Anomalous Atmospheric Circulation, Heat Sources and Moisture Sinks in Relation to Great Precipitation Anomalies in the Yangtze River Valley

2001 ◽  
Vol 18 (5) ◽  
pp. 972-983 ◽  
Author(s):  
Yang Hui
Keyword(s):  
Atmospheric Circulation ◽  
Yangtze River ◽  
Yangtze River Valley ◽  
River Valley ◽  
Heat Sources ◽  
The Yangtze River ◽  
Anomalous Atmospheric Circulation ◽  
Precipitation 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

scholarly journals Forecast of summer precipitation in the Yangtze River Valley based on South China Sea springtime sea surface salinity

2019 ◽  
Vol 53 (9-10) ◽  
pp. 5495-5509 ◽  
Author(s):  
Lili Zeng ◽  
Raymond W. Schmitt ◽  
Laifang Li ◽  
Qiang Wang ◽  
Dongxiao Wang
Keyword(s):  
South China Sea ◽  
South China ◽  
Yangtze River ◽  
Summer Precipitation ◽  
Yangtze River Valley ◽  
River Valley ◽  
Sea Surface ◽  
Sea Surface Salinity ◽  
The Yangtze River ◽  
Surface Salinity

scholarly journals Cotton N rate could be reduced further under the planting model of late sowing and high-density in the Yangtze River valley

2020 ◽  
Vol 3 (1) ◽  
Author(s):  
Xinghu SONG ◽  
Ying HUANG ◽  
Yuan YUAN ◽  
Atta Tung SHAHBAZ ◽  
Souliyanonh BIANGKHAM ◽  
...  
Keyword(s):  
Yangtze River ◽  
Block Design ◽  
Yangtze River Valley ◽  
River Valley ◽  
High Density ◽  
Cropping Pattern ◽  
The Yangtze River ◽  
Cotton Yield ◽  
N Rates ◽  
Late Sowing

Abstract Background An optimal N rate is one of the basic determinants for high cotton yield. The purpose of this study was to determine the optimal N rate on a new cotton cropping pattern with late-sowing, high density and one-time fertilization at the first flower period in Yangtze River Valley, China. A 2-year experiment was conducted in 2015 and 2016 with a randomized complete block design. The cotton growth process, yield, and biomass accumulation were examined. Results The results showed that N rates had no effect on cotton growing progress or periods. Cotton yield was increased with N rates increasing from 120 to 180 kg·hm−2, while the yield was not increased when the N rate was beyond 180 kg·hm−2, or even decreased (9∼29%). Cotton had the highest biomass at the N rate of 180 kg·hm−2 is due to its highest accumulation speed during the fast accumulation period. Conclusions The result suggests that the N rate for cotton could be reduced further to be 180 kg·hm− 2 under the new cropping pattern in the Yangtze River Valley, China.

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