Estimating missing weather data for agricultural simulations using group method of data handling

Contacting weather stations via modems to obtain weather data for crop simu lations has become a common practice. Users sometimes encounter gaps in the se data, and techniques are needed to estimate weather variables for days w hen the data are absent. The authors hypothesized that such estimations can be made using data from before and after the day with no data. Dependencie s of weather variables of a particular day on weather variables from severa l days before and after could be very complex. To find and to express these dependencies, group method of data handling (GMDH), which is a tool for mo deling complex "input-output" relationships by building hierarchical polyno mial regression networks, was used. Data on daily solar radiation, maximum and minimum temperatures. and wind runs collected daily in Stoneville, Miss issippi, during May-September of 1982-92 were used. Fourteen-hundred sequen tial 7-day datasets from the database were extracted. For each dataset, the authors assumed that weather variables on the fourth day were unknown and had to be found from the weather variables of days 1, 2, 3, 5, 6, and 7. Se venty-five percent of these data were used to iind the hierarchical polynom ial regression, and 25% were used to evaluate it. Correlation coefficients between calculated and actual parameters were similar for training and eval uation datasets. Coefficients of determination (R-2) were about 0.88 for mi nimum temperature. 0.80 for maximum temperature, and 0.80 for wind run. Acc uracy of the solar radiation and precipitation estimates was lower, and R-2 was about 0.2-0.3 but improved to 0.5-0.6 for the training dataset and 0.3 for the validation dataset for both variables when an additional indicator variable that shows the presence or absence of rain was included. The next day after the day with missing data gave the most essential information. I ncreasing the number of missing days resulted in gradual deterioration of t he accuracy for ail variables but wind run. GMDH fan be a useful tool for f illing gaps in weather data from weather stations installed in the field.