Numerical weather models such as WRF (Weather Research and Forecasting) are increasingly used in studies on water resources. However, they have suffered from relatively poor performance in rainfall estimation. Among the various influential factors, a critical parameter in the WRF model rainfall retrieval is raindrop size distribution (DSD), which has not been fully explored. The analysis of sensitivity and uncertainty of the DSD model accuracy is significant for rainfall forecasts based on mesoscale numerical weather prediction (NWP) models. A WRF-disdrometer integrated error assessment framework is developed to analyze the accuracy and sensitivity of DSD parameterizations of gamma distribution in WRF rainfall simulation. This study adopts three different microphysics parameterizations (Morrison, WDM6, and Thompson aerosol-aware) to simulate the DSD of approximately one hundred rainfall events in Chilbolton, UK that are categorized into 12 scenarios based on the season, rainfall evenness, and rainfall rate. The Thompson aerosol-aware microphysics scheme shows the best performance among the three. In comparisons of WRF rainfall simulations across different scenarios of evenness and rainfall rate, a higher accuracy is obtained with more even rain and a higher rainfall rate. The sensitivity results of different DSD parameterizations indicate that the sensitivity to the intercept parameter 0 is pronouncedly higher than those to the shape parameter μ and slope parameter λ for all studied schemes. The overall WRF rainfall shows a trend of slight underestimation followed by overestimation as μ increases; further, the rainfall is overestimated when 10 0 or λ decreases and is underestimated when it increases and then remains constant. Comparisons of different scenarios reveal that variations of DSD parameters of even rain have a relatively high impact on rainfall recognizability, and the DSD parameterizations show a higher sensitivity for rainfall with a low rate. Moreover, the sensitivity discrimination is not clear among the rainfall of different seasons. The uncertainty assessment of the WRF rainfall retrieval caused by the shape parameter suggests that a gamma DSD model with a variable shape parameter should be developed according to the evenness, rainfall rate, and microphysics parameterizations by using the WRF model. Some modified algorithms of the WRF gamma DSD model for achieving better accuracy in WRF rainfall retrievals will be explored in future studies with various climatic regimes by adjusting the DSD parameterization based on the assimilation of measured data.
Radar‐gauge rainfall discrepancies are considered to originate from radar rainfall measurements while ignoring the fact that radar observes rain aloft while a rain gauge measures rainfall on the ground. Observations of raindrops observed aloft by weather radars consider that raindrops fall vertically to the ground without changing in size. This premise obviously does not stand because raindrop location changes due to wind drift and raindrop size changes due to evaporation. However, both effects are usually ignored. This study proposes a fully formulated scheme to numerically simulate both raindrop drift and evaporation in the air and reduces the uncertainties of radar rainfall estimation. The Weather Research and Forecasting model is used to simulate high‐resolution three‐dimensional atmospheric fields. A dual‐polarization radar retrieves the raindrop size distribution for each radar pixel. Three schemes are designed and implemented using the Hameldon Hill radar in Lancashire, England. The first considers only raindrop drift, the second considers only evaporation, and the last considers both aspects. Results show that wind advection can cause a large drift for small raindrops. Considerable loss of rainfall is observed due to raindrop evaporation. Overall, the three schemes improve the radar‐gauge correlation by 3.2%, 2.9%, and 3.8% and reduce their discrepancy by 17.9%, 8.6%, and 21.7%, respectively, over eight selected events. This study contributes to the improvement of quantitative precipitation estimation from radar polarimetry and allows a better understanding of precipitation processes.
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