A Real-Time Climate Information System for the Midwestern United States

Home , Western United States

Kenneth E. Kunkel, Stanley A. Changnon, A Real-Time Climate Carl G. Lonnquist, and James R. Angel Midwestern Climate Center Information System Climate and Meteorology Section Illinois State Water Survey 2204 Griffith Drive for the Midwestern Champaign, Illinois 61820 United States

Abstract mate information is not used more fully by agribusiness. These include: a) lack of a delivery system to The Midwestern Climate Information System (MICIS) is a near provide timely access to information; b) perceived real-time system which provides access to a wide variety of cli- complexity of the decision-maker's agricultural prob- mate information products. These include current temperature lem of which climate is only one part; c) attitude by and precipitation data for several hundred midwestern United States stations, historical temperature, and precipitation for about potential users that climate information is of question- 1500 stations, climate summaries, long-range predictions, re- able economic utility; d) lack of process models or gional soil moisture estimates, and crop yield risk assessments. organizational resources to fully exploit the available The region covered includes the states of Illinois, Indiana, Iowa, information; and e) lack of availability of agriculturally Kentucky, Michigan, Minnesota, Missouri, Ohio, and Wisconsin. important variables, such as soil moisture and solar Because agriculture is a major sector of the Midwestern economy and is sensitive to climate fluctuations, some products have been radiation. oriented to the needs of agriculture. However, many other prod- The Midwestern Climate Center's (MCC) Midwest- ucts have generalj applicability. Users of this system include agri- ern Climate Information System (MICIS) is designed businesses and researchers. to partially address these problems, particularly points MICIS has several unique features: a) regional coverage pro- a), d), and e). vides climatic information for a major part of the United States corn and soybean belt; b) daily temperature and precipitation data The development of MICIS built upon the experi- are obtained daily from an average of 500 stations providing an ence of another climate information system called Cli- up-to-date assessment of current climatic conditions; c) process mate Assistance Service (CLASS). This system models provide an estimate of potential impacts on soil moisture (Changnon et al. 1984; 1987) provided products for and corn and soybean yields. Illinois and was operated for the period 1984-1988. Partially as a result of the CLASS experience, MICIS 1. Introduction is oriented to the needs of the private sector in general, and agribusiness in particular. However, most products also have applicability to the needs of those in Recent analyses (Lamb et al. 1985; Changnon et al. government agencies and the scientific research com- 1987) pointed to the need for a computer-based sys- munity. tem which would allow access to a wide range of climatic information on a regional scale. In the mid- Prior to MICIS, the best source of climate infor- western United States, climate anomalies have major mation on a regional scale was the information system economic impacts on agriculture and water resources. of the Climate Analysis Center (CAC) (Finger et al. The need for close monitoring of actual conditions 1985). However, the CAC system is designed to pro- during such anomalies was emphasized by events vide a national and international view of climate con- occurring during the 1988 drought. As an example, ditions and does not address the specialized and often during a single three-week period in June, corn prices detailed informational needs of any particular region. on the Chicago Board of Trade rose by more than In addition, many CAC products are updated only 35% in response to growing concern about yield re- weekly, and this is not adequate for many needs during ductions. However, discussions with commodities bro- the growing season when the agricultural weather sit- kers indicated that these changes occurred largely in uation can change rapidly. By contrast, MICIS is de- the absence of quantitative estimates of how the cur- signed to supply more timely information (updated rent climatic conditions might affect crop yields. Lamb daily) with products specifically tailored to midwestern et al. (1985) identified several major reasons why cli- needs. The system covers a nine-state region (Illinois, Indiana, Iowa, Kentucky, Missouri, Minnesota, Mis- souri, Ohio, and Wisconsin), although a few products also include North Dakota, South Dakota, Nebraska, ©1990 American Meteorological Society Kansas, New York, Pennsylvania, Vermont, and

