, QC 995 . U61 no. 56 c . 2 F ;hnical Memorandum NWS CR-56

THE PREDICTION OF DAILY DRYING RATES

Jerry D. Hill WSO/AG Lexington, Kentucky

Scientific Services Division Central Region Headquarters November 1974

NATIONAL OCEANIC AND National Weather noaa ATMOSPHERIC ADMINISTRATION Service I

NOW TFCHNICAL MEMORANDA National Weathar Service, Central Region 6ubaarlea r> w„.hBr Service Central Region (CR) sube.rl.e provldea an Infernal medium for the documentation and quick dl.e.minatlon The National Weather^Servlcetentr ^ for publication. n,e eerie. la uaed to report on work In progress, to deacrlbe * h*?U«l,nroredurra and practices or to relate progr.se to a limited audience. Thoae Technical Memoranda will report on lnvestlga- ufn? IwX .f mainly to regional p.rconn.1. a^ hence will not be widely dlatribuUd.

Pane re 1 to 11 are In the former aeries. ES5A Technical Memoranda, Central Region Technical Memoranda (CRTH); papers 16 to 36 are ln^the former^serles, FSSA Technical Memoranda, Weather Bureau Technical Memoranda (WBTM). Beginning with 37, the papers are now part of the aeries, NOAA Technical Memoranda NWS. Papers that have a FB or COM number are available from the National Technical Information Service, U. S. Department of Connerce, ►JjL Prt»-t RovaI Rond SDrinrfield Va. 22151. Price: $3.00 piper copy; $0.95 microfiches Order by Accession number shown in parenthe9ls°at end of each entry.’ All other papers are available from the National Weather Service Central Region, Scientific Service* Division, Room 1836, 601 E. 12th Strest, Kansas City, Mo. 66106.

ESSA Technical Memoranda

Precipitation Probability Forecaat Verification Sunmary Nov. 1965-Mar. 1966. OSD Staff, WDCRH - May 1966 CPTM 1 A Study of Summer Showers Over the Colorado Mountains. Wm. C. Sullivan and James 0. oeveraon - Juno 1966 CRTM 2 Areal Shower Distribution - Mountain Versus Vallsy Coveraga. Ms. C. Sullivan and James 0. ...verson - June 1966 CPTM 3 Heavy Rains In Colorado June 16 and 17, 1965. SSD Staff, WBCRH - July 1966 CPTM u The Plum Fire. Wm. C. Sullivan - August 1966 her 10AA CPTM 5 Precipitation Probability Forecast Verification ouimapy Nov. 1965-July 1966. SoD Staff, WBCRH - Septaaber 1966 CPTM 6 7.rfeet of Diurnal V.'eather Variations on Soybean Harvest Efficiency. Leonard P. Hand - October i960 CRTM 7 Climatic Frequency of Precipitation at Central Region Stations. SSD Staff, WBCRH - November 1966 CRTM 8 Heavy Snow or dailng. Harry W. Haldheuser - December 1966 CPTM 9 Detection of a Weak Front by VSR-57 Radar. Harry W. Waldheuser - December 1966 CRTM 10 Public Probability Forecasts. SSD Staff, WBCRH - January 1967 CPTM 11 Heavy Snow Forecasting In the Central United States (An Interim Report). SSD Staff - January 1967 CK7M 12 Diurnal Surface Ceostrophic Wind Variations Over the Great Plains. Wayne E. Songster - March 1967 CPTM 13 Forecasting Probability of Summertime Precipitation at Denver. Wm. G. Sullivan and James 0. Severson - March 1767 CRTM 14 Improving Precipitation Probability Forecasts Using the Central Region Verification Printout. Uwrenoe A. Hughes - May 1967 CRTM 15 Small-Scale Circulations Associated With Rallatlonal Cooling. Jack R. Cooley - June 1967 WBTM CR 16 Probability Verification Results (6-Month and 18-Month). Lawrence A. Hughes - June 1967 CR 17 V/BTM On the Use and Misuse of the Drier Verification Score. Lawrence A. Hughes - August 1967 (PB 175 771) WBTM CP 18 Probability Verification Resulta (24 Months). Lawrence A. Hughes - February 1968 V/BTM CP 19 Radar Depiction of the Topeka Tornado. Norman E. Prosser - April 1968 WITH l CP 20 WDTM CR 21 Wind Waves on the Great Lakes. Lawrence A. Hughes - May 1968 ism /pb isi Tit) Seasonal Aspects of Probability Forecasts: 1. Summer. Lawrence A Hughe. - June 1968 (PB 185 733) wn*m CR 22 Seasonal Aspects of Probability Forecasts: 2. Fall. Lawrence A. Hughss - September 1968 (PB 185 734) 104, WBTM CR 23 The Importance of Areal Coverar. In Precipitation Probability Forecasting. John T. Curran and Uwr.no. A. Hughes ‘ ‘‘•Pj V/BTM CR 24 Meteorological Conditions ae Related to Air Pollution Chicago, Illinois, April ^ Sw*n 0c‘°b#r WBTM CR 25 Seasonal Aepecta of Probability Forecasts: 3. Wlnlsr. Lawrence A. ughe. - December 1968 PD 185 735) WBTM CR 26 Seasonal AflDfcta of Probability Forecast.: 4. Spring. Lawrence A. Hughes - February 1969 JPB 185 736# WBTJt CR 27 Minimum Tem^rature Foreca.ting During Poeeible Fro.t Periods at Agricultural Weather Station, in Weatcm Michigan. WBTM CR 28 torn hall A. Goderberg - torch 1969 . u . . 1QAo An Aid for Tornado Warnings. Harry W. Waldheuser and Lawrence A. Hughes - V/BTM CR 29 *VT±1 1969 An Aid in Forecasting Significant Lake Snows. H. J. Rothrock - November 1969 VBTM CR 30 A Forecast Aid for Boulder Winds. Wayne E. Sangster - February 1970 1

