The Impacts of Atmospheric Moisture Transportation on Warm Sector Torrential Rains over South China

Zhong, Shanshan; Chen, Zitong

doi:10.3390/atmos8070116

Cited by 11 publications

(7 citation statements)

References 29 publications

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“…Based on forecast funnel thinking, the forecast process could be initiated by synoptic-scale weather pattern type. However, it should be realized that in many instances of severe convective weather, the synoptic-scale information at the top of the funnel may not be particularly useful in determining the meso-and microscale details of the convective event (Zhong and Chen 2017;Tian et al 2018;Wu et al 2018). In other words, the occurrence and development of severe convective weather are often affected by various scales in the forecasting process and the factors of various scales should be considered comprehensively.…”

Section: Multiscale Fusionmentioning

confidence: 99%

On the Construction Principle of Conceptual Models for Severe Convective Weather Forecasting Operations in China

Liu

Zhou

MAO

et al. 2020

Weather and Forecasting

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It is argued here that even with the development of objective algorithms, convection-allowing numerical models, and artificial intelligence/machine learning, conceptual models will still be useful for forecasters until all these methods can fully satisfy the forecast requirements in the future. Conceptual models can help forecasters form forecast ideas quickly. They also can make up for the deficiencies of the numerical model and other objective methods. Furthermore, they can help forecasters understand the weather, and then help the forecasters lock in on the key features affecting the forecast as soon as possible. Ultimately, conceptual models can help the forecaster serve the end users faster, and better understand the forecast results during the service process. Based on the above considerations, construction of new conceptual models should have the following characteristics: 1) be guided by purpose, 2) focus on improving the ability of forecasters, 3) have multiangle consideration, 4) have multiscale fusion, and 5) need to be tested and corrected continuously. The traditional conceptual models used for forecasts of severe convective weather should be replaced gradually by new models that incorporate these principles.

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Section: Multiscale Fusionmentioning

confidence: 99%

On the Construction Principle of Conceptual Models for Severe Convective Weather Forecasting Operations in China

Liu

Zhou

MAO

et al. 2020

Weather and Forecasting

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“…X. Zhong et al: ML MSKF land-sea contrast, topography, and urban landscape (Zhong and Chen, 2017;Luo et al, 2017;Jian et al, 2002;Di et al, 2006;Xia and Zhao, 2009;Zhang and Ni, 2009). The complex terrain and heterogeneous land surface of the south China region are crucial in promoting active convection.…”

Section: Introductionmentioning

confidence: 99%

Machine learning parameterization of the multi-scale Kain–Fritsch (MSKF) convection scheme and stable simulation coupled in the Weather Research and Forecasting (WRF) model using WRF–ML v1.0

Zhong,

Yu,

2024

Geosci. Model Dev.

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Abstract. Warm-sector heavy rainfall along the south China coast poses significant forecasting challenges due to its localized nature and prolonged duration. To improve the prediction of such high-impact weather events, high-resolution numerical weather prediction (NWP) models are increasingly used to more accurately represent topographic effects. However, as these models' grid spacing approaches the scale of convective processes, they enter a “gray zone”, where the models struggle to fully resolve the turbulent eddies within the atmospheric boundary layer, necessitating partial parameterization. The appropriateness of applying convection parameterization (CP) schemes within this gray zone remains controversial. To address this, scale-aware CP schemes have been developed to improve the representation of convective transport. Among these, the multi-scale Kain–Fritsch (MSKF) scheme enhances the traditional Kain–Fritsch (KF) scheme, incorporating modifications that facilitate its effective application at spatial resolutions as high as 2 km. In recent years, there has been an increase in the application of machine learning (ML) models across various domains of atmospheric sciences, including efforts to replace conventional physical parameterizations with ML models. This work introduces a multi-output bidirectional long short-term memory (Bi-LSTM) model intended to replace the scale-aware MSKF CP scheme. This multi-output Bi-LSTM model is capable of simultaneously predicting the convection trigger while also modeling the associated convective tendencies and precipitation rates with a high performance. Data for training and testing the model are generated using the Weather Research and Forecast (WRF) model over south China at a horizontal resolution of 5 km. Furthermore, this work evaluates the performance of the WRF model coupled with the ML-based CP scheme against simulations with the traditional MSKF scheme. The results demonstrate that the Bi-LSTM model can achieve high accuracy, indicating the promising potential of ML models to substitute the MSKF scheme in the gray zone.

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“…In recent years, greater efforts have been made to study WSR and the related convective initiation in South China a Deceased. by field experiments, numerical simulations and case studies (Zhang et al 2011;He et al 2016;Liu et al 2016;Wu and Luo 2016;Luo et al 2017;Zhong and Chen 2017).…”

Section: Introductionmentioning

confidence: 99%

Object-Oriented Composite Analysis of Warm-Sector Rainfall in North China

Zhang

Hewson

2020

Monthly Weather Review

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Warm-sector rainfall (WSR) occurs, by definition, in a warm-air region that is isolated from any forcing related to synoptic frontal boundaries at the surface. This study explores the use of an object-oriented technique to objectively and automatically identify various WSR events over North China from June to September in 2012–17. A total of 768 substantive events are identified over the 6 years. They have a mean maximum rainfall accumulation of 35 mm h−1. Most such events occur over the plains, with two-frequency maxima: one to the south of the Yanshan Mountain Ranges, and the other near the junction of Henan, Shandong, and Jiangsu provinces. WSR-related rainstorms can form in all warm-season months but are most commonly observed between mid-July and mid-August (40% of all events occurred then). Geographically, the region at greatest risk moves gradually northward from mid-June to mid-August, consistent with the progression of the East Asian summer monsoon. There are two diurnal peaks in WSR activity, one from late afternoon to early evening and the other from late evening to early morning. Three classes of upper-level synoptic pattern seem to be conducive to WSR: (i) a “Mongolia front pattern,” (ii) a “northern China front pattern,” and (iii) a “southern front pattern.” All of these patterns are accompanied by warm and moist southwesterly flow at low levels. Prior to WSR events, there is usually an upper-level trough. According to other studies, such a feature is not usually seen for WSR events in South China.

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The Impacts of Atmospheric Moisture Transportation on Warm Sector Torrential Rains over South China

Cited by 11 publications

References 29 publications

On the Construction Principle of Conceptual Models for Severe Convective Weather Forecasting Operations in China

On the Construction Principle of Conceptual Models for Severe Convective Weather Forecasting Operations in China

Machine learning parameterization of the multi-scale Kain–Fritsch (MSKF) convection scheme and stable simulation coupled in the Weather Research and Forecasting (WRF) model using WRF–ML v1.0

Object-Oriented Composite Analysis of Warm-Sector Rainfall in North China

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