General Disclaimer
One or more of the Following Statements may affect this Document
This document has been reproduced from the best copy furnished by the organizational source. It is being released in the interest of making available as much information as possible.
This document may contain data, which exceeds the sheet parameters. It was furnished in this condition by the organizational source and is the best copy available.
This document may contain tone-on-tone or color graphs, charts and/or pictures, which have been reproduced in black and white.
This document is paginated as submitted by the original source.
Portions of this document are not fully legible due to the historical nature of some of the material. However, it is the best reproduction available from the original submission.
Produced by the NASA Center for Aerospace Information (CASI) :low, 0,
USE O!" THERMAL INERTIA DCTFRMINED BY HC *1 TO PREDICT NOCTURNAL COLD PROiI% AREAS IN FLORION E84 - 100 05 7"r ;2e^' HC,d9 DATA INVESTIGATION 1:s0-002 CONTRACT NAS5-26453 "Mak available under NASA spousorsOlp in toe interest uf early and wit t dis. serr'saticr, ei Earth Resources Survey Program. informazloa and WitWU1 trabillix r il any bst mbdL fherew." L.H. Allen, Jr., Principal Investigator University cf Florida Institute of Foci! and Aariculti..ril Sciences Building 164, A3roriciay kysiolugy Lab. t Gainesville, FL 32611! N84-11546 (584-100051 USE OF THE SAL INEit'TIA DETERAINLD BY HLM IN TU PhXIDILI NOCTURNAL COLD ke fiUNE Ai EAS IN ELLIAIUA Final Report, Aar- 1 y 81 — Aar. 1983 IFiorida Unirr.) 74 p UDCLas CSCL 059 G3/43 00005 8C A03/5F ► 01 Coinvestigators Ellen Cheri, Cooperator, Research Associate J.D. Martsolf, Cooperator, Professor, Fruit Crops Department P.H. Jones, Ccoperator, Research Associate Toby N. Carlson, Cooperator, Professor, Meteorology Department, Pennsylvania State University
i October 1983 gECE^^ED ^^ Final Report, March 1981 to March 1982 DVI i
Prepared for NATIONAL AERONAUTICS AND SPACE ADMINISTRATION GODDARD SPACE FLIGHT CENTER Greenbelt, MD 20771
9 V USE OF THERMAL INERTIA DETERMINED BY HCMM TO PREDICT NOCTJRNAL COLD PRONE AREAS IN FLORIDA
HCMM DATA INVESTIGATION HFO-002 CONTRACT NASS-26453
L.H. Allen, Jr., Principal Investigator University of Florida Institute of Food and Agricultural Sciences Building 164, Agronomy Physiology Lab. Gainesville, FL 32611
Coinvestigators Ellen Chen, Cooperator, Research Associate J.D. Martsolf, Cooperator, Professor, Fruit Crops Department P.H. Jones, Cooperator, Research Associate Toby N. Carlson, Cooperator, Professor, Meteorology Department, Pennsylvania State University
wy Dom= orist"al she«pw ,._. Cron l0 Data Cartor gipps ^13a,^ x7199
October 1983 Final Report, March 1981 to March 1983
Prepared for NATIONAL AERONAUTICS AND SPACE ADMINISTRATION GODDARD SPACE FLIGHT CENTER Greenbelt, MD 20771 ^, o
ORIGINAL DUA OF POOR QLITY
TECHNICAL REPORT STANDARD TITLE PAGE 1. Report No. 2. Government Accession Me. 3. Recipient's Catalog No.
1. ill* and Suittitle . ,eport Date SE OF THERMAL INERTIA DETERMINED BY HCMM TO Ocv,her 1983 REDICT NOCTURNAL COLD-PRONE AREAS IN FLORIDA 6. Performing Organization Code
7. Auther(s) 8. Performing Organization Report Me. L.H. Allen, Jr., et al. 9. Performing Organization Nome and Address 10. Work Unit No. I nstitute of Food and Agricultural Sciences ni versi ty of Florida 11. Contract or Grant No. ainesville, FL 32611 I 5- 13. Type of Report and Period Covered 12. Sponsoring Agency Home and Address TYPE III Final Report ational Aeronautics and Space Administration March 1981 to March 198 boddard Space Flight Center 1 4. Sponsoring Agency Code Preenbelt, MD 20771 15. Supplementary Notes repared in cooperation with the Soil and Water Research Unit, Agricul-1, ural Research Service, U.S. Department of Agriculture, at Gainesville through AgRISTARS (Early Warning/Cro p Condition Assessment). 16. Abstract The Heat Capacity Mapping Mission (HChWI) satellite was launched on April 26, 1978, in a polar orbit with nominal equatorial ascending an descending crossing times of 2:00 pm and 2:00 am, respectively. Pair of HCMM day-night thermal infrared (IR) data were selected during the 1978-79 winter to examine patterns of surface temperature and thermal inertia (TI) of peninsular Florida. GOES and NOAA-6 thermal IR, as well as National Climatic Center temperatures and rainfall, were also l used. HCMM apparent thermal inertia (ATI) images closely corresponded to the General Soil Map of Florida, based on soil drainage classes. Areas with low ATI overlay well-drained soils, such as deep sands and drained organic soils, whereas areas with high ATI overlay areas with wetlands and bodies of water. The HCMM ATI images also corresponded well with GOES-detected winter nocturnal cold-prone areas. Use of HCM M data with Carlson's energy balance model showed both high moisture availability (MA) and high thermal inertia (TI) of wetland-type sur- faces and low MA and low TI of upland, well-drained soil-. Since soil areas with low TI develop higher temperatures during the day, then antecedent patterns of highest maximum daytime surface temperature can also be used to predict nocturnal old-Drone areas in Florida. 17. Key Words (S, leered by Author(%)) 18. Distribution Statement CMM, Thermal-Inertia, Cold-Prone- reas, Well-Drained-Soil, Wetlands, Soil-Temperature
19. Security Clossif. (of this report) 20. Security Clossif. (of this page) 21. No. of Pages 22. Price 'UNCLASSIFIED UNCLASSIFIED
'For sale by the Clearinghouse for Federa! Scientific and Technical Information, Springfield, Virginia 2.2151•
Figure 2. Technical Report Standard Title Page Table of Contents Pa e Title Page ...... Acknowledgements ...... i i i Executive Summary ...... 1 List of Figures ...... 5 List of Tables ...... 7 1.0. INTRODUCTION ...... 9 1.1. Previous Work with GOES-IR Surface Radiance Temperature ...... 9 1.2. HCMM System and System Objectives . . . . 10 2.0. OBJECTIVES OF THIS HCMM INVESTIGATION ...... 10 3.0. MATERIALS AND PRODUCTS ...... 11 3.1. HCMM Products ...... 11 3.2. GOES Products ...... 13 3.3. TIROS-6 Products ...... 13 4.0. RESULTS AND FINDINGS ...... 13 4.1. Information Assembled . . . 13 4.2. Comparison of thermal propertiesfrom kW and other data sets ...... 16 4.2.1. Comparison of 1978-79 winter max-min air temperatures with surface condi- tions which affect thermal inertia . . . . 16 4.2.2. Comparison of HCMM and GOES images using the GE Image-100 and PDP 1145 System at KSC ...... 24 4.3. Thermal properties of organic and mineral soils in Florida ...... 27 4.3.1. Thermal properties of organic soils in south Florida ...... 27 4.3.2. Thermal properties of mineral soils . . . . 27 4.4. Thermal inertia analysis and interpretation . . . . 41 4.4.1. Thermal inertia classification using the GE Image-100 system ...... 41 4.4.2. Correlation of HCMM thermal inertia with HCMM surface infrared tempera- tures ...... 47 4.4.3. Fourier analysis of GOES temperatures 52 4.4.4. Prediction of moisture availability and thermal inertia from Carlson's model ..54 4.4.5. Comparison of HCMM IR data with GOES* IR data ...... 60 4.4.6. Calculation of apparent thermal finer- . tia (ATI) using temperatures from GOES satellite data ...... 61 4.4.7. Spatial resolution and thermal prop- erties ...... 64 4.4.8. HCMM thermal pattern of organic soil attributable to land use ...... 68 4.4.9. Nocturnal surface temperatures, ther- mal inertia, and soil or surface properties ...... 68 5.0. SUMMARY AND CONCLUSIONS ...... 73 6.0. REFERENCES ...... 74 MAN" ii Lq - 0+ ^4i
Acknowledgements
This investigation was supported in part also by the Soil and Water Unit, Agricultural Research Service, U.S. Department of Agriculture, at Gainesville, Florida, through AgRISTARS (Early Warning/Crop Condition Assessment). Special thanks go to Gary Del Valle for computer program- ming support for processing HCMM data and running Carlson's models, to Pattie Everett for assistance in preparation of the final report, and to personnel at the NASA Kennedy Space Center for assistance in use of the GE Image-100 facility. We also thank personnel of the HCMM program at the Goddard Space Flight Center and their contractors for providing data and assistance, and research participants of the HCMM program for valu- able discussions.
iii D
EXECUTIVE SUMMARY
Rationale and Objectives
Previous satellite remote-sensing studies have shown the presence of persistent cold-prone and warm-prone areas in Florida during winter nocturnal conditions. These differences in surface temperature patterns can determine whether or not severe damage to vegetable, fruit, and other specialty crops occurs during cold air mass frontal movements across the peninsula. Qualitatively, the surface temperature patterns appeared to be related to soils and surface moisture conditions. The surfaces with low soil moisture and/or few bodies of water in the area tended to show low nocturnal surface temperatures, whereas the surfaces with high soil moisture and/or associated bodies of water tended to show high nocturnal temperatures. Since water has a high heat capacity, sur- faces with high water content tend to cool slowly relative to surfaces with low water content. Thermal inertia is a property that embodies both the heat capacity and the ability to conduct heat through the medium, i.e., high thermal inertia implies high heat capacity and/or high heat conductivity, and low thermal inertia implies either low heat capacity and/or low heat conductivity. This investigation was undertaken to de- termine thermal inertia features associated with she nocturnal cold- and warm-prone areas.
The objectives were to (a) determine thermal inertia of peninsular Florida surfaces from Heat Capacity Mapping Mission (HCW) satellite thermal data obtained from day-night overpasses, and compare thermal inertia maps with maps showing relevant surface features, (b) refine patterns of winter nocturnal cold-prone areas and warm-prone areas from HCMM IR daytime and nighttime overflights, and compare these patterns with data from other satellite systems and ground-based observations, and (c) adapt a boundary-layer, energy-balance model to quantify mois- ture availability and thermal inertia of surface features in peninsular Florida that are associated with the observed day-night surface tempera- ture patterns.
Scope of Work
The investigation focused on peninsular Florida, which includes the following types of surfaces: shallow lakes (e.g., Lake Okeechobee), broad rivers (e.g., St. Johns River), extensive wetlands of both organic (e.g., Everglades) and mineral (e.g., Suwanee River floodplain) soils, drained organic soil (e.g., Everglades Agricultural Area), poorly drained mineral soil (e.g., complex of the South Florida Flatwood Land Resource Area), well-drained mineral soil (e.g., complex of the Central Florida Ridge Land Resource Area, and excessively well-drained mineral soil (e.g., Ucala National Forest).
The HCMM data source was restricted to the winter of 1978-1979, since data availability was limited by satellite problems later on. Un- fortunately, this winter was exceptionally cloudy and rainy over Flor- ida, which eliminated the use of many of the overflight data. Further- more, Florida was outside the latitude zone of the 12-hour, day-night sequences of overflight coverage by the HCMM satellite. Fortunately, we
AV 0
found one time-window sufficiently cloud-free to obtain adequate data for display and analysis along the peninsula: January 29 to February 3, 1979, with a daytime overflight on January 29, a nighttime overflight on February 1, and a following daytime overflight on February 3. We used maximum and minimum temperatures from the National Climate Center aata to estimate corrections For day-night temperature differences for appro- priate climatological regions in Florida.
During other winters, we also used GOES IR data and NOAA-6 IR data to complement and supplement HCMM data in this investigation. Surface temperature data were also examined in view of surface moisture fea- tur..; at each site and antecedent moisture (i.e., antecedent rainfall) conditions.
Both computer-compatible magnetic tape data WCT) and image data from HCMM were utilized in this analysis and report..
Results, Accomplishments, and Significance
Comp arison of thermal properties from HCMM with other data sets. SelectedClimate Center maximum and minimum temperatures for 7 zones in Florida were examined for January and February, 1979. Stations with large ranges of differences of maximum and minimum temper- ature were found to be located in areas generally having well-drained or excessively well-drained soils, and those with smaller differences in maximum and minimum temperatures were found to be located in areas gene- rally having poorly drained soils, or were near wet or coastal areas. HCMM temperature difference and thermal inertia images were found to closely agree also with a general soils map of the state based on drain- age properties of the soil. That is, areas where HCMM data or images showed larger day-night temperature differences or, concomitantly, smaller thermal inertia, overlaid areas with extensive well-drained to excessively well-drained soils. Likewise, areas where HCMM data showed smaller day-night temperature differences and larger thermal inertia were associated with wetter surfaces.
Direct comparison of a HCMM nighttime surface temperature image with a GOES nighttime surface temperature image showed that cold areas overlay closely. However, the HCMM IR image showed more detail than the GOES image, since it has a 0.6 x 0.6 km resolution at nadir, whereas the GOES in Florida has about a 6 x 8 km resolution.
HCMM detects similar surface patterns that GOES or surface measure- ments can detect, but it obviously shows more detail.
Thermal Proeerties of or anic and mineral soils. A HCMM apparent therms nertia A image of the whole peninsu a showed patterns re- lated to surface conditions. In organic soil areas of south Florida, the areas drained for agriculture (Everglades Agricultural Area) had dis- tinctly lower ATI values than the undrained Everglades or undrained Wa- ter Conservation Areas located on organic soils. The low ATI values cor- responded closely to low nocturnal surface temperatures detected by GOES. Within the undrained Everglades, the ATI image showed that the ATI values of parts of the Everglades National Park were lower than parts of the Water Conservation Areas. The im.,,es show the impact of water man- agement, but at this point one could not say whether the differences from the natural conditions would be mainly due to retention of water in conservation areas, or r^eyennt ion of natural flow ma nta n ng higher water (and higher ATI) in the Tverglades National Park.
Thermal properties of mineral soils in north Florida in the Suwanee River Area showed that upland areas were persistently colder at night and had lower ATI than did floodplain areas. These patterns show that surface tooling properties, not nocturnal drainage properties, predomi- nate. An exception, which is usually at about the limits of resolution of HCMM, is found in karst depressions of the Central Florida Ridge Land Resource Area. In the spring of 1981, following severe freezes in Jan- uary, 1981, we observed severe citrus tree damage in dry karst depres- sions, but lack of damage around karst depressions that contained water (small pt^rmanent lakes, ponds, or marshes).
Therma l, inertia analysis and interpretation. The apparent thermal inertia i a e of e ruary 1-3, 1979, was ana y2ed on the GE Image-100 at the NASA Kennedy Space Center and separated into 5 major thermal inertia classes. These classes overlaid the general soils map of Florida quite precisely, so we adopted the general soils map as a first approximation thermal inertia map of peninsular Florida. The close correspondence of ATI images to surface soil drainage classes implies that ATI could be useful in classifying wetlands in remote areas of the world.
.Predictinlg moisture availability and thermal inertia. Carlson's energy-balance model was used to compute moist,-ire avai a ility and ther- mal inertia from three wet areas and two dr y areas in north central Florida. Under these conditions, it was found that m*isture availability was much more unstable than thermal inertia. Moisture availability ranged from about 0.05 to 0.96 for the driest to the wettest site. Ther- mal inertia ranged from about 0.085 for the wet sites to a^92t O . g55 for the dry sites, or a difference of about 0.03 cal cm sec °K .
Calculation of ATI from GOES data. GOES temperatures averaged 5.9°C and 5,2 0C higher than the HCMM temperatures for two nights examined (January 10, 1979, and February 1, 1979, respectively). This suggests that the HCMM standard processed data may represent temperatures that are about 5.5°C too low.
The ATI in the HCMM User's Guide, 1980, was used with two GOES day-night periods (January 12-13 and January 18-19, 1981) to compute ATI for the Everglades Agricultural Area (drained organic soil with sugar- cane) and for an area of excessively well-drained and well-drained soils south of Ocala, Florida. ATI was about 17.5 for this mineral soil site on both occasions, but it dropped from 36.1 to 28.5 in the organic soil area. We interpret the drop in ATI in the Everglades Agricultural Area to be due to killing of transpiring sugarcane and other vegetation leaf tissue during the first freeze event, since the day-night temperature difference changed from 19°C to 25°C. If this is the case, then evapora- tion from living vegetation can decrease ATI, and confound the interpre- tation of results. This also would imply that an energy balance model = that accounts for evaporation should be used with HCMM data. Indeed, in
3 GY1
many applications, moisture availability and evaporation are surface parameters that are actually more useful.
Recommendations
Information gained from HCW studies can be applied to other polar-orbiting or geosynchronous satellites that have thermal IR sen- sors. In many cases, GOES may be more desirable where coverage can be obtained, because the data stream is available in continuous, one-half hour intervals. In the case of many weather events, such as freezes, continuous data of a coarse resolution, such as GOES, are much more use- ful for both prediction of incipient freeze severity, and documentation of frieze severity, than would be 12-hour coverage from a polar-orbiting satellite. In any event, coverage at the right time every 12 hours is needed, and polar orbiters can supply this information, albeit local times and solar angles may not be very uniform.
The thermal inertia approach has value in identifying wetlands and in quantifying their condition at °ny given time, especially in remote and inaccessible areas. This methoeuiogy should also be useful in de- tecting soil moisture availability and drought severity in agricultural cropping regions, in grasslands, and perhaps even in forests. An energy balance model such as Carlson's model could probably be simplified, and certainly a better procedure for quantifying surface moisture availabil- ity and thermal inertia needs to be devised. Perhaps initializing with observed temperature data at one overpass and running to the next over- pass might yield more meaningful moisture availability and thermal iner- tia values (although it would sacrifice whole-day evapotranspiration and other predictions).
4 List of Figures
Fig. 1. Climatological regions of Florida, showing the 6 regions where rainfall data were averaged. From Climatological Data--Florida, National Climatic Center, Asheville, NC.
Fig. 2. HCMM infrared (IR) image of lower Florida at approximately 0200 EST Feb. 1, 1979, obtained by digitizing HCMM film transparency using the Image 100 computer at Kennedy Space Center.
Fig. 3. GOES digital image of Florida at 0200 EST, Feb. 1, 1979. The coldest area south of Lake Okeechobee (symbol Y) was blocked for comparison with the HCMM image showing the coldest area, green.
Fig. 4. Map of lower Florida showing location of the conservation areas and Everglades National Park, and original extent of the Ever- glades Marsh (from Central and South Florida Flood Control Dis- trict, Vol. 1, No. 5, 1972).
Fig. 5. HCMM-derived apparent thermal inertia from daytime Feb. 3 and nighttime Feb. 1, 1979, data. Areas in south Florida were clear and were used to compare with GOES images.
Fig. 6. GOES infrared digital data showing average temperatures for Lake Okeechobee, the Everglades Agricultural Area, and Water Conser- vation Areas #1, #2, and #3.
Fig. 7. Diurnal surface temperatures obtained from GOES infrared digi- tal data for the night of Feb. 26-27, 1980.
Fig. 8. Diurnal surface temperatures obtained from GOES infrared digi- tal data for nights of Jan. 11-12 and 12-13, 1981, during a se- vere freeze period in Florida.
Fig. 9. GOES infrared digital map for 2100 EST, Jan. 12, 1981, showing surface temperatures within the Suwannee River area of north Florida. Coldest areas are outlined. Circles show areas where diurnal temperatures were plotted (Fig. 11). A temperature sym- bol scale is shown on the map. Black dots are NWS stations listed in Table 9.
Fig. 10. HCMM-derived apparent thermal inertia image from nighttime Dec. 15 and daytime Dec. 17, 1978, data. Areas in -,rth Florida in the Suwannee River Basin were clear although overall sky condi- tions were poor southward. The thermal inertia patterns of the Suwannee River Basin agreed with diurnal GOES thermal data.
Fig. 11. GOES diurnal surface temperatures for 5 areas in the Suwannee River Basin (see Fig. 9).
rig. 12. General soil map of Florida, showing the 5 thermal inertia classes for Florida constructed from HCMM and GOES data (see Table 11 for description). Source: R.E. Caldwell and R.W.
5 Johnson, USDA-Soil Conservation Service, IFAS-Agricultural Ex- periment Station and Soil Science Department, revised 1982.