Bulletin American Meteorological Society 1601

Unauthenticated | Downloaded 09/26/21 08:19 AM UTC southern Canada. Products (data and information) are widely from day to day, depending on the weather. On presented for points in the region, climate divisions, a day with no precipitation, there may be as few as states, and the region as a whole. The system has ten reports in some states. On a day with widespread been in operation since April 1989 and is open to public precipiation, the number may increase to over 100 in access with a fee charged to users to help maintain some states. On an average day, about 500 stations the system. in the 9-state region report. There are also occasional This system is unique in its timeliness, geographical obvious errors in the data necessitating quality control coverage, and sophistication of certain products. This procedures. paper briefly describes the components of MICIS and There are a few other sources of daily data that are the products available on MICIS. The specialized soil- not regional in extent. For instance, the Minnesota moisture and crop-yield products are described to il- Agricultural Extension Service and the Minnesota De- lustrate how two highly used products are calculated. partment of Natural Resources obtain daily data from a network of 40 observers in and around the state. 2. System design Updates of these data are made available on MICIS once a week. The components of the system include the database, All of these daily data are stored in the same da- hardware, data-processing procedures (software), tabase, constituting a seamless record for a station public products, and the means of dissemination. from the beginning of record to the latest report. As more reliable information is obtained, it overwrites the a. Database previous, less reliable data. For example, the near real- A major component of the database is a set of daily time data obtained by satellite are overwritten with observations of total precipitation, maximum and min- preliminary NCDC data, which in turn are overwritten imum temperature, total snowfall, and snow depth. by the final NCDC data. These observations are obtained from a number of A second component of the database is a set of different sources, but there are two basic types: those daily average values of dew point temperature, wind of the recent past (received from NC DC), and those speed, wind direction, air pressure, and cloud cover. updated daily. These are calculated from hourly surface airways re- Historical records of observations from the National ports. Recent data are available for about 100 stations. Weather Service's (NWS) cooperative observer net- Historical data, in some cases back to 1948, are avail- work were obtained from the National Climatic Data able for about 50 stations. In addition to the above Center (NCDC) (TD-3200 dataset). Data for the entire variables, daily total solar radiation is calculated using period of record for all active stations are stored online. the method of Meyers and Dale (1983) and potential There are about 1500 active stations in the 9-state evaporation is estimated using the Penman-Monteith region. The digitized records for many of these stations formula (Monteith 1965; Thorn 1975). extend back to 1948 and earlier. These data are reg- Other elements in the database include the 3-5-day, ularly updated. A preliminary version of the latest avail- 6-10-day, 30- and 90-day NWS forecasts. The first able data is obtained from NCDC about five weeks two are obtained directly from the Domestic Data Plus after the end of a month (e.g., May's data are obtained service while the latter two are obtained from CAC. in early July), and the final edited version of the data Weekly updates of the Palmer Drought and Crop Mois- is obtained 2-3 months after the end of the month (e.g., ture Indices are also obtained from CAC. Finally, his- May's data are obtained in mid-August). These data torical monthly values of temperature, precipitation, the represent a very dense network of observation stations Palmer Drought Severity Index, and the Palmer Hy- but they are not very timely in monitoring rapidly drological Drought Index for the period 1895-present changing climatic conditions. averaged over climate divisions were obtained from More timely data are received daily from a variety NCDC. of NWS networks, including first-order, aviation, agricultural, and hydrologic networks. These data are ob- b. Hardware tained by satellite transmission from Zephyr Weather The system is implemented on a SUN 4/110 computer Information Service, Inc. (their Domestic Data Plus running under the Unix operating system with two service). These near real-time data are less reliable 325MB disk drives. One disk drive is devoted exclu- than the data received from NCDC. Many stations are sively to the storage of daily data. Data are stored in criterion reporters; that is, they report only when some a binary format with one station-day of five climate criterion is met (e.g., occurrence of measurable pre- elements occupying eight bytes. Thus one disk drive cipitation). As a result, the number of reports can vary provides a capacity of about 100 000 station-years.

1602 Vol. 71, No. 11, November 1990

Unauthenticated | Downloaded 09/26/21 08:19 AM UTC With the exception of the soil-moisture and crop-yield CERES-Maize (Jones and Kiniry 1986). The soybean risk assessment products, no derived variables are product is based on a similar model for soybeans, stored in disk memory. All other products using daily SOYGRO. These unique products are explained in data are calculated when requested by a user. This further detail later. provides a number of operational advantages: Many applications, such as accumulation of degree days (growing, heating, and cooling), require complete temperature data. Because of the aforementioned 1) only a single database needs to be updated problems with the reliability of data received by satellite when new data become available. All products transmission, most stations are characterized by at are in essence immediately updated; least a few missing values in the most recent one to 2) the user can be provided the option to choose two months. It was therefore necessary to provide a the period over which calculations are made; capability for estimating missing values. The modified and Barnes objective analysis method of Achtemeier 3) disk memory requirements are minimized. (1989) is used to produce gridded datasets of maximum and minimum temperatures on a daily basis. A The tradeoff, of course, is that the response of the two-analysis approach is used to automatically ex- system may be slowed by the necessity to process clude obvious erroneous values, which could distort the data each time a request is made. In practice, this the gridded field. In the first analysis, the radius of has not been a major problem because of the speed influence for each grid-point value is relatively large, of the computer. Most products can be processed with minimizing the effect of any single measured value. little delay. For instance, the calculation of a monthly Each measured value is then compared with an es- precipitation summary for a station with 40 years of timated value from the gridded field. If the difference data requires less than one second. between these values is greater than 5°C, that mea- The data received from the Zephyr satellite link are sured value is excluded from the second analysis. In captured continuously. At frequent intervals (10 min), the second analysis, the radius of influence for each those data of interest to MICIS are extracted and grid-point value is smaller which allows some of the stored in the appropriate files. At that time they become smaller scale structure of the temperature field to be available to users. FORTRAN and C are the two pro- retained. gramming languages used for computations and database access, with C shell (a standard Unix command interpreter) being used for the human interface. This d. Products and dissemination arrangement takes advantage of the speed of the high- Table 1 lists the wide range of products generated level languages when needed. The C shell environ- from the processed data. These include both mapped ment allows for the easier development of the interface and tabular displays. Either recent or historical values because shell scripts can be developed somewhat of daily climate observations may be obtained. Monthly independently of the programs and changes in the and annual values of statistically processed data are interface do not require recompilation. Therefore, most also available. A series of standard climatological sum- products on the system are actually shell scripts which maries are provided. Certain long-range forecast prod- in turn call the underlying program(s) or other shell ucts of the NWS are made available. In addition, scripts. values of temperature, precipitation, and Palmer Drought Indices, which have been averaged over climate divisions, are available for the period 1895-pres- c. Data processing ent. Finally, specialized products include soil-moisture There are two categories of data processing. One cat- estimates and crop-yield risk assessments. These are egory is standard statistical analysis; statistical pro- explained in more detail in the following sections. The cedures include means, extremes, standard user has the option to choose whether to estimate deviations, number of days above and below thresh- missing temperature data. For a chosen station, val- olds, ranking, probability distributions, and the incom- ues are estimated from the aforementioned gridded plete gamma distribution for precipitation probabilities. data by linear interpolation of the values at the four The second category is physical process modeling. nearest grid points. There are three classes of products resulting from The system is available for public access by phone models: regional soil-moisture estimates, corn-yield with a 1200- or 2400-baud modem or through national risk assessment, and soybean-yield risk assessment. communication networks, such as Internet. A menu The first two of these products result from a standard system provides a user-friendly environment for simulation model of corn growth and development, choosing products.