NOAA Technical Memoranda NWS

Forecasting Maximum and Minimum .Surface at Topeka, Kansas, Using Guidance from the PE .Numerical Prediction NWS CR 37 Model (FOUS). Morrie S. Webb - November 1970 (COM-71-OOH8) Snow Forecasting Tor Southeastern Wisconsin. Rheinhart W. Harms - November 1970 {COM-71-00019) _ - b (COM-73l-C0369) NWS CR 38 A Synoptic Climatology of Bli«arda on the North-Central I lain, of JU:. United State^. E. toukTib 1971 (COM 71-00369) NWS CR 39 Forecasting the Spring 1969 Kldweat Snowmelt Flood.. Herman F. Hondecheln - Ttbr^rj im (C0H-71-00489) CR 40 NWS The Temperature Cycle or Lake Michigan 1.(Spring and Summer). Lawrence A. Hughee - April 1971 (COH-71-00545) NWS CR a Du*t Devil Meteorology. Jack R. Cooley - Hay 1971 (COM-71-00628) CR 42 NWS Sumner Shower Probability in Colorado as Related to Altitude. Alois G. Topil - toy1971 (c0M NWS CR 43 An Investigation of the Resultant Transport Wind Within the Urban Complex. ,h™ iSiaaAV6 . NWS CR 44 The Relationship of Some Cirrus Formation, to Sevsre Local Storms. William E. Williams - Juljr 1971 (C*"71"O08W( COM-71-01039 / MS CR 45 Ths Temoerature Cycle of lake Michigan 2. (Fall and Winter). Lawrence A. Hugh*. - September 1971 (X—VXUJ7/ MS CR 46 Practical Application of a Graphical Method of Geoetrophlc Wind Determination. C. B. Johneon - November 1971 NWS CR 47 (COM-71-01084) ‘

(Continued on back inside cover) 00

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NOAA Technical Memorandum NWS CR-56

THE PREDICTION OF DAILY DRYING RATES

Jerry D. Jmi WSO/AG Lexington, Kentucky

Scientific Services Division Central Region Headquarters November 1974

ATMOSPHERIC suit library

FEB 2 7 1-

N.O.A.* U. S. Dept. oJ

UNITED STATES NATIONAL OCEANIC AND National Weather DEPARTMENT OF COMMERCE ATMOSPHERIC ADMINISTRATION Service F Frederick B. Dent, Secretary Robert M. White, Administrator George P. Cressman, Director

To THE PREDICTION OF DAILY DRYING RATES

Jerry D. Hill National Weather Service WSO/AG Lexington, Kentucky

ABSTRACT

Weather observations on 92 rain-free days were used to develop a relation­ ship between open-pan and various meteorological parameters. A regression analysis determined that 86% of the variance in total daily evaporation could be explained using maximum saturation vapor pressure deficit, average daytime wind speed, and total solar radiation. Predictions obtained using the regression equation have an average absolute error of . 02 inches/day as compared to an average absolute error of . 038 inches/ day when Penman's equation was used. A simple nomogram is presented so that forecasts of wind speed, radiation, maximum temperature, and minimum relative can be used to solve the regression equation graphically for total daily evaporation and categorical drying conditions.