Fig. 13. Five by five blocks of HCMM pixels averaged from Night IR data of Feb. 3, 1979 (A) and from Apparent Thermal Inertia data based on Feb. 1-3, 1979, day-night overflights (8), for use in relat- ing apparent thermal inertia with nighttime surface tem- peratures. A ... A' identifies the row of pixels listed in Table 13. See also Fig. 12.
Fig. 14. Linear regression of thermal inertia (grey scales) and temper- atures for the cross section A ... A' (Fig. 13).
Fig. 15. Fourier fit of GOES diurnal temperatures of the Everglades Agri- cultural Area for data from Jan. 12-i3, 1981.
Fig. 16. HCMM (AAO-281-065804) temperature difference data from Feb. 1-3, 1979. This set of data is used to obtain a day temperature (6°C was added to the temperature to correct HCMM data) to use with Carlson's model. Areas used are labelled 1, 2, and 3 (wet) and 4 and 5 (dry). The areas are also located by x's in Fig. 12.
Fig. 11. Map showing the eight areas (1-8) where HCMM-derived surface temperatures were compared with GOES surface temperatures. ATI was calculated using AT obtained from GOES from Area 4 and Area 9. County lines are shown on the map.
Fig. 18. Diurnal surface temperature (GOES) for the Evergiades Agricul- tural Area (Area 4 in Fig. 17) for two days in January, 1981.
Fig. 19. HCMM night IR temperatures from February 1, 1919, showing de- tailed (600 m resolution) land-water temperature boundaries in the Lake Apopka area.
Fig. 20. HCMM night IR image of February 1, 1979, showing detailed (600 m resolution) land-water temperature boundaries of Newnan's Lake area in North Central Florida.
Fig. 21. GOES IR image of Florida from February 1, 1979, 0200 EST, shows the land-water temperature boundaries for small lakes of 1 to 2 pixel area.
Fig. 22. HCMM nighttime IR image on Jan. 15, 1979, obtained by digitiz- ing HCMM film transparency using the Image 100 computer at Kennedy Space Center. The image illustrates thermal patterns which can be generated due to different land use.
Fig. 23. Average low temperatures in the state during 1979-80 and 1980-81 winter seasons. Temperatures are in °C. Lines are time-expansion of cold areas initiated from cold areas. Thick lines indicate average coldest conditions during midnight hours and dashed lines indicate average coldest conditions during predawn hours.
6 List of Tables
Table 1. HCMM data tapes (CCT) received. The tapes are lis t ed in the order received, according to shipment dates from GSFC.
Table 2. Polar-orbiting NOAA•6 satellite film transparencies covering the sane period as in Table 8 obtained from Environmental Data Service.
Table 3. Oates, route, and sky conditions during transects of surface temperature (Raytek radiation thermometer), 1.5-m air temper- ature, and dewpoint temperature (from E. Chen and J.F. Gerber, unpublished).
Table 4. Florida nocturnal surface temperature transects and HCMM cov- erage, January and February, 1319.
Table 5. Rainfall preceding January and February, 1979, transects.
Table 6. Average rainfall for each climatic region of Florida for the period from Dec. 16, 1980, through Jan. 16, 1981: Northwest (1); North (2); North Central (3); South Central (4); Ever- glades and SW Coast (5); and Lower East Coast (6). Data were from Climatological Data for Florida, National Climatic Cen- ter, Asheville, NC, Dec. 1980 and Jan. Ml.
Table 7. NCC-NOAA climate data for selected Florida stations that show average daily temperature ranges indicative of high or low surface thermal inertia.
Table 8. Maximum range of average daily temperature observed at cli- mate stations within zones from NCC-NOAA climate data, 1979.
Table 9. Maximum and minimum temperature from Jan. 12-13, 1981, for NOAA cooperative observer stations in the Suwannee River Ba- sin of north Florida. Approximate location, of the cities are indicated in Fig. 16 (Mack circles). Source: Climatological Data--Florida, 85, No. 1, National Climatic Center, Ashe- ville, NC, 1981.
Table 10. Thermal inertia classes for Florida from Feb. 1-3, 1979, Day-Night HCMM data.
Table 11. Thermal inertia classes for Florida based on HCMM image AAO- 281-6580-5 and using the Image-100 Image Analyzing System to determine areas with equal thermal inertia as defined by grey scales. Data digitized: HCMM A-A0281-06580-5 (HCMM14, File 5). Data formatted from Feb. 1 (night) and Feb. 3 (day), 1979, IR temperatures.
7 Q
Table 12. Rainfall and departures from the means for 1979 and for 1980 that determine antecedent surface moisture conditions for periods immediately before the Jan. - Feb. winter period of 1980 and 1981, respectively. Source: Climatological Data-- Florida, Annual Summary, National Climatic Center, Asheville, NC 83, No. 13, 1979, and 84, No. 14, 1980.
Table 13. HCtw thermal inertia and night IR temperatures for day-night pail ..f Feb. 1-3, 1979. Data pair was taken from a cross-sec- tion in North Central Florida (Fig. 13-A and 13-8, A...A'). Temperatures are uncorrected.
Table 14. Day-night temperature pairs for input into Carlson's model to predict thermal inertia and moisture availability.
Table 15. Predicted thermal inertia (TI) and moisture availability (MA) for areas 1, 2, 3, 4 and 5.
Table 16. Comparison of surface IR temperatures derived from HCW film transparen:ies with GOES surface IR temperatures for b areas in South Florida. Temperature is in °C.
Table 17. Values used ir calculation of apparent thermal inertia (ATI) for 2 areas in Florida: Area 4, the drained organic soils or the EverglaL;es Agricultural Area (EAA) in south Florida, and Area 9, the c~ &a classified as excessively drained and well- drained mineral sc,!ls 25 km directly south of Ocala #n the west north central Florida peninsula. Data usea were from Jan. 1981.
8 GY1
1.0. INTRODUCTION
1.1. Previous Work with GOES IR Surface Radiance Temperature
Earlier studies by Chen (1979) and Chen et a1. (1981, 1982) showed that nocturnal GOES thermal imagery gave very persistent patterns of cold-prone areas throughout Peninsular Florida. These patterns have been borne out by two subFequent studies (Chen and Martsolf, 1981; Chen, Allen, and Martsolf, 1982). Transects of nocturnal air temperatures at 1.5 m measured from a moving vehicle generally showed the same tempera- ture patterns as determined by the GOES s , tellite (Chen et al., 1983). However, later transects of air temperature at 1.5 m dur gn the daytime across wetland areas and across well-drained, upland areas (from Gainesville to Cedar Key, and from Gainesville to Palatka) seemed to be uncoupled from GOES-derived surface temperature (Chen, Allen and Martsolf, 1982). (In soils terminology, well-drained may refer to good internal drainage of the soil profile as well as relief that provides good surface drainage.) The daytime GOES-derived surface temperatures were cooler than air temperatures in wetland areas and warmer than air temperatures in well-drained upland areas. Either rapid mixing resulted in the daytime 1.5-m air temperature being represented by air-mass tem- perature conditions, or the 1.5-m air temperature measurements were "contaminated" by road and roadside conditions, or both. Nevertheless, the close tracking of surface and 1.5-m air temperatures at night, and the decoupling of surface and 1.5-m air temperatures during the daytime seems at first unexpected, since current wisdom suggests that stably- stratified nocturnal air should effectively decouple surface and air temperatures, whereas unstably-stratified daytime air should cause close coupling of surface and air temperature. However, the findings of Chen et al. (1983) and Chen, Allen and Martsolf (1982) suggest that 1.5-m air temperature is closely coupled to the surfaf.e temperature of the nearby locale at night, and is closely coupled to the air mass temperature dur- ing the daytime.
Drained organic soil of the Everglades Agricultural Area south of Lake Okeechobee was a persistent cold-prone area identified by GOES thermal data (Chen, 1979; Chen et al., 1981). However, wet or flooded Water Conservation Areas mainta neU-by the South Florida Water Manage- ment District were found to be considerably warmer at night during win- ter cold weather conditions.
In most of the mineral soil areas of Florida, patterns of cold- prone areas appeared to be related to deep, well-drained to excessively well-drained sandy soils (such as the Central Florida Ridge Land Re- source Area) whereas warm areas were related to the presence of bodies of water (such as the broad downstream St. Johns River) or to poorly drained soils or wetlands areas (such as the Green Swamp area), Chen et al. (1982).
Recent antecedent rainfall appears to reduce the winter nocturnal cold tendency of well-drained soils (Chen et al., 1982), whereas long- term drought tends to increase the winter nocturnal cold tendency of usually high water able soils that have drained or had soil water re- moved by evapotranspiration (Chen, Allen and Martsolf, 1982).
9 (4)
1.2. HCMM System and System Objectives
The HCMM satellite was launched on April 26, 1978, into a near- polar orbit with equatorial crossing times at near 1430 and 0230 local times. In the latitude band of about 35° to 85°, 12-hour day-night over- pass intervals were available. However, in the 22 0N latitude to 35 0N latitude, day-night sequence images were available at only 36-hour in- tervals. This problem was recognized in the project proposal so that enough HCMM products were planned for request from NASA so that paired day-night sequences under similar weather conditions could be selected for analysis. The project proposal also called for use of other satel- lite products, such as GOES, for supplying additional and supplemental information for missing data sequences or different years.
Earlier uses of HCMM data have been to evaluate water surface tem- peratures ana motion or mixing patterns, geologic formations, snow cover, evapotranspiration and land cover patterns. Perhaps one of the most analytic applications was the use of HCMM day-night overpass se- quences of surface temperature patterns to compute surface thermal iner- tia and surface moisture availability at several test sites within the U.S.A. (Carlson et al., 1981).
2.0. OBJECTIVES OF THIS HCMM INVESTIGATION
The rationale and objectives of this particular investigation were derived form several sources, first being the overall objectives of the HCMM program. Second, findings from previous work in Florida with GOES IR data provided background for ideas that needed to be investigated. Third, early HCMM investigations elucidated further applications oppor- tunities.
The specific objectives of this report of HCMM investigations are:
(A) Determine Thermal Inertia of Peninsular Florida surfaces from winter Heat Capacity Mapping Mission (HCMM) thermal data under a range of surface and soil moisture conditions, and develop maps that compare thermal inertia with relevant surface features.
(8) Refine patterns of winter nocturnal cold-prone areas and warm-prone areas from both nighttime and daytime overflights and compare these patterns with other data from other satellite systems.
(C) Adapt a boundary-layer, energy-balance model (Carlson's model) to determine patterns of moisture availability and thermal inertia from HCMM data over different surface features in Florida, and show how nighttime minimum surface temperature can be predicted from daytime surface temperature and thermal inertia information. 3.0. MATERIALS AND PRODUCTS
3.1. HCMM products
The products available to HCHM users were computer-compatible tapes and negative or print images of daytime visible, daytime IR, or night- time IR data. Also, rectified day-night temperature differences or ap- parent thermal inertia could be obtained as negatives, prints, and com- puter-compatible magnetic tapes. We obtained these types of data for the winter of 1978-79 (Table 1).
The lack of 12-hour, day-night sequence of HCMM satellite over- flights in Florida, and the lack of clear periods in Florida during HCMM overflights during the winter of 1978-79 were two major uncontrollable problems in identification and selection of suitable data for analysis. Lack of "user-friendly" documentation on computer-compatible tapes, lapse of time between, order and receipt of products, and a few defective or missing products were minor controllable problems. All problems con- tributed to delays in completing this HCMM investigation task.
(A) Lack of 12-hour, day-night sequence of HCMM satellite overflight in Florida latitudes
This problem was detailed in the First Quarterly Report (HCMM Data Investigation HFO-002, Contract NAS5-24653, March 16 to June 15, 1981). We solved this problem partially by calculating sequential daily maximum and minimum temperatures from climatological data (Climatological Data for Florida, Asheville, NC, NOAA, USOC) aver- aged over the region of Florida where satellite data were to be ana- lyzed. The daytime maximum and nighttime minimum climatological tem- peratures were used to adjust the 36-hour (or greater) overflight interval day and night temperatures of HCMM so that a reasonable pair of temperature data were obtained. This procedure will be dis- cussed in more detail in a later section. Also, GOES IR temperatures were used extensively to supplement and calibrate (or corroborate) the HCMM data.
(8) Lack of clear periods during 1978-79 HCMM overflights
The winter of 1978-79 had more cloudy periods than usual. Therefore, it was difficult to find clear day and night pairs of images of HCMM data that were reasonably close together in time, and which repre- sented cold weather conditions. This problem was compounded by the lack of 12-hour, day-night overpass sequences. From the available HCMM data, we selected the period from January 29 to February 3, 1979, as the best period for data analysis. This period gave a good nighttime overpass on February 1, sandwiched between good daytime overpasses on January 29 and February 3. This period coupled well with GOES IR data which were available to us, and represented a rel- atively moist period, bit a period with clear-sky conditions during HCMM satellite overpasses. GOES IR temperatures from 1980-81 were used to represent a dry period. GOES 12-hour, day-night temperatures were used in place of HCMM data in this latter period.
11 n+ ORIWIX PAGE 19 OF POOR QUALITY
Table 1. HCMM data tapes (CCT) received. The tapes are listed in the order received, according to shipment dates from GSFC.
Date Received Scene ID Local Comments Files Tape Name
18 Sept 1981 259-065703 1-4 HCMM1 265-070703 5-8 " 267-182801,2 9-15 of 1 Oct 1981 233-07130,P -- HCMM2 233-07130,P -- HCMM3 278-183506,7,8 -- HCMM4 278-183506,7,8 -- HCMM5 278-183506,7,8 -- HCMM6 12 Oct 1981 265-065806,7,8 -- HCMM7 Tape is bad, data 265-065806,7,8 -- HCMM8 no longer available 265-065806,7,8 -- HCMM9 30 Oct 1981 281-070403 1-7 HCMMM10 283-183001,2 8-14 " 264-064903 15-18 HCMM11 233-071303 19-22 262-183701,2 1-7 235-183611,1 8-14 278-183501,2 1,7 HCW12 Not readable 278-183601,1 8-13 " 9 Nov 1981 281-06580P(20/3) 1-8 HCMM13 281-06580P(30/3) 1-6 HCW14 281-06580P(10/3) 1-6 HCW15 10 Nov 1981 259-064806 -- HCW 16 259-06480 -- HCW17 259-064804,5,6,7,8 -- of 12 Nov 1981 262-18320 -- HCW 18 First record bad, no readable directory 262-18320 -- HCW 19 Continuation of 18? 262-18320 -- HCMM20 Empty 3 Mar 1982 283-132801,2 1-7 HCMM21 233-071303 1-4 HCMM22 Replacement for a previous bad tape 12 July 1982 278-183501,2 1-7 HCMM32 Replacement tape for 281-065803 8-11 to previous bad tape 259-064803 12-?5 if 262-183201,2 16-22 If
12 (C) HCW tapes documentation
The HCMM tapes lacked "user-friendly" information on the tapes as they were sent to us. The label on each tape contained minimal in- formation on scene ID. Sometimes a scene that was not completed on one tape was put on another tape without clear documentation. When an attempt was made to read such a tape, errors were encountered because no header information was found. After repeated failures, each tape was dumped and the content examined to obtain each file contained in each tape. This process was time-consuming and expen- sive. One tape was not readable. The first record of one tape was bad and there was no readable tape directory. One tape was empty. A total of 23 tapes were received (Table 1).. NASA-Goddard personnel and contractor personnel provided assistance and replacement tapes.
3.2. GOES Products
GOES infrared digital printouts of the Florida sector from the Sat- ellite Freeze Forecast System (SFFS), J.O. Martsolf, Principal In- vestigator, were used extensively to supplement the HCMM data. Data usu- ally covered the nighttime period of January and February of each year (1977-1981). Average low temperature maps (1979-80, 1980-81 winters) developed from GOES for another project (Chen and Martsolf, 1982) were also used as a basis for qualitatively extending thermal inertia ob- tained from HCMM to the entire state of Florida.
3.3. TIROS-6 Products
NOAA-6 positive film transparencies that were obtained in the win- ter of 1980-81 are shown in Table 2. Day-night sequence of NOAA-6 frames for the 3 periods classified as wet-cool, intermediate dry-cool and dry-cold were selected from the film transparencies and digital magnetic tapes were ordered.
4.0. RESULTS AND FINDINGS
4.1. Information assembled
In addition to HCMM, GOES, and NOAA-6 satellite products, other data, such as National Climatic Center cooperative-observer temperature and rainfall data, and surface transect observations from a moving vehi- cle, were assembled. These data were tabulated for comparisons, analy- ses, and interpretations that are discussed in later sections.
During January and February, 1979, surface transects were made for 11 overnight periods in Florida during cold night conditions by Ellen Chen and J.F. Gerber (Table 3). The data collected included radiation surface temperature, air temperature, dewpoint temperature, windspeed, and sky condition observations.
13 W
Polar-orbiting NOAA-6 satellite film transparencies obtained from Environmental Data Service, covering the same period as in Table 4.
Date Time Orbit Number Weather Condition uec. 1/, ivou 352:01:08:32 7655 wet, cool
Dec. 17, 19801 352:13:29:22 7662
Dec. 18, 19801 352:23:10:22 7669
Dec. 18, 1980 353:13:08:32 7676
Dec. 31, 1980 366:00:53:20 7854 intermediate
Dec. 31, 19841 366:13:16:01 7861 dry, cool
Jan. 1, 19811 001:12:56:02 7875
Jan. 3, 198111 003:13:49:16 7904
31 010:00:31:30 7996 dry, cool 31 010:12:55:02 8003 9111 012:13:48:11 8032 311! 012:23:33:28 8039 91 013:13:26:46 8046
9 digital magnetic tapes ordered. 1 ORIGINAL f ^L`^. OF POOR QUALITY
vi c 4A NV N r^ r-I N r-1 r-1 N r-1 r-I N N
H V) Z W ro i •1 d v ai O 4M roa ^ ex O of NI O • 01 O 4J L +O+ 4J QJ Lp ^ 7 •r N r r +J r- n a n v ra F L C G1 f O O Nl N) Nl ^ b a! ro ♦J Vt U 0 V N N N H 0 0 VI f- E C tol w ti r r^ w ^u aw a 4VLu .-^ M L o N L 4- • N 4-`^^ C :1.1 N 0 O O t 4.J N C +> > = N +^ 01 .-^ }i •r •r O O Z7 ro ri V O O n w VI 3 O N N N N G/ CJ '- w O H N C C! U 7 L L > > > L L U L r E W to a L ro ro L L L 7 ro ro ro Q1 C'3 L >1 L w w L L L_ O O w w 3 N O E }J ^ Y •r r-- r- •r •r .3 r- r r — O r^ L N 4 C N u V V u C7 u U u V r- U -4 rn b 4J Q w r w u C L cu VI r-I 7 L C w r♦ 7 ro tp 41 L O C ro a-1 w +1 QJ M ^O roro 4J O +J n +J O O o O v pp po n t w 1-4 •r E V) OC to N13' to N Nq*, 'ct tt N +J O C +J L O O E V L > CJ O •r' O ^ i >1 ro 4- V) E v► 01 O^ 01 v► O► .Y .^ O I f^ 01 I^ 1^ I^ !^ f^ f^ VI L LA 01 01 I^ 01 01 01 01 C1 Q1 01 01 •ro O C •- 1^ 01 • -t •--I .-^ r-, I- .-I ri r-4 O > 3 ro.-1 01 .^ 01 01 44 ro w •.,.I w .•-1 r-I w w w 0 w Gl Ln • 1 C) M Lt 1 .-I O Cp Vl 0 +J 4J ^^ 4 N w 0 --I N M w .--I N N L i V 4J L'0 ro I Q1 .1 I 1 1 N m 1 I I N '7 Q) 7 GJ N O b 1 1 -ct N q:l 1 1 O 01 I*l 4- C VI O +1 t N Q7 Q1 '-I N N •-4 r4 N N t C L W N L ro E .r • • • • • . w fr w Q r- C C C C C C C Ln +J N E d ro ro ro ro ro ro v QJ w w w O CI G! L 7 7 7 7 7 7 'D LL- LL- LL Li LL •r Lt! Gl to = C r 4 +1 m O O V M E +1 L A O (A N v -u L O r Ln u- f .0 E ^. z r-I N M et Ln t0 P^ 00 0) ° --4I -41 N I M
15 Periods of no rainfall and antecedent rainfall during January and February, 1979, shown in Table 4 and Table 5 helped to determine condi- tions for wet and dry fronts, and hence, wet and dry periods. Nine of the 11 noctu rnal transect periods had HCMM overpasses before, during, or after the transects that were in overlapping range to give surface cov- erage. The February 8-9 period had daytime coverage both before and af- ter. Six potential day overflights and 4 potential night cverflights were available. However, HCMM data for daytime overflights were missing on January 8, February 8, and February 9, 1979.
