Assessing the performance of WRF ARW model in simulating heavy rainfall events over the Pune region: in support of operational applications

Valappil, Vineeth Krishnan; Kedia, Sunita; Dwivedi, Arun K.; Pokale, Sagar S.; Islam, Sahidul; Khare, Manish

doi:10.1007/s00703-023-00952-7

Cited by 4 publications

(2 citation statements)

References 69 publications

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“…Many studies (Gunwani & Mohan, 2017; Hu, Nielsen‐Gammon, et al., 2010; Hu et al., 2012, 2019; Hu, Klein, & Xue, 2013; J. X. Wang & Hu, 2021) have evaluated the performance of various modern PBL schemes, with most of them focusing on continental cloud‐free PBL. Compared to continental clear PBL, much less is known about the performance of PBL schemes in presence of clouds (Angevine et al., 2012; H. Y. Huang et al., 2013; Supinie et al., 2022; Valappil et al., 2023; Yang et al., 2019).…”

Section: Introductionmentioning

confidence: 99%

Effects of Lower Troposphere Vertical Mixing on Simulated Clouds and Precipitation Over the Amazon During the Wet Season

Huang

Xue

et al. 2023

JGR Atmospheres

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Planetary boundary layer (PBL) schemes parameterize unresolved turbulent mixing within the PBL and free troposphere (FT). Previous studies reported that precipitation simulation over the Amazon in South America is quite sensitive to PBL schemes and the exact relationship between the turbulent mixing and precipitation processes is, however, not disentangled. In this study, regional climate simulations over the Amazon in January–February 2019 are examined at process level to understand the precipitation sensitivity to PBL scheme. The focus is on two PBL schemes, the Yonsei University (YSU) scheme, and the asymmetric convective model v2 (ACM2) scheme, which show the largest difference in the simulated precipitation. During daytime, while the FT clouds simulated by YSU dissipate, clouds simulated by ACM2 maintain because of enhanced moisture supply due to the enhanced vertical moisture relay transport process: (a) vertical mixing within PBL transports surface moisture to the PBL top, and (b) FT mixing feeds the moisture into the FT cloud deck. Due to the thick cloud deck over Amazon simulated by ACM2, surface radiative heating is reduced and consequently the convective available potential energy is reduced. As a result, precipitation is weaker from ACM2. Two key parameters dictating the vertical mixing are identified, p, an exponent determining boundary layer mixing and λ, a scale dictating FT mixing. Sensitivity simulations with altered p, λ, and other treatments within YSU and ACM2 confirm the precipitation sensitivity. The FT mixing in the presence of clouds appears most critical to explain the sensitivity between YSU and ACM2.

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Section: Introductionmentioning

confidence: 99%

Effects of Lower Troposphere Vertical Mixing on Simulated Clouds and Precipitation Over the Amazon During the Wet Season

Huang

Xue

et al. 2023

JGR Atmospheres

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“…It should be noted that the simulation results obtained via the WRF model vary significantly under the different parameterization schemes in terms of the amount and spatial distribution of precipitation. Therefore, an appropriate parameterization scheme is crucial for the simulation results of weather processes [26][27][28][29][30][31][32][33]. For example, Hu et al conducted an experimental evaluation of the different boundary layer schemes and found that the YSU boundary layer scheme can well solve the overestimation problems for the near-surface temperature and wind speeds [28].…”

Section: Introductionmentioning

confidence: 99%

Simulations of a Heavy Snowfall Event in Xinjiang via the WRF Model Coupled with Different Land Surface Parameterization Schemes

Ai,

Wang,

Zhi

2023

Atmosphere

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Frequent heavy snowfall in Xinjiang plays an important role in the land water cycle. In this study, 18 groups of simulation experiments are conducted on the heavy snowfall event in Xinjiang during 9–13 December of 2015 using the Weather Research and Forecasting (WRF) model. In these experiments, the combination of six land surface parameterization schemes (the Noah scheme, Noah-MP scheme, RUC scheme, CLM4 scheme, PX scheme, and TD scheme) with three microphysical parameterization schemes (the WSM6 scheme, Thompson scheme, and Lin scheme) are adopted, where the observed snowfall data are used for performance evaluation. Results show that the simulated snowfall intensity and snowfall range in different areas are very sensitive to the selection of the land surface scheme. The snowfall in southern Xinjiang is overestimated by almost all six schemes, where the Noah-MP scheme performs more reasonably than the others. The Noah scheme shows its advantage in northwestern Xinjiang. The three different microphysical schemes vary significantly in producing snowfall amount. The WSM6 scheme produced the largest snowfall amount, and the Lin scheme resulted in the smallest snowfall amount. In addition, the accumulated snowfall amounts above 10 mm are generally underestimated by all six land surface schemes, while the accumulated snowfall amounts below 10 mm are overestimated by most of the schemes. The Noah-MP scheme performs the best in the simulation of the snowfall amount in the whole region. However, the Noah scheme shows an advantage in areas with a large snowfall amount.

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Assessments of the WRF model in simulating 2021 extreme rainfall episode in Malaysia

Chen,

Chan,

Ooi

et al. 2023

Air Qual Atmos Health

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Assessing the performance of WRF ARW model in simulating heavy rainfall events over the Pune region: in support of operational applications

Cited by 4 publications

References 69 publications

Effects of Lower Troposphere Vertical Mixing on Simulated Clouds and Precipitation Over the Amazon During the Wet Season

Effects of Lower Troposphere Vertical Mixing on Simulated Clouds and Precipitation Over the Amazon During the Wet Season

Simulations of a Heavy Snowfall Event in Xinjiang via the WRF Model Coupled with Different Land Surface Parameterization Schemes

Assessments of the WRF model in simulating 2021 extreme rainfall episode in Malaysia

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