利用地基GPS估计PWV(precipitable water vapor)时,除GPS观测数据外,GPS测站地表的气温和气压也是必要参数。针对我国多数GPS网并未配备相应的气象传感器的情况,利用美国环境预报中心气候预报系统第2版提供的逐6h产品,并顾及测站高程转...利用地基GPS估计PWV(precipitable water vapor)时,除GPS观测数据外,GPS测站地表的气温和气压也是必要参数。针对我国多数GPS网并未配备相应的气象传感器的情况,利用美国环境预报中心气候预报系统第2版提供的逐6h产品,并顾及测站高程转换时的平均海平面高改正,提出一种GPS测站气象参数的插值新方法。以香港卫星定位参考站网实测GPS数据进行试验研究,结果表明,平均海平面高对地表气压的插值结果影响较大,而对地表气温的插值结果影响较小;经平均海平面高改正后,地表气压插值结果的平均均方根误差(RMSE)为1.61hPa,地表气温插值结果的平均RMSE为1.93 K;由插值气象参数估计的PWV的平均RMSE为2.76mm,验证了所提方法的有效性。展开更多
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....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.展开更多
作为东亚冬季风的关键系统,西伯利亚高压的变化对欧亚大陆冬季天气及气候异常产生重要影响。本文系统地评估了美国国家环境预测中心第二代气候预测系统(NCEP-CFSv2,National Center for Environment Prediction-Climate Forecast System...作为东亚冬季风的关键系统,西伯利亚高压的变化对欧亚大陆冬季天气及气候异常产生重要影响。本文系统地评估了美国国家环境预测中心第二代气候预测系统(NCEP-CFSv2,National Center for Environment Prediction-Climate Forecast System,version 2)对冬半年(11~2月)及逐月西伯利亚高压强度的预测效能。结果表明,NCEP-CFSv2模式仅对11月西伯利亚高压强度的预测效能较好,研究其成因发现11月西伯利亚高压强度主要受该地区热力、动力过程以及西伯利亚地区积雪状况的影响。在热力过程方面,NCEP-CFSv2模式可以较好地再现11月西伯利亚高压强度及其相联的该地区表层土壤温度、对外长波辐射等热力因素;在动力过程方面,模式能较好地再现11月西伯利亚高压强度及其相联的该地区对流层低层辐散环流、中高层下沉运动;同时,模式也能较好地再现11月西伯利亚高压强度与该地区积雪覆盖率之间的相互作用。因此,与11月西伯利亚高压相联的热力、动力过程和该地区积雪状况可能是11月西伯利亚高压强度的可预测来源,且NCEP-CFSv2模式能较好地再现这些可预测来源。展开更多
This study depicts the sub-seasonal prediction of the South China Sea summer monsoon onset(SCSSMO)and investigates the associated oceanic and atmospheric processes,utilizing the hindcasts of the National Centers for E...This study depicts the sub-seasonal prediction of the South China Sea summer monsoon onset(SCSSMO)and investigates the associated oceanic and atmospheric processes,utilizing the hindcasts of the National Centers for Environmental Prediction(NCEP)Climate Forecast System version 2(CFSv2).Typically,the SCSSMO is accompanied by an eastward retreat of the western North Pacific subtropical high(WNPSH),development of the cross-equatorial flow,and an increase in the east-west sea surface temperature(SST)gradient.These features are favorable for the onset of westerlies and strengthening of convection and precipitation over the South China Sea(SCS).A more vigorous SCSSMO process shows a higher predictability,and vice versa.The NCEP CFSv2 can successfully predict the onset date and evolution of the monsoon about 4 pentads(20 days)in advance(within 1–2 pentads)for more forceful(less vigorous)SCSSMO processes.On the other hand,the climatological SCSSMO that occurs around the 27th pentad can be accurately predicted in one pentad,and the predicted SCSSMO occurs 1–2 pentads earlier than the observed with a weaker intensity at longer