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