Bulletin American Meteorological Society 1603

Unauthenticated | Downloaded 09/26/21 08:19 AM UTC TABLE 1. MICIS Products (1985) and Jones and Kiniry (1986). This is a multiple- 1. Daily Climatological Observations (temperature, precipitation, layer model which includes the effects of partial can- snowfall, wind, dew point, pressure, cloud cover) opy cover on soil evaporation and plant transpiration. a. Regional or state map of recent data This model is used to generate climate-division esti- b. Tabular listing by state of recent data c. Tabular listing by month or year of historical data for single mates of soil moisture status on a real-time basis. The stations model is run daily at mid-morning and provides up-to- 2. Statistically Derived Products date soil-moisture values. Details of the model pro- a. Monthly and annual values of means, extremes, degree days, number of days above and below thresholds, and freeze data cedures are givenin Kunkel (1990) and a few major b. Tabular listing of days which meet user-chosen threshold points will be presented here. criteria The model requires data about certain soil char- c. Daily, monthly, or annual values of estimated solar radiation and potential evapotranspi ration acteristics, including soil-water conductivity, albedo, 3. Single Station Climatological Summaries—1-page and soil water-content for the following conditions: tables of climatological statistics including lower limit of plant available water, drained upper limit a. Monthly temperature means, extremes, and number of days above and below thresholds (or field capacity), and saturation. Soil characteristics b. Monthly precipitation means, extreme, and number of days were obtained from a database assembled by the above and below thresholds United States Department of Agriculture (Dyke et al. c. Monthly heating, cooling, and growing degree day means d. Average and extreme dates of last spring and first fall freeze 1985). For each climate division, the dominant soil was e. Daily temperature means and extremes (climate calendar) chosen as representative of the entire division. Soil f. Daily temperature probability distribution by month type acreages by county were obtained from the g. Precipitation probability distribution by month and season 1 h. Sunrise/sunset times SOILS database. The county acreages were com- 4. Climate Atlas bined to yield climate division acreages. a. maps of the climate statistics available in item 3 above Daily values of temperature, precipitation, and solar 5. Long-Range Forecasts a. 3-5-day radiation are required as input to the model. For tem- b. 6-10-day perature and precipitation, the daily values consist of c. 30-day simple averages of the data from all stations reporting d. 90-day e. 7-day minimum/maximum temperature on a particular day in a climate division. Solar radiation 6. Regional Soil Moisture Estimates is estimated from hourly cloud-cover observations a. Maps and tables of several variables including plant available based on the work of Meyers and Dale (1983). An water (inches), deviation from long-term mean, and % of potential plant available water operational problem arises when there are no report- b. Text advisory pointing out potential problem areas ing stations on a particular day in a climate division. 7. Corn and Soybean Yield Risk Assessment This is not a problem for days on which NCDC data a. Probability distribution of model yields b. Categorization by type of weather in historical year have been obtained, that is, for days at least one to c. Listing of values for historical years corresponding to latest two months before the present day. However, the long-range forecast more recent data are less dense, with a larger per- d. Text advisory pointing out areas with model yields above and below average centage of missing values. 8. Regional Data Averaged over Climate Divisions- A 4-month comparison (June-September 1989) be- Historical and Current Values of tween the near real-time data identified about 200 sta- a. Palmer Drought Indices b. Temperature tions which are highly reliable in the following sense: c. Precipitation when no data are received, an assumption of no precipitation is usually accurate. For these stations, this assumption was made. For other stations, their precipitation reports are included in the climate division average when received; no assumption is made when no report is received and the station is excluded from 3. Soil moisture the averaging for that day. Temperature is handled differently. When no reports are received for a climate Analyses of climate information needs in the Midwest (Wendland and Vogel 1986) revealed a widespread interest for regularly updated information on soil moisture on a regionwide basis, particularly during the growing season. Hence, a procedure was developed 1The SOILS data provided were compiled as a cooperative effort for estimating soil moisture values for each of the 75 between the Environmental and Technical Information System climate divisions in the Midwest. The system utilizes (ETIS) of the University of Illinois, Department of Urban and Re- the CERES-Maize corn-simulation model, which con- gional Planning, and the U.S. Army Corps of Engineers, Construc- tion Engineering Research Laboratory (CERL), Champaign, Illinois. tains a soil-water balance model based in part on work The source of the original SOILS data is the USDA Soil Conservation by Ritchie (1972) and described in detail in Ritchie Service (SCS).

