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 the forecast of soil conditions critical for agricultural applications. The approach is to optimize weather forecasts from several real-time weather prediction models used in the NCAR Dynamic, Integrated Forecast System (DICast) system and to combine those data with the High-Resolution Land Data Assimilation System (HRLDAS) output to generate high-resolution soil temperature and moisture forecasts. These soil condition predictions 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. This study focuses on retrospective studies in which HRLDAS was driven by National Centers for Environmental Prediction (NCEP) weather analysis data. The improvements in HRLDAS are then incorporated into the operational demonstration forecast system.

To approximate the vegetation state, HRLDAS originally used climatological vegetation data. In this project, real-time NASA MODIS satellite data are used to improve the HRLDAS initial land and vegetation conditions. These MODIS data sets, such as leaf area index and green vegetation cover, provide higher temporal and spatial resolution than their climatological predecessors. The incorporation of these additional data leads to a better description of vegetation phenology and hence better estimates of plant transpiration and soil state across the forecast domain. Yet, the MODIS data cannot be used without care as several challenging issues were faced. Properly handling of these issues is critical to successfully using the MODIS data.