GOES maps were also available during the transect periods listed in Tables 3 and 4. Rainfall conditions preceding the cold fronts (cold nights) are listed in Table 5. From the array.ot HCMM coverage, GOES maps and transect periods, rainfall conditions, and cloud problems, we selected suitable periods to determine the effects of surface thermal inertia on predicting nocturnal low temperatures. Several data sources were needed to obtain adequate surface temperature coverage of the state because of the prevalence of clouds in some part of the state in many of the HCMM scenes.
NOAA -6, polar-orbiting satellite infrared (10.5 to 12.6 microns) digital magnetic tape data for December, 1980, and January, 1981 (Table 2), were selected according to rainfall information from climatological data (Table 6). Geostationary satellite (GOES) infrared digital images showed that most of peninsular Florida was clear during periods of no rainfall on December 17-18, 1980, the period from the night of December 31, 1980, through the morning of January 3, 1981, the night of January 10, through the morning of January 13, 1981, and finally, from January 17-19, 1981. The period of Uecember 17-18 was classified wet and cool (Table 2), because an average of 0.63 cm (0.25 inches) or more of rain fell in each climatological region of peninsular Florida (Fig. 1) during the period of December 16-17 (Table 4). Nighttime temperatures were about 3-5 °C. The clear period from December 31, 1980, to January 3, 1981, was classified intermediate dry and cool. Rainfall for the 4 days (December 28-31) preceding the clear period was from no rainfall to trace amounts (Table 6). Nighttime temperatures in the peninsula were around 0°C and below. The period of January 10-13, 1981, was classified as dry and cold. Table 6 showed that there was negligible rainfa ll in peninsular Florida during the period of January 4-9, 1981, which en- hanced the antecedent dry condition of the state. Nighttime temperatures were below freezing for the entire state, with minimum temperatures of -10 to -11°C in northern Florida and parts of peninsular Florida.
4.2. Comparison of thermal properties from HCMM and other data sets
4.2.1. Comparison of 1978-79 winter max-min air tempera- tures with surface conditions which affect thermal inertia
Selected National Climate Center (NCC) maximum and minimum tempera- tures for 7 zones in Florida (Northwest, North, North Central, South Central, Everglades and S.W. Coast, Lower East Coast, and Keys; NCC- NOAA, 1979) were examined for January and February, 1979. The monthly
16 ORIGINAL PAGE 19 OF POOR QUALITY
Table 6. Average rainfall for each climatic region of Florida for the period from December 16, 1980, through January 16, 1981: Northwest (1), North (2), North Central (3), South Central (4), Everglades and SW Coast (5), and Lower East Coast (6). Data were from Climatological Data for Florida, National Cli- matic Center, Asheville, NC, December, 1980, and January, 1981.
Climatic Regions (1) (2) (3) (4) (5) (6)
• No. of Date Stations 15 20 13 28 14 11
1980 Dec. 16 .23 .11 .18 .15 .04 .04 17 .04 .13 .09 .37 .49 .21 22 .06 .02 .21 .30 .06 .01 23 .09 .141 .092/ .09 .12 .37 24 .06 Tr- NR- .01 .10 .15 25 .10 .09 Tr .03 .02 .01 26 .01 Tr NR .01 Tr .02 27 .02 Tr NR .01 NR NR 28 Tr .01 Tr Tr NR Tr 29 .06 .05 Tr Tr Tr .03 30 Tr Tr Tr Tr Tr Tr 31 NR NR NR Tr NR NR
1981 Jan. 7 .56 .40 .09 .0j. Tr Tr 8 NR .03 .02 .01 Tr Tr 14 .01 Tr Tr NR NR NR 15 .04 .11 .06 Tr NR NR 16 NR Tr Tr Tr .01 .02
Tr = Trace NR = No Rainfall QFt WaL PAGE It OF DOOR QUALITY
N1C bN .mil N ^ (O N N M N N N
C C A A b q W W W Cr ^1p 710 "2 7A "D 7 "7 U. U-W 4.W ILW IL li aiN Q• ¢. C @ N E N N N N N N pN^ yy Nyy yy q^ qNq^^ Nyy q T T T T T ^1 ui S C C nO+ v NO+ N7 L L >N >N N7 a a a L L p L.L ML s,a L q q L L L O 7 7 q q U q V Y CC L W W L L L O O O W ^ i- ++ Of L V7 O O r- O q v •u u u •u u •u u u v u u^ u n wO aL W h a A U OI 07 OI G q V) G C C q t' N NNy Ny N N yNy NNy N Ny y y •q v N T T ^ y1qT N •N 7^ T TyIq T qL q E E c q L r+ v W cm m W W rn W cm cm O r- N L ^ ^ L 6 L L ^ G OI W 4 q w L L w w v L V10 L L q CO L L W 7 7 W W W 4. W 7 Z. OF- d a a q d d g a A a a a 0 L u 0 4, W L 5s L c Y, ?^ ?^ L 71 L L O1 01 10 OI Al q q q q O q P 01 E q q a a v a v a = Zz •c c e •e •c i aLi 0 W' aC U u q G L N W O .u-I N 61 .^+ t(1 ti N 0 M m u r. NW w z o ^" o ~ o qc L QLJ N A W E9 N L n n co co Ch 7 C.) Z .^ .-+ ^ •..I pm.i .-^ H N N N I.i Lq i NN W co N .H W N N co 01 N N N Lq ~ I O ZO ^1'0 "'^q q A q O W W L,W W 07 OI W ^1 7 7 L la_ U. LL LL 4 r u Wq v c vnr 4 q -0 O ^6J1 L G G L N L N q q WL Q Vq ^+N 's G t L L n n n O Ey q V W W ^J I «. .+ c L L W u N\ L L \ C C G `1 q \ w U W 1: ?^ C7 C7 ?^ s' 1 Z . 01 ?^ N G ate+ ', q N - W \\ W W O O O ^+ m q L V Y- c O Y ^.+ .1.0 Y Y ++ — 2 L c0 q! q O L q a L L W W W O a T L G 7 L c L O O L L W W W n c L N H q q a q q p L q n p Y Y ^ E a r W L W ^7 b W 0! A A b L O w q L Oi U m Y Y N IL •L 7 r O m U U Y U' O Ln r L. r+7 Lq.. rW N N N N N Iq 2 E67 I OII 1 •firO N V I I I O O. 1^ I^ F- M 0D p1 •-. N N .^ O 0p .-^ .^ N N W _W N C C G c C C A A d a A A A A q q q q q q H ^^ 7 7 •7 "^ 7 lLW tiW lvW 1VW U.W 1LW U.W \
18 Table 5. Rainfall preceding transects in January and February, 1979.12Y
Jan. 2-3 Generally greater than 1 inch throughout the state, mostly on Jan. 2, with some on Jan. 3.
Jan. 8-9 Almost all rain on Jan. 8. Generally less than 0.5 inch.
Jan. 9-10 Almost all rain on Jan. 8. generally less than 0.5 inch.
Jan. 14-15 Almost all rain on Jan. 13, small amount on Jan. 14. Gener- ally less than 1.0 inch.
Jan. 22-23 Almost all rain on Jan. 21. Generally greater than 1.0 inch N, less than 1 inch NC and SC.
Jan. 24-25 All rain on Jan. 24, generally greater than 1 inch.
Feb. 1-2 Almost all rain on Feb. 8, locally heavy, generally less than 0.5 inch.
Feb. 10-11 No rain since Feb. 8, locally heavy, generally less than 0.5 inch.
Feb. 19-20 Light rain in north section, generally less than .25 inch.
Feb. 27-28 Most rain on February 25, variable, but generally greater than 1 inch. Variable rain on Feb. 26, generally less than 0.5 inch.
Monthly rainfall was about 7 inches for January for the North, North Central, and South Central parts of the peninsula. This amount is over twice the normal.
?/ February rainfall was about 3, 2, and 1.5 inches in the North, North Central, and South Central zones.
19 OF MM
Mg. 1. Climatological `regions of Florida, showing the 6 regions where rainfall were averaged. From Climatological Data - Florida, National Climatic Center, Asheville, N.C. I(- 20 D'
average minimum air temperature was subtracted from the monthly average maximum air temperature for each selected station to give the average daily temperature range. A large range indicates low surface thermal inertia, and a small range . indicates high surface there. inertia. These ranges of temperature were tabulated in descending order for specific stations within each zone in Table 7. The maximum range of average daily temperature averaged across all stations within each climate zone are given in Table 8. Specific stations within each zone are discussed be- low.
Northwest Zone (1). Station Fountain shows low thermal inertia soil ef?ectss , wgereas palachicola shows the moderating high thermal inertia effects of the Apalachicola River wetlands and perhaps Apalachi- cola Bay.
North Zone (2). Stations High Springs, Cross City, and Live Oak showarT ge average daily temperature ranges indicative of well-drained soils with low thermal inertia. Lake City, only about 25 miles from Live Oak, shows high t hermal inertia assoc i ated with poorer-drained soil. Hastings anj Federal Point show higher thermal inertia associated with the St. John's River. This area was identified as a warm area by Chen et al. (1979).
North Central Zone (3). Station Ocala is in the middle of a cold- prone area extending down the middle of the state. Alexander Springs is near the edge of the Ocala National Forest, an area with excessively drained soils. Inv prness represents a strip of land on the west side of the state north of Tampa Bay that has been shown to b y cold-prone. Deland is on an "island" of cold-prone soils. All these stations are on well-drained soils. Sanford and Clermont stations have smaller tempera- ture ranges caused by wetlands and lakes, respectively. Clermont repre- sents the warmer area of the central lake region of Florida.
South Central Zone (4). Stations Bartow, Archbold, and Lake Alfred are in well-drained soil areas and have large average daily temperature ranges. St. Petersburg shows the very strong moderating effect of Tampi Bay and the Gulf of Mexico. Okeechobee Hurricane Gate N6 Station is ad- jacent to the north side of Lake Okeechobee, and shows the lake modera- tion effect on the temperature range even though the site is on the up.. wind side of the lake when northerly flows occur. Fellsmere shows the effects of a wetlands region, near the St. John's marsh in the headwa- ters region of the St. John's River.
Everglades and S.W. Coast Zone (5). Station Belle Glade is just south of ake Okeechobee located on drained organic soil, but the 1 .1ter- mediate value of average daily temperature range suggests that the low thermal inertia of drained organic soil is modified by proximity to the lake. Tamiami Trail shows the high thermal inertia of the undrained Everglades. There is no NCC reporting station within the drained Ever- glades that would give the sharp definition of this cold-prone area shown by Chen et al. (1979) from GOES data. LaBelle is in an agricul- tural area of somewhat poorly drained sandy soils (high water tables), but it had 5.64 inches of rain in January and only 0.46 inches of rain
21 i (J1
Table 7. NCC-NOAA Climate data for selected Florida Stations that show average daily temperature ranges indicative of high or low surface thermal inertia.
Range (*F) Zone Station Jan. Feb. Surface Conditions
1 Fountain 3 SSE 27.6 28.0 well drained Apalachicola 19.8 18.8 wetlands/bay
2 High Springs 31.6 (28.3) well drained Cross City 27.5 25.9 well drained Live Oak 25.8 25.2 well drained Lake City 22.7 21.5 poor drained Hastings 22.8 21.1 St. Johns River Federal Print 21.1 20.6 St. Johns River
3 Ocala 27.5 29.5 well drained OeLand 28.6 28.6 well drained Alexander Springs 30.0 27.1 Ocala Nat. For. Inverness 27.3 27.6 well drained Sanford 23.7 22.4 wetlands Clermont 21.5 23.8 lakes
4 Bartow 28.7 28.3 well drained Archbold 26.5 27.7 well drained Lake Alfred 26.2 27.1 well drained Fellsmere 22.3 23.0 wetlands Okeechobee 21.O 19.2 Lake Okeechobee St. Petersburg 16.8 17.4 bay
5 La Belle 23.9 28.2 high water tablet/ Tamiami Trail ?2.2 Everglade, Belle Glade 20.8 X2.5 muck/ lake-'
6 Loxahatchee 23.4 27.4 mixed31 Miami Beach 11.7 11.5 island
17 See text for January vs. February rain effect.
?/ The drained organic soil effect is probably masked by proximity to Lake Okeechobee.
3/ Confrunding effects of January vs. February rainfall and proximity to drained organic soil, undrained water conservation arei, and well drained sandy soil.
I i S[ 0+-
Table 8. Maximum range of average daily temperature observed at climate stations within zones from NCC-NOAA climate data, 1979.
Max. Range Min. Range Zone Month (°F) (°F) Difference
1 Jan. 27.6 19.8-11 9.5 Feb. 28.0 18.31, 9.7
2 Jan. 31.6 21.15 11.5 Feb. (28.3) 20.631 8.9
3 Jan. 30.0 21.5 8.5 Feb. 29.5 22.441 7.1
4 Jan. 28.7 20. 15 8.6 Feb. 28.3 19.25 9.1
5 Jan. 23.9 19.6 4.3 Feb. 28.2 20.6 8.6
6 Jan. 23.4 18.961 4.5 Feb. 27.4 17.861 9.6 a I^ excludes Pensacola exc,iu%es Jacksonville Beach excludes Jacksonville Beach and Fernandina Beach excludes Daytona Beach excludes St. Petersburg 6^ excludes Miami Beach and Miami in February. Thus, -it showed higher thermal inertia properties in Janu- ary than in February.
Lower East Coast Zone.(6). Station Miami Beach shows the extreme case of effects of an is an surrounded by water. Loxahatchee is near the boundary between deep sands, drained organic soil, and an undrained organic soil water conservation area. It showed evidence of a higher thermal inertia during January than during February.
There is some evidence in the climate records that some stations had a much larger increase in the average daily temperature range in February, 1979 (a dry month) than in January, 1979 (a wet month). In the North Zone, Stations Gainesville and Usher Tower increased by +2.7 and +2.1°F, respectively (neither of these two stations are listed in Table 7). Also, there was a general increase in the average daily temperature range in the South Central (4), Everglades and S.W. Coast (5), and Lower East Coast (6) regions due to the significantly lower February rainfall (Table 8). Furthermore, 18 out of 25 Stations showed this ef- fect. Of the 18 stations not near a shoreline, 16 showed the effect of higher ranges of average daily maximum and minimum temperatures during the dry February than during the wet January.
These air temperature data show wide ranges at different stations throughout the state. Surface temperature ranges would probably be even wider, because low thermal inertia surfaces would rise higher in temper- ature during daytime solar loading, and drop lower during nocturnal ra- diative cooling. Nevertheless, these air temperature data show clearly that thermal properties of the underlying surfaces vary considerably. These differences are due largely to thermal inertia mediated through water content of the surfaces (soils, or complexes of soils, wetlands, and water bodies). The stations with the largest temperature ranges gen- erally had both higher than average maxima and lower than average minima within a climate zone. The sparse NCC climate station measurements such as these can be used to help verify satellite observations.
4.2.2. Comparison, of HCMM and GOES Images using the GE Image-100 and PDP 1145 Systems at KSC
In order to determine the feasibility of using GOES images to sup- plement HCMM data analysis, HCMM film transparencies were digitized by using the Image-100 system and one resultant image (Fig. 2) was compared with GOES thermal patterns. Images compared were for 0200 EST, February 1, 1979. Fig. 2 showed a HCMM nighttime temperature image digitized into eight themes (represented by colors) after eliminating grey scales not found on the surface of Florida. The coldest areas in the HCMM scene are indicated by arrows. They corresponded well with surface temperature patterns of the GOES coldest pixels (@, y) pointed out in Fig. 3, which lie in the cold-prone Everglades Agricultural Area (Chen, 1979; Chen et al., 1979). Because of different levels of temperature slicing, the high resolution HCMM image (Fig. 2) showed small clusters of both cold areas and warm areas, whereas the low resolution GOES image showed larger ex- tended areas of cold or warm pixels. However, the important point is that the shape of the thermal patterns were similar.
24 re-;^ r^^
0R1(Ai;AL OF ppOR `U - - ,
.^
Key 1. Green (coldest) 2. Orange 3. Dark blue 4. Yellow 5. Magenta 6. Light blue 7. Black 8. White (warmest)
Fig. 2. 11011 infrared (IR) image of lower Florida at approximately 0200 EST Feb. 1, 1979, obtained by digitizing HCMM film trans- parency using the Image 100 computer at Kennedy Space Center. Arrows show coldest areas.
25
Q
ORIGINAL PAU' i OF POOR QUALITY
t/. y't -'7711 t i i%7
GOES IR digital map of Florida 0200 EST, Feb. 1, 1979 77
HCMM GOES 1f^7 - , ]:^ti '7U:.0 r` Temp( C) Symbol ^F^_ v J, ,, f ^y ; r^_v•...r- 1. Green 4.3 - 4.8 Y @ Ltr1„1 r 2. Orange 5.3 - 6.8 A• B C D _ 1 + 1 ; L. u. .L. 3. Blue, Dk 7.3 - 8.3 9 $ FY! _^^^_..^_ 1,:=„'• +. lr;% 4. Yellow 8.8 -10.3 * 6 7 8 L.^ .. 7 5. Magenta 10 .8 -11.3 5 % f -- \ ::.7. .,-tier•;- - •- 6. Blue, L 18. -123. 4 3 ^,^ -• , f ^; 7. Black 2.8 -13.3 2 8. White 13.8 1- v •., .. .r ,, .. .
i-:.7(: :11•^ r ! /.'_ r,=yV L 11 - = _'t i 1 • i l (;^. 7 77G•v C} f =^ !^ ^' 7 .L"- ;: !'^rV l/ rr \rrl y= ;: .1: V i' -1( : : '" ^;.n^ i, l.,iL•(1: rr j/. : 7? q t. y f-i--^- 1.Y=hrr-:'rl i"VV 1 (^ r-r )'Ii -I - - a S;R 7.4 't t-:F\' VAC- ,.: r-.:.ti ^. V ^7 +(: L %.
'VVV'z7i C \11 -• i':^;lt'r- -r nr,r-:^ • F, '^:. ^: yy'-1/ ' : :'It f t ti, r, _ -h ::^•.L n/i7ti': i: c 17^;,aU _r !- % '?.1 VV -.7?7"'A%7lru.-1, 7fd7 ,4(".If ej r.'- 1 +;IC yrr .r2:,,lr•c...;;%^- t l't f = /-F fi __^.-_. [. ••, r- rte. t:(;11 ( f .^ 1 • ( 1 r,•4(i. ^.-.O JUC BEE
c lu 77 f , °•,. `•' C r u l ,,,, a C( . , v ( +^+. / 7 7 ^: n r'. 1_ C Q 7 f- -7 77:._>r..
L L. 1. ? 3 r. . "> '. j ? h i.. L ^t : j •. `:, i. L. L r i "% ^ ^ 7 r. ^ 7 L c f' H. {' . _ . _ - . t ^ : a :.. - `i I
7 l .. . ., Jt'%i^f. ."<%a/ .:_/ %(r. i7^ :i l/f7^ _ ' t •. (. (^ `' j / i7 •G:4 S % '1.^ ... L ' ^ C ' ., .. .! .. ^ . r. '1^ ? ? :j) 1 %(a C. ^ t /. - 1 % I '^ ! 1•, u i r ^, . -} L: • 1^ .. c r. a 10. •. -
^.^. / JL ty ^ ,r. L• L^. G. .'^l- ^- .: r,l .'.]' i;1 (, -r_7 j:r f. l.:. .. .. _• 7 C. .=1.. =. .. ., r7/- _ .. . i-z Jr: :^1: L:. {L' - :`^^. r. 7, nn!-n ^r_-. .•7: / r ^-__ _
t 27
r t i,- • • t r r .- , .^ .. ] HCMM Yellow Area ? l
, ^ ^_ .r .1'. .r.. i 1'7r 'r.. • ^.. :. -7- - t t]i'_, • r.j r. !., - -i ce.
. L - ^ . iii 1f:. n -t -'i 1 i•.;C' t' ] } r.n „1. , ;.1: , rr l.. n ^,1( • 1 __. t ^.•. .., ' t i s. ^ ^i -rr j:. -^.- ../: .. • .n r:.++...,rl. i.. ^r11.:'I I .. . I Fig.3. GOES digital image of Florida at 0200 EST, Feb. 1, 1979. The coldest area Giuth of Lake Okeechobee (symbol Y) was blocked for comparison with the HCHM image showing the coldest area, green.