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Unauthenticated | Downloaded 09/26/21 08:19 AM UTC division, a value is estimated from the objectively ana- (1990) and only a brief outline will be given here. These lyzed gridded temperature fields discussed in the pre- products provide a quantitative assessment of the po- vious section. Although this treatment of missing tential for current climate conditions to affect corn and precipitation data may not be adequate for some ap- soybean yields.The approach is similar to that pro- plications, we do not expect that significant errors re- posed by Duchon (1986) and also builds on work by sult in this case since soil moisture is an integrative Hodges et al. (1987). In order to produce a yield, an and rather slowly varying quantity. entire growing season of weather data is required. Since any model applied on a large scale may con- Year-to-date data are available up to the time of the tain biases, it is likely that absolute values are less model run. The growing season is completed by using accurate than comparisons between current estimates data from past years. For example, if the model is run and the long-term average of estimates for past years on 1 July 1989, daily weather data for each station in since the differencing should largely remove system- a climate division are averaged for 1 January 1989- atic biases. In order to obtain long-term averages, the 30 June 1989. For the rest of the growing season (1 model was run continuously for each climate division July 1989-end of the growing season), weather data for 1949-1988. Weekly average values for each year from one year of the historical record, for example are stored in a file. Current estimates can then be 1965, are used. The composite of 1989 data to date compared with historical modeled estimates for the and 1965 as future data is used to run the models. same calendar week of the year for each year back The resulting yield represents what might happen if to 1951. The first two years of this period (1949 and the weather during the rest of the growing season is 1950) are not used because the values may be influ- like 1965. For each climate division, the model is run enced by the values of soil moisture used to initialize for each year of the historical record back to 1949. the model. The resulting yields provide probabilistic information These soil moisture estimates differ from the Crop about potential crop outcomes. Moisture Index (CMI) (Palmer 1968) in the following ways:

1) the use of a multilayer (nine levels) model allows a greater level of vertical detail. The depth of dry or wet layers can be identified; 2) because the model is run continuously (through the winter), carryover of dryness from the previous growing season is allowed. By contrast, the CMI begins at zero (normal conditions) at the beginning of the growing season. This is especially important for the (drier) western part of the Corn Belt where carryover dryness occa- sionally affects crop production; and 3) crop water use is more accurately estimated in FIG. 1. Map of Iowa precipitation for 24 h ending in the early our procedure. Since the stage of vegetative morning of 8 September 1989. Values are in hundredths of inches (e.g., 94 = 0.94 inches). development affects transpiration rates, the use of a crop development model allows realistic estimates of transpiration.

These soil moisture estimates are most appropriate for corn and other similar row crops (e.g., soybeans). Other crops, such as alfalfa and wheat, will have a different seasonal dependence of transpiration. These estimates would not be as applicable for those crops.

4. Crop yield probability assessment

These products are derived from the CERES-Maize FIG. 2. Map of model estimates of soil moisture by climate and SOYGRO models, respectively, for corn and soy- division for 7 November 1989. Values are deviations from 1951-88 beans. Details are given in Kunkel and Hollinger average in inches.

Bulletin American Meteorological Society 1605

Unauthenticated | Downloaded 09/26/21 08:19 AM UTC TABLE 2. Monthly summary of precipitation for Urbana, Illinois for the period 1903-89.

