Sensitivity of summer precipitation simulation to the physical parameterizations in WRF over the Tibetan Plateau: A case study of 2018

Zhou, Peifeng; Ma, Mengnan; Shao, Min; Tang, Jianping

doi:10.1016/j.atmosres.2023.107174

Cited by 4 publications

(1 citation statement)

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“…The initial and boundary conditions are from ERA5 reanalysis dataset, which is a newly developed reanalysis product by the European Centre for Medium-range Weather Forecasts and offers a comprehensive record of the global atmosphere, land surface, and ocean waves [33]. This dataset is widely used as forcing dataset for regional climate simulation over China [18,21,34]. Table 1 lists the physical parameterization schemes used in the sensitivity simulations, with a total of seven sensitivity experiments conducted.…”

Section: Methodsmentioning

confidence: 99%

Investigation on the Sensitivity of Precipitation Simulation to Model Parameterization and Analysis Nudging over Hebei Province, China

Li,

Tian,

Chen

et al. 2024

Atmosphere

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The physical parameterizations have important influence on model performance in precipitation simulation and prediction; however, previous investigations are seldom conducted at very high resolution over Hebei Province, which is often influenced by extreme events such as droughts and floods. In this paper, the influence of parameterization schemes and analysis nudging on precipitation simulation is investigated using the WRF (weather research and forecasting) model with many sensitivity experiments at the cumulus “gray-zone” resolution (5 km). The model performance of different sensitivity simulations is determined by a comparison with the local high-quality observational data. The results indicate that the WRF model generally reproduces the distribution of precipitation well, and the model tends to underestimate precipitation compared with the station observations. The sensitivity simulation with the Tiedtke cumulus parameterization scheme combined with the Thompson microphysics scheme shows the best model performance, with the highest temporal correlation coefficient (0.45) and lowest root mean square error (0.34 mm/day). At the same time, analysis nudging, which incorporates observational information into simulation, can improve the model performance in precipitation simulation. Further analysis indicates that the negative bias in precipitation may be associated with the negative bias in relative humidity, which in turn is associated with the positive bias in temperature and wind speed. This study highlights the role of parameterization schemes and analysis nudging in precipitation simulation and provides a valuable reference for further investigations on precipitation forecasting applications.

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