26 4.3. Thermal properties of organic and mineral soils in Florida
4.3.1. Thermal prope r ties of organic soils in south Florida
From Lake Okeechobee southward, the topography of Florida is flat and lies at low elevations. A wide strip from Lake Okeechobee to Florida Bay consists of organic soils (Fig. 4). An area south of Lake Okeechobee is drained and used for agriculture; it is called the Everglades Agri- cultural Area. Large canals extend from Lake Okeechobee to the southeast coast. Three Water Conservation Areas are located in organic soil to the southeast and south of the Everglades Agricultural Area. The Everglades National Park is located south of these Water Conservation Areas onward to Florida Bay.
HCMM apparent thermal inertias and temperature differences derived from daytime February 3, 1979, data and nighttime February 1, 1979, data (Fig. 5) for south Florida showed similar patterns to GOES-detected sur- face temperatures of the same area and for approximately the same time (Fig. 6). The HCMM-derived data showed distinct boundaries between the drained organic soil of the Everglades Agricultural Area and the un- drained organic soil of the Water Conservation Areas #1, #2, and #3, managed by the South Florida Water Management District (Fig. 4), as well as between the Water Conservation Areas and the southeast coastal land area. Differences among the three Water Conservation Areas are present but they are less distinct. The GOES surface pattern for 0100 EST, Janu- ary 29, 1979, showp ^ a difference of 5-6°C between the Everglades Agri- cultural Area and ths. Water Conservation Areas, and a difference of only 1-2°C among the three Water Conservation Areas. Data from February 1, 1979, showed generally equivalent differences (Fig. 6). The HCMM-calcu- lated difference in apparent thermal inertia indicates a difference in temperatures and thermal properties of the surface. The region has the same organic soil base, but a different surface water content due to differences in land use and water management. Differences in surface water content contributed to the difference in thermal inertia. Atmo- spheric conditions would affect the regions equally because of the prox- imity of the areas. We could not accurately quantify the apparent ther- mal inertia from the HCMM prints that were received, but the patterns of the HCMM-derived apparent thermal inertias for the areas are supported by the GOES surface thermal patterns (Fig. 6) and the GOES diurnal sur- face temperature wave for different dates (Figs. 7 and 8). The diurnal surface temperature range of the Everglades Agricultural Area is the largest (Fig. 7) which corresponds with the light gray scale of the ap- parent thermal inertia image (Fig. 5), whereas the smaller diurnal tem- perature ranges of the Water Conservation Areas corresponds closely to the darker gray scales in Fig. 6.
4.3.2. Thermal properties of mineral soils
The Suwannee River (Fig. 9) flows through an extensive area of well-drained sandy soil in north Florida. GOES surface temperatures in- dicated that the area appeared persistently colder than surrounding areas (Fig. 9). The colder areas corresponded closely to well-drained sandy soils and to LANDSAT-identified cleared areas. HCMM apparent ther- mal inertia for the area (Fig. 5 or Fig. 10, circled area) showed the
27
I 1W eA 6N
ORMINAL OF POOR QUALIW
N -" 0 o
Sandy Soil Area
SOUNDART, CONSERVATION AREAS k~SOYIIOART, 0010INAL 1444914 Of &VC&OLAOt SS0^1«
9VIOSlA^tT7 MATIONM' PARR
{CAIt IN NILEO
3 -1.....J 0 0 IO 1! 1
X
0 tYEROLAN S 1^1 O Q YA RR L 1 47
CAPE `r SABLE Ca
^ ' 2
P
Fig. 4. Map of lower Florida showing location of the conservation areas and Everglades National Park, and original extent of the Everglades Marsh ( from Central and South Florida Flood Control District, Vol 1, No. S, 1972).
28 Fig. 5. HCMM-derived apparent thermal inertia from daytime Feb. 3 and nighttime Feb. 1, 1979, data. Areas in south Florida were clear and were used to compare with GOES images.
PRECEDING PAGE BLANK NOT F;LtJ M
30
ORIGINAL PAGE 19 OF P" QUALITY
3 p A
< L. (<- .a > > 1 W > 0 %k w \ .c v po w c 0 c >- C rc lz- a kv w .9
"T Q > LL LL p-i C cd a c LL >- r-. > t—; C > > > f*,, 0 in cc 1 QI O tY > LL' LL 11; U: UL.L W - 7 u Lt- LL, I I M^ > i . LL LL'll LL N--, IJ 0 >- LL. LI-,, - LL :-k •-4 c U U u 44 > LL L-u a=> LL 0) en r 0 r lftt N. r .0 W I it -LL LL >- > > > 0 00 s C L^ LL LL L-,, >- > r, X = -c 0E! 41 L, C. L 2. :i > be -r LL >- > M co 60 lw N. r u^LLiz>=r m 4 .L u. LL co 0 0 :7 Z; co u 00 m $W w > r= 7 z
I " p I 'F
r T 0 sw CU
> a 3., Z L.L LL L: >- >- 4F. wh ME Lr rf LL Ll, I.L.:.L LL L: VC < ftr- :-,L u. > :^ L: - a = < • LL 6L C, L L, ^.f L. - - I . w•1 f0 LL. 4 m W " w .1 LL Lt U-= > L i •4 LL ^ 1'.L LL u T N Ic w L.L. LL Ul I > * -ic >L-. 0 >,-> tr. C >7 > LL . Tn La I >C-. 0 r-r-cr o l cr cc a: r-Ic a =L.;-QAC N I- V L* > LL > I. > rt: > > > v L.: x L: L. 3 L. m CL 0• 0 . o - c r- Cr CL r- c: v i: % I= > >Plz- t-' cc tr L. k; o- CL c Z-wc c: a r- r- r- 0 r- It t.;:r L: L-: IL -Q 4-c C, cc a ON tr X cc fl- 1^ cr LL %1.4 < xI . -L .LL > > 'Z^ -F ON L.; L.: a cc r cv.%. LL L >- > L.: :X t_• S• X 4.:, M 4,r CL C r,- CV N N q G r. L: N L! L-1 (f Lt > L.^;-: Z c.; L-: k.: t:^ t.; cr x L.: L.;.% -c a m-'rN, m r, M 1. U 6L LL L;- U L: -c Cr L: L; Z, :r -N N M. 7. w I; L:.-,, LL LL > b=- L; Z L.: c t_; < a: c C, c: 4a ol p t (-, N gr u v LL Ll c L: 9 *,, - fz N, tic r. M 4; T Cd - C V • to U, LL Lt (Z ^X < if if re, N N [2 -T — •-. J; U. LL d u > Y r, rL L: Lr mcnN-NUL- S g >- tz Z L: 01 .4 1 in 4 .4 K K f0ri, to • t j :r --, :^ (.L t 1 rl 4.^ Ic 77 . t,,,c: c- <1,1 I M'I -4- .4 tr -j (\,N.ftA -,4 - -V T C U. f". P., -L -L .L >-> :;> L.: Q: T. rp N ff fr, C\ > -,Lj -% N r 7 Lr tv 'IJ U, C\ L-- > k.: L: C L 7., LA * -Z Lf "s 3 Lr% 4* cr -4 --c C -N 7 C^ Z < ^ -, 3' w I- -i! Lr tr (N- M N c N iz i•: Lt -n r-- -Z ff
PRECEDING PAGE BLANK NOT FILMED
—BLUM 33 ^e
3 ^I OAQV AL PAGE W OF POOR QVALI nr _30
28 O--o Everglades Agricultural Area, organic soil, drained. 26
0_--O Water Management Conservation 24 Area #1
n —•n Water Management Conservation 22 t Area #2
^1----+^ Water Management Conservation 20 Area #3 ^---.^ Urban Area (Miami)
18 U °^ 16 Night of Feb. 26-27, 1980 m x
14 w ^n
H 12
o 10 r ^ 8 0
6 '-.01-011, u 0`O_0-1. O `0 4 C O G O
t t 1 I t L_, t 1 t t I 16 18 20 22 24 02 04 06 08 10 12 TIME (GMT) a^ lll
ORIWNAL PAGE IN OF POOR QUALITY
d V
.y N N
V O O 7 5 \\\ ~ " 1 ro ro y w.. I 0 rJ al u v e f 1 u<< mss. i O C R< ^`^ I N u .Oi M m O ro ~ G 0 al to N .1L 'roj nI tl r4 • y ° w a m 1.c I "! / o 0. id ^C e u C? i o .. C
^ . W0 • Q ^
IJ • N .r
` d o Gil d / A4
• +O. O
!_ O VI W ' N 41
I W V y .°.. Q ( W Y ( u00\ c rE c \ y t
O M y N J I 4 ;^ •i • y ^ ro ♦ ry ro N O ~ O / ^ aQ1 0 • :c ...1 O y w I N , \ O
/ ^) • CIO N T
0 I O • C ^
1 Z4 .' ^4aTuPTf: a
4 N ♦ ! ^ asnsesad^ai
O 1 O N O OO 'V J N 7 N .T ^
35
4
W- r+) f ' I ` f ORONAL P V '65 OF POUR QUAL11T i
to---\`- ^ hil"^ www.^^ww]II\ IaL (XDACBt9?)-9;i8779O=it:OT \I -t iittw t.t-1lwwlww " 117111 Tt r4EDCDP?*-P;8S76SS3(ARAJRWI"^' \t\7" "lI""7\ QW CDACB-)(;88766,-269:BEFOTt" ww11 .w,.www Suwauwa River Basin Cold III)\I^ www lZll FDCBAa? ( 9t998S75S:AHLNQUZb'): ` i l ; I Z l ^ Areas I t 2 0 and 3. I 1 )1 ] " ] "" -1 M .DACE @?) (9 t 98543 T Z l'»- wI ► , ^ w' •w _ I wwwwww..w•.y _ ww w!^)IZ\l7lw wwww J lI \Z. I L .Jan. 12, 19(31,1 I100 857: I_CC ~« 7 •: "tit" 1 \ J wwww ^11 ^3Z ^" 4% ( Y 11 _ L IJG --- w.ww wA I]iwwwlw t w\'] \ \j ^ A. ^1 " [ V O : " C«-- ''^" _•_^ I7]Z] "" -' ICJI . R AAN= ?^<;85ib69 Fig. 10. HCMM derived apparent thermal inertia image from nighttime Dec. 15 and daytime Dec. 11, 1978, data. Areas in north Florida in the Suwannee River Basin were clear although overall sky conditions were poor southward. The thermal inertia pattern, of the Suwannee Iaver Basin agreed with diurnal GOES Thermal data. PRECEDING FACE GLA^:K NOT FILMED IT ('*) I + ORIGINAL P,:. - 'bii Kmm v'lArn. vnan• OF POOR QUALITY VIM* y1:0• V12 • T J.r U u r r 3 ^ ^ - ,^ f• T '^ •^•'^•• ^ ^^ •may ^ ' ` ti r ^:\ ^► • ^, mow. A' ar `. m t V6 y^ ,. A61. I ^ ~ t q^i^ll / f op •"^ !Y ml •'. ^ .A ^'^._ ^^.+ ^r^ ~ •.. ^ yr • - 11, • •sue ^~ ' ' ^ • 1 same general pattern as GOES and LANDSAT false color imagery. Therefore, we decided to examine the thermal properties of the area and to obtain thermal inertias for the region in preparation for construction of a thermal inertia map for Florida. GOES surface temperatures from January 12-13, 1981 (Fig. 11), were used to obtain cyclic diurnal surface tem- perature curves for five areas in the Suwannee River Watershed. As an accuracy check for GOES temperature, maximum and minimum temperatures from NOAA cooperative observer stations in the Suwannee River Basin were tabulated in Table 9 (approximate location of each site indicated by black dots but not individually identified on Fig. 9). The five areas, shown in Fig. 9, are regions which appeared colder than surrounding areas early in the evening (1900 to 2100 EST). Of the five areas, Area 2 appeared generally the earliest and also the coldest. The diurnal sur- face temperatures are shown in Fj'g. 11. Area 2 has a larger diurnal am- plitude than other Areas, which indicates different thermal inertia and thermal properties. Areas 1, 2, and 3 are found in higher well-drair:ed elevations (100 to 150 ft) whereas areas that are warmer at night (4, 5) are found in lower, poorly-drained elevations (less than 100 ft). A strip of mineral soil is found along the southeast coast of Flor- ida. This mineral soil has been highly drained. It is highly urbanized, with agricultural development at the south and north ends. This strip is thermally very distinct from the wetter Everglades to the west (Figs. 2 and 3). The diurnal GOES surface temperatures for the Miami urban area are shown in Figs. 7 and 8. This area shows an urban heat island effect because the midday temperatures are high, but the nighttime temperatures do not drop as low as the Everglades Agricultural Area. Fig. 8 also shows the distribution of diurnal surface temperatures for Lake Okeecho- bee and for a mineral soil area west of Lake Okeechobee. 4.4, Thermal inertia analysis and interpretation 4.4.1. Thermal inertia classification using the GE Image- 100 system The HCMM apparent thermal inertia map (Fig. 5) of February 1-3, 1979 (AAO-281-06580-5, HCMM14, File 5) was digitized using the GE Image 100 and POP 1145 Mini-Computer System. Results showed that the state can best be divided into 5 thermal inertia classes. Each class contained 5 grey scales (grey scales range fre^. 0 to 255), beginning with grey scale 40 (Table 10) and ending at grey scale 80. This range (40-80) of grey scales covered the entire state except for water areas (Table 10). On comparison with a general soil map of the state (Fig. 12), an apparent thermal inertia image from HCMM data (Fig. 5), and a low-temperature map generated from GOES (such as Fig. 9), the following thermal inertia classes (Table 11) are formed, based on information from HCMM scene AAO-281-06580-5 digital data processed using the Image 100 system. Ther- mal inertia class 2 corresponds well with coldest areas detected by GOES in the Central Florida Ridge and in the drained Everglades Agricultural Area, which we have monitored since 1977 (Chen et al., 1979). Thermal inertia lass 1 included small cold pockets scattered within class 2. These small cold areas are known to be present from reports from citrus growers and Cooperative Extension Service personnel. These details are not resolved by the low resolution (8-km) GOES images, but appeared in 41 N^ ^ _INTENTIONALLY r-RECEDING IRAQ BLANK NOT MIMED D OMWAII. PAGE W' Of POOR QuAuTY Cd cdw goCd a v C 3 W u C vc0 u^1 w0 J.! a +. L) I uE U W IHW i .-4 C C rl b US n W rn O C^ 00 . W .r .r 41d 00 v rl GL 42 Table 9. Maximum and minimum temperatures from January 12-13, 1981, for NOAA cooperative observer stations in the Suwannee River basin of North Florida. Approximate location of the cities are indicated in Fig. 9 (black dots). Source: Climatological Data, Florida, 85, No. 1, National Climatic Center, Asheville, NC, 1981.— Temperature (°C) Station Max. Min. Cross City 2 WNW 5.6 -12.2 High Springs 10.0 - 7.8 Jasper 5.0 -11.7 Lake City 2 E 4.4 -10.6 Live Oak 10.0 -12.2 Madison 4 N 8.9 -10.0 Mayo 4.4 -11.1 Perry 8.3 -11.7 Steinhatchee 6 ENE 7.2 -11.? Usher Tower 10.0 -11.7 Average 7.4 -11.0 43 AD 1W IV ile 10. Thermal Inertia Classes for Fiorida from Feb. 1-3, 1979 Day-Night HCMM Data. *** 1-CHANWEL, LEVEL SLICER *** WARNING- WIPES OUT THEME TRACKS TYPE ChAN111EL AND *--OF-SLICESs OR ECX?IT 1 8 EQUAL. SLICES? tY/N ) N lNP THEME( 1) - 40 TO 44 THEME( 2) - 4S TO 49 ORKA AL PAGE VW THEME( 3)- 50 TO S4 OF rOOR QUALITY TtE14E ( 4) - SS 'TO 59 -1 t S) - 60 TO 64 THEMEi 6) • as TO 69 T t ?J'- ?0 TO 74 THEME( 8) - 75 TO 80 E(X)IT OR (R)ECYCLE ? *** AREA SUMMARY (D8/1. 0) ^Ic I f ETIORY I LANDSAT I I PIXELS PERCENT I PIXELS ACRES HECTARES I I I ------I I Z I TRAIN I I I AREA I 0. O.Ox I 0. 0.0 0.0 I I I I ALOM I 0. 0.071 I 0. 0.0 0.0 I I I I 7WF7'E 1 I 2600. 1.071 I 2610. 2918.1 1181.0 I I I FO ME 2 Z 1207. 0. Sx I 1212. 1354.7 548.2 I Z I I TFETE 3 I 1S4'?6 . 5.9% I 15S37. 1?369 .7 7028 . S I I I I 'I HE14E 4 I 71647. 27.3% I 719L". 80414.1 32543.6 I I I I S I 232!08. 8.9x I 23299. 26047 . 8 10541 .6 I I I I THEME 6 I 6962. 2.3% I 5985. 6691.5 2708.1 I I I I THEME 7 I 4038. 1.5% Z 4054. 4532.1 1834.1 I I I I T^ErE 8 I 4396. 1. ex I 4312. 48M.6 1950.9 I 44 ORK91NAL PAGE J OF POOR QUALITY a ^ R G I i ^ 11 II ^• wNIM UYU atlIN.•tIYIu I`-- -.\ I.U. ` ? J _ . • ' _.^ III I• ^ I ♦ i wunww l ,f it - 1 T^ ^-.. — • ^•^"'^ ll '.'^'...^.Y .f • ... , IY f I 1111{IIwW U C , a IIII I I •Axv„ I. ^ I \ I ^,^ - • m r MI 1 • to n fi «.. is 20 34' .; 4 V 71 ^ ^ ^ , la- MTIn 4 /^ ^ M h } a! -I` 31 I I " n MIl ii 7 ,f y " 1 Z 70 t IWII n 4 CT L N^ 1♦ t ^ ^.. ^ of s an aT iJ fi ..- 1, I ^i s ` ^ n 1^' i Fig. 12. General soil map of Florida, showing the 5 ' thermal inertia classes for Florida constructed from RCMM and GOES data (see Table 11 for descrip- tion). Source: R.E. Caldwell and R.W. Johnson, USDA-Soil Conservation Service, IFAS-Agricultural Experimeat Station and Soil Science Department, ^« t rt vised 1982. ^.+ 45 - 1 ^1 Table 11. Thermal inertia classes for Florida based on HCMM image AAO-28106580-5 and using the Image-100 Image Analyzing System to determine areas with equal thermal inertia as defined by grey scales. DAta digitized: HCMM A-A0281-06580-5 (HCMM14, File 5). Data 'Formatted from Feb. 1 (night) and Feb. 3 (day), 1979, IR temperatures. % Area Thermal Inertia Class Thermal Inertia Area 1.04% 1 (Theme 2) Lowest thermal inertia class. Numerous small areas scattered within drained organic soils of the Everglades Agricultural Area and in the deep, sandy, well-drained soils of thg, Central Florida Ridge Land Resource Area.— 12.3% 2 (Theme 3) Drained organic soils of the Everglades Agri- cultural Area and deep, sandy, well-drained soils of the Central Florida Ridge Land Re- source Area. 56.9% 3 (Theme 4) Peninsular Florida Flatwoods Land Resource Area, except areas covered by Themes 5,6,7,8. V, 18.5% 4 (Theme 5) Co astal Zones and Wetlands: 1. Between Waccasassa Bay and Tampa Bay along west coast of central Florida 2. St. Augustine and Kennedy Space Center along east coast of central Florida 3. Tsala Apopka Lake region 4. Green Swamp 4.8% 5 (Themes 6, Shoreline, river, Water Conservation Area #1. 