Station: (118740) Urbana, IL Station: (118740) Urbana, IL From Year-1903 To Year-1989 Missing Data: 0.3% Total Precipitation Snow #Days Precip. Mean High Yr Low Yr 1 -Day Max Mean High Yr > = 0.10 > = 0.50 > = 1

Ja 2.09 7.62 50 0.06 86 2.43 26/1967 5.9 28.3 79 5 1 0 Fe 1.90 5.70 9 0.15 47 1.78 19/1939 5.7 18.5 5 4 1 0 Ma 3.23 8.35 22 0.38 10 2.93 13/1917 4.3 32.0 6 7 2 1 Ap 3.82 9.55 64 0.59 76 3.09 20.1964 0.6 8.0 20 8 2 1 Ma 3.98 11.20 43 0.22 25 4.50 26/1921 0.0 2.5 29 7 3 1 Jn 3.94 9.38 47 0.32 88 3.89 19/1983 0.0 0.0 0 6 3 1 Jl 3.71 10.96 71 0.47 16 4.43 30/1987 0.0 0.0 0 6 3 1 Au 3.55 10.01 77 0.68 53 3.90 20/1924 0.0 0.0 0 6 2 1 Se 3.22 9.76 26 0.25 54 3.91 15/1931 0.0 0.0 0 5 2 1 Oc 2.72 9.01 41 0.16 64 3.72 21/1983 0.1 3.3 89 5 2 1 No 2.66 10.08 85 0.00 4 4.07 2/1936 1.7 11.2 32 5 2 1 De 2.38 6.63 67 0.12 19 2.74 7/1966 4.8 19.1 83 5 1 0

An 38.21 55.64 27 24.68 14 4.50 26/05/21 23.3 51.9 77 69 25 9 Wi 6.37 16.33 50 1.40 20 2.74 7/12/66 16.4 49.6 78 14 4 1 Sp 11.03 19.78 44 4.42 32 4.50 26/05/21 5.0 32.0 6 21 7 3 Su 11.20 20.17 81 4.69 13 4.43 30/07/87 0.0 0.0 0 18 8 3 Fa 8.60 17.25 41 3.02 53 4.07 2/11/36 1.9 11.2 32 15 6 2

TABLE 3. Daily maximum temperature for Morris, Minnesota for 1988.

Station: (215638) Morris, MN Year: 1988 Element: Maximum temperature (F)

Day Jan Feb Mar Apr May Jun Jul Aug Sep Oct Nov Dec 1 0 3 42 43 77 92 80 103 87 66 53 23 2 1 -4 42 56 82 89 69 100 82 65 50 30 3 22 3 30 44 83 90 84 83 77 59 51 46 4 12 4 33 50 81 89 88 79 68 49 56 30 5 -10 -3 37 58 78 90 100 77 71 50 43 36 6 -12 -1 49 58 81 91 101 81 70 55 32 45 7 -2 15 46 67 84 91 102 93 75 61 41 37 8 13 7 48 81 80 95 90 91 81 65 44 23 9 1 11 33 84 76 84 83 78 73 71 50 15 10 3 1 44 52 68 76 89 86 80 74 51 18 11 17 -3 50 56 72 86 80 92 78 57 31 6 12 23 1 45 62 79 91 79 99 89 51 38 16 13 11 11 19 77 85 96 86 95 61 57 37 40 14 9 22 18 53 65 87 91 78 77 71 49 39 15 17 22 24 47 83 73 90 86 73 80 36 17 16 35 17 29 54 70 80 92 99 62 71 35 23 17 38 33 35 76 64 82 88 100 73 62 24 24 18 35 39 40 46 77 85 86 86 85 49 22 27 19 28 43 35 47 90 98 86 72 86 53 30 39 20 28 36 33 59 75 101 83 78 52 52 19 44 21 14 17 29 49 72 95 77 84 49 49 19 25 22 19 43 47 49 67 97 86 80 64 49 30 35 23 20 35 54 57 62 86 89 81 64 56 30 36 24 23 24 51 57 79 82 91 81 71 36 38 30 25 30 23 46 71 76 104 85 85 78 44 47 14 26 -9 38 48 55 85 81 87 76 65 42 39 13 27 13 45 37 40 91 83 97 80 74 48 34 29 28 19 43 48 59 83 89 102 70 59 39 22 10 29 23 45 43 69 93 96 98 71 51 32 19 2 30 35 40 77 90 75 98 73 54 37 29 19 31 34 46 91 93 81 48 24

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Unauthenticated | Downloaded 09/26/21 08:19 AM UTC TABLE 4. Monthly total precipitation fob Hillsboro, Ohio for 1949-89.

