scholarly journals A Case Study of Double Ridges of Subtropical High over the Western North Pacific: The Role in the 1998 Second Mei-yu over the Yangtze River Valley

2008 ◽  
Vol 86 (1) ◽  
pp. 167-181 ◽  
Author(s):  
Rui-Fen ZHAN ◽  
Jian-Pin LI ◽  
Jin-Hai HE ◽  
Li QI
Keyword(s):  
North Pacific ◽  
Yangtze River ◽  
Western North Pacific ◽  
Yangtze River Valley ◽  
River Valley ◽  
Subtropical High ◽  
The Yangtze River

scholarly journals Forecasting Summer Rainfall and Streamflow over the Yangtze River Valley Using Western Pacific Subtropical High Feature

Water ◽  
2021 ◽  
Vol 13 (18) ◽  
pp. 2580
Author(s):  
Ranran He ◽  
Yuanfang Chen ◽  
Qin Huang ◽  
Wenpeng Wang ◽  
Guofang Li
Keyword(s):  
Yangtze River ◽  
Western Pacific Subtropical High ◽  
Summer Rainfall ◽  
Yangtze River Valley ◽  
River Valley ◽  
Western Pacific ◽  
Subtropical High ◽  
Exceedance Probability ◽  
The Yangtze River ◽  
Extreme Flood

The western Pacific subtropical high (WPSH) is one of the key systems affecting the summer rainfall over the Yangtze River Valley in China. In this study, the forecasting capacity of the WPSH for summer rainfall and streamflow is evaluated based on the WPSH index (WPSHI) derived from the NCEP/NCAR reanalysis dataset. It has been found that WPSHI can identify extreme flood years with a higher skill than normal wet years. Specifically, exceedance probability forecasting based on WPSHI has higher skills for higher thresholds of rainfall. For streamflow, adding WPSHI as a predictor only enhances the skill for higher thresholds of streamflow relative to models based on antecedent streamflow. Under the same framework, performances of two postprocessing approaches for dynamical forecasts, i.e., the model output statistics (MOS) approach and the reanalysis-based (RAN) approach are compared. Hindcasts from Climate Forecast System version 2 from the National Center for Environmental Prediction (CFSv2) are used to calculate WPSHI, which is used as the predictor for rainfall and streamflow. The result shows that the RAN approach performs better than the MOS approach. This study emphasizes the fact that the forecasting skill of exceedance probability would largely depend on the selected threshold of the predictand, and this fact should be noticed in future studies in the long-term forecasting field.

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 Summertime Moisture Sources and Transportation Pathways for China and Associated Atmospheric Circulation Patterns

2021 ◽  
Vol 9 ◽  
Author(s):  
Ruonan Zhang ◽  
QuCheng Chu ◽  
Zhiyan Zuo ◽  
Yanjun Qi
Keyword(s):  
South China ◽  
North Pacific ◽  
Moisture Transport ◽  
Northeast China ◽  
Yangtze River ◽  
Western North Pacific ◽  
North China ◽  
Yangtze River Valley ◽  
The Yangtze River ◽  
Moisture Sources

Based on the Lagrangian particle dispersion model, HYSPLIT 4.9, this study analyzed the summertime atmospheric moisture sources and transportation pathways affecting six subregions across China. The sources were: Midlatitude Westerly (MLW), Siberian-Arctic regions (SibArc), Okhotsk Sea (OKS), Indian Ocean (IO), South China Sea (SCS), Pacific Ocean (PO), and China Mainland (CN). Furthermore, the relative contributions of these seven moisture sources to summertime precipitation in China were quantitatively assessed. Results showed that the CN precipitation source dominates the interannual and interdecadal variation of precipitation in most subregions, except Southwest and South China. The Northeast China vortex and Pacific–Japan (PJ) teleconnection, which transport water vapor from the MLW, OKS and PO sources, are crucial atmospheric systems and patterns for the variation of precipitation in Northeast China. The interannual variation of precipitation in Northwest and North China is mainly dominated by mid–high-latitude Eurasian wave trains, which provide the necessary dynamical conditions and associated moisture transport from the MLW and SibArc sources. In addition, an enhanced western North Pacific subtropical high (WNPSH) accompanied by the East Asian–western North Pacific summer monsoon and PJ teleconnection, transports extra moisture to North China from the SCS and PO sources, as well to the Yangtze River Valley and South China. The Indian summer monsoon (ISM) is also critically important for the interdecadal change in precipitation over the Yangtze River Valley and South China, via the southwesterly branch of moisture transport from the IO source. The interdecadal changes in precipitation over Southwest China are determined by the IO and SCS sources, via enhanced WNPSH coupling with a weakened ISM. These results suggest that the interdecadal and interannual variations of moisture sources contribute to the attendant variation of summertime precipitation in China via large-scale circulation regimes in both the mid–high and lower latitudes.

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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