7, and 8) 3.1% Shoreline, Water Conservation Area #1, part . of natural Everglades. 3.3% Water Conservation Area #2, natural Everglades. 0 i 1/ Generally too small to be resolved by GOES 46 - the high resolution (600-m) HCMM images. The thermal inertia classes agree well with information obtained from GOES images (Chen and Martsolf, 1982). The area north of Lake Okeechobee appeared quite cold (GOES images) during the 1980-81 and 1981-82 winters, but did not appear to be relatively as cold based on apparent thermal inertia image (Fig. 5). This area generally has flat topography and high water table, poorly drained soils of the South Florida Flatwoods Land Resource Area, which usually will have a higher thermal inertia, and therefore will not usu- ally appear cold. However, if drought conditions exist, as in 1980-82, the area can become cold. During the winter of 1978-79, climatological data (National Oceanic and Atmospheric Administration, 1979) showed higher than average rainfall amounts (Table 12), whereas several subse- quent years showed lower than average rainfall amounts. 4.4.2. Correlation of HCMM apparent thermal inertia with HCMM surface infrared temperatures A computer program was developed to read the HCMM CCT's, average the values in a 5-pixel x 5-pixel array, and print a symbol map of these averaged pixels. The IR pixels of such a symbol map represent 20 to 25% of the area of a GOES IR pixel. This process reduced the symbol map data to a manageable size for displaying the Florida peninsula. Reduced sym- bol maps for both nighttime IR temperature data of February 3, 1979, and apparent thermal inertia based on February 1 and 3 day-night overflights were developed (Figs. 13A and 13B). Data were extracted from a west-to- east cross-section of north central Florida (Figs. 12 and 13A, 13B, la- beled A ... A'), and the values tabulated (Table 13). This cross-section passes through persistent cold areas as indicated by GOES and therefore includes some of the lowest temperatures in the state during this per- iod. Values in Table 13 are plotted in Fig. 14. Symbols that indicated surface waters were not included in Fig. 14 because thermal inertia of surface water areas were found to be nonlinearly related to temperature, causing errors in linear regression analysis of grey scale versus tem- peratures. Regression statistics of apparent thermal inertia (y) and night-IR (x) (Table 13) are: Number of points (n) = 69 Mean temperature (z) = -2.9°C Mean apparent thermal inertia (y) = 56.8 Standard deviation (Sx) = +1.36°C Standard deviation (Sy) = +11,95 Linear regression equation: y = 7.07x + 77.5 Correlatio n coefficient (r): 0.81 Comparing Table 11 and Fig. 14, thermal inertia classes derived from February 1 and 3, 1979, nighttime and daytime data, respectively, can be associated with the following nocturnal temperatures of February 47 ^^ v V1 I N N N M 4 S ao ch ¢ 1 1 1 1 1 1 -1 dr r w b^ L D L m 41 ^ I to ^ 0 p Ci CJ r G^ U r ^ 4J CH Ln C%) ^O n O C *-1 u c n Ln W O A N N N N N M u E r aJ v •^ r 4J O r t0 a .^ 4- to L C C! r aJ n tv co _ c Or ^ Ln r4 ^ •^O N O M n CT f^ r-1 L L 4-1 4a CD O O I 1 1 1 1 C) C b (A •-4 C 4) 3 Z to ^ w 7 M v N 4+ L L bb b O = = EQ b Q1 LA 4m 4+ L E O 4J ON N qd* <71 LLn C O ► O c n co LL. Ln ii Li `^ v u O) 1 r L ^ L ^ C i O C 4- 4- C ¢ Of C ^ ^ w -4 n ^ M Ln b C! 13 CJ O N t •- O O r-1 M N M .--1 M 4+ L 00 ^ 14 f^. O Gt M C) •'- . 4 V 14 L LL. Cf N O w 0 O E t4- w M ^ i O C) b .^ i L .0 41 C1 r^ a) 4- to t ^ CL 7 4-) to c n C C ^ r •^ a O b 4 1 10 w M M M CV) M M E '^ • CO r C) to CJ O CEE!_O^ b b C 4+ E O to C \ N r 4J .^ N N E to w f^ s Ls) L1) O 1n E rn cc v M o 00 kD ^ L v •^ r. u O b r 4- O CA O C] ^ N tC1 f^ Ln O a ^ n► 1 .-4 O C E C7 N L Of O N C- CJ () • • w 1--t C s- L C1 C7 M b 4) Q) 7 L^ ^--1 t 4••1 4J L L N L O \ a••1 I C fo 4- 00 L C •O fS V) Z t0 C"i O O 41 41 N M N O O M 4- r b ^ C •1 CL O M r O N -0 •t- >> 00 to L°o LO L C) to r C r b L O to •.- C! w 4- 4- C > U ^ r C .— Z C •1- •- O 4J to i L b V V •• f^% CA .. O 4- CI W C) \ r-1 N 4J CL C C) CL r C r" L CJ •— L to r O ^ ^ N O b L b O CJ •^ N O r r C b 41 n OC 4J L > .-4 to to to O O Cf 4- U ^ L L U 4+ 'M O ^ 4J 4+ N 4.•f .. 4a 11 II L N to N N N c)(1 a N b^ CJ E CU to a W ..i 3 r 3 4-3 Cl E _O r C L C t Oft L 41 m Cl U 4f ++ 4+ 4+ L 41 w N CL Z L L L O N O 3 b to O O O O > O 00 Z Z Z N ui Ln .J U •-4 1 NI MI r •,00000e0000eeeeooeeeee000eee000 O 000000 r 00 -•Fr7000 fly f7 `:,^T. •7 ^ V 11 y.i •. 1' n+. ••U •.K .•••.• • ^• •• (+"Y 00e0000e000e0e00000e000000000e0 0 000000 0 0 0 0 0 0 0 0 0 0 f,•7t.>10.l1 11 1wb4'1'O 1901)^V^ II QUMQWmOV r•p.: w1e 0 009 eoeeeooe000eoeeeeooeeooe000aeeo eAOeooeseoee t7W N7FNFU)c 1eWU14 /171w•CM WWN7WA1ti t7w 13 .7 .7^0 pr)w tl - 000.000000000000000000000000000 1 'J U' -10 r--'Ot)0 M" f71.1b N•7 t47l ILUM a N•7.4m 7fo 0.41.70 O ooeesesooeoeetleeeooaeoeeooeooeo 0040000.0 ^rt 1 •i'7N f•'7'a^NWt.: ',n.Te.H1r01C f17W.tl090 O>•J]9 9'•+'•I.7nN0.7 eOOOOOe000e0Otl O0ee00000000a r COTNMf >t O MM 77: n wn domown tW .106 Y•-1`41UMw. Nn 00.00OOOOe0000e Otle00000• K eelY>.FO.► y T•7 Q.1 `716 p' O ra O)U 01 =ZA. 0 OO ee 000000 eee00tleee0 • E TNO ed:lf m•6Z f,'7 01414140 9 OeM 0000000tlO0e 000000 .1 1/1J 1 11V WIgl UC gf6 Gt'Y11 q 00 DOCMNSISWIQIn^ IQIaa 0 0a000ee0tl0000 Kw7p.a 74 CJ •n NtiMIhL7UtJm T.000 d 07 ► ►t we. 4,• ••U H r0 N 1•. N4'N n VnM- -10011M+W. v o e a e e e e e 7c16rmut7caa I UU7wt7u ro uU• MOO ►. w I6W Mesat1•• N.tVV •• 4h7V011.N w7r.on V N 111 M V V 9 10 W.f::(Jn e a^ IUVWIm .rnn •.ONna1M1r.IL.t M..1 OIM..V V4vfl-•V -t"...e fam •7>•onh 3 r Vn6:m:.t HO.O IA"7CO1.1 mum:nVMUV 00,000 rqtUJ 11 OMO. •7^ It n h:0 00 M•%DnO O et V wm V NmA'O •A14A M 0N pV n V M n 1•.^J-176 *m(. 4- 111411a OnCO•aO MONnON.•••VnA••wlN:..4f••'1IAM^'=I •• V OOMIJ7U'OMNYh 3X14 VM VV ••t O.e Y1N10 r m •. OO t= 1WO a"••UWr9Vn N m VO.11 n VnnlW.iMF 11411 0094 ••t'7 ..J ..n W_ n •.1f1 N I,DON,OP V •t: V W ►•.n 1101'• N COW ro 10.ca c 0a.1c-c ICwM^M00 •O4 N- /J^^ .a.ONS Is.. IN00 X14 M:1^01-4M•• 17013f n t•Nro n,oJ VNYNA^hY • W \ON^INtntnJ ..InhminVl:!/t ^, V N V O O O oCOCOT •wN 000 O 0 r•m nO 0 0 0 0 Moe) M•7: a•.40;J p0III O O O O•• H:rUM.on ^h min 00'30,3.- na•OOn,Ontw.11. 9M 9 •. I. NU 0000000000"00 Vol Wmuoo000001q 4,4 N nm U Wrd, ••UM.ws^ nV VM:N'oas 1'0X3•'? . ne• O N1, ..V 1,^70 p0 a" ►0000 DmN 070 • V-c ol •")^ O •r f' W1V7•:u O:m V UM .10,M76.6 UM1Oa .-0,ON N a ••00!0 •.:,n •eV11'f•O,ONCO,OP 1 ii ,10•100 N V O7:h N W7M .•^tpn narhA V mnnht^ •. N V fVN,A W p.PO 'O 0 0 4 V n q 1ma O mA um•.o•O e e s aa10i•A =wuouin ^•^r•e1 !nMUN MN •.V W •^••:.•NryYN\Vlahno...... U1n'0,0 on-40 a ^I r10 LIA •,OmaOai .,a N1•a (+Oh•.•••.•tl••.•VN^w7y0O OOa p f• •.w • I •,p •\071301. .OI^m Wnh .. ^0'Ao"D aY n `",VV WIT "a) P MNwfe-iry .• •ONOV ,aA•.Whn COWTIN N,OV •. •n•Dry nuON u M•Ol.eNmCO ngCOl WlmOH:O,•7bmOn11 -4 Fie L.t3n1•)5 mr.W GOunrfl0m ^ONYX • • 1 1 •^<7 •i0 q::nP.e a. 1.•F .149 •7 ••VL n • .f• .Y \I-1 n . .N NNV • 4,0 ••PN•r 1W0 "'I'MM7M177mM nAWMm VUPOd .tn UAMN PVV1,D mLe' • `ONInY,nONnA•4M •.ONCOONm,pnONN>a 740.1 tq On Uf4NCO nnlN •.1V11MON..n ••mONN N ,fOOVVUi NM Vantn.:rfk0 l1+ NO We O 0 0O 0 O 0 V011hM 70W rOw•T 41 14:ro•.MV N P••U•7 OCON rC4JA f •JO+ S 1YON ••D:n ••V ro9RIO N,OnON .•0/hlpUgwnO,lg ro M'4A,.0 b,011 M1M1tpO'• ••N .••.ON .•U-t : N :DroMO a 00:7.0 0 0 0 0 0w••tnn V •r •11.,0,0 .• mo.• a \ml_ •,^^V ••'1 NMnrtl •.n,OM1Y1Y.•V71 ••o:s70 a-.100chM1 NO n...... 0...... 1+V •.•,CO N.'W pe be a IJO 0,ammo +40 00 0 0 o e 1740 1 \YON V O,nN a ^n + o^o ` •ti ••••V N NO JI'••• U-00 •.Vm,OONhh.•••VVV NO NO amhhmn n M^--E- 0,0,0040,00II WO P• ••• 39 uON .,m V ••ONh .... •.Mm ..h nn N Vh_ C DOO Vw001r1•ha- 140010 •..Nmomtnr loo •t4• •+ooa%omShO\\o • • ---MV••y(!Dt • 0 •\1 w t^•.[000..0 ^n0♦^,D,O•.nacvvomv p V.•AV n mus7-1 itV ••wf]AV .fi 16 11 h>. y .1H0 0 m^ •7n^O\ YmU •1X13Mf111 ••M1'D ••^P N t_ •.D .b Gnaw • hl\ \Nh:M.1'070V •.WWIOwFWiO..1W.aH 0 n'e.1 Uhn:14 '02 O:O O O O O 7t ••[Hn,ov11w14 f n0 O 1 n W COm t7 f•! :J 13 V 11 70 11 1 > 1 •.VWY0 •7 •• l.mtfl•'NhnmNn n 0=273 .411 WIU91mf:O..7 oW .. ••II ••OIY:M b p 4J]WO 00 MVONV1 •r970 O .7N MV••.•..•JfO 11 r.^VOO 7^. •^ 11 n -YONn1 . -W,!N •m.tdMV V V V94:77rotJ•+Km W1041 m'e^•!014130.. •.VN O O O O O7D O O MON,O,nNONMYtONO W.t •• •M •M„JM mn 001%S Z.7mV ,nw:r•o WO•I• •m14AI.) a •V,O1,7Mw11a.mCOUUVUVe1•!0•rD'101111MD\ OV p0 m0 O ••n.nh am" n VH H nA N .••• V ••9477^ ••• ,nM••V n " A'^ hCO N"! um•7h •.N ^a W:O W/4^U^4mM •„!m^hw7P00 MO bC40 •.mON.-N14hNN090 p Wre 16 mOVIN.V ••m•• VONn ••P •.'em7cC • H ••ON 1.'•1X 3' VOTNMOCII•..')O••o.a1V•-0aos7 4,/O ..eptgmMr.Nr•D.00 n4, 1 I NNNY 1.7 •N^nOa••m .•Me.Mr•ONVm'7V O'nM1 Tr`'o00h 117 i -Vyt?n•t^•. pVMAAW..Mp.m llh g.7Ni7U m m 11/0 M-c..l 0..14 M V W a. a• iw w^mLa,n► •n \YV1V•n J ,OaONnON .•l W1W.Ob•.M aIP,OV.•:,7Q► l••: NtiOU W J •e 11 nn,MM00 414111 1100 .N♦ •.14 4(41 ON♦ •• .* • t • • ♦.,►• w *Gar ..^ ••N^MInNJO N- zr ov rn W1W•7 ..'nmm .1Y \f. • •Vm011/1 rm• :0111 ••V ^ 100 drO ..V •.114^♦ •;•w aNl • Ww • • r-r• I1••.•M..VMMr^ Mm••1 14.0?00► 7000,^r • I . \\•I •.. \ O.,,A u^!;N^COh0 il•\7•DMt/gnt•9nm•O IO:OVCO,Oh m•M•.16Ir 1 '."ONN Vmn•• lN4mUrOm•67tO fO V l w .+.. • .OaO r1N\\\N I f \+w 1 O,DhN^mh YcipNI I ••lmU tJM•6MUOw CO ur"n COON 01333:7 I.•1401 ••CO.pV •• NO O m 10tJmm0 O O •3 • • • .1 \^N^3731-N 1 O\ . Y10w I hPY,01nCO14tACO\t 1 WON.l71:GUCOWtJM u 11 •dT VY •\H .n \c9 maaP• .mom 01.1u,700eoe^ •\1 •1 C;1 14 Pon .w ,7 •\ D rO lN 3^ .. • •\O + + •w \1, W.7 W •7 76 77 i r110 11 . •CO Y •. ^.v M i.+ ( 1 7n'^111Y .•,e• Q 'o,DMUmR.7tm NtA ••CO f01 ,\\N 1 1 1 • r0+ .1 • Wr^A ,nlq.7 0 •••+•N 1 +++\ootomwa •IY:W a7l Me. .•1414144 r/J . cm10371,01 :flow" NNl am=hJ ,NO•• ,\O\ •\+IA+ •I \nrvny.•.a+ \nOOM► +n0 f\•ON^MO10VloiMro13Mnn••ONNnYONt- ]O Vnnn •M Q1 $ 7^rn•077t711ONO11 r•ln\O^N\\O^ •+..•AR •+ •1 ♦ •Innh\O' •M. nmU ••Otn\^Y • • WUM f'• 11 MAON••MN •. ••h •• •,m YNO^ ♦ Q ]n,O D+aN\^t f'• 1111 p 110 .•ro -A%D%DJmt1` NV10\^ N++.,....A .1 ♦ ♦ •.V in a KW Y lr1V •w.ny+Jnn ']nh JNO^^• 0t h,- A1O 1Plr .•n.O,eV V..{1'1n wn V.^•O\\ •^•••a 1 •NID\^ •Nyl M091D7G0.701ONYY1nVIA^• ^n.nVMre •M•.1nON1nh.^:M.. ll V •.l ••..•M•'-nwl.•Vn N ..'013.3^1 v •^''^1 \♦^\Y,3. •O O •Oma010 ..•a 0[6 14 ••O,rTn..h,n•O^ .IN••'+..•.. ,^\y v Nhv UMP1 1'lOOmW•0,0,0 .00Otnln\^N r1Mh Nln/nwA V31I V.O YM1tn • ' CL •N'V l O • Q ,^ 7^CO1eryh •.W q .•., N COON" V ••YYYrv1AJ^A ^a 0 \N\ ,3 II ulr ti m•7.)ro /•.wmK•.Oh .,YrvnO\ •\ •\!+ • , ♦ •♦ I b^Th00 7 1 4,0\ .1 v r1^ I NIOWPP 3,3X3,0 ..ON •Yn •hN\♦= ^.O^ •n am •••e N t1.tNe..•Wm•D,OON'O •Now i+..\\\ 331 •a 34••'0.3\, ^\ :y\^^ e0 •\ .N\O•nNCOhnO 1 ^\•••• \Vn l + 1 +• • ^•♦ ^•0143 VIWI,. y7`•PNn•0 • wY^'•^Na m. i .1 s0 -4w., w 1 ^ •••^,Y 1 1 ^a 1 •NO COYnnO\^ ryA 1 + 1,r • w •.. w• •^v1 •,1.0 NCO '1001 • :^•^[.^'• \ l \ + 1 NV11_1 NX) ri • .twiv. ^A\N . •w • \10X,0.403 \\' 1 +^ . w ^r .,w ONNJ17!'rn ,n t\ 1 10Dh\I u 1 ^.. \\OON^.-h "'. .YON^•w W1 ♦1 nY 1?II Y•- Ne0\ iA ♦ 1 GIM04 . 00i mNN1A \•"N^ V \ . 1 i•^OYL a Q tt 101•!1 101„1^Oi •,N^a•••,..rwA+l^711111\.•awi •O\\ •••++ • vfnh^n,n,DON,ntn0JM0 I \^ n",mfn 10 •\•:7 Y19NJ1mN ••77W 11 •L 8 „^•, • \;^ 1C LV • • •\A A • .i s..r A w0 1 -,..r. / A ...• NM1N I H • N R 1 .owtnahONON'4Y wmh •• X,11\NNrnn •-..n ••y Nh1EW4 11\1+ ••+a •^•.+..` ••ra• • r O r/VPV1•..+ a •r• . \\1 •\ ••IV^^hmoaannvY.ar1.-\ 3 Ihn nn ..wv..NVr.- . •'•^\•:J+\^ • In0 •r++l a`+...• •1• faw •nuU0a1+ •\•+w•1 .--R • .ONY11n ••M1'77Nn•.14m10 .'•1'91 \1nu'7nn.rV C"V'0 "UN 1 LL. 0 1 ^ 1 \ ,\ •\^vn\ •oN^\314 oNYOwNJUOmIO .. ^1 a •r I +.,4 •A Ohnhryn^nl•1^N\+^140 ^•4 •.V ••n II Vmm7.7 Y.,n^ 1D . ••11 Dry \•-•OTN nh •... It ,e ^ ro lMr)NIWU Wm'•1UU13o ♦ ♦♦• ♦4,...i +•w1 ^ •+ Ow 1 .1 N\ •rVTP N COP m oo 03 .V.•^:4NC Q ,;^-f0^NO• •143,0%mV^110V n illmy OlP.1W0wWOONw4UN • ,I \^'z I • N NI .•, •ri ^wl •1++^\••^mmro•.n p11 •t•^ •JIM 114 1\^70V141nJ N-- %Dn.ow ••n^I%4J6bJml mM f. V1& 11 1! •1-t,4ON mm:9. •tnhln-pol ^13 .Am V=M9!1ft.0 WA Yo .a V ••V II •CM1<09ON\• YA0.WIWVV'ON •M•N V .lWWK ••n VCOV1NJ1 Nna•Wn•i 1rOYIn,4 000KM•nN\\\.,T 1,O^ .R,0.30♦ . • •♦• I • \N no"• 11. .•ON Ur•r••Or1\=7 1 .,..On ONON n..r M? M1 ••ON\ • •• rON;J N1Wn7'On••nMMMVmlf013YN3••Vwy KOw W070. n7c 3.VONY,01n0 • •1 'D .. n W 1•.:••ilnln A r•UI` uvnln • ,a•• .^t3.YmhhY fAiA 1 .I ON[•1 U110.t1tMMn UIrO •e Mr. ..7nnzrV m^ar. MZw 7^0)Fdf^ r,7^V•`1N^ON%otllm U1Xflrt1.cN1` v •nr•^Ja •,n ^^r • ♦ • 1 . •nN • ..w O anfD.t!7W W07r•M0••pnr.0013A^V On•IpM 00 F TiaQV :Wfl^••ONn.-n U4Li ••C1W^ .•V n.?:n 1:'7.• Y:7 ••O•w .. •OON •• •M•MUVH7 QOCU90t![•7W 0000'1.1 7001:00 N11 '• }7^IIN\7 I. D•.II ^,f T^ \ \ •SRa . 4-• ^^VN1.-.-!,m OUMn'a 07 Y) 00000 013:07•.hMOV0 fR 3 n ^: fno \pc 1° \N -D l l+. ^ K •*. CJ . O O 000090 000000000000 U14Nn1. ._, •^ --0 •. ••AN `.i l •\t9 .•hlnm,n^ .\•. • •1.K^l0:'7w[q o 0 0 o u 0 0 00 O 0 0 0 0 '0 0 0 0 0 0 0 0 0 d -r1 Y 11 N h 13^ V^ M1 1n'J l' :1^•^•..^]!+\•'1 it 1•..yVr^14\^•^hV ,• IIC l;•, J1:c0000900 (10'. c 00^OJG:O^OC00000 00000'0000000000000090 O O O O 00 Q O O n0 T'9ry14.n^•-+70'n•nl.J ^.:7 -' -.. ..- 111 - 1. .-•- •.."' .. .. . 1.334. •• nn•, o!, na ^n 33 . 030 7 oo J n^ f:._.• I .n.. r. t. -.no 1111_ . _...... _.^. •...... _.._...... •.. .. _ _ sn.7.^r...7amazan..lnaezoTalnnra.7.a9ri .i.17ca10oomOO. 0o0aoan.7crun^1 In ,c: ^OC^^^NNNY••' .ir.7:x^1e7en )e7e.1..17eaaE7e :mn7ec .a.,aaa ••^.^azs zzr 000 r_ 000 aaaa..1..7•^V w^ne . _.-. _ . -_ ...... -.....^ .. 1111. _ ....w,n n..•.•.._•.^,n,en mn••n.n•n ne h.e.nnN""N-INNP4410O n7 n PJn JGOO • . 1 ONNIMD cm 014 NNN '•1'7M^7M.i..1nO>q:R 1110••i•i••i•i.i•.lr•-t+]v1 T.aa+r s^.. ,•u -v rr •1 •1 \^^• ..•. ^-••...... -.- _. .. __. _N^^N^1NN^I.-.-N ^^f Xa• .a nf'fp n•a.1.aa .•7..7.a..1..a..l.anrnn.1 • I \^\ • O^^O^^000^^NNnN^7/X4,073 N^.•N•••-- ^^(n n O JYJn70a 7: i1..1.-114 74 77 10 77.-74,.-lar-1 ra •C •Ion t N•\^I NNNOO \^ o\\\000 '•.f'J,nzN"nOr-NO^0\0\OO^000^71,00/h .. ..m y 0000"U •'-•'•O^^vlrr,noo >470-107.7.414 t4X7C.•1l7C4K.4967W 11 h• .I \\\\\\ •\00000\\\•-^^^ ON \\?^\\\ •\\\NVr.V•'^^n y.+-^^O\^ \\O \00••• •'^\\\O • .a•:.a.a74wwnH•7'7.77C•7 mVro •\\\ • •\\O^•••\\\\\0\ \\ .^O^^000 000 \OON^^\000► 71G' 76 70 76 16 7.7:+1^••mP••V • 1 1 1 1 •\\ •\\O^00\ .\\OOnY ^Y^ 0\ • .rve•-\O\\\077000\\O",00 O^NN N'v^,•-O +9St71nY yOmp u • ^ryn,9 1 •1 1 1 • 1 •a • •1 • \\\\1 1 • 1I \\1 +\\\\\\\\O^^OMV13^\\\\\\00 \\\^O^^NOO 001 \1 1 \O\ 1 O,nO\^\\\X113\\Of,\\\^X0303^\ ^ •a• a + ♦ a •1 •1 1 1 1 • • • • • • • . •\\O\\\\O\\ •\\\OnnNX^ . •140••\\^'OyN\7000\N \•'•^O\\00NNNN.-N ♦ • •++ . .♦ • • . •. ••1 1 I • 1 .\\\\ \\\ •\ poo^\ •\ . . • • • o^^oNnavN\\\\moo\\^ooaooN 1vle.rt o, s+.4,• •1 I 1r 7•+• •a •. ♦ •• ♦ • I •1• 1• • • • •\^ 1 I • \o• • •t •\\\O^\1^\1\al • ♦ • 1 \YrD •- .Or. VO\ • •\ \^00\\\\OOr•14+tYh w •f+ •1 ♦ w • • •• ♦ 1 1 1 •\\1 1 •\+ 1 1 • 1 1 1 \ •\0J •\ • • 1 1 1 •I • •\Dn.N •\O^\1 • \000\ •^ .n 1,103 nN .+•+,.^i^+at •.IU+l+^•11 \\ t+l • 1\\. • \\II .\^ • •.11 4, 11 •••• SON •.01. •1 \\\\OO\NN.1141^^^NJ7m,a . .N •• ♦ ii• •1111+•+++• ••I • .•7111\O•• •00 ••,1 •1 •11111 •• •11 1X..01\ n ^ \a^\\\\\O\\M1nON\^^11 Ir +•aa+ws •N ♦ ww •♦ 1 •++ •• I I ••11 •^V \ • : •I • 1 ,n0\ • 0 ^\•nD^ • 10\\ \\7\\\\\\\\\y00.D-+•'^O7I . ♦ .♦ w A • , •+r a • I n0• .+4, ♦ 1 1 1 1 1 t 1 1 N^OONO • 1 • . •1 1 • \Nf^- \0\ •N-'N.D'V • 1 #N • • \^N-;I • •b •\ r Otn•-•-•"pta^O •i ♦ • w •1313 • •OnYm N •D\ •+♦ 1 t 1 . 1 •N ••V 11 ••ON 1 • .. • •\ • •\N ..