Station: (333758) Hillsboro, Ohio From year 1949-89 Total precipitation (in)

Year Jan Feb Mar Apr May Jun Jul Aug Sep Oct Nov Dec Ann 1949 8.30 3.57 4.02 2.83 0.98 5.53 2.93 3.35 1.61 1.98 1.63 3.95 40.68 1950 11.25 5.41 2.13 4.92 3.87 4.41 4.11 3.49 6.11 2.09 5.46 1.72 54.97 1951 6.59 4.35 4.81 3.15 3.12 2.15 0.53 1.45 3.44 1.39 4.39 5.97 41.34 1952 6.09 2.37 4.79 3.57 2.84 2.26 2.86 4.42 3.97 1.57 1.50 2.34 38.58 1953 4.99 1.27 3.43 3.66 5.65 2.28 3.68 1.14 0.55 0.93 1.58 2.72 31.88 1954 3.14 1.95 4.54 3.23 2.93 2.83 9.80 6.72 1.40 5.94 1.68 3.43 47.59 1955 1.85 6.33 5.91 2.58 3.01 3.38 3.22 1.10 4.40 3.45 2.69 0.94 38.86 1956 1.51 4.95 4.78 5.05 4.77 2.95 8.00 2.45 4.01 1.78 2.69 3.07 46.01 1957 3.46 3.42 1.99 6.22 4.09 9.14 2.03 0.77 3.69 2.54 5.79 6.13 49.27 1958 3.02 0.64 1.52 6.04 4.42 5.80 8.47 6.06 5.05 1.89 2.88 1.22 47.01 1959 5.64 3.08 3.21 3.52 3.45 2.07 7.75 3.00 1.14 4.86 3.39 3.13 44.24 1960 3.08 4.05 1.26 1.08 4.87 3.17 3.74 3.65 2.53 1.65 2.74 1.74 33.56 1961 2.50 2.43 4.79 5.88 5.96 3.30 5.35 3.91 0.88 1.70 3.13 2.85 42.68 1962 4.36 5.35 4.48 1.87 8.67 1.11 7.50 1.04 3.96 3.76 3.19 1.80 47.09 1963 1.39 0.74 11.53 2.82 4.21 3.46 4.47 5.30 1.13 0.03 1.30 1.01 37.39 1964 2.56 2.27 12.51 7.90 1.24 4.25 2.12 3.01 1.43 0.96 3.05 5.00 46.30 1965 3.05 2.89 3.50 5.13 1.81 1.71 9.86 3.90 8.29 4.50 1.11 0.44 46.19 1966 5.13 2.65 1.69 5.36 2.61 1.47 8.20 2.10 3.14 1.97 3.65 3.37 41.34 1967 0.61 1.37 3.88 3.09 5.86 0.85 5.77 0.94 2.97 3.32 3.84 3.18 35.68 1968 1.62 0.65 4.95 3.59 10.05 1.16 3.93 1.95 2.42 1.27 3.21 3.49 38.29 1969 3.76 0.82 1.75 4.05 2.90 4.25 2.09 3.87 0.98 1.86 3.58 2.48 32.39 1970 1.59 2.14 3.92 7.17 3.17 1.90 2.63 5.31 5.10 2.85 2.76 3.65 42.19 1971 2.24 4.82 2.71 0.87 4.60 7.35 4.37 5.46 5.72 1.28 2.06 4.39 45.87 1972 2.36 2.11 4.38 5.75 6.30 5.40 4.67 3.08 5.54 1.86 7.73 4.16 53.34 1973 1.63 1.34 3.96 7.40 4.20 6.48 7.70 4.73 2.21 4.23 5.40 3.11 52.39 1974 3.22 1.92 4.49 2.51 6.08 5.87 2.17 8.75 5.80 1.65 2.37 3.48 48.31 1975 4.11 4.84 5.47 3.76 2.40 5.94 5.27 5.53 7.11 4.15 1.59 3.30 53.47 1976 3.43 3.30 2.01 1.36 1.67 7.18 2.87 7.09 2.49 3.03 0.73 0.72 35.88 1977 1.96 0.46 4.03 3.78 2.60 4.32 3.43 5.18 2.96 4.12 2.58 3.65 39.07 1978 5.86 0.43 3.22 3.14 4.30 4.41 3.89 4.63 1.00 6.46 2.05 4.70 44.09 1979 4.45 4.62 0.73 4.06 3.70 3.43 4.51 6.74 8.79 1.63 3.81 2.52 48.99 1980 2.24 1.38 4.76 3.16 4.38 4.21 7.36 5.25 2.72 2.50 2.20 1.42 41.58 1981 0.35 3.14 2.07 5.81 6.98 2.58 6.03 2.83 2.80 2.26 1.54 1.87 38.26 1982 5.40 2.38 6.15 1.57 4.64 4.13 1.36 2.57 1.14 0.33 3.80 3.80 37.27 1983 2.51 1.49 1.47 4.01 8.44 4.91 3.52 1.36 2.09 9.03 4.52 2.88 46.23 1984 1.12 2.57 4.09 4.20 4.98 1.41 3.08 3.17 2.69 2.45 4.36 3.53 37.65 1985 1.69 1.49 5.18 1.10 4.71 3.25 2.73 3.27 0.54 4.31 10.94 1.19 40.40 1986 1.01 4.12 2.12 1.20 2.30 3.74 3.15 2.87 6.61 3.65 4.25 3.15 38.17 1987 1.16 0.66 3.70 4.51 3.46 2.45 3.62 2.10 1.72 1.04 1.61 2.96 28.99 1988 2.16 3.92 3.65 4.30 0.88 0.41 5.92 5.75 3.82 1.77 3.90 2.38 38.86 1989 2.14 4.50 5.60 6.25 5.64 3.22 5.21 4.96 2.44 4.16 2.32 2.01 48.45