I. INTRODUCTION

One of the most observable effects of the weather at any location is the dry­ ing rate or the power of the air to remove water by direct evaporation. While this effect is of interest to nearly everyone, it is probably one of the least understood of all items which confront the meteorologist. Whether it be the housewife wanting to know how well her laundry will dry, the far­ mer wanting to know how soon his fields will dry, or the football coach wanting to know if his playing field will dry before tonight's game, there is a universal need for drying forecasts. Because of the difficulty in pre­ paring evaporation predictions though and possibly the difficulty in express­ ing them to the public, such forecasts have only been prepared as part of very limited programs in the Weather Service.

Unlike precipitation which is highly dependent upon 3-dimensional models of the atmosphere, evaporation takes place near the surface and is dependent primarily on the meteorological factors only at that level. Since there is considerable local variation in these meteorological factors due to influences of shade, sheltering, etc. , it is not unusual to find considerable local varia­ tion in daily drying. Generally, however, well-exposed sites should exhibit uniform day-to-day variation in drying representative of the varying weather conditions. The basic meteorological observation related to drying conditions is open- pan evaporation. The pan, which is made of metal, has standard dimensions consisting of a four-foot diameter and 10 inch depth. It is usually mounted on a slatted wooden frame to hold it slightly above the ground and prevent the conduction of heat from the soil into the pan. Evaporation observations are made daily just as precipitation might be observed. The water's depth in the pan is measured and, after making an allowance for any additions due to rainfall, the reduction in depth is reported as the amount of evaporation taking place during the preceding 24 hours. A nominal amount in mid-latitude, sub-humid climates is about .25 to . 30 inches on a warm rain-free day.

The amounts can vary widely though and are a representation of the drying power of the air. The purpose of this paper is an attempt to develop a method to predict what these variations may be.

II. THE NATURE OF OPEN PAN EVAPORATION

Water is continually being lost from a saturated surface due to the difference in vapor pressure between the water and the unsaturated air adjacent to it. Any increase in vapor pressure gradient would be expected to increase the rate of movement of water molecules from the water to the air. Since latent heat must be added to water in order to change it from a liquid into its vapor state it would seem likely that the rate of evaporation would be greatest when radiation falling upon the evaporating surface provides this necessary energy.

In a study of open-pan evaporation, Cook (1968) instrumented a pan and con­ tinually monitored the loss of water from it. Using 26 selected rain-free days from September 16 through October 30, he determined the percent of total daily evaporation occurring during each hour of the day. Figure 1 shows the daily distribution of evaporation and indicates more than half of the loss occurred during the afternoon and evening hours.

Fig. 1. Percent of total daily evapora­ tion occurring during each hour.

2 In the past, it has been extremely difficult to quantify the effects of vapor pressure gradient or solar radiation upon evaporation due to the nature of the observing time. An evaporation observation taken at 7:00 A. M. on a particular day and ascribed to that date actually is a measure of water loss taking place mostly under the previous day's weather influences. Likewise, observations taken at 5:00 P. M. measure only a portion of the evaporation on that day since additional losses will occur before sunset. The instrument developed by Cook to continually record the water level in the evaporation pan allows water losses to be ascribed to the same time period for which other meteorological observations are representative, e. g. total daily solar radiation.

In Figure 2 the recording trace for several consecutive days is shown. The horizontal lines are intervals of . 02 inches and the vertical lines are time increments of 2 hours.

Figure 2. Portion of continuous trace from evapora­ tion recorder, July 11-12, 1973. The water level in the pan is shown to change little during the period from midnight to noon. The major loss occurs from noon until about 8:00 P. M. and the slope of the curve is an indication of the rapidity with which water is being removed.

On typical rain-free days the saturation vapor pressure deficit is greatest during the afternoon period thus favoring the peak loss at that time. Sur­ face winds are generally strongest in the afternoon and insure a continual flow of unsaturated air over the water surface to replace the boundary layer before it becomes saturated and reduces the vapor pressure gradient.

III. PREDICTION OF EVAPORATION

Evaporation is an important part of the hydrologic cycle but probably one of the least measured elements. In many areas there are 25 to 50 rainfall ob­ serving substations for each evaporation observing station. Because of the lack of such observations, meteorologists and climatologists have strived for years to predict evaporation from more commonly available meteorologi­ cal data. Some of the early methods were completely empirical using what­ ever data might be available to develop them.