•-\O 1 1 1 •\oN\1 \ • 1 1 •\•-\^n 7000 • NNNO\On OU^ •^ 1 NwaYV.•..1nhV1111'`\t+VO• •1 I • 1 •0311 ,0,301 +\111 •\I I •\^NO\ .1 I \I\ 1 •t 11 •O^Y^-1\\0.014\- \\\.•-303.3 •^• •.^• V1V^ r.DM1M1Y 1 ♦+w +0134+ • • • V ••0 .o l •1 . 1 \ . • • 1 •M1^\Ih\ ..1 \• • \O`- • •• 11 \I •\\\ I 1 •. \\O. •O .XN^• ,\n \\nn\O^14?\O\\\\^-]o•-\ • •\\ n • •"1'^•O •,3111 V•^ ,• • •• w r w i rOn • N . I . 1 r . ♦• .\1( .1( lbmnnll^:0001 1 NY •-,no\ •^^ N1\ . • • 1 •I •171 \1 •N\•\\^Nr0\\ •ON7 •WYhN f• 11400•w^•+•-•-^r •1111.-. .."^«w.+w ^www •-+-hNP•-•-1 •.Y\• •O\+ r•Ow w ♦ ^N1 Inn^.3333VN •11 --+• •\1 pow .\•\\\•I I\1 ) •I •\[]000^..-\•NPn K-9 4,a., .•.r-r te •rw+l •.-.i0 rw i.•.r •\. mf.rrr.r.(...a 11 ,11 • ••4, ♦ • I •, .+. •+1 ••1 • II •1 •\^No\\O.-o .\N^n•. •wr••••Pt.JwN.. •+w aNli..♦ O\ 111\NY .OY^ MM n f.an • ••Iw r +r • 1 •♦•4, w •+• •I 1 •t\ ^\^OnN^\.00oo\\\ •+^'•rwi^,^..,. . r 1 r•• •NONN11mo• •h •'••M ) 1 • • •Nb+ r! .0) 1 •a •+w + •• • 1\1 .1 IN\OvNNYN\^ .O\\\\\N ^w^•.. W..... /+A.+.+.-.N ♦ . •♦ .1 • •• •.#.+ •r\\•+ ♦++ ar.N •a.44, •7.. 11 •.4,+r•+ .1 .aM1 Cp \\\= = . +v O\\^O\ ,^,n .d-wK. w-•M!!.-..-.....•^•- ♦ ••I t •,1 • •1++4 . 11 • 1 ♦ ••r4, . •+ir 4, ♦ ..1 ^\♦ .1 .IU1M M.o\ NOO,4 n^\\•^n0^nn^ . w...A • • ..r.i.^•...r.^-..r 1 1 • •\ 1 ♦ ••- 1 ..N ►a + r r + + •a f w + 1111 . N .•7C^ ..+D'd B .B r.N \OON\^V1 • \^ NNO.•1N _ ...•..,.oar••..•--^-•R •+^••Ir\\wh+r+wrw•a+:44+••10•- \oNn.D••a:,I•..lu.o^^nu1.-a .10\0\•.- • \\\•,\•1. ••,.,.•..Riw..r..•- •• •1 •mowiw+•mN•rwrww+r+r •.+ .1^o^ay.p' 1vo,_n •.^ •u.bOn.t+,ln^ 1 to\o .. •I .lo w ... ,.,•., ^.- ... ^'-^ N 1 + •O • •+ r •-r w • 1 V...A w • A • • • ♦ . • ♦ • --_^ • N • •'311141. l 1 4 • ^ • • • N d! I ♦ 1 1 n \0 0 1 1 • I 1 • • I -w. • ^••-•..N.- wri....• • +^wr+^lrwrafwNa+ r +++r .•• . 03330\\...1 lei •I .,.1 .\\0011 .1 -.. ^t,..,4w`•'+..Mr w..,+.. ♦1011: 1': wrM• •+ •1 •OU1 :4. I 1 1 • N ( . Aso .-.-..Y .++ • 4, ..1 1 •N • •N\\, . •o\O r .\n ww , 1 • . •\ .\y \\1 + NO •1 1 \ • •V^11.03 ,+ •\\OO11A-rVnO •nN^O\\aaN -Y t r1•-..++•+ ♦ n^ . • •r 1 •1 .IN 1 •77 c • •• ^^'-140 01.00) . 71314\\ •\ • mv .• oryA ••r7\\ •.^0 -.1314 -nN 1 •, w...w t • 1 + •+ .. • •1 • .1 ►•o •1 1 1 • • .• 1 .\ • •r•, ... M.mV .^9N• • • 1 •\ n V1.ONtnY \n • I \OaO N^•'1X0 4,r •1 1 1 1 1 • • •1 1 • • 1 1 •! ••O , • • ♦ • .P ..NMON I • , • 1 \rIN^N .-O^O \\\'^\\00 ^^^ 1. c 1 0 w ♦1 ♦ w+ • 1ri •+ r 1l l t l l • h .1 .\0•••y 140^•-n^^p•\ .•. I\ -'43 N1, O ' C '.O '•• w J m . I Oar . •. 1 . •\ . ...t 1 1 ... •r-1'!V •f • 1/♦ . w r .ICI• • • a. 1. I 1 • \0^70^^O \ ,U \\ • 1 1 I • 1 \••• o • 1 4 w + • N i • . 1 1 • •+ • • •+ • t • I \4X14 r r r .•• •„1 ,OO\oo .\ \\.o Rl10ONm1• ♦••\w ,....•...•ter 1•,....1 ^. N.^.••.r.^^. ♦ N •+f•.01+1w+•7\1 • 11\000\••1, \ .O n -'=C•01.EMdV J N ♦ ♦ r a0 ••M1O O^ N.."'.-.r...^.•+.^mow ..w..r...w ^f N..^...w ♦N • .a•♦ •a• • • . a!. • • • ( \ \ ,\\^70N r= O v v « u r • • a •O\vn •..."^1•^....--...r • r^=..r•..1 f ..^...... r,^,...w l..w w a♦ • •.\ ..I . ,•a f 1• I.4, •\I• • \\ 1• • 00^^\ . •\\\^ ^ ♦•a+110.•..y1te.••'•te• + a .pxhn..•...•-.•^..^r ..-111..1: w ^._+^ r r .-.^ .•.• ! .. a • 1 r a ♦ • aaa ♦ .1 \'^O"O^N'^•'^• \\00.-n\ • .\\1 ♦♦ ..i 1111.^te.1•i• 11\^^••w ♦ •♦•+♦+• ,.n. y.. \++•1 .f te. ^^ •.-.r.-.^.. -te \\\000•-O'J0\\\•' \\• f^ y v ... y • r•0 ♦ + • a ^.... 1111. -w f f ^ • • • 1 I I I • \ • \ \\0 \\ \\ \ r _ +\ •1 11•te,.r..^w mow ^ ^ -..-^..^ 1111 • r 1 1 ^:i t• •,1\ •. ♦ . ; • . 1 • • \ , \ • :`\ ; i r r « A rf.....• r•-.....ww..,^^ ^^ ..+a+ \1•-^+^^\I •I 1 •1 1 1 O\ \ •\:!\ ^ - « wr a ^ V C ^ mH ..,1+\1 ♦ ♦I ^ w r r 1111• ,+w .-. . ,,,^...te...te.. • • \I i . \^ O D \ • I • 1 •+ . •• • ^ \ • \ , . ♦ • 1 • I • 1 \\ .- '> > r7 y 9 ••+ •f .. +^ ..M I • • f ^r ..r .. r•w te.`•'r ♦ •\^-\^O\" •0'7 1 •♦ 1 • • •+ •a 1 0 •♦ r • • • 1 \^ 1.111..♦ IIt\IV\rIN ••^\ •4,11r^.•...•+r ♦ .4 .4, rl ^^% ^c[« ..c. .r^.-r 111.1. •1111 --.r .. ^• 1014, ♦ •♦ •4• • «c «. .• ..,,.....r .. 1111.. ^:11.11 _•••1.♦.4,4,1^ny .1\O^3.0^^ . .4w r w ....sfrr.♦ \I, •11 >•: .r,e 1111 .. 1111. -._. ••+1•....-•. r te. .. •^. :.14, •1:11.1!• •II .+i ,1 -a v 0000 ^ ^ ..•^•^ r^ .V.i.•..+,Jy. ^•-•• •4, ♦ NNrr ,.,a.w.lw+r♦ , • 1 ; 1 i, •^ •j ^^ 0 w ':^"• •-•^r • `•f ^i r ..+ .f w rr^wv .. ^.. r w =N• .1 .^O I 1 • . I • .1 ^ w >^ r1111 c e .•, u.1 c i • ... w.... • ww- .1r + 1. ^ :.^ -•'....I f w ..• 1..+4, .+ i :.. 1 •. ..^ • 1 • . 1 \^c .9 TY L !.. w1.,.tMWw Wte W-._ w1r. "^mar .•. •) ^1 11+.1 .11\57 .1 bn - •" r; ..xW.,wwrw^wJww• w,• ... -1111 ♦. .1 .^ .1 •1 I •1 • 1.0 _1 1 .c .• o p ... w ..• ,w 1111 w J - .w...... 4, 1 1 • .0...... + ^s 4, r a • .. • 1 . 1 • 1 4 1 • I •4 4, a o\ \\ nF '• } r '- 1 ••♦+• •1 ••4, w wiir..ri11 ••♦• •n ....•+`^^- ,.,.vw1i •1 ♦ •.1 •.•1111 ••••1 •• \.- 0n^^Nn u r .^. x oc .1 i1l4, ♦•4, r 4,r 4,1 ♦ .♦+ .a rrf^-. .•. N.,••...r 4444 •!^.flrl •.• \00\^N'v-•^ry+1n-t, ." •.M ..l +l 4,• .•♦♦.• r w rr • ♦ •♦ • •• ...••••.r1.w r w 1....r • • 1A.. •♦ • I I •11\^NNMnNN-„^14X0 7r --. 't_...r.T _. %-., u 7_111 w ♦ .4 ♦ • •i!4,.4,a ••• , r •4,0\1.fr9 141 •• •♦ ••♦4, r 44, . •4,I l l 11 ^^ry^n3YY11n1n •9X0•'•^ - .- ^ .•1J w• `- '1 •• t •1 • ♦ . • • 1 1 • t 4,.w • • .f •^1n\ •^ • • • • • 1 1 1 • . 1 • 1 • 1 ,\^^•-^•'•^nN7iN n ynN•-C '7...••tWWM7tww .+•J. • Ww• t• 1.1r • . , • • •4,f •w • .a .+• te., • .te! . . \00\^•7t.,'\ • ,\ • • t . , •-•-N•-n \^SO \N :1n'Vn.7 • Jw"- •wmw1.+• • . 014 14w-.► •• ^1 • •+ •• ♦ •1 •+ .1 + . •♦ + f - ^•' 4 9 ^- \•- \\\\0 •NN^^N^^.- 179.7X7 'J- -. .. • w• 'J• - Mt W COW W ^ • 4,r •♦ • 4 • 1 • N • • 1 1 1 I 1 .1.914 113 r0.914•nrlN r•^^^ ^•-\ •'714X•• 1 n3^•--^ .X^^^ t••t 'J• v • -^ Wwr^JW 0.. ..,.. .. ► -.-r • r • 4, 1 4,j .,...•r . 9.r, TIT •r 77V17•V•r1Un11vv o •- . 1 \^10\•-•914^ 0 ••-•-•^^ - . w•--.• •,-r -++ ••a •.. i nN^\J • •.•, 1 114 J••14:,•P•'.• uM1 . VVV VV m09 •O. .N^n.• :• t, N-c r., wW'd• 4,^ ww 4,• •1 •111 •n ^;, :r %I^il y• : •' 1.,IV••VVt'4 i•1 VV H V:n, 0v ..\\.-r, ,1'^^n^•. r1^..,..w - .I•• - .^. r• •.w „.•,-^ .. ... •1 J'!V ••u l 1 i.1 .^..Vr'IV 9Vyv Vy .. 1. U•'• •1^.1 .N^1- Table 13. HCMM thermal inertia and night-IR temperatures for day-night pair of Feb. 1-3, 1979. Data pair was taken from a cross-sec- tion in North Central Florida (Fig. 13-A and 13-8, A ... A'). Temperatures are uncorrected. Thermal Inertia Night-IR Thermal Inertia Night-IR (Grey Scales) (°C) (cont.) (cont.) 62 -2.7 79 0.10 63 -2.7 83 -1.5 52 -3.5 69 -1.5 43 -4.8 68 -1.1 39 -5.2 59 -2.7 40 -4.8 52 -3.9 42 -4.8 61 -3.5 41 -5.2 61 -3.1 45 -4.8 52 -3.1 42 -4.8 55 -3.1 41 -5.2 61 -3.5 44 -4.4 62 -2.7 55 -2.3 60 -2.7 70 -0.7 61 -3.1 74 -1.1 61 -3.1 65 -2.3 65 -2.3 70 -2.3 80 -0.33 52 -3.5 61 -2.7 51 -3.5 52 -3.5 45 -3.9 56 -3.1 38 -4.2 57 -3.1 39 -4.2 61 -2.7 40 -4.8 76 -1.5 41 -4.2 80 -1.5 41 -4.2 65 -1.9 39 -4.2 72 -1.5 54 -2.7 71 -1.5 45 -3.9 63 -2.3 44 -3.9 64 -2.3 42 -4.4 58 -2.7 54 -3.5 61 -2.3 49 -0.7 65 -2.3 66 0.9 71 -2.3 62 -3.9 62 -1.9 47 -0.7 f 50 13 OF POO'-2 Q^A^-1'i1f V . 7 "A7v 77 c 90 80 u^o y 70 a 60 ^a w 50 .a E 40 a^ F Inertia s —S —4 —3 —2 —1 0 1 2 Temperature (oC) Fig. 14. Linear regression of thermal inertia (grey scales) and temperatures for the cross section AA' (Fig. 13). 51 ^i 1, 1979. It can be seen that in this case, the thermal inertia classes are separated by temperatures of approximately 1°C: Thermal inertia class 5 5.5 to 3.5°C Thermal inertia class 4 3.5 to 2.8°C Thermal inertia class 3 2.8 to 2.0°C Thermal inertia class 2 2.0 to 1.4°C Thermal inertia class 1 colder than 1.4°C Temperatures given above have been adjusted by adding 5.5°C to cor- rect for the HCMM calibration (HCMM Users' Guide, 1980). 4.4.3. Fourier analysis of GOES temperatures Diurnal temperatures from GOES for two data sets (February 26-27, 1980, and January 11-13, 1981), one each from wet (1979-80) and dry (1980-81) winter seasons illustrate diurnal amplitudes which resulted from differences in relative surface water content of the areas result- ing from long-term wet or dry antecedent conditions (Figs. 7 and 8). Table 12 shows amounts of rainfall for periods immediately before Febru- ary, 1980, and January, 1981. The entire state showed higher than aver- age rainfall for 1979 and lower than average rainfall for 1980, especi- ally for the latter half of the year (July-Dec. columns). Diurnal tem- perature amplitudes of the drained Everglades Agricultural Area were larger than those of the three Water Conservation Areas, which indicates that the former has a smaller thermal inertia than the latter. Water Conservation Area #E 1 showed a larger diurnal amplitude than Water Con- servation Area #F 2 for wet antecedent conditions (Fig. 7), whereas the difference between the diurnal amplitudes was less for dry antecedent conditions (Fig. 8). Average diurnal amplitudes for the Water Conserva- tion Areas were larger for data from January 12-13, 1981 (dry) than for data from February 26-27, 1980 (wet). Diurnal amplitudes from GOL- S could be used to estimate thermal inertia to compare with HCMM apparent ther- mal inertia. An equation representing diurnal surface temperatures can be writ- ten in a Fourier series as follows: 00 8(0,t) = 0a + E (Ak cos kwt + 8k sin kwt) [1] k=1 where a is the temperature in °C, t is the time in sec, a is the aver- age temperature at the surface, k is the number of harmonics, A and 8 are the Fourier coefficients, and w is the diurnal frequency (a 2,r/86460 sec ). Surface temperatures from GOES were used to obtain the coeffi- cients and the equation which describes the diurnal temperature wave. One result is shown in Fig. 15 for Everglades Agricultural Area hourly data from GOES over the interval 0600 GMT (0100 EST), January 12, 1981, to 0600 GMT (0100 EST), January 13, 1981. The equation (with coeffi- cients) is: ©(O,t) - 3.7 8.33 cos (wt) - 0.7 sin NO + 3.91 cos (2wt) 0.51 sin (2wt) - 0.92 cos (3wt) + 0.12 sin (3wt) - 0.45 cos (4wt) + 0.09 sin (4wt) [21 52 L+V_ - ______- --- O 00 ITUPTW co p 1r U0014 S L. P* O 49-UPT14 I ao %V I? rq Iz CID (D.) ain3riadmi 53 11. w L w The result could be used to estimate thermal inertia independent of HCMM apparent thermal inertia both as a check and also to be used to fill the HCW diurnal surface temperature data gap, assuming the majority of the contribution can be extracted from the first harmonic. 4.4.4. Prediction moisture availability and thermal in- ertia from uarison's model HCMM data were used in conjunction with Carlson's model (Carlson and Boland, 1978; Carlson al., 1981) to predict thermal inertia and moisture availability of tie surface. Carlson's model was selected be- cause it is a thorough energy balance model and because it was applied earlier to HCMM data. HCMM data from the day-night sequenco of February 3 (day) and Feb- ruary 1 (night), 1979, were selected to ap p ly to the model. This day- night sequence was selected because of good sky conditions and because nocturnal GOES images for February 1, 1979, were available. The model was applied to a wet and a dry area in north central Florida (Fig. 16). They are: (1) Waccasassa Flats (wet, area 1, 2, and 3). (2) Newberry Sandhills (dry, area 4 and 5) The HCMM (HCMM13) data and products used are: (1) February 1, 1979, Night-IR (2) Apparent thermal inertia, from February 3 and February 1 day-night pair (3) Temperature difference, from February 3 and February 1 day-night pair Parameters that we spec i fied using in the model were: (1) Lat (Latitude): 29.5°N (2) Long (Longitude): 82.5°W (3) Emissivity of the surface: 1.0 (4) Albedo: 0.32, the high value is used to account for brighter win- tertime surfaces and for the presence of score clouds; the high al- bedo has the effect of decreasing solar radiation reaching the ground (5) Z roughness length: 100 cm (fcrest--longleaf pines and turkey oaks) (6) BTemn, 2.95-m depth ground temperature, constant: 289°K (7) Initial shelter-height. temperature (at 0600 local time): 271.5°K, determined from an average of NCC data for the region 54 C^ n o^t11k^ P, OF POOR QUALITY JT:,J( Fig. 16. HCMM (AA0-281-065 9 J4) temperature difference data from Feb. 1-3, 1979. This set of data is used to obtain a day temperature s,-° wc1., dUUeU LO tale temperature to correct 11CM,1 data) to use with Carlson ' s model. [areas used! are labeled 1, 2, and 3 (wet) and 4 and 5 (dry). The areas are also loca t ed by x ' s in Fig. 12. 55 ) (8) Temperature, dewpoint temperature, wind speed, and wind direction with height based on radiosonde data from Ruskin, Florida, at 12002 (0700 EST), February 1, 1979 (9) Precipitable water estimated from RADCOM (Weinreb and Hill, 1980): 0.5 cm (10) Surface temperature gradients and surface pressure gradients, esti- mated from weather maps (11) KM, the turbulent diffusivity: 500 cm 2/sec for flow through and over a forest canopy surface, which has roughness elements that protrude into the surface boundary layer.. (12) Several other parameters, such as a table of solar transmission coefficients as a function of wavelength, HCMM satellite overpass times, frequency of printed output summary, month, day, ground depth parameters, and other program control parameters, such as start-Ing time (usually 0600 local time), ending time, and time zone with respect to Greenwich mean Time The Carlson model uses satellite day-night overpass data in three basic steps (Carlson et al., 1981): ' Model is run for about 24-hour time conditions, with a range of fixed moisture availability (MA) and thermal inertia (TI) values that predict daytime surface temperatures and nighttime surface tem- peratures at the time of satellite overpass. Clear sky conditions are assumed, and the energy balance is computed at 4-minute time steps during the day and 2-minute time steps at night. (2) Multiple regression equations are then used to develop predictor equations for MA and TI based on the daytime surface temperatures and nighttime surface temperatures. (3) Registered satellite day and night pixel values of surface tempera- ture are then used to predict MA and TI by pixels or by clusters of pixels. Th;- Carlson model can print out the energy budget at any time as specified. Final outputs can also include total daytime net radiation, total nighttime net radiation, daytime latent heat exchange, nighttime latent heat exchange, the Bowen ratio, total evapotranspiration, daytime sensible heat exchange, nighttime sensible heat exchange, daytime ground temperature (at the 10-cm depth at the satellite overpass time) and nighttime ground temperature (at the 10-cm depth at the satellite over- pass time). Parameters that were specified for the model were listed earlier. However, because of our inexperience in Florida with the model, we had to make a number of computer runs to develop day-night pairs of model- predicted surface temperatures that were within the range of observed HCMM values. Finally, because HCMM values were estimated from February 3, 1979, daytime values and February 1, 1979, nighttime values, the tem- 56 perature differences had to be readjusted also. Sequential runs led us tic.to make the following adjustments in input parameters to be more realis- (1) Shelter height temperature throughout the north and north-central climate region was used for the 1000 mb temperature rather than the Ruskin radiosonde data at the surface. (2) Albedo was raised from 0.24 to 0.32 in order to bring model-pre- dicted daytime temperature in range with the HCMM daytime tempera- ture. (3) Turbulent diffusivity for both heat and water vapor transfer was increased to be more realistic with forest and rough surfaces that protrude into the free air stream. (4) Ground temperature at 2.95 m was decreased from an estimated 292°K to an estimated 289°K, which improved model predictions. The day-night temperature pairs for input into the model to predict thermal inertia and moisture availability are shown in Table 14. Since a February 3 (day) reading was not available on our CCT's, we used the February 1 (night) plus the temperature difference (February 3 to February 1) to find a February 3 day-IR image. The NASA-Goddard tem- perature difference pixels overlaid exactly the February 1 (night) IR temperature pixels. Since they were elongated HCMM data sets that in- cluded more than one primary scene, they covered most of the state. Cli- matic data (National Oceanic and Atmospheric Administration, 1979) were examined to obtain the climatic mean daily maximum and minimum tempera- tures of the north and north central Florida climate zones over the per- iod of January 28 to February 3, 1979. We found the day-night pair of February 1 to be 55°F and 29°F, but the day-night pair for February 3 was 61°F and 34°F. Therefore, we reduced the February 3 day-IR data by 6°F (3.3°C). 