include means, extremes, degree days, and the num- 5. Product examples and usage ber of days above and below various thresholds. Figure 2 illustrates one of the soil moisture products, Figure 1 is an example of a map product showing daily a map of soil moisture in a 6-foot profile by climate precipitation values for the region. These are 24-h val- division. In this case, the model estimates show de- ues ending on the morning of the current day. Gen- ficient conditions (less than - 2 inches) relative to the erally, most data are received and available to a user 1951-88 mean in much of Iowa, southern Minnesota, by about 8:30 A.M. Table 2 illustrates a precipitation north-central and southwestern Wisconsin, western Il- summary product for Urbana, Illinois. A user can spec- linois, and northern Michigan. ify any time period over which the summary is cal- Table 5 is an example of a corn yield risk assess- culated. Table 3 illustrates a tabular product of daily ment produced for Illinois for a model run on 15 July maximum temperatures for 1988 for Morris, Minne- 1989. For each climate division, 40 model yields were sota. These data are part of the NCDC database. obtained corresponding to finishing the growing sea- Table 4 is an example of monthly values of total pre- son with each of the historical years of 1949-88. cipitation for Hillsboro, Ohio for 1949-88. The ele- These model yields are categorized according to the ments which can be obtained on a monthly basis type of weather in the historical data used to finish the

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Unauthenticated | Downloaded 09/26/21 08:19 AM UTC TABLE 5. Corn yield risk assessment product for 15 July 1989. Values are deviations (%) from the 1979-88 average. Values are categorized according to the type of weather in the years used to finish the growing season for the period 15 July-13 August.

Illinois 7/15/1989 Corn Yield Risk Assessment Percent deviation from USDA Average (categorized by type of weather in the simulation year) (9999 = no years in that weather category)

USDA 79-88 climate < — — dry > < normal > < wet > average divis. cool norm hot all cool norm hot all cool norm hot all (bu/ac) 1 -40 -26 -32 -33 -19 -4 -8 -9 5 6 -8 0 116 2 -3 -3 -5 -3 0 6 -4 0 6 3 -3 1 118 3 -42 -29 -42 -39 -12 -8 -17 -12 5 1 9999 3 115 4 -27 -22 -18 -21 0 0 -4 -1 6 7 9999 7 119 5 -5 -16 -12 -12 1 1 -2 0 3 4 -1 3 116 6 -20 -27 -27 -25 -2 -4 -6 -4 16 9 8 11 120 7 -7 -6 -14 -9 4 3 -1 1 14 10 0 10 111 8 7 9 -6 2 21 17 6 14 25 13 7 15 90 9 10 9 -6 3 13 6 2 7 13 3 -4 4 92

growing season. This product illustrates what might be Table 6. Usage of MICIS by product category for the possible yield outcomes for several combinations 27 June-28 September 1989.This does not include usage by Midwestern Climate Center staff members. of temperature and rainfall over the rest of the growing season. In this case, corn is at the greatest risk for Category Queries % of Total subsequent dry weather in climate divisions 1 and 3. Daily Climatological During the 3-month period 27 June to 28 Septem- Observations 666 55 ber 1989, the system was monitored for usage by Statistically Derived external subscribers. Using the more general catego- Products 53 4 Climate Summaries 93 8 ries of Table 1, Table 6 shows how often each category Long-Range Forecast 99 8 was queried, plus the total. These numbers are an Regional Soil Moisture 62 5 underestimate of how many products were accessed; Corn & Soybean Yield Risk Assessment 148 12 other accounting information indicates that an aver- Regional Data by age of 8-9 products are obtained each time a user Climate Division 81 7 enters into one of these major menu options. Total 1201 Daily climatological data was by far the most popular item on the system and shows that timely, basic climatological data can satisfy the needs of many users. stations, which are updated in a timely manner; The corn and soybean yield risk assessment products 3) statistical tools which provide standard types of were also very popular. Each of the other categories climatological analysis; experienced significant use. 4) standard climatic summaries for single stations; 5) regional view of climate conditions and statistics; and 6. Conclusions 6) specialized products devoted to the special needs of the midwestern United States. These This regional climate information system has been include soil moisture estimates and crop yield available for public use since 1 April 1989. Many sub- risk assessments for corn and soybeans. scribers are medium to large agribusinesses although state agencies and research scientists are also heavy Our experience in the first year of operation has users. This system has been designed to be complete been instructive in indicating the types of climate infor- and provides access to the following types of climate mation which are valuable. These are in line with the information: needs indicated by Lamb et al. (1985). Based on our interactions with users, the following MICIS character- 1) current climate conditions updated daily; istics are of particular interest and economic utility, 2) historical data for all active cooperative observer particularly among agribusinesses:

