Abstract
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.
Highlights
Precipitation is an important component of the water cycle
EXP2 shows slightly smaller mean absolute difference (MAD), significantly smaller false alarm ratio (FAR), and significantly higher CCs, probability of detection (POD), and Equitable Threat Score (ETS) compared with the open loop estimates (OPL) experiment, suggesting that hourly assimilation windows improve hourly precipitation estimates better than six-hour windows do
We looked at the statistical metrics of six-hour accumulated precipitation estimates from the two assimilation experiments and the OPL experiment (Figures not shown); the results were similar to those of hourly estimates and further suggest that the hourly windows have better performance than six-hour
Summary
Precipitation is an important component of the water cycle. It influences hydrologic variables, such as groundwater flow and runoff, which are vital to our daily life. Accurate estimates of precipitation are of great value for society. Model simulations of precipitation usually have uncertainties [1,2]. One reason is the uncertainty of the initial condition of the model [3]. Very small differences in the initial condition can cause diverging simulations of the climate system. One possible solution to the uncertainty of the initial condition is to integrate precipitation observations into the model [4,5]
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