5°C was added to all of the IR data to correct for HCMM calibration offset (HCMM User's Manual, 1980). The vertical moisture profile was estimated from the nearest radio- sonde sounding, taken at Ruskin, Florida, at 1200 GMT (0700 EST). The sounding from Ruskin is near Tampa Bay and represents the best compro- mise for an average sounding along the length of the peninsula. However, there may be some local effects of Tampa Bay or the Gulf of Mexico near the earth's surface. Moisture, temperature and pressure from soundings were inputted into the RADCOM model (Weinreb and Hill, 1980) to compute effect of moisture and gases on surface temperature. Based on RADCOM model calculation, HCMM-derived surface temperatures were increased by 1°C to account for atmospheric absorption. Predicted thermal inertia (TI) and moisture availability (MA) pre- dicted from model calculations for areas 1, 2, 3, 4, and 5 are shown in Table 15. Carlson (personal communication) showed that MA is equivalent to R a /(R a + R s ), where R is a surface (e.g., canopy) aerodynamic resis- 57 L^ U ------T Table 14. Day-night temperature pairs for input into Carlson's model to predict thermal inertia and moisture availability. Temperature Area Feb. 3 (day) Feb. 1 (night) 1 Waccasassa Flats (wet) 285.0°K 215.3°K 2 It 284.5 274.9 3 if 285.1 274.5 4 Newberry Sandhills (dry) 287.0 272.4 It 5 272.8 58 I ^wJ & Table 15. Predicted thermal inertia ( TI) and moisture availability (MA) for areas 1, 2, 3, 4 and 5. Predicted TI Predicted MA Area cal/cm 2 /sec t/2OK Dimensionless 1 Waccasassa F',ts (wet) 0.0886 0.717 2 0.0866 0.965 3 0.0796 0.771 4 Newberry Sandhills (dry) 0.0539 0.379 5 " 0.0550 0.046 59 tance and R is a surface internal resistance (e.g., ensemble stomatal resistance for a canopy). However, this generalized form can be applied to any surface whether it is vegetated or riot. Moisture availability (MA) by definition would normally be expected to be between 0.0 and 1.0, i.e., near 0.0 for a nonevaporating surface and near 1.0 for a freely evaporating surface. MA predicted by Carlson's model (Table 15) appeared to be sensitive to several parameters in addi- tion to the combination of day and night satellite temperatures, includ- ing the initial shelter-height temperature, the temperature at the bot- tom of the substrate (2.95 m), the albedo, and the turbulent diffusiv- ity. The substrate temperature was an average value obtained from data taken during freeze nights in 1978 from stations in Central Florida and is similar to other observations (DuCharme, 1971). We experimented with substrate temperatures of 289+3°K. We found that at the higher tempera- ture (292°K), the predicted MA for the 5 temperature pairs in Table 15 had a range of 1.12, but had values as high as 1.32. At the lower tem- perature (286 0K), the range had decreased to 0.697, but had MA values as low as -0.17. MA was also found to be sensitive to changes in initial surface input temperature (at 0600 local tirre). According to model out- puts, an increase in daytime surface temperature (accompanied by a larger diurnal temperature amplitude) had the expected effect of de- creasing MA. A decrease in daytime surface temperature (accompanied by a smaller diurnal temperature amplitude) had the expected effect of in- creasing MA. The model thus illustrated that if the diurnal temperature amplitudes are too large or too small, its predicted MA will not always fall between 0 and 1. Thermal inertias (TI) predicted from the 5 temperature pairs (Table 15) seemed to bE less sensitive to changes of temperature at the sub- strate layer or any other of the input parameters. Differences in TI between wlt 2 aand l dry areas were predicted to be on the order of 0.03 cal cm sec K under these conditions. 4.4.5. Comparison of HCMM IR data with GOES IR data Since cloudy conditions were prevalent over Florida during the win- ter of 1978-79, many frames of HCMM data were of limited use. To partly compensate for lack of usable HCMM data, GOES images were used to pro- vide information to calculate apparent thermal inertia (ATI). A large quantity of GOES winter nighttime infrared data were archived for Flor- ida. By using the Florida data from GOES, we can calculate ATI and pro- vide information to help determine usefulness of the ATI data product. One advantage of using GOES data is that the availability of data each day allowed us to select similar cold events more frequently than with HCMM, and allowed us to calculate ATI over the same surface within a short time period, thereby giving some assurance that thermal inertia of the soil would not have changed significantly during this period. GOES surfz=e temperatures were compared with calibrated HCMM night-IR data from film transparencies. Also, GOES data one week apart (January 12-13 and 18-19, 1981) were used to calculate ATI using methods in the HCMM User's Guide. 60 To compare GOES surface temperatures with HCMM surface tempera- tures, HCMM film transparencies for night -IR data from January 10 and February 1, 1979, were used. Film characteristics were: January 10, 1979: Night-IR Contrast Range: 27-76 Corresponding temperature range: -1.5 to 16.6°C °C/grey scale: 1.13°C February 1, 1979: Night-IR Contrast Range: 18-75 Corresponding temperature range: -5.2 to 16.2°C °C/grey scale: 1.34°C GOES surface temperatures used were from 0400 EST, January 10, and 2300 EST, February 1, 1979. GOES temperatures obtained from the above two images were adjusted to account for time discrepancy between GOES image time and time of passage of HCMM at approximately 0200 EST. A 0.5°C/hour drop in temperature was assumed and is a reasonably conserva- tive value when nights are not excessively cold. Therefore, 1°C was added to GOES temperatures obtained from January 10, 1979, and 1.5°C was subtracted from GOES temperatures obtained from February 1, 1979. Re- sults for temperature estimation for eight areas in south Florida are compared and listed in Table 16. Areas where temperatures were analyzed are shown in Fig. 17. Average GOES temperatures were 5.9°C and 5.2°C higher than HCMM temperatures, respectively, for the two nights examined (Table 16). Therefore, HCMM data gave temperatures lower than GOES data by ap- proximately 5 to 6°C. Since GOES temperatures compared well with 1.5-m air temperatures (standard error of estimate, +1.6°C; correlation coef- ficient, 0.86; Chen and Martsolf, 1981), GOES temperatures are probably closer to true values. Perhaps the -5.5°C offset that has been applied to all standard processed HCMM data should be added back to HCMM data, as we did in an earlier section. Results in Table 16 are in agreement with previous HCMM user experience (HCMM User's Guide, 1980). 4.4.6. Calculation of apparent thermal inertia (ATI) using temperatures from GOES satellite data Equations used to calculate ATI were developed for HCMM data (HCMM Users' Guide, 1980). They are listed below: ATI = NC(1 - a)/AT [31 where N = 1000, C is from Table 11-3, p. 14, HCMM Users' Guide, 1980, AT is obtained from GOES digital IR temperatures, a = Kr/(sinesino + cosecosocosa ) e is the latitude on earth, m is the solar declination, where 61 Table 16. Comparison of surface IR temperatures derived frum HCMM film transparencies with GOES surface IR temperatures for 8 areas in South Florida. Temperature is in °C. January 10, 1979 February 1 1979 Area Compared HCMM GOES- aT- HCMM GOES- ATV 1. Lake Okeechobee 9.0 13.01 4.0 6.6 13.01 6.4 2. Lake Kissimmee 5.6 10.3 4.7 3. Blue Cypress Lake 5.6 8.8 3.2 4. EAA -1.6 8.5 10.1 -1.2 4.8 6.0 5. Cons. Area #1 6.2 11.0 4.8 4.2 10.0 5.8 6. Cons. Area #2 7.9 11.5 3.6 5.9 10.5 4.6 7. Everglades 5.7 11.5 5.8 3.7 9.5 5.8 8. EAA, West 2.0 9.3 7.3 2.2 7.0 '.8 Average AT 5.9 5.2 Temperature was estimated by adding 1.0°C to the temperature obtained from the 0400 EST GOES image in order to bring the GOES temperature closer to the time of passage of HCMM (ti 0200 EST). AT = GOES temperature - HCMM temperature. Temperature was estimated by adding -1.5°C to the temperature ob- tained from the 2300 EST GOES image to bring the GOES temperature closer to the passage of HCMM (ti 0200 EST). Water temperatures of Lake Okeechobee, Lake Kissimmee, and Blue Cy- press Lake were allowed to stay the same because it has been shown that GOES-detected water temperature of Lake Okeechobee decreases only tit °C or less throughout a 24-hour period during cold weather events (Chen and Martsolf, 1981). 62 .^ Fig. 17. Map showing the eight. areas (1 to 8) where HCMM-derived surface temperatures were compared with GOES surface temperatures. ATI was calculated using A T obtained from GOES from Area 4 and Area 9. County lines are shown on the map. 63 GY 80.3) 0.4091 sin (2,r(Da 365 6 is the hour angle at local noon, K 1 + 0.0167 sin^2,r(Da - 93.5)1 r is reflectivity. AT was derived from GOES surface temperatures from 2 day-night per- iods (January 12-13 and January 18-19, 1981) for 1 area each in central and south Florida (Fig. 18). Area 4 (Fig. 17) is an agricultural area of drained organic soils, known as the Everglades Agricultural Area. Area 9 (Fig. 17) is in an area of excessively well-drained and well-drained sandy soils directly south of Ocala, in vest north central Florida. Tem- peratures obtained from GOES images showed consistently that the well- drained and excessively well-drained areas are colder during winter noc- turnal conditions and hence have different climate characteristics from wetland areas. Values obtained for inputs to the above equation and used to calculate ATI are listed in Table 17. The difference in apparent thermal inertia (36.1 vs. 28.5) in value for organic soil from the same area and within a week may indicate that other factors (wind, atmospheric absorption) were neglected which could influence surface temperatures. Rainfall was scanty during the week of January 12-19, 1981. It ranged from 0 to a maximum of 0.18 inch, as re- ported in the Clewiston and Belle Glade area (Climatological Data, Flor- ida, 85, January 1981). Diurnal surface temperatures for the Everglades Agricultural Area (Area 4, Fig. 17) for the 2 days showed a difference of 6°C during daytime (Fig. 18). This difference in surface temperature (increase in AT for January 18-19 compared to January 12-13) for the same area over a 6-day period caused the difference in the calculated ATI's. Wind data were insufficient to determine whether wind was a sig- nificant contributing factor to the temperature difference. We did not obtain vertical moisture profiles to correct for atmospheric absorption. One factor which may also contribute to the difference (6°C) in daytime surface temperature may be the extensive damage to crops in the area from the earlier freeze (January 12-13, 1981). This freeze killed leaves of the widespread sugarcane crop of the Everglades Agricultural Area, which would allow more solar radiation to reach the surface of the dark organic soil as well as reduce evapotranspiration from the vegetated surface. Thus, we conclude that moisture availability for evapotranspi- ration from the surface may affect thermal properties and perceived thermal inertia of the surface. 4.4.7. Spatial resolution and thermal properties North of Lake Apopka in central Florida is a large area of organic soils. The temperature of this organic soil could not be resolved from the water temperature of Lake Apopka by the low spatial resolution of GOES. However, HCMM data symbol maps showed that these types of surface features were clearly discernible at the 0.6-km resolution. HCMM images (Fig. 19) showed that the water temperature of Lake Apopka was 10-11°C. A large area with a temperature of 2-3°C surrounds the lake; this area includes the cropped, drained, organic soil area and excessively drained 64 f0 mmm(RI ORIG NAL PAQLr I, 01F POOR QUALIr 4 F—♦ Jan 12-13, 1981 Jan 18-19, 1981 Time (EST) .9 1A 16 18 20 22 24 02 04 `\b Fig. 18. Diurnal surface temperature (GOES) for the Everglades Agricultural Area (Area 4 in Fig- 17) for twc days in January, 1981. 65 J, -1 Table 17. Values used in calculation of apparent thermal inertia (ATI) for 2 areas in Florida: Area 4, the drained organic soils of the Everglades Agricultural Area (EAA) in South Florida, and Area 9, the area classified as excessively drained and well- drained mineral soils 25 km directly south of Ocala in the west North Central Florida peninsula. Data used were from January, 1981. Area 4 Area 9 Area where ATI is calculated Organic Soil Mineral Soil Date (January, 1981) 12-13 • 18-19 12-13 17-18 N 1000 1000 1000 1000 8 (latitude) 26.50 26.50 29.00 29.00 0 (solar declination) -21.63° -20.58° -21.63 -20.580 d (hour angle) -60 -60 -60 -6° C (Table 11-3, HCMM User's Guide) 0.981 1.004 0.926 0.950 AT (GOES), °C 19.3 25.0 24.6 23.1 r (reflection, from Geiger) 0.2 0.2 0.35 0.35 K (calculated from Eq. C31) 0.967 0.968 0.967 0.968 a (calculated from Eq. C21) 0.2014 0.2016 0.5370 0.5375 1/ ATI (calculated) 36.07 28.51 17.42 17.56 Unit for ATI is scaled to 0-255 rather than to physical units (HCMM User's Guide, p. 10). 66 ORIM, AL PAGE 11 OF POOR QUALITY +r• ;718^7IIDAe 72. /243?-2745497 51214; ; 710/00/-. /. • -. ---. --«++.. -. -/ 11' '! vC* 749->3DDF 7• nnC> 1^ +4 p 10244772/1 . -. /4,`0- • t, •---, . w, t-. -. J :';Ci•:.3.DFDCO•.:3 RC@tllAFACC. ^^ 21,34^M!•<93/1 .^y 7t>-: 40//--. 00///. MCAM IMA66-N16MI /. -. ---1. -. /, /01 , /J--- ,.::•'^,t:c'.^^,% BCC4792110. ia9=i ccDC:+A; r? r;r::DD3CCCCDDD8DCA; 211/.. 