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Unauthenticated | Downloaded 09/26/21 08:19 AM UTC 1) easy and timely access to basic climate data. References Our users in general do not have access to data from NWS's specialized hydrologic and Achtemeier, G.L., 1989: Modification of a successive corrections agricultural networks because these data are objective analysis for improved derivation calculations. Mon. transmitted in SHEF format and the index of Wea. Rev., 117, 78-86. Changnon, S.A., J.L. Vogel and W.M. Wendland, 1984: New climate station identifiers is not widely available. They delivery system developed in Illinois. Bull. Amer. Meteor. Soc., have thus been mostly restricted to data from 65, 704-705. NWS offices and airports. The very simple ser- —, W.M. Wendland and J.L. Vogel, 1987: Usage of near real-time vice of decoding the SHEF data has greatly in- climate information. J. dim. Appl. Meteor26, 1072-1079. creased the density of observations available to Duchon, C.E., 1986: Corn yield prediction using climatology. J. Clim. them. This has been particular value after sig- Appl. Meteor., 25, 581-590. Dyke, P.T., W.W. Fuchs and G. Wistrand, 1985: Organization of nificant summertime precipitation events, which national data bases for use in process models. Proceedings of can have a large impact on grain prices, since National Resource Modeling Symposium, Pengree Park, Col- most of our data are available by 9:00 A.M., be- orado, Oct. 16-21, 1983, D.G. DeCoursey, Ed., United States fore the grain markets open; Department of Agriculture, Agricultural Research Service, ARS 30, 129-131. 2) temperature estimation. A frequently requested Finger, F.G., J.D. Laver, K.H. Bergman and V.L. Patterson, 1985: item by agribusiness users is accumulated grow- The Climate Analysis Center's User Information Service. Bull. ing degree days for specific locations, which Amer. Meteor. Soc., 66, 413-420. requires complete (i.e., no missing days) data. Hodges, T., V. French and S. LeDuc, 1985: Estimating solar radiation Since the most recent data transmitted by satel- for plant simulation models. AgRISTARS Technical Report JSC- 20239, YM-15-00403, 21 pp. lite are often incomplete, the MICIS temperature —, T., D. Botner, C. Sakamoto and J. Hays Haug, 1987: Using estimates obtained from objective analysis of the CERES-Maize model to estimate production for the U.S. the temperature field have been popular; corn belt. Agric. Forest Meteor., 40, 293-303. 3) corn and soybean yield modeling. A segment Kunkel, K.E., and Hollinger, 1990: Operational large area corn and of our users has shown great interest in this soybean yield estimation, Agric. Forest Meteor, in press. —, 1990: Operational soil moisture estimation for the midwestern feature since their primary interest in climate United States. Submitted to J. Appl. Meteor. data is to estimate grain yields. However, in Lamb, P.J., S.T. Sonka and S.A. Changnon, 1985: Use of climate most cases they do not have the organizational information by U.S. agribusiness. NOAA Technical Report skills or resources to run these crop models NCPO 001. U.S. Dept. of Commerce. themselves. Although they have taken a "wait- Meyers, T.P., and R.F. Dale, 1983: Predicting daily insolation with and-see" attitude regarding the accuracy of the hourly cloud height and coverage. J. Clim. Appl. Meteor., 22, 537-545. MICIS yields, it is obvious that this type of Monteith, J.L., 1965: Evaporation and environment. Symp. Soc. product fills an economic need; and Exp. Biol., 19, 205-234. 4) availability of climate variables other than tem- Palmer, W.C., 1968: Keeping track of crop moisture conditions, perature and precipitation. In particular, there nationwide: The new crop moisture index. Weatherwise, 21, 156-161. has been a demand for solar radiation and po- Ritchie, J.T., 1972: Model for predicting evaporation from a row crop tential evaporation data, not widely available. with incomplete cover. Water Resour. Res., 8, 1204-1213. —, 1985: A user-oriented model of the soil water balance in wheat. Wheat Growth and Modeling: W. Day and R.K. Atkin, eds., Acknowledgments. We thank Peter Lamb for his helpful comments NATO-ASI Series. Plenum Publishing Corp. during the development process and his manuscript review. We Thorn, A.S., 1975: Momentum, mass and heat exchange of plant also thank Steve Hollinger, Michael Richman, and Beth Reinke for communities. Vegetation and the Atmosphere, Vol. 1, J.L. Mon- their comments during system development. The Climate Analysis teith, ed., Academic Press, 57-108. Center and the National Climatic Data Center provided valuable Wendland, W.M., and J.L. Vogel, 1986: Assessment of need for climate station information. Finally, we thank Jean Dennison for her real-time climate data and information in the upper Midwest. professional preparation of this manuscript. This work was sup- Illinois State Water Survey Contract Report 386, 28 pp. ported under NOAA grant NA87AA-D-CP119.

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