Impacts of the thermal effects of sub-grid orography on the heavy rainfall events along the Yangtze River Valley in 1991

2007 ◽  
Vol 24 (5) ◽  
pp. 881-892 ◽  
Author(s):  
Lei Feng ◽  
Yaocun Zhang
Keyword(s):  
Thermal Effects ◽  
Yangtze River ◽  
Heavy Rainfall ◽  
Yangtze River Valley ◽  
River Valley ◽  
The Yangtze River ◽  
Rainfall Events ◽  
Heavy Rainfall Events

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.

How Were the Eastward-Moving Heavy Rainfall Events from the Tibetan Plateau to the Lower Reaches of the Yangtze River Enhanced?

2021 ◽  
Vol 34 (2) ◽  
pp. 607-620
Author(s):  
Yang Zhao ◽  
Deliang Chen ◽  
Yi Deng ◽  
Seok-Woo Son ◽  
Xiang Wang ◽  
...  
Keyword(s):  
Tibetan Plateau ◽  
Yangtze River ◽  
Heavy Rainfall ◽  
Diabatic Heating ◽  
Convective Motion ◽  
Convective System ◽  
The Tibetan Plateau ◽  
The Yangtze River ◽  
Rainfall Events ◽  
Heavy Rainfall Events

AbstractThis study investigates eastward-moving summer heavy rainfall events in the lower reaches of the Yangtze River (LRYR), which are associated with the Tibetan Plateau (TP) vortices. On the basis of rainfall data from gauges and additional atmospheric data from ERA-Interim, the dynamic and thermodynamic effects of moisture transport and diabatic heating are estimated to determine the physical mechanisms that support the eastward-moving heavy rainfall events. As the rainband moves eastward, it is accompanied by anomalous cyclonic circulation in the upper and middle troposphere and enhanced vertical motion throughout the troposphere. In particular, the rainfall region is located in the fore of the upper-level trough, which is ideal for baroclinic organization of the convective system and further development of the eastward-moving vortex. The large atmospheric apparent heat source (Q1) also contributes for lifting the lower-level air into the upper atmosphere and for enhancing the low-level convective motion and convergence during the heavy rainfall process. Piecewise potential vorticity inversion further verifies the crucial role that the diabatic heating played in developing the anomalous geopotential height favorable for the enhanced rainfall. The combined action of the dynamic and thermodynamic processes, as well as the rich moisture supply from the seas, synergistically sustained and enhanced the eastward-moving rainfall.

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.

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