Seasonal prediction of June rainfall over South China: Model assessment and statistical downscaling

2015 ◽  
Vol 32 (5) ◽  
pp. 680-689 ◽  
Author(s):  
Kun-Hui Ye ◽  
Chi-Yung Tam ◽  
Wen Zhou ◽  
Soo-Jin Sohn
Keyword(s):  
South China ◽  
Statistical Downscaling ◽  
Seasonal Prediction ◽  
Model Assessment ◽  
China Model

Sub-seasonal prediction of the 2008 extreme snowstorms over South China

2020 ◽  
Vol 55 (7-8) ◽  
pp. 1979-1994
Author(s):  
Lina Zheng ◽  
Yaocun Zhang ◽  
Anning Huang
Keyword(s):  
South China ◽  
Seasonal Prediction

scholarly journals Predictability of Persistent Thailand Rainfall during the Mature Monsoon Season in 2011 Using Statistical Downscaling of CGCM Seasonal Prediction

2015 ◽  
Vol 143 (4) ◽  
pp. 1166-1178 ◽  
Author(s):  
Yukiko Imada ◽  
Shinjiro Kanae ◽  
Masahide Kimoto ◽  
Masahiro Watanabe ◽  
Masayoshi Ishii
Keyword(s):  
Statistical Downscaling ◽  
El Niño ◽  
Rainy Season ◽  
El Nino ◽  
Seasonal Prediction ◽  
Precipitation Pattern ◽  
Prediction System ◽  
Central Pacific ◽  
Central Pacific El Niño ◽  
Local Rainfall

Abstract Predictability of above-normal rainfall over Thailand during the rainy season of 2011 was investigated with a one-tier seasonal prediction system based on an atmosphere–ocean coupled general circulation model (CGCM) combined with a statistical downscaling method. The statistical relationship was derived using singular value decomposition analysis (SVDA) between observed regional rainfall and the hindcast of tropical sea surface temperature (SST) from the seasonal prediction system, which has an ability to forecast oceanic variability for lead times up to several months. The downscaled product of 2011 local rainfall was obtained by combining rainfall patterns derived from significant modes of SVDA. This method has the advantage in terms of flexibility that phenomenon-based statistical relationships, such as teleconnections associated with El Niño–Southern Oscillation (ENSO), Indian Ocean dipole (IOD), or the newly recognized central Pacific El Niño, are considered separately in each SVDA mode. The downscaled prediction initialized from 1 August 2011 reproduced the anomalously intense precipitation pattern over Indochina including northern Thailand during the latter half of the rainy season, even though the direct hindcast from the CGCM failed to predict the local rainfall distribution and intensity. Further analysis revealed that this method is applicable to the other recent events such as heavy rainfall during the rainy seasons of 2002 and 2008 in Indochina.

scholarly journals Seasonal Forecast of Early Summer Rainfall at Stations in South China Using a Statistical Downscaling Model

2020 ◽  
Vol 35 (4) ◽  
pp. 1633-1643
Author(s):  
Zheng Lu ◽  
Yan Guo ◽  
Jiangshan Zhu ◽  
Ning Kang
Keyword(s):  
South China ◽  
Statistical Downscaling ◽  
El Nino ◽  
Forecast Error ◽  
Summer Rainfall ◽  
Seasonal Forecast ◽  
Early Summer ◽  
Statistical Downscaling Model ◽  
Seasonal Forecasts ◽  
Independent Test

AbstractCurrent dynamic models are not able to provide reliable seasonal forecasts of regional/local rainfall. This study aims to improve the seasonal forecast of early summer rainfall at stations in South China through statistical downscaling. A statistical downscaling model was built with the canonical correlation analysis method using 850-hPa zonal wind and relative humidity from the ERA-Interim reanalysis data. An anomalous southwesterly wind that carries sufficient water vapor encounters an anomalous northeasterly wind from the Yangtze River, resulting in a wet anomaly over all of South China. This model provided good agreement with observations in both the training and independent test periods. In an independent test, the average temporal correlation coefficient (TCC) at 14 stations was 0.52, and the average root-mean-square error was 21%. Then, the statistical downscaling model was applied to the Climate Forecast System, version 2 (CFSv2), outputs to produce seasonal forecasts of rainfall for 1982–2018. A statistical downscaling model improved CFSv2 forecasts of station rainfall in South China with the average TCC increasing from 0.14 to 0.31. Forecasts of South China regionally averaged rainfall were also improved with the TCC increasing from 0.11 to 0.53. The dependence of forecast skill for regional average rainfall on ENSO events was examined. Forecast error was reduced, but not statistically significant, when it followed an El Niño event in both CFSv2 and the downscaling model. While when it followed an EP-type El Niño, the significantly reduced forecast error (at the 0.1 level) could be seen in the downscaling model and CFSv2.

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

Improving Summer Precipitation Prediction in China Using Deep Learning

2021 ◽  
Author(s):  
Weixin Jin ◽  
Yong Luo
Keyword(s):  
Deep Learning ◽  
General Circulation ◽  
Dynamic Models ◽  
Summer Precipitation ◽  
Seasonal Prediction ◽  
Representation Learning ◽  
Feature Representation ◽  
Model Assessment ◽  
State Variables ◽  
Precipitation Prediction

<p>Summer precipitation in China exhibits considerable spatial-temporal variation with direct social and economic impact. Yet seasonal prediction remains a long-standing challenge. The dynamical models even with a 1-month lead still shows limited forecast skill over China in summer. The present study focuses on applying deep learning to summer precipitation prediction in China. We train a convolutional neural network (CNN) on seasonal retrospective forecast from forecast centres in several European countries, and subsequently use transfer learning on reanalysis and observational data of 160 stations over China. <span>The Pearson’s correlation coefficient (PCC) and the root mean square error (RMSE) </span><span>are used to evaluate the performance of precipitation forecasts.</span> <span>The results demonstrate</span> that deep learning approach produces skillful forecast better than those of current state-of-the-art dynamical forecast systems and traditional statistical methods in downscaling, with <span>PCC increasing by 0.1–0.3, at 1–3 months leads</span>. <span>Moreover, experiments show that </span>the data-driven model is capable to learn the complex relationship of input atmospheric state variables from reanalysis data and precipitation from station observations, with PCC of about 0.69. Image-Occlusion technique are also performed to determine variables and  spatial features of the general circulation in the Northern Hemisphere which contribute maximally to the spatial distribution of summer precipitation in China <span>through the automatic feature representation learning</span>, and help evaluate the weakness of dynamic models, in order to gain a better understanding of the factors that limit the capability to seasonal prediction. It suggests that deep learning is a powerful tool suitable for both seasonal prediction and for dynamical model assessment.</p>

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