1, Thornthwaite' s Equation

Thornthwaite (1948) attempted to estimate the drying power of the air by a method to predict monthly potential using the relation- ship:

E 1. 6

where E monthly potential evapotr anspir ation (cm) T monthly mean temperature (°C) I a heat index which is a constant for a given location a an empirically determined exponent which is a function of I.

A day-length correction is to be used with the Thornthwaite equation in order to adjust for longer or shorter periods of sunlight. The relationship has been used to forecast water loss for a period on the order of 7 days but, as one might imagine, the use of mean temperature and day length alone provide poor estimations of daily demand.

2. Penman's Equation

One of the more widely used methods for predicting evaporation is the equation by Penman (1948) which is based on a combination of aerodynamic and energy balance equations. In its common form:

4 Qn S' + T Ea E s + r

where E = estimated daily evaporation from a free water surface § = slope of the saturation vapor pressure curve at the temperature of the air 0 - a constant in the wet and dry-bulb psychrometer equation (assumed to be . 27)

E has the form: E = b ( a+U) (es - ea)

where U = total daily wind movement at pan height es = saturation vapor pressure ea = actual vapor pressure of the air a and b are constants.

Qn is the net radiation estimated as: 4 Qn = Ra (1 - r) (0. 18 + . 55 n/N) -

where Ra = mean extraterrestrial radiation expressed in equivalent mm /day of evaporation r = reflection coefficient n/N = ratio of actual to possible hours of sunshine

Data collected at Lexington, KY for 92 rain-free days from April through October of 1973wereused to test Penman's equation. The observed values of daily evaporation ranged from . 04 to . 38 inches and the average absolute error of the predicted values was . 038 inches. The absolute error was dis­ tributed as follows:

magnitude of error: no. of cases . 05 inches 66 .05- . 10 inches 22 . 10 inches 4

From these data it might be expected that Penman's equation can be used to forecast daily open pan evaporation within . 05 inches more than 2 days out of 3.

5 In order to simplify the use of Penman's equation in routine forecasting, Purvis (1961) has developed a nomogram for the solution to it. The nomogram was actually developed to estimate evapotranspiration from a crop rather than evaporation from a water surface and the original Penman equation solution has been reduced by a factor of . 7 but the technique still provides a reasonable estimate of the day to day variation in drying quality of the air.

3. Development of a Multiple Regression Equation:

The 92 observations of evaporation used in the Penman equation test, along with the other meteorological observations made on those days, were used in a multiple regression program to determine which of the variables were most important in the prediction of drying. The observations were made at a completely open, exposed site surrounded by extensive areas of grass cover. The greatest simple correlation was between total daily solar radiation and evaporation with a correlation coefficient of . 81.

In order to determine the most meaningful relationship among all the variables., a computer program was used which fitted all possible combinations of pre­ dictors ahd selected the best combination based on reduction of unexplained variance between predicted and observed values.

The results of this step-wise fitting procedure are shown in Table 1:

Table 1: Sequential fitting of variables in the regression analysis:

variable variance explained

solar radiation . 657 solar radiation, saturation vapor . 722 pressure deficit solar radiation, saturation vapor . 863 pressure deficit, avg. daytime wind

The addition of the next 2 most important variables, average daily wind speed and percent of possible sunshine increased the amount of variance explained to only . 870 which is very little improvement for the additional forecast input.

It is interesting to note that the analysis procedure selected average daytime wind speed (defined as the mean of observations at 1000, 1300, 1600, 1900) rather than the average daily wind speed as the third variable in the regression. If the average daily wind speed is substituted for daytime wind the variance explained drops to . 772.

6 The saturation vapor pressure deficit used in the analysis was calculated as:

VPD = es - ea

where: VPD = the saturation vapor pressure deficit = the saturation vapor pressure of the water surface calculated using its maximum temperature for the day = the actual vapor pressure of the air calculated from ea the minimum relative humidity and maximum air temperature for the day

The maximum temperature of the water surface was not always equal to the maximum air temperature but exceeded it on sunny days and fell below it on cloudy days. In order to determine the amount of difference between maxi­ mum air temperature and maximum temperature of the water in the pan, ob­ servations of the difference were plotted against values of total radiation re­ ceived during the day. Figure 3 shows the relationship and a straight line has been fitted to the data.

