Improving the Drought Monitoring Capabilities of Land Surface Models by Integrating Bias-Corrected, Gridded Precipitation Estimates

Improvement in the depiction of land use, soil type, and vegetation allows estimation of drought-informative parameters such as soil moisture, evapotranspiration, and streamflow at fine spatial resolutions in the North American Land Data Assimilation System (NLDAS). However, the accuracy and representativeness of the precipitation data in NLDAS is lagging behind the other information. Precipitation forcing can be improved by integrating a radar-based quantitative precipitation estimate (QPE) product in place of the currently operational dataset that uses daily gauge analysis from by the Climate Prediction Center (CPC). We have developed methods for correcting the QPE for beam blockage and also mean-field, range-dependent, and two-dimensional biases in an extensively tested three-step algorithm. We will integrate bias-corrected, gridded QPEs into the NLDAS precipitation forcing dataset to improve the modeling of drought informative variables in the NLDAS LSMs, which use the NASA Land Information System (LIS) modeling and data assimilation environment.