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Seasonal Prediction for May Rainfall over Southern China Based on the NCEP CFSv2

Journal of Tropical Meteorology 2022年第1期28卷 29-44页

作者：李婵珠杨崧莫伟强张劲梅魏维 Dongguan Meteorological Service DongguanGuangdong 523082 China School of Atmospheric Sciences Sun Yat-sen University(Zhuhai)ZhuhaiGuangdong 519082 China Guangdong Province Key Laboratory for Climate Change and Natural Disaster Studies Sun Yat-sen UniversityGuangzhou 510275 China Southern Marine Science and Engineering Guangdong Laboratory(Zhuhai) ZhuhaiGuangdong 519082 China

In this study,we assess the prediction for May rainfall over southern China(SC)by using the NCEP CFSv2 outputs.Results show that the cfsv2 is able to depict the climatology of May rainfall and associated circulations.However,the model has a poor skill in predicting interannual variation due to its poor performance in capturing related anomalous circulations.In observation,the above-normal SC rainfall is associated with two anomalous anticyclones over the western tropical Pacific and northeastern China,respectively,with a low-pressure convergence in between.In the cfsv2,however,the anomalous circulations exhibit the patterns in response to the El Ni?o-Southern Oscillation(ENSO),demonstrating that the model overestimates the relationship between May SC rainfall and ENSO.Because of the onset of the South China Sea monsoon,the atmospheric circulation in May over SC is more complex,so the prediction for May SC rainfall is more challenging.In this study,we establish a dynamic-statistical forecast model for May SC rainfall based on the relationship between the interannual variation of rainfall and large-scale ocean-atmosphere variables in the cfsv2.The sea surface temperature anomalies(SSTAs)in the northeastern Pacific and the centraleastern equatorial Pacific,and the 500-h Pa geopotential height anomalies over western Siberia in previous April,which exert great influence on the SC rainfall in May,are chosen as predictors.Furthermore,multiple linear regression is employed between the predictors obtained from the cfsv2 and observed May SC rainfall.Both cross validation and independent test show that the hybrid model significantly improve the model’s skill in predicting the interannual variation of May SC rainfall by two months in advance.

关键词： rainfall May southern China NCEP CFSv2 prediction

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维普期刊数据库

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SEASONAL PREDICTIONS FOR SPRING AND AUTUMN SURFACE AIR TEMPERATURES OVER SOUTHERN CHINA BY THE NCEP CFSv2

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Journal of Tropical Meteorology 2019年第4期25卷 448-461页

作者：李婵珠杨崧李春晖刘俊荷 School of Atmospheric Sciences Sun Yat-sen UniversityZhuhaiGuangdong 519082 China Guangdong Province Key Laboratory for Climate Change and Natural Disaster Studies Sun Yat-sen UniversityGuangzhou 510275 China Southern Marine Science and Engineering Guangdong Laboratory(Zhuhai) ZhuhaiGuangdong 519000 China Guangzhou Institute of Tropical and Marine Meteorology/Guangdong Provincial Key Laboratory of Regional Numerical Weather Prediction Guangzhou 510641 China Thai Meteorological Department BangnaBangkok 10260 Thailand

In this study, we investigate the variations of spring and autumn air temperatures in southern China(SC) and associated atmospheric circulation patterns. During the boreal spring, the SC air temperature is mainly influenced by tropical sea surface temperature anomalies(SSTAs). On the one hand, the El Nino SSTA pattern may induce a stronger-than-normal western Pacific subtropical high, which leads to warming in SC. On the other hand, the warm SSTAs in the tropical Indian Ocean may trigger anomalous Rossby wave trains, which propagate northeastward and result in anomalously high temperature in SC. During the boreal autumn, however, the SC temperature is more likely to be affected by the mid-latitude atmospheric circulation, such as the wave trains forced by the North Atlantic SSTAs.The ncep Climate Forecast System version 2(cfsv2) is able to capture the climatology of SC air temperatures during both spring and autumn. For interannual variation, the cfsv2 shows a good skill for predicting the SC temperature in spring, due to the model's good performance in capturing the associated atmospheric circulation anomalies as responses to tropical SSTAs, in spite of the overestimated relationship with the El Nio-Southern Oscillation(ENSO). However,the model has a poor skill for predicting the SC temperature in autumn, primarily due to the unrealistic prediction of its relationship with the ENSO.

关键词： southern China temperature NCEP CFSv2 prediction spring autumn

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维普期刊数据库

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Regional PWV Estimation Using Interpolated Surface Meteorological Data from NCEP CFSv2

Regional PWV Estimation Using Interpolated Surface Meteorolo...

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第四届中国卫星导航学术年会

作者： Junjie Wang Xiufeng He Institute of Satellite Navigation ＆ Spatical Information System Hohai University

In addition to the GPS data, site-specific surface meteorological data is essential to derive the precipitable water vapor （PWV） using ground-based GPS. Furthermore, many GPS networks don’t have the meteorological sensors installed together with the GPS antenna at all when built. This thesis has provided in detail a new method to interpolate the surface meteorological data of GPS sites by using National Centers for Environmental Prediction （ncep） Climate Forecast System Version 2 （cfsv2） 6-hourly forecast products. Based on Hong Kong Satellite Positioning Reference Station Network （SatRef）, the method is tested, and the results show that the method can be helpful to estimate the regional PWV even when there are no meteorological sensors.

关键词： GPS meteorology NCEP CFSv2 PWV interpolation

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Prediction skill and Predictability of Eurasian Snow Cover Fraction in the ncep Climate Forecast Version 2 Reforecasts

Prediction skill and Predictability of Eurasian Snow Cover F...

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第32届中国气象学会年会

作者： Qiong He Zhiyan Zuo Renhe Zhang Song Yang Wanqiu Wang Ruonan Zhang Emily E.Riddle Chinese Academy of Meteorological Sciences Department of Atmospheric Sciences Sun Yat-sen University Climate Prediction Center NCEP/NWS/NOAACollege Park Department of Geosciences University of Massachusetts

Eurasian snow cover fraction（SCF） prediction is analyzed using the recently developed ncep Climate Forecast System,version 2（cfsv2） monthly retrospective forecasts for 1983-2009.Generally,the cfsv2 is capable of reproducing the observed Eurasian SCF seasonal cycle and climatology except some systematic biases.This study focuses on the prediction skill and predictability of Eurasian SCF in snowmelt and snowfall seasons since the most robust variability occurs in these months.The cfsv2 reasonably predicts the interannual variations,long-term trend and leading EOF pattern in snowmelt season several months ahead.Compared with the prediction skill in snowmelt season,although the cfsv2 shows a slightly better prediction skill in climatological values,it underestimates the observed interannual variability in snowfall season.Additionally,The cfsv2 predicts a downward trend whereas theobservations exhibit an upward trend in snowfall season.The systematic biases,the larger climatology and smaller interannual variations in both snowmelt and snowfall seasons,might be the result that the cfsv2 predicts the cooler atmosphere in the low to middle troposphere and more precipitation and weaker variability in them compared with the observations.It is interesting that the prediction skill is higher and more stable after late-1990 s than before in the two seasons.It is possibly caused by the changesbothin the initial conditionin the cfsv2 and the observed SCF in late-1990 s.

关键词： Eurasian SCF NCEP CFSv2 Prediction Predictability Tropospheric temperature

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