4567 Fig '4. HCMM night 1R temperatures from Feb. 1, 1979, showing detailed (600 m resolution) land-water temperature boundaries in the Lake Apopka area. 67 jVP sandy soil. Southeast of the lake is a large area with warmer tempera- tures (3- 4 0C). Farther north, near Gainesville, HCMM images showed that water temperatures of Newnan's Lake, Orange Lake, and Lake Lochloosa ranged from 8-10°C (Fig. 20), whereas the surrounding land surface tem- perature was 0-3°C. Payne's Prairie, a wetland area (marsh) southwest of Newnan's Lake, was 4-5°C. In both cases, the difference between land and water temperature was approximately 7 0f. The GOES image (Fig. 21) showed 7°C (warmest) pixel for Lake Apopka and 3.5°C for surrounding areas. Newnan's Lake was not discernible at all in the coarse-resolution GOES imagery. The comparison of HCMM pixel resolution (0.6 x 0.6 km) with GOES pixel resolution (6 x 8 km) points out that there may be many small cold pockets or warm lakes within the coarse regional delineation that the GOES imagery gives for peninsular Florida., r 4.4.8. HCMM thermal pattern of organic soil attributable to land use A HCMM nighttime IR image (Fig. 22) of January 15, 1979, showed thermal patterns of lower Florida including the original Everglades marsh. The thermal image showed that although the soil base is the Fame, the different water contents on or near the surface resulting frun! dif- ferent land use or management created the thermal patterns shown in Fig. 22. This pattern is also discernible in Fig. 2. The different surface water and near-surface water contents were partly due to different land use in the are4, for example, agricultural (3), detention for water con- servation (1 and 2), and natural (4) (Fig. 4). The Everglades Agricul- tural Area (3) generally contains the coldest areas in south Florida at night during winter cold weather conditions. The pattern of the agricul- tural area (Fig. 4) appears quite distinct in HCMM images (Figs. 5,22). It is obvious in comparing Fig. 22 (HCMM nighttime IR image) with Fig. 5 (HCW apparent thermal inertia image) that drainage for agricul- ture resulted in colder nighttime surface temperatures mediated through a decrease in surface thermal inertia. Likewise, it is obvious that de- tention for water conservation results in an uneven surface water cover- age as shown by sharp boundaries in the apparent thermal inertia iri the organic soil area of the Florida Everglades. 4.4.9. Nocturnal surface temperatures, thermal inertia, and soil or surface properties The climatic feature which usually separates the thin line between severe crop damage and little or no crop damage daring winter cold air mass events in peninsular Florida is the temperature at the surface- atmosphere interface at night., and the length of time that it persists. Patterns of nocturnal low temperatures for 1979-80 and 1980-61 are shown in Fig. 23. These patterns bear a resemblance to both the apparent ther- mal inertia image (Fig. 5) and to the 5 thermal inertia classes superim- posed on the Gene-al Soil Map of Florida (Fig. 12). First, in the north- ern half of the pe, ,.insula, the low nocturnal winter temperatures are largely associated with low thermal inertia patterns, which in turn are associated with the deep, sandy, well-drained soils of the Central Flor- ida Ridge Land Resource Area (thermal inertia classes 1 and 2 of Fig. 12). Exceptions are generally related to areas with high densities of 68 ^^1 i t CWGINAL PAGE 0 OF POOR (QUALITY ` +1*YYt )+r +, i, , •r, w.,,, ,-. ,,.-. .. .I +wi•,•vrrr«!^«w+-. r , '4w. wy . -,.+!.!.--.-0*<- , ^) *t•i -liil lN-. b. ++•- -+..-+. ^`i)1f-F*^•^t'Al ^'_iit- 1-. N ,r+Nip . ^1.-.-/^7-}'$-1 ^) a f +) , - J •.+ t i :1 :, s, i+#-. -+, . -i, )) i ^,) sMwi# (i , ++, «i) - , ) -.. *, #-+ x*, -) +--023 ^• i `?a+, atw) az:, i# +.+, +++,+^ ^ )*)0 t +,«r)+)i++t., +.I.t,x-++*,x)*. 4:?. f ; S) t i . +)++z;•.) ##, . +, )*, i, --*+++#'► ► i ). ia+) ) )t- +i, ++-- /. --+x+) (. , ... {)36: •. i(f))v•,i.,i+:s'+t#i,+«-,+„-,+),+()+s-► ?#))"t)' , •r'•.+,+,-'-^+•))fr ##, +,. at..:'^► aa+-(; . 77972+, +#. , ++)+ . -, -,+)+)#, . +, , 0. +'-• ^, •'. -„ /+ /1.+•+ a `) . ( +. t. ---*, )+t#-++ , !F+aF, , , ++, + , , . +. / 9C -?-<;< QI +) o) +t++. C ' ? +) +, -, . -1--. -+.. ++. +- r;;+ J) a:! I 1+*+. #.. +042- , -+, . -tx+*) ) *+. + .,- j •! ? ) f)) : +. , +++ •t ++ik+'30-+-) +) . +-, , #, , - /, / J .^. lfil^Ti~ -'-+, "'-iF, -, -, , --r 1Ft. -, ++-. ), . , +x•#-+, +11 ;sx ! a x+#+++x, + at, +, , +-12., -, ^►++«++, a:C^^ s • 7 0. +.. 0, ++st 3 ?)+, .. , +, ,+, i. , --i21. +).. 't+►, . / : C--; : 8t /-/. t. /*0, +. +, , --++#. -!J. -- +')+v'::'++. , . +-l--. , 0 . 2131-*^, -. -, . -- J . /, 1-. G+--- . °, , . +-+.. w+, k, y^ +?=•{ , +;;I . •3u. -!1003574 ,+.- +, + I. - !. -/-•+-^ ► -+11a tisa+: i t+} jf!^ 44 a 224 1 'k 623-. ++r -, . , -. -. -, J_.-_ .0 , . - + : ')) x = ' `3 :^ G, ---+--i, ---, -+. +++/321/ J 15ca: 4 >*'3rrt'$3432--/1n - :!! J )+* `, , /--/3 1 3474633 1 332530, .. 12. - -+, -i--1 , --+, ++--, 2” ' ^ ? ? . t t ' +) , . - L ='31012223412 . 0+, -. -U. - ±+r - - G -/, ^ l ^ -+-+-. --.► +++/ s+L -i 1 -13112/+, , ---- /.. - ^_:. -/-.. -- +r. , - t. /O-O --/003;1, , , !, -1 //0. 0. /+, ►. Q. , ! ---. +, , +-++, , . #00-, , +^ f-+. +, -!r +, , s•+. ---03.111, . #O/. ---/0. -, - . -/Gr--... - t+. -, +-. -. )/+. +•+/ j - _, . } ce -+,•, 1, -s+• , . , +, -Ui ... +, . //.... /. - . - .. +-+. /.. -/. -+.. -. , - /!i='. 0. , O, . - -3 1' C 1. +s+ U1 :1, , , . *, s. +-. --/+^.. .. /. / , --- / +/-/:) ), /. 1422 -s:+ • !^1Gi '3/, ++*, --174 -++++- /0'-' -, , , -. 0, +/ -+,, -/41 +++,__ ; , .: _ , _ +++Y• 0 5/, . , +-GCi2C3/.2+ , , ++. -++,•'. +0.0,n" , , 01 . - - 0/i I - ;++xi , /-- --ll cl {3cC's•+1 , -, . +'.), - - ,) '^=J ': , 464-•^, ---^-- O. . y s+ -- - _5 ?_:-+«)•4,1x),+i-- /12,3•?/21, - .-.'1.•.23<:==''".^- h ^ - -+,/a„i % S!S5c---. -+w+, , a +, +c.. --- 1' --0-.. - Q :--n > !mot /-.O 0. , - +<_ - !- ,n ! l--. !. 747 ^'• !-. .-///O,. O +. ,+ST.°mss:- +++tcT-. lJSz--x, -- 1, Sr'T •'1. r, +J/ 7 •-.r .:li '^= `.c -Q--00//, -.fin 2,' +/,r#. -+. !3774/+. -- = : 63 ?'-. -/ :?-. - - .!— +2 -- - - +. <- -• . _ '7' ,_^.C_- - lfi- -'Z ,- -, •R , -it+j+x±-r{., __^e 1 97 _^ !' J^+..7.4=- ♦ 1 1 !- V/--!!-, ' . ^. w -+, i •-• _)!+: -+T.+T 'L ^ !. 41- .:,3 ^.-`_7 i/V/ --. +V']-. J 2:_ -353?^ /ice.- - --, ... ^- +++, _, -+)++- ?+c ^•+, J =^> 3254^ J11- O/. -/0/0'!--/: •' - -r.^:),++,.,+et-r--tx„-)-,.J^f`a'.'^? ^:J4^, i.0//.•-J/11//0..-l. 1 - + +;: i+. -!+) >+, -. - - 5; _ 7 135 /0. - 1. 1 -// - , 00 -//. 1 .FFBRuARy 1 1q 74 . :77. /: /. 12-, 1. -:. . H MM iMA6E - NtCGHT IR r, fi. , -: , -+ • ) #, , , , 4+-«+r , . '^'-. i _^ 4353 -# , +, , )+-, ++-. [•: -- -+ -os.o, , ++ +a+++--^ ^. - ' _: ?o6ti: 624 i+, , +})) !. #. x/. /i - ^''. . ++, ' + Y?++, a++. - _ - - raw ^ - n-. ) ..- .. /i'1^ -rs, +) r..... > •}s.+- (J s+, . ----%. C:. --. 0/C)/. /i^Cl // Fig. 20. HCMM night IR image of Fe'-.. 1, 1979, showing detailed (600 m resolution) land-water temperature boundaries of Newnan's Lake area in North Central Florida. 69 cl+ A.V UNiGINAL PAGE 19 OF POOR QUALITY t 4.4 1 Y L V TO 7 -07btt il:t im A4 1k t I ECT ;in I Mo P f IC Y Y . i 'i • I f I f 11 I I A ?. M F7,. I N/ I T 1 r...... J.: 7 T T .1 1 1 f V r 7- 1 1/•.1.!: Z; 7 7 7 -7 7 7 F, 1, c 7 -1 7-1 7 i IL A - r.=' L ,77 7 z z 7 1 II T I 1' 77 '7- 1 T 7 Va - Y, 06 • I, I. . m a:./: 1 1 +11 r =. _ = :- '- :: I 'I-' - 4. ; P 7! ;Q, 'i" t z--' r_-. -77 'F =Y -y 7 -- = I'll 1. 'I D 2 7-;L,F , 7' 7, - -77=_ =7T r 7 I. ^777 :r, 1^ 7 7 7 ) 7, z 7 7 -7 7:- _- - - _-__-"•v- -z 1 LAKE Mbrl" cl -I - 'I f. L LL a LL Z, z r -•-7 7- =Zv, Z -,V L I ► V=^ 7 1 a = y L I -Z ' Al Il 7 IL 7 - ==r.- 7 ; F: r r P ,-: z -Z -c 1-- Z Z. Z v 1:, 'I r. r r-. r^ r. =Y!- 7 PVY Z.- - - X 7 :k Q P ; )Vyr V r _ ^t 'U = 1 L ,: C 4 C y V 7^ ;7^ f =v V 17 r at L'ds y 7 ..!, -*!.'- C " T - 11 t V v v LAC'.." ? j; 4 7 7C7 7 77 W/ 7 W - - i t Z 7 Fig. 21. GOES IR image of Florida from Feb. 1, 1979, 0200 EST, showing the land-water temperature boundaries for small lakes of 1 to 2 pixel area. 10 n ORMINAL PAC-1: f^q OF POOR QUALITY tdk .d6 s 3 f Key 1. Green (coldest) 5. Magenta 2. Orange 6. Light blue 3. Dark blue 7. Black 4. Yellow 8. White (warmest) Pig. 22. HCPM nighttime IR image on Jan. 15, 1979, obtained by digitizing HCMM film transparence using the Image 100 computer at Kennedy Space Center. The image illustrates thermal patterns which can be generated due to different land use. 71 ORIGINAL PAGE OF 9 N U1 o I m cb v 0) ^ °u CA Qj O E W c^ v v u ^ ^ C 0 cc r-i U C -4 m = m tU r- * ry O ^ O l0+ LJ 00 4-1 •p ^ N m rn v 14-1 0 r r! ro .n c e ^ ^ O J = 1 a M YOI 00 S r O ^ H co M tl .-a. j N L 'b 00 m2 ^^ 60 00' Y +. d a .^, S a 00 M ro aN 6 = aN F+ N i. N n N 1a 1 E ^ CE O^ c0 G 0 a o 1 rl v O ,all w ca C u M b = 1+ .-c QO 0 0 -H 'C U C b c vw•.4 N 4J O E m N C GO N w O C C a M M N v V1 1^ JJ C, b C k cA M v C y .O 41 v E 41 C O vIr >+^ U ^ GS L 3 ^ ' N 1 v ai v ca 4J C b ^C1 N >a 0 u a v a^a 00 M Z 41 U Ov0 rCl too ct! >~ ca O v v G N ^(} co • O^ a o ^ ¢ Q v n1 C n H v 0 O^ fb L .r.l -4 1N s ^ M IJ - cv1 to u •.i oc Y N v -H rj ^ - Y ^ Z rRrC Z y ^ h C 7 U T F N CO L C O GaN E i ^ 1 W s E I^ ^ d C E is £ F N ^ ^ 1 ` r E I S J L 72 lakes, which produce patterns of high thermal inertia although surround- ing soils may have low thermal inertia. Second, a cold air area is usu- ally found extending southward from the Tampa Bay Area in the western half of the peninsula. This area, although it is a part of the South Florida Flatwoods Land Resource Area, does have good natural (as well as manmade) surface drainage. Since 1978-79 was a wet winter, Fig. 5 shows only intermediate apparent thermal inertia. In the drier 1979-80 and 1980-81 winters, cold areas were present and persistent in the southwest peninsula. Cold areas showed up north of Lake Okeechobee in 1979-80 and 1980- 81 GOES data (Fig. 23) that had not been observed in three previous win- ters. These areas have naturally high water tables, but with dry weather coupled with artificial drainage for pastures, these small areas appar- ently decreased in thermal inertia and became relatively cold-prone. South of Lake Okeechobee, the drained organic soil of the Ever- glades Agricultural Area appears as an isolated cold-prone area year after year. Sharp thermal inertia demarcations appear between the drained organic soil and the Water Conservation Areas. In summary, we found broad agreement among patterns of HCMM appar- ent thermal inertia, cold-prone areas, and the soil drainage classes of the General Soil Map of Florida. Land use and management, especially drainage (or flooding) can alter thermal inertia and cold-prone rela- tionships. A15`:, antecedent rainfall conditions can affect thermal iner- tia properties and cold susceptibility, especially of the South Florida Flatwoods Land Resource Area. 5.0. SUMMARY AND CONCLUSIONS Generally cloudy sky conditions during the project period (1979) when HCMM data were available {or thermal inertia studies in Florida caused us to depend more extensively on GOES temperatures than ori- ginally planned. The comparison of GOES data with the HCMM data had ad- vantages because we found that the use of high resolution (HCMM, 600 m) versus low resolution (GOES, 8 km) data, and low frequency (HCMM, 36 hours) versus high frequency (GOES, hourly) data, permitted us to infer information not possible if we were restricted to either set of data alone. We were able to extend thermal inertia information for small areas (HCMM) to the peninsula (GOES), and were able to confirm GOES di- urnal temperatures with HCMM data. Results from this project indicate that future work of this type should rely on both GOES and the high res- olution polar-orbiting satellites (NOAA-6,7). Future work should include improving the first order thermal iner- tia map generated in this project and refining land-water boundary ef- fects of small lakes and coastal areas. The diurnal temperatures from GOES can then be investigated to yield more accurate thermal inertias for the state for use in specific small-area temperature predictive mod- els for short-term forecasting of cold events. Further summarization of results, accomplishments, significance, and recommendations is given in the Executive Summary. 73 6.0. REFERENCES 1. Carlson, T.N. and Boland, F.E. 1978. Analysis of urban-rural canopy using a surface heat flux/temperature model. J. Appl. Meteorol., 17:998-1013. 2. Carlson, T.N., J.K. Dodd, S. Benjamin, and J.M. Cooper. 1981. Sat- ellite estimation of the surface energy balance moisture availabil- ity and thermal inertia. J. Appl. Meteorol. 20:67-81. 3. Chen, E. 1979. Estimating nocturnal surface temperature in Florida using thermal data from geostationary satellite data. Ph.D. disser- tation, University of Florida. 280 pp. 4. Chen, E., L.H. Allen, Jr., J.F. Bartholic, R.G. Bill, Jr., and R.A. Sutherland. 1979. Satellite-sensed winter nocturnal temperature pat- terns of the Everglades Agricultural Area. J. Appl. Meteorol. 18:992-1002. 5. Chen, E. and J.D. Martsolf. 1981. The development of nocturnal GOES infrared data as a source of climate information. Final Report, Na- tional Climate Program Office, NOAA Contract No. NA80AA-D-00129. University of Florida, Institute of Food and Agricultural Sciences. 106 pp. 6. Chen, E., L.H. Allen, Jr., J.F. Bartholic, and J.F. Gerber. 1982. Delineation of cold-prone areas using nighttime SMS/GOES thermal data: Effects of soils and water. J. Appl. Meteorol. 21:1528-1537. 7. Chen, E., L.H. Allen, Jr., J.F. Bartholic, and J.F. Gerber. 1983. Comparison of winter nocturnal geostationary satellite infrared sur- face temperature with shelter-height temperature in Florida. Remote Sens. Environ. 13:313-327. 8. Chen, E., L.H. Allen, Jr. and J.D. Martsolf. 1982. The application of geostationary satellite surface infrared irradiance temperatures to provide mesoscale climate temperatures. Final Report, NOAA Post- Doctoral Grant Contract No. NA81AA-0-00098. University of Florida, Institute of Food and Agricultural Sciences. 9. DuCharme, E.P. 1971. Soil temperature in Florida citrus groves. Tech. Bul. 747, Agric. Exp. Stat., Inst. Food & Agric. Sci., Univ. Fla. 15 pp. 10. Hanks, R.J. and F.L. Ashcroft. 1980. Applied Science Physics. O Springer-Veriag, New York, V. Advanced Series in Agricultural Sci- ence, No. 8. 11. HCMM User's Guide,1980. NASA, Godoard Space Flight Center, Green- belt, MD. Second Revision, October 1980. 120 pp. 12. Kimball, B.A. and Jackson, R.D. 1979. Soil Heat Flux in: Modifica- tion of the Aerial Environment of Plants. B.J. Barfield and .F. Gerber, eds. ASAE, St. Joseph, MI. 74 n J 13. National Oceanic and Atmospheric Administration, National Climatic Center. Climatological Data, Florida, Annual Summary, 82 (12) 1978, 83 (13) 1 , and 85 (15) 1981. Asheville, NC. 14. Price, J.C. 1980. The potential of remotely sensed thermal infrared data to infer surface soil moisture and evaporation. Water Resources Res., Vol. 16, No. 4. pp. 787-795. 15. Rosema, A., J.H. Bijleveld, P. Reiniger, G. Tassone, K. Blyth, and G.J. Gurney. "Tell-Us," A combined surface temperatur-, soil mois- ture and evaporation mapping approach. 12th Int. Symp. on Remote Sensing of Environment. Manila, Phillippi,nes, April 20-26, 1978. 16. Soer, G.J.R. 1977. The Tergra model, a mathematical model for the simulation of the daily behavior of crop surface temperature and ac- tual evapotranspiration. Niwars Publ. 46, Delft, Netherlands. 17. Soer, G.J.R. 1980. Estimation of regional evapotranspiration and soil moisture conditions using remotely sensed crop surface tempera- tures. Remote Sens. Env. 9:27-45. 18. Sutherland, R.A. 1980. A short-range objective nocturnal temperature forecasting model. J. Appl. Meteorol. 19:247-255. 19. Weinreb, M.P. and M.L. Hill. 1980. Calculation of atmospheric radi- ances and brightness temperatures in infrared window channels of satellite radiometers. NOAA Tech. Rep. NESS 80. 40 pp. 75 v