Sensitivity analysis of raindrop size distribution parameterizations in WRF rainfall simulation

Abstract

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.

Comparison of rainfall microphysics characteristics derived by numerical weather prediction modelling and dual‐frequency precipitation radar

“…The domain setting in the WRF model and GPM region collected for the present study in the UK terrain elevation map. GPM, global precipitation measurement mission; WRF, weather research and forecasting sensitivity and uncertainty of the WRF DSD model through simulation of typical rainfall events (Brown et al, 2016;Kala et al, 2015;Morrison et al, 2015;Yang et al, 2019).…”

The role of land cover on the climate of glacial Europe

“…2c and 2f). The remaining small differences could be interpreted as a part of the internal variability and uncertainties predominantly caused by parameterisations in the models, e.g., cloud formation and microphysical processes (Casanueva et al, 2016;Rajczak and Schär, 2017;Shrestha et al, 2017;Knist et al, 2018;Yang et al, 2019).…”

Response to Referee #1 Comments

“…Printer-friendly version Discussion paper ing text has been added in the Section 5 to enrich the discussion: "The reliability of the WRF model is heavily dependent on the model-driving initial data provided by mesoscale or global models and complicated scheme setting and parameter adjustment (Liu et al, 2013;Thompson and Eidhammer, 2014;Kumar et al, 2017). However, numerous uncertainties are observed in the parameterization of the WRF simulation, and the choice of microphysical schemes has a significant influence on the inverted DSD (Ćurić et al, 2009;Yang et al, 2019). Therefore, combining the DSDs obtained by an increasing number of disdrometers and the WRF model is valuable.…”