scholarly journals Association of the Rainy Season Precipitation with Low-Level Meridional Wind in the Yangtze River Valley and North China

2012 ◽  
Vol 25 (2) ◽  
pp. 792-799 ◽  
Author(s):  
Gang Zeng ◽  
Wei-Chyung Wang ◽  
Caiming Shen
Keyword(s):  
Rainy Season ◽  
Yangtze River ◽  
North China ◽  
Precipitation Amount ◽  
Meridional Wind ◽  
Yangtze River Valley ◽  
River Valley ◽  
The Yangtze River ◽  
Intergovernmental Panel ◽  
June July

Abstract This study first used measurements to establish the association between the rainy season precipitation in the Yangtze River valley (YRV) and north China (NC) and the 850-hPa meridional wind, and then evaluated the Intergovernmental Panel on Climate Change (IPCC) Fourth Assessment Report (AR4) models’ simulations of both the associations and precipitation amount. It is shown that there exists a statistically significant positive correlation in the June–July precipitation and wind gradient over the YRV, and in the July–August precipitation and wind over NC. These associations are robust at daily, monthly, and interannual scales. Although many models are found to be capable of simulating the associations, the precipitation amount is still quite inadequate when compared with observations, thus raising the issue of the importance of lower-level wind simulations.

scholarly journals The Monsoon Rainband over China and Relationships with the Eurasian Circulation

1999 ◽  
Vol 12 (1) ◽  
pp. 115-131 ◽  
Author(s):  
Arthur N. Samel ◽  
Wei-Chyung Wang ◽  
Xin-Zhong Liang
Keyword(s):  
Time Series ◽  
South China ◽  
Yangtze River ◽  
North China ◽  
Yangtze River Valley ◽  
River Valley ◽  
Subtropical High ◽  
The Yangtze River ◽  
Total Rainfall ◽  
Siberian High

Abstract Yearly variations in the observed initial and final dates of heavy, persistent monsoon rainband precipitation across China are quantified. The development of a semiobjective analysis that identifies these values also makes it possible to calculate annual rainband duration and total rainfall. Relationships between total rainband precipitation and the Eurasian circulation are then determined. This research is designed such that observed rainband characteristics can be used in future investigations to evaluate GCM simulations. Normalized daily precipitation time series are analyzed between 1951 and 1990 for 85 observation stations to develop criteria that describe general rainband characteristics throughout China. Rainfall is defined to be “heavy” if the daily value at a given location is greater than 1.5% of the annual mean total. Heavy precipitation is then shown to be “persistent” and is thus identified with the rainband when the 1.5% threshold is exceeded at least 6 times in a 25-day period. Finally, rainband initial (final) dates are defined to immediately follow (precede) a minimum period of 5 consecutive days with no measurable precipitation. A semiobjective analysis based on the above definitions and rainband climatology is then applied to the time series to determine annual initial and final dates. Analysis application produces results that closely correspond to the systematic pattern observed across China, where the rainband arrives in the south during May, advances to the Yangtze River valley in June, and then to the north in July. Rainband duration (i.e., final − initial + 1) is approximately 30–40 days while total rainfall decreases from south to north. A significant positive correlation is found between total rainfall and duration interannual variability, where increased rainband precipitation corresponds to initial (final) dates that are anomalously early (late). No clear trends are identified except over north China, where both duration and total rainfall decrease substantially after 1967. The Eurasian sea level pressure and 500-hPa height fields are then correlated with total rainfall over south China, the Yangtze River valley, and north China to identify statistically significant relationships. Results indicate that precipitation amount is influenced by the interaction of several circulation features. Total rainfall increases over south China when the surface Siberian high ridges to the south and is overrun by warm moist air aloft. Yangtze River valley precipitation intensifies when westward expansion of the subtropical high along with strengthening of the Siberian high and monsoon low cause moisture advection, upward motion, and the thermal gradient along the Mei-Yu front to increase. North China total rainfall increases in response to intense heating over the landmass, westward ridging of the subtropical high, and greater moisture transport over the region.

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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