scholarly journals Energy budgets on the interactions between the mean and eddy flows during a persistent heavy rainfall event over the Yangtze River valley in summer 2010

2016 ◽  
Vol 30 (4) ◽  
pp. 513-527 ◽  
Author(s):  
Shenming Fu ◽  
Huijie Wang ◽  
Jianhua Sun ◽  
Yuanchun Zhang
Keyword(s):  
Yangtze River ◽  
Heavy Rainfall ◽  
Yangtze River Valley ◽  
River Valley ◽  
Rainfall Event ◽  
Energy Budgets ◽  
Heavy Rainfall Event ◽  
The Yangtze River ◽  
Persistent Heavy Rainfall ◽  
The Mean

Two Distinct Types of 10-30-day Persistent Heavy Rainfall Events over the Yangtze River Valley

2021 ◽  
pp. 1-44
Author(s):  
Yifeng Cheng ◽  
Lu Wang ◽  
Tim Li
Keyword(s):  
Yangtze River ◽  
Heavy Rainfall ◽  
Yangtze River Valley ◽  
The Yangtze River ◽  
Rainfall Events ◽  
Anticyclonic Circulation ◽  
Persistent Heavy Rainfall ◽  
Circulation Anomalies ◽  
Upper Level ◽  
Heavy Rainfall Events

AbstractLarge-scale circulation anomalies associated with 10-30-day filtered persistent heavy rainfall events (PHREs) over the middle and lower reaches of the Yangtze River Valley (MLYV) in boreal summer for the period of 1961-2017 were investigated. Two distinct types of PHREs were identified based on configurations of anomalies in western Pacific subtropical high (WPSH) and South Asian High (SAH) during the peak wet phase. One type named as PSAH is characterized by eastward extension of the SAH while the other named as NSAH is featured by westward retreat of the SAH, and they both exhibit westward extension of the WPSH. Both types of PHREs are dominated by Mei-yu frontal systems. The lower-level circulation anomalies play a crucial role in initiating rainfall but through different processes. Prior to rainfall occurrence, a strong anticyclonic circulation anomaly is over the western North Pacific (WNP) for the PSAH events and the related southwesterly wind anomaly prevails over the south-eastern China, which advects moisture into the MLYV, moistens the boundary layer, and induces atmospheric convective instability. For the NSAH events, the WNP anticyclonic circulation is weak while a strong northerly wind is observed north of the MLYV. It brings cold air mass southward, favoring initiating frontal rainfall over the MLYV. The formation of upper-level circulation anomalies over the MLYV is primarily due to the shift of anomalous circulations from mid-high latitudes. After the rainfall generation, the precipitation would influence the lower- and upper-level circulation anomalies.

scholarly journals Seasonal Prediction of Summer Precipitation in the Middle and Lower Reaches of the Yangtze River Valley: Comparison of Machine Learning and Climate Model Predictions

Water ◽  
2021 ◽  
Vol 13 (22) ◽  
pp. 3294
Author(s):  
Chentao He ◽  
Jiangfeng Wei ◽  
Yuanyuan Song ◽  
Jing-Jia Luo
Keyword(s):  
Neural Network ◽  
Machine Learning ◽  
Yangtze River ◽  
Climate Models ◽  
Summer Precipitation ◽  
Yangtze River Valley ◽  
River Valley ◽  
Training Data ◽  
The Yangtze River ◽  
Learning Methods

The middle and lower reaches of the Yangtze River valley (YRV), which are among the most densely populated regions in China, are subject to frequent flooding. In this study, the predictor importance analysis model was used to sort and select predictors, and five methods (multiple linear regression (MLR), decision tree (DT), random forest (RF), backpropagation neural network (BPNN), and convolutional neural network (CNN)) were used to predict the interannual variation of summer precipitation over the middle and lower reaches of the YRV. Predictions from eight climate models were used for comparison. Of the five tested methods, RF demonstrated the best predictive skill. Starting the RF prediction in December, when its prediction skill was highest, the 70-year correlation coefficient from cross validation of average predictions was 0.473. Using the same five predictors in December 2019, the RF model successfully predicted the YRV wet anomaly in summer 2020, although it had weaker amplitude. It was found that the enhanced warm pool area in the Indian Ocean was the most important causal factor. The BPNN and CNN methods demonstrated the poorest performance. The RF, DT, and climate models all showed higher prediction skills when the predictions start in winter than in early spring, and the RF, DT, and MLR methods all showed better prediction skills than the numerical climate models. Lack of training data was a factor that limited the performance of the machine learning methods. Future studies should use deep learning methods to take full advantage of the potential of ocean, land, sea ice, and other factors for more accurate climate predictions.

Contribution of Water Vapor to the Record-Breaking Extreme Meiyu Rainfall along the Yangtze River Valley in 2020

2021 ◽  
Vol 35 (4) ◽  
pp. 557-570
Author(s):  
Licheng Wang ◽  
Xuguang Sun ◽  
Xiuqun Yang ◽  
Lingfeng Tao ◽  
Zhiqi Zhang
Keyword(s):  
Water Vapor ◽  
Yangtze River ◽  
Yangtze River Valley ◽  
River Valley ◽  
The Yangtze River
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