Application of Atmospheric and Land Data Assimilation Systems to an Agricultural Decision Support System

Application of Atmospheric and Land Data Assimilation Systems to an Agricultural Decision Support System

Agriculture is a critical sector of the US economy. Both weather and soil conditions are important input to agricultural decision-making process. However, both the weather and the soil predictions necessary to adequately model the agricultural environment at field scales are currently lacking. For example, phenological pest models predict the evolution of an organism's life stages based on the temperature of its environment. These models generally use only daily maximum and minimum air temperatures to estimate the continuum of conditions affecting the organism. These gross temperature bounds can be poor surrogates for temporally higher resolution air and soil temperature forecasts that are specific to a farm's microclimate.

The National Center for Atmospheric Research (NCAR) and DTN/Meteorlogix are working together on a NASA-funded project to improve these forecast of soil conditions critical for agricultural applications. The approach is to optimize weather forecast from several real-time weather prediction models in NCAR DICast system and to combine that with the High-Resolution Land Data Assimilation System (HRLDAS) to generate high- resolution soil temperature and moisture forecasts. These forecasts will be used to drive agriculture-specific models, such as pest and crop models. The output of these models will be provided to over 60,000 agricultural users via the DTN/Meteorlogix Decision Support System, DTN Online.

Real-time NASA MODIS satellite data will be used to improve the HRLDAS initial land and vegetation conditions. HRLDAS incorporates several MODIS data sets, such as land surface temperature and emissivity, leaf area index, green vegetation cover, and others. The incorporation of these additional data is expected to lead to a better description of vegetation phenology and hence better estimates of plant transpiration and soil state across the forecast domain. We will discuss our forecast system components (DICast and HRLDAS), the utilization of site-specific and remote sensing data in HRLDAS, and evaluation of our forecast system against data collected at from soil observation mesonets such as Soil Climate Analysis Network (SCAN) and the Oklahoma Mesonet.