Precipitation estimates from numerical weather prediction (NWP) models are uncertain. The uncertainties can be reduced by integrating precipitation observations into NWP models. This study assimilates Version 04 Integrated Multi-satellite Retrievals for the Global Precipitation Measurement (GPM) (IMERG) Final Run into the Weather Research and Forecasting (WRF) model data assimilation (WRFDA) system using a four-dimensional variational (4D-Var) method. Three synoptic-scale convective precipitation events over the central United States during 2015–2017 are used as case studies. To investigate the effect of logarithmically transformed IMERG precipitation in the WRFDA system, this study reports on several experiments with six-hour and hourly assimilation windows, regular (nontransformed) and logarithmically transformed observations, and a constant observation error in regular and logarithmic spaces. Results show that hourly assimilation windows improve precipitation simulations significantly compared to six-hour windows. Logarithmically transformed precipitation does not improve precipitation estimations relative to nontransformed precipitation. However, better predictions of heavy precipitation can be achieved with a constant error in the logarithmic space (corresponding to a linearly increasing error in the regular space), which modifies the threshold of rejecting observations, and thus utilizes more observations. This study provides a cost function with logarithmically transformed observations for the 4D-Var method in the WRFDA system for future investigations.
This is an open access article distributed under the Creative Commons Attribution License
which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited