scholarly journals Sub‐seasonal prediction of rainfall over the South China Sea and its surrounding areas during spring–summer transitional season

2020 ◽  
Vol 40 (10) ◽  
pp. 4326-4346
Author(s):  
Qingquan Li ◽  
Juanhuai Wang ◽  
Song Yang ◽  
Fang Wang ◽  
Jie Wu ◽  
...  
Keyword(s):  
South China Sea ◽  
South China ◽  
Seasonal Prediction ◽  
The South China Sea ◽  
The South ◽  
China Sea ◽  
Surrounding Areas

Pn anisotropic tomography and mantle dynamics underneath the South China Sea and surrounding areas

2021 ◽  
pp. 104796
Author(s):  
Qi Mi ◽  
Jianshe Lei ◽  
Mofei Du ◽  
Jing He ◽  
Hongbin Lu ◽  
...  
Keyword(s):  
South China Sea ◽  
South China ◽  
The South China Sea ◽  
The South ◽  
Mantle Dynamics ◽  
China Sea ◽  
Surrounding Areas

Sub-seasonal prediction of rainfall over the South China Sea and its surrounding areas during spring-summer transitional season

2020 ◽  
Author(s):  
Qingquan Li ◽  
Juanhuai Wang ◽  
Song Yang ◽  
Fang Wang ◽  
Jie Wu ◽  
...  
Keyword(s):  
South China Sea ◽  
South China ◽  
Lead Time ◽  
Function Analysis ◽  
The South China Sea ◽  
Seasonal Migration ◽  
Northwestern Pacific ◽  
The South ◽  
China Sea ◽  
Surrounding Areas

<p>        The sub-seasonal characteristics and prediction of rainfall over the South China Sea and surrounding areas during spring-summer transitional season (April-May-June) are investigated using a full set of hindcasts generated by the Dynamic Extended Range Forecast operational system version 2.0 (DERF2.0) of Beijing Climate Center, China Meteorological Administration. The onset and development of Asian summer monsoon and the seasonal migration of rain belt over East Asia can be well depicted by the model hindcasts at various leads. However, there exist considerable differences between model results and observations, and model biases depend not only on lead time, but also on the stage of monsoon evolution. In general, forecast skill drops with increasing lead time, but rises again after lead time becomes longer than 30 days, possibly associated with the effect of slowly-varying forcing or atmospheric variability. An abrupt turning point of bias development appears around mid-May, when bias growths of wind and precipitation exhibit significant changes over the northwestern Pacific and South Asia, especially over the Bay of Bengal and the South China Sea. This abrupt bias change is reasonably captured by the first two modes of multivariate empirical orthogonal function analysis, which reveals several important features associated with the bias change. This analysis may provide useful information for further improving model performance in sub-seasonal rainfall prediction.</p>

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