Precursor Conditions to Heavy Precipitation Events Over the United States Corn Belt Region

Agriculture in the United States depends strongly on precipitation, and precipitation forecasts. While several studies note that precipitation over the Great Plains is largely dominated by the seasonal cycle, subseasonal variations in precipitation are arguably most important to agriculture. Unfortunately, numerical weather predication has struggled with precipitation forecasts beyond day seven. This study aims to identify large-scale precursor conditions to heavy rainfall events, with the goal of expanding predictability of precipitation over the United States corn-belt region (CBR) beyond days seven to ten.

Precipitation variability over North America is complicated, ranging over a wide variety of spatial and temporal scales. While several previous studies have shown precipitation variability over North America is closely linked to rapid changes in topography, soil moisture and sea surface temperature changes, few have tried to identify systematic synoptic to planetary scale precursor signals to precipitation over the CBR. This study uses a multifaceted approach to identify precursor conditions of strong precipitation events over the CBR.

Methods employed include lagged composite analysis, cluster analysis and time-extended empirical orthogonal analysis. A 30-year climatology of precipitation over the CBR was first created using the Climate Prediction Center Unified Gauge-Based Precipitation dataset, which was the focus of each analysis. Geopotential height, anomalies, wind anomalies, and specific humidity from the Climate Forecast System reanalysis were also utilized in each analysis. Each method details synoptic scale precursor conditions to heavy precipitation events over the CBR, from which a convenient conceptual model of these conditions is obtained. Results from each analysis will be presented, as well as the conceptual model. Future studies involve applying results from this analysis to develop a statistical model for precipitation prediction over the CBR.