Figure 3. Difference between maximum daily temperature of air and water in evaporation pan vs. total daily solar radiation.

7 The simple linear relationship fitted is:

difference (°F) = . 03 (solar radiation) - 10

On days when solar radiation exceeds about 350 Langleys the maximum temp­ erature of the evaporating surface exceeds that of the air while on days when less radiation is received, the water surface generally fails to reach the maxi­ mum air temperature.

Using values for solar radiation and maximum air temperature, the maximum water surface temperature can then be determined and used to compute the saturation vapor pressure of the water. The minimum relative humidity and maximum air temperature are used to calculate the actual vapor pressure of the air. The difference between the two is the vapor pressure deficit. On sunny days when water surface temperature is elevated, the vapor pressure is increased thus making the saturation deficit greater. The simple corre­ lation between evaporation and vapor pressure deficit computed in this manner is . 779. In a study of evaporation in Canada, Baier and Robertson (1965) com­ puted the vapor pressure deficit using mean air temperature and mean daily dew point. The correlation coefficient with evaporation was . 65 in that study. Since, for the same mean conditions, one could have a large amplitude of the diurnal temperature cycle (and large evaporation) or small amplitude (smaller evaporation), the difference in correlations is as expected.

The regression coefficients were determined for the 3 variables in the model and the prediction equation became:

Evaporation = . 15 (VPD) + .013 (WIND) +.000194 (S.R.)-.OBO

where: wind is in knots solar radiation is in Langleys VPD is in inches of mercury

When tested on the 92 days of dependent data, the average absolute error was . 020 inches which is about half of the average absolute error from Penman's Method. The distribution of errors can be compared to that for Penman's equation given earlier. Part of the difference, of course, is due to the fact that the data were independent for the Penman equation and dependent for the equation given here. magnitude of error no. of cases . 05 inches 90 .0 5 - .10 inches 2 s' . 10 inches 0

8 Table 2 shows an analysis of variance for the data:

Table 2: Analysis of variance: Degrees of Sum of Mean Prob, of Sour ce freedom Squares Squares F >F

R egression 3 . 38114 . 12705 184. 7 . 0001 Error 88 .06052 .00068

Corrected total 91 .44166

R - Squar e = .8629

IV. GRAPHICAL SOLUTION TO THE REGRESSION EQUATION

A nomogram has been prepared to graphically solve the regression equation. It requires a prediction of maximum air temperature, minimum relative humidity, total solar radiation, and average daytime wind speed to produce a forecast of daily open pan evaporation. The nomogram shown in Figure 4 uses the upper right-hand quadrant to solve for the VPD term from fore­ cast values of temperature and relative humidity. Since the VPD has been computed from 2 different temperatures, one representative of the air temperature and the other representative of the water temperature, there must be some method of considering the differences. This is done by es­ timating the difference as a function of solar radiation using the relation­ ship shown on page 8.

The upper right-hand quadrant of the nomogram has 2 sets of curved lines on it. The solid set represents the forecast minimum daily relative humid­ ity and the dashed set without labels represents the temperature difference scale. The user simply moves upward from the forecast temperature to the expected relative humidity. At this point the temperature difference correction is made by moving either upward and to the right (+ correction) or downward and to the left ( - correction) an appropriate distance between the 10 degree dashed intervals. This correction is made while moving along a constant value of relative humidity.

From this point one moves horizontally to the left until reaching the forecast daytime wind speed. Moving downward to the solar radiation then to the right, the lower portion of the y-axis is intercepted at a value equal to the predicted daily evaporation total.

9 Forecast solar Temperature Forecast solar Temperature radiation: correction: radiation correction:

<217 Langleys -4°F 450-482 Langleys +4°F 217-250 -3 483-515 +5 251-283 -2 516-548 +6 284-317 -1 549-581 +7 318-350 0 582-614 +8 351-383 +1 615-647 +9 384-416 +2 648-680 +10 417-449 +3 681-713 +11 714-746 +12

20% WIND SPEED (KTS) DASHED LINES ARE TEMPERATURE CORRECTION 20 15 10 5 0 / 40% RELATIVE AT INTERVALS OF 10 °F / HUMIDITY I S o sa A •T \i

To forecast daily open-pan evaporation amounts:

1. Predict total solar radiation. 2. Enter chart with maximum temperature forecast. 3. Move upward to minimum relative humidity forecast. 4. Along a line of constant relative humidity, move right or left a SOLAR RADIATION distance equal to the temperature (LANGLEYS) correction as noted by the dashed lines. 5. Move horizontally to the forecast average daytime wind speed. 6. Move down to the forecast solar radiation. 7. Move to the right to read evaporation amounts from the vertical scale.

