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 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.

scholarly journals Assimilation of SMOS Sea Surface Salinity in the Regional Ocean Model for South China Sea

2019 ◽  
Vol 11 (8) ◽  
pp. 919 ◽  
Author(s):  
Ziyao Mu ◽  
Weimin Zhang ◽  
Pinqiang Wang ◽  
Huizan Wang ◽  
Xiaofeng Yang
Keyword(s):  
South China Sea ◽  
South China ◽  
The South China Sea ◽  
Sea Surface ◽  
Sea Surface Salinity ◽  
Observation Data ◽  
The South ◽  
Surface Salinity ◽  
China Sea ◽  
Ocean Salinity

Ocean salinity has an important impact on marine environment simulations. The Soil Moisture and Ocean Salinity (SMOS) mission is the first satellite in the world to provide large-scale global salinity observations of the oceans. Salinity remote sensing observations in the open ocean have been successfully applied in data assimilations, while SMOS salinity observations contain large errors in the coastal ocean (including the South China Sea (SCS)) and high latitudes and cannot be effectively applied in ocean data assimilations. In this paper, the SMOS salinity observation data are corrected with the Generalized Regression Neural Network (GRNN) in data assimilation preprocessing, which shows that after correction, the bias and root mean square error (RMSE) of the SMOS sea surface salinity (SSS) compared with the Argo observations can be reduced from 0.155 PSU and 0.415 PSU to −0.003 PSU and 0.112 PSU, respectively, in the South China Sea. The effect is equally significant in the northwestern Pacific region. The preprocessed salinity data were applied to an assimilation in a coastal region for the first time. The six groups of assimilation experiments set in the South China Sea showed that the assimilation of corrected SMOS SSS can effectively improve the upper ocean salinity simulation.

A Novel Dual Path Gated Recurrent Unit Model for Sea Surface Salinity Prediction

2020 ◽  
Vol 37 (2) ◽  
pp. 317-325 ◽  
Author(s):  
Tao Song ◽  
Zihe Wang ◽  
Pengfei Xie ◽  
Nisheng Han ◽  
Jingyu Jiang ◽  
...  
Keyword(s):  
Neural Network ◽  
Time Series ◽  
South China Sea ◽  
South China ◽  
Prediction Accuracy ◽  
Sea Surface ◽  
Sea Surface Salinity ◽  
Surface Salinity ◽  
Intrinsic Correlation ◽  
Gated Recurrent Unit

AbstractAccurate and real-time sea surface salinity (SSS) prediction is an elemental part of marine environmental monitoring. It is believed that the intrinsic correlation and patterns of historical SSS data can improve prediction accuracy, but they have been not fully considered in statistical methods. In recent years, deep-learning methods have been successfully applied for time series prediction and achieved excellent results by mining intrinsic correlation of time series data. In this work, we propose a dual path gated recurrent unit (GRU) network (DPG) to address the SSS prediction accuracy challenge. Specifically, DPG uses a convolutional neural network (CNN) to extract the overall long-term pattern of time series, and then a recurrent neural network (RNN) is used to track the local short-term pattern of time series. The CNN module is composed of a 1D CNN without pooling, and the RNN part is composed of two parallel but different GRU layers. Experiments conducted on the South China Sea SSS dataset from the Reanalysis Dataset of the South China Sea (REDOS) show the feasibility and effectiveness of DPG in predicting SSS values. It achieved accuracies of 99.29%, 98.44%, and 96.85% in predicting the coming 1, 5, and 14 days, respectively. As well, DPG achieves better performance on prediction accuracy and stability than autoregressive integrated moving averages, support vector regression, and artificial neural networks. To the best of our knowledge, this is the first time that data intrinsic correlation has been applied to predict SSS values.

scholarly journals Impact of Spring Soil Moisture on Surface Energy Balance and Summer Monsoon Circulation over East Asia and Precipitation in East China

2011 ◽  
Vol 24 (13) ◽  
pp. 3309-3322 ◽  
Author(s):  
Renhe Zhang ◽  
Zhiyan Zuo
Keyword(s):  
Soil Moisture ◽  
Yangtze River ◽  
Surface Energy Balance ◽  
Summer Precipitation ◽  
Western Pacific Subtropical High ◽  
Yangtze River Valley ◽  
River Valley ◽  
East China ◽  
Monsoon Circulation ◽  
The Yangtze River

Abstract Numerous studies have been conducted on the impact of soil moisture on the climate, but few studies have attempted to diagnose the linkage between soil moisture and climate variability using observational data. Here, using both observed and reanalysis data, the spring (April–May) soil moisture is found to have a significant impact on the summer (June–August) monsoon circulation over East Asia and precipitation in east China by changing surface thermal conditions. In particular, the spring soil moisture over a vast region from the lower and middle reaches of the Yangtze River valley to north China (the YRNC region) is significantly correlated to the summer precipitation in east China. When the YRNC region has a wetter soil in spring, northeast China and the lower and middle reaches of the Yangtze River valley would have abnormally higher precipitation in summer, while the region south of the Yangtze River valley would have abnormally lower precipitation. An analysis of the physical processes linking the spring soil moisture to the summer precipitation indicates that the soil moisture anomaly across the YRNC region has a major impact on the surface energy balance. Abnormally wet soil would increase surface evaporation and hence decrease surface air temperature (Ta). The reduced Ta in late spring would narrow the land–sea temperature difference, resulting in the weakened East Asian monsoon in an abnormally strengthened western Pacific subtropical high that is also located farther south than its normal position. This would then enhance precipitation in the Yangtze River valley. Conversely, the abnormally weakened East Asian summer monsoon allows the western Pacific subtropical high to wander to south of the Yangtze River Valley, resulting in an abnormally reduced precipitation in the southern part of the country in east China.

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