Role of PBL and Microphysical Parameterizations During WRF Simulated Monsoonal Heavy Rainfall Episodes Over Mumbai

Verma, Saurabh; Panda, Jagabandhu; Rath, Sudhansu S.

doi:10.1007/s00024-021-02813-z

Cited by 16 publications

(1 citation statement)

References 77 publications

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“…Many studies explored the parameterization of multiple physical schemes available in the WRF model for simulating rainfall events, such as the microphysics (MP) scheme [23][24][25], cumulus (CU) parameterization scheme [26,27], land surface model (LSM) options [28][29][30], and planetary boundary layer (PBL) scheme [31,32]. The study [33] conducted on Mumbai, India's west coast discovered that parameterizing MP WRF Single-Moment 6-class WSM6 schemes with CU Betts-Miller-Janjic (BMJ) has the ability to accurately predict and simulate extreme events in the region.…”

Section: Introductionmentioning

confidence: 99%

Assessment of Weather Research and Forecasting (WRF) Physical Schemes Parameterization to Predict Moderate to Extreme Rainfall in Poorly Gauged Basin

et al. 2022

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Incomplete hydro-meteorological data and insufficient rainfall gauges have caused difficulties in establishing a reliable flood forecasting system. This study attempted to adopt the remotely sensed hydro-meteorological data as an alternative to the incomplete observed rainfall data in the poorly gauged Kuantan River Basin (KRB), the main city at the east coast of Peninsula Malaysia. Performance of Weather Research and Forecasting (WRF) schemes’ combinations, including eight microphysics (MP) and six cumulus, were evaluated to determine the most suitable combination of WRF MPCU in simulating rainfall over KRB. All the obtained results were validated against observed moderate to extreme rainfall events. Among all, the combination scheme Stony Brook University and Betts–Miller–Janjic (SBUBMJ) was found to be the most suitable to capture both spatial and temporal rainfall, with average percentage error of about ±17.5% to ±25.2% for heavy and moderate rainfall. However, the estimated PE ranges of −58.1% to 68.2% resulted in uncertainty while simulating extreme rainfall events, requiring more simulation tests for the schemes’ combinations using different boundary layer conditions and domain configurations. Findings also indicate that for the region where hydro-meteorological data are limited, WRF, as an alternative approach, can be used to achieve more sustainable water resource management and reliable hydrological forecasting.

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