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Sensitivity analysis of raindrop size distribution parameterizations in WRF rainfall simulation

Research output: Contribution to journalArticle

  • Qiqi Yang
  • Qiang Dai
  • Dawei Han
  • Yiheng Chen
  • Shuliang Zhang
Original languageEnglish
Pages (from-to)1-13
Number of pages13
JournalAtmospheric Research
Volume228
Early online date18 May 2019
DOIs
DateAccepted/In press - 17 May 2019
DateE-pub ahead of print - 18 May 2019
DatePublished (current) - 1 Nov 2019

Abstract

Numerical weather models such as weather research and forecasting (WRF) 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 100 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 N 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 log 10 N 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.

    Research areas

  • Disdrometer, Microphysics parameterization, Morrison, Raindrop size distribution, Rainfall simulation, Thompson aerosol-aware, WDM6, WRF

Documents

Documents

  • Full-text PDF (accepted author manuscript)

    Rights statement: This is the accepted author manuscript (AAM). The final published version (version of record) is available online via Elsevier at https://doi.org/10.1016/j.atmosres.2019.05.019 . Please refer to any applicable terms of use of the publisher.

    Accepted author manuscript, 1 MB, PDF document

    Embargo ends: 18/05/20

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    Licence: CC BY-NC-ND

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