Figure 4. Nomcgram for prediction of daily open-pan evaporation. 10 Some users of evaporation forecasts may find it difficult to relate to actual predicted amounts and might prefer a categorical forecast of relative dry­ ing conditions. To satisfy this need the following categories have been ar­ bitrarily established:

predicted daily evaporation categorical drying forecast

0 to . 10 inches poor .11 to . 15 fair .16 to . 25 good >. 25 excellent

V. PREDICTION OF SOLAR RADIATION

Most forecasters are familiar with the techniques available for the prediction of maximum temperature, minimum relative humidity, and wind speed but some may not be quite so accustomed to forecasting solar radiation. The amount of radiation received on a horizontal surface during a day depends upon cloud cover, atmospheric turbidity, latitude, and time of year. For a given location and time of year, studies reveal an excellent correlation between solar radiation and cloudiness. The Smithsonian Meteorological Tables (List, 1963) provide considerable material dealing with the amount of solar radiation reaching the earth's surface and can be of assistance in preparing forecast aids.

Table 151 in the Smithsonian publication gives a linear relationship between sunshine amounts and radiation. Table 135 and its accompanying discussion give a method to estimate total solar radiation reaching the earth on a clear day with various transmission coefficients. It could be used with estimated values of cloudiness to make a forecast of solar radiation on any given day.

VI. SUMMARY AND CONCLUSIONS

Daily open-pan evaporation can be estimated by the use of commonly observed meteorological variables. The regression analysis described in the paper is able to provide an estimate of total water loss which should be a relative measure of the drying quality of the air. Early morning forecasts of total solar radiation, maximum temperature, minimum relative humidity, and average daytime wind speed can be used in Figure 4 to predict specific evaporation amounts or to prepare a categorical drying forecast.

Even though predictionsof evaporation amounts are not routinely made in most forecast offices, there are occasions when such inquires are directed to the meteorologist. The regression analysis presented here is useful in pointing out those weather parameters which have the greatest effect on evaporation as well as how they can be utilized to make a specific forecast.

11 REFERENCES

Baier, W. and G. W. Robertson, 1965: Estimation of Latent Evaporation from Simple Weather Observations. Can. J. Plant Sci. 45:276-284

Cook, D. , 1968: Diurnal Changes in Evaporation; the Process and its Prediction. Unpublished Master ' s Thesis, University of Kentucky Dept, of Agronomy.

List, R.J. 1963: Smithsonian Meteorological Tables, 6th Rev. Edition, Published by the Smithsonian Institution, Washington, D. C.

Penman, H. L. , 1948: Natural Evaporation from Open Water, Bare Soil, and Grass. Proc. of the Royal Soc. of London, Series A, 193: 120-145.

Purvis, J. C. , 1961: Graphical Solution of the Penman Equation for Potential Evapotranspiration. Monthly Wea. Rev. 89: 192-196.

Thornthwaite, C. W. , 1948: An Approach Toward a Rational Classification of Climate, Geographical Rev. 38: 55-94. (continued from front, inside cover)

NWS CR 43 Manual of Great Lakes Ice Forecasting. C. Robert Snider - December 1971 (C0M-72-10143) _ NWS CR 49 A Preliminary Transport Wind and Mixing Height Climatology St. Louis, Missouri Donald E. Ymerch, Albert J. Courtois, Carl Ewald, Gary Ernst June 1972 (COM-72-10859) NWS CR 50 An Objective Forecast Technique for Colorado Downslope winds Wayne E. Sangster December 1972 (COM-73-10280)

NWS CR 51 Effect on Temperature and Precipitation of Observation Site Change at Columbia, Missouri March 1973 (COM-73-10734)

NWS CR 52 Cold Air Funnels. Jack R. Cooley and Marshall E. Soderberg September 1973 (COM-73-11753) NWS CR 53 The Frequency of Potentially Unfavorable Temperature Conditions in St. Louis, Missouri October 1973 Warren M Wisner

NWRCR 54 Objective Probabilities of Severe Thunderstorms Using Predictors from FOUS and Observed Surface Data Clarence L. David May 1974

NWS CR 55 Detecting and Predicting Severe Thunderstorms Using Radar and Sferics John V. Graff and Duane C. O'Malley June 1974