Effects of Typhoon In‐Fa (2021) and the Western Pacific Subtropical High on an Extreme Heavy Rainfall Event in Central China

Rao, Chenhong; Chen, Guanghua; Ran, Lingkun

doi:10.1029/2022jd037924

Cited by 7 publications

(3 citation statements)

References 69 publications

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“…This blocking effect can increase the convergence of moist air, intensifying precipitation processes [29][30][31]. Rao et al found that the coupling effect of typhoons and WPSH played an important role in the July 2021 Zhengzhou, Henan extreme rainfall [32].…”

Section: Data Sources and Processingmentioning

confidence: 99%

Contribution from the Western Pacific Subtropical High Index to a Deep Learning Typhoon Rainfall Forecast Model

Fang,

Cheung,

Yang

2024

Remote Sensing

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In this study, a tropical cyclone or typhoon rainfall forecast model based on Random Forest is developed to forecast the daily rainfall at 133 weather stations in China. The input factors to the model training process include rainfall observations during 1960–2018, typhoon information (position and intensity), station information (position and altitude), and properties of the western Pacific subtropical high. Model evaluation shows that besides the distance between a station and cyclone, the subtropical high properties are ranked very high in the model’s feature importance, especially the subtropical ridgeline, and intensity. These aspects of the subtropical high influence the location and timing of typhoon landfall. The forecast model has a correlation coefficient of about 0.73, an Index of Agreement of nearly 0.8, and a mean bias of 1.28 mm based on the training dataset. Biases are consistently low, with both positive and negative signs, for target stations in the outer rainband (up to 1000 km, beyond which the model does not forecast) of typhoons. The range of biases is much larger for target stations in the inner-core (0–200 km) region. In this region, the model mostly overestimates (underestimates) the small (large) rain rates. Cases study of Typhoon Doksuri and Talim in 2023, as independent cases, shows the high performance of the model in forecasting the peak rain rates and timing of their occurrence of the two impactful typhoons.

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Section: Data Sources and Processingmentioning

confidence: 99%

Contribution from the Western Pacific Subtropical High Index to a Deep Learning Typhoon Rainfall Forecast Model

Fang,

Cheung,

Yang

2024

Remote Sensing

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“…The average accumulated precipitation over Zhejiang Province was 191 mm [23] as a result of the slow movement in Hangzhou Bay for 16 h, breaking the record for total typhoon landfall precipitation in Zhejiang. The early stages of In-Fa supplied a tremendous amount of moisture to central China, contributing to the 21.7 heavy rainfall event in Henan Province [24,25].…”

Section: Description Of Typhoon In-fa (2021)mentioning

confidence: 99%

Improved Gravity Wave Drag to Enhance Precipitation Simulation: A Case Study of Typhoon In-Fa

Liu,

Yu,

et al. 2023

Atmosphere

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Traditional gravity wave drag parameterizations produce wind stresses that are insensitive to changing horizontal resolution in numerical weather prediction (NWP), partly due to the idealized elliptical assumption. This study employs the modified subgrid-scale orography scheme based on the Fourier transform into gravity wave drag scheme of the China Meteorological Administration Global Forecast System (CMA-GFS) to assess its impacts on simulating precipitation during the slow-moving period of Typhoon In-Fa after its landfall in Zhejiang Province, China. The simulation with the updated scheme can effectively reduce the accumulated precipitation bias of the control one and improve the simulation of precipitation distribution and intensity, especially in the hourly precipitation simulation. The improved scheme primarily influences the wind field of the low-level troposphere and also changes the convergence of the integrated water vapor transport and ascending motions related to the reduced precipitation biases. The modified scheme enhances the tendencies of the horizontal winds caused by the varying horizontal resolutions in the model, strengthening the sensitivity of the gravity wave drag across the horizontal scales. Results from medium-range forecasts indicate the modified scheme benefits the statistics scores of precipitation over China and also reduces root-mean-square errors of 2 m temperature and 10 m winds.

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“…China's coastal regions are struck by an average of seven typhoons each year. These typhoons can cause meteorological calamities such as storm surges and severe convective weather, which can have a significant impact on residents of coastal and adjacent areas [3][4][5]. The track errors may lead to an accuracy decrease of weather event predictions.…”

Section: Introductionmentioning

confidence: 99%

The WRF Simulation Influence of Assimilating GNSS Water Vapor and Parameterization Schemes on Typhoon Rumbia

Li,

Ma,

et al. 2024

Atmosphere

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The Weather Research and Forecasting (WRF) model was used to simulate Typhoon Rumbia in this paper. The sensitivity experiments were conducted with 16 different parameterization combination schemes, including four microphysics (WSM6, WSM5, Lin, and Thompson), two boundary layers (YSU and MYJ), and two cumulus convection (Kain–Fritsch and Grell–Freitas) schemes. The impacts of 16 parameterization combination schemes and the data assimilation (DA) of Global Navigation Satellite System (GNSS) water vapor were evaluated by the simulation accuracy of typhoon track and intensity. The results show that the typhoon track and intensity are significantly influenced by parameterization schemes of cumulus and boundary layers rather than microphysics. The averaged track error of Lin_KF_Y is 104.73 km in the entire 72-h simulation period. The track errors of all the other combination schemes are higher than Lin_KF_Y. During the entire 72-h, the averaged intensity error of Thompson_GF_M is 1.36 hPa. It is the lowest among all the combination schemes. As for data assimilation, the simulation accuracy of typhoon tracks can be significantly improved by adding the GNSS water vapor. Thompson_GF_M-DA combination scheme has the lowest average track error of 45.05 km in the initial 24 h. The Lin_KF_Y-DA combination scheme exhibits an average track error of 32.17 km on the second day, 28.03 km on the third day, and 35.33 km during 72-h. The study shows that the combination of parameterization schemes and the GNSS water vapor data assimilation significantly improve the initial conditions and the accuracy of typhoon predictions. The study results contribute to the selection of appropriate combinations of physical parameterization schemes for the WRF-ARW model in the mid-latitude region of the western Pacific coast.

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Effects of Typhoon In‐Fa (2021) and the Western Pacific Subtropical High on an Extreme Heavy Rainfall Event in Central China

Cited by 7 publications

References 69 publications

Contribution from the Western Pacific Subtropical High Index to a Deep Learning Typhoon Rainfall Forecast Model

Contribution from the Western Pacific Subtropical High Index to a Deep Learning Typhoon Rainfall Forecast Model

Improved Gravity Wave Drag to Enhance Precipitation Simulation: A Case Study of Typhoon In-Fa

The WRF Simulation Influence of Assimilating GNSS Water Vapor and Parameterization Schemes on Typhoon Rumbia

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