The Impact of Radar Data Assimilation on Warm Season Rainfall Forecasts for use in Hydrologic Models: Examples from Extreme Rain Events in Iowa

Warm Season convective rainfall is one of the most poorly forecasted parameters in numerical models. Yet it is also one of the most important, because in many areas of the world including the Upper Midwest it often accounts for a large percentage of the annual rainfall. In addition, this rainfall often occurs with very high rates which can lead to flooding if the duration of the event is sufficiently long. Unfortunately, quantitative precipitation forecasting (QPF) skill has traditionally been so poor that these forecasts are not used in hydrologic modeling for stream flow. Instead, stream flow forecasts are made using estimates of precipitation that has fallen, reducing the amount of lead time for warnings from what could exist if forecasts were used. Thus a continued focus in the meteorological community has been on increasing the forecasting accuracy of warm season convective rainfall.

This study expands upon our prior work on the impact of radar data assimilation on Weather Research and Forecasting (WRF) model runs by focusing on extreme rainfall events (i.e. those that are likely to create significant flooding) during the last ten years. Also, the study will quantify the impact of such assimilation on hydrologic forecasts that use the QPF.

Forecasts are made using a convection-allowing grid spacing version of the WRF over a domain covering roughly 800 x 800 km centered over Iowa. The extreme rain events simulated are those where the 24-hr rainfall total (NWS 24-hr COOP reports) exceeds 5 inches for at least two stations in the state of Iowa during the period from May 1, 2001 to September 1, 2011. A few events that occurred during the IFloodS project in 2013 will also be simulated, comparisons will be made with QPF from NASA's Unified WRF model for these cases. The skill of the model over the first 12 forecast hours with radar data assimilation will be compared to the skill of the same model without radar data assimilation. The use of radar data assimilation in the Center for the Analysis and Prediction of Storms (CAPS) ensemble has been found to noticeably improve forecasts, especially over the first 6-12 hours. The present study will focus on quantifying the impact of such assimilation on rainfall forecasts for extreme events in Iowa, and on hydrologic forecasts that use the QPF. Most importantly, whether or not the improvement in QPF skill is great enough to result in a statistically significant increase in the skill of the hydrology model's stream flow predictions when all cases are considered will give an idea if radar data assimilation might be able to aid in the prediction of flood events before the corresponding heavy rain events occur.