leadtimes.Warm SST biases appear over the western equatorial Pacific preceding the SCSSMO.These biases induce a weaker-thanobserved WNPSH as a Gill-type response,leading to weakened low-level easterlies over the SCS and hence an earlier and less vigorous SCSSMO.In addition,after the SCSSMO,remarkable warm biases over the eastern Indian Ocean and the SCS and cold biases over the WNP induce weaker-than-observed westerlies over the SCS,thus also contributing to the less vigorous SCSSMO.展开更多
利用代表南海夏季风季节内振荡特征的850 hPa纬向风EOF分解的前两个主成分,定义南海夏季风季节内振荡指数,并利用美国国家环境预测中心第2代气候预报系统(NCEP Climate Forecast System Version 2,NCEP/CFSv2)提供的1982 2009年逐日回...利用代表南海夏季风季节内振荡特征的850 hPa纬向风EOF分解的前两个主成分,定义南海夏季风季节内振荡指数,并利用美国国家环境预测中心第2代气候预报系统(NCEP Climate Forecast System Version 2,NCEP/CFSv2)提供的1982 2009年逐日回算预报场计算了南海夏季风季节内振荡指数的预报值,用于我国南方地区持续性强降水的预报试验。试验结果表明:利用南海夏季风季节内振荡实时监测指数与模式直接预报降水量相结合的统计动力延伸预报方法,能够有效提高季节内降水分量的预报效果。同时,该方法能够避免末端数据损失,修正了对模式预报降水直接进行带通滤波而导致的负相关现象,并起到消除模式系统误差的作用。展开更多
基于国家气候中心气候系统模式(Beijing Climate Center Climate System Model,BCC_CSM1.1m)和美国NCEP/NCAR的气候预测模式(The NCEP Climate Forecast System Version 2,CFSv2)分别建立针对长江流域汛期降水的动力与统计相结合的降尺...基于国家气候中心气候系统模式(Beijing Climate Center Climate System Model,BCC_CSM1.1m)和美国NCEP/NCAR的气候预测模式(The NCEP Climate Forecast System Version 2,CFSv2)分别建立针对长江流域汛期降水的动力与统计相结合的降尺度预测模型,并比较两模式对应模型的预报技巧和差异来源。分别选择两模式2月起报的500 hPa及200 hPa全球位势高度场为预报因子,结合年际增量及经验正交分解(EOF)迭代法建立降尺度模型(分别简称DY_CSM1.1m和DY_CFSv2),研究发现:(1) EOF迭代法中截断解释方差的递增增加了预报因子的协同性和稳定性,从而显著提高预报技巧,并由此确定98%的截断解释方差为模型的最优参数。(2)两模型基于最优参数的预测效果均优于模式原始的降水预测,其中DY_CSM1.1m预测技巧更高,对应29 a距平相关系数(ACC)平均评分可达0.43,尤其在长江干流区域预报效果显著提高。将两模型预测的降水年际增量百分率转换为降水距平百分率时,ACC多年平均评分降为0.27和0.22,仍高于模式原始预测。(3) DY_CSM1.1m的ACC历年评分和长江流域汛期降水年际增量均与西太平洋副热带高压的一系列指数具有高相关性(以西太平洋副高脊线位置指数为例,DY_CFSv2则无此关系),因此BCC_CSM1.1m在西太平洋地区模拟性能优于CFSv2是导致该模式降尺度后预报技巧更高的重要原因,这一点在典型洪涝年1998和2020年中得以佐证。展开更多
文摘利用地基GPS估计PWV(precipitable water vapor)时,除GPS观测数据外,GPS测站地表的气温和气压也是必要参数。针对我国多数GPS网并未配备相应的气象传感器的情况,利用美国环境预报中心气候预报系统第2版提供的逐6h产品,并顾及测站高程转换时的平均海平面高改正,提出一种GPS测站气象参数的插值新方法。以香港卫星定位参考站网实测GPS数据进行试验研究,结果表明,平均海平面高对地表气压的插值结果影响较大,而对地表气温的插值结果影响较小;经平均海平面高改正后,地表气压插值结果的平均均方根误差(RMSE)为1.61hPa,地表气温插值结果的平均RMSE为1.93 K;由插值气象参数估计的PWV的平均RMSE为2.76mm,验证了所提方法的有效性。
基金National Natural Science Foundation of China(42088101,41975074)Project of Guangdong Province Key Laboratory for Climate Change and Natural Disaster Studies(2020B1212060025)。
文摘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.
文摘作为东亚冬季风的关键系统,西伯利亚高压的变化对欧亚大陆冬季天气及气候异常产生重要影响。本文系统地评估了美国国家环境预测中心第二代气候预测系统(NCEP-CFSv2,National Center for Environment Prediction-Climate Forecast System,version 2)对冬半年(11~2月)及逐月西伯利亚高压强度的预测效能。结果表明,NCEP-CFSv2模式仅对11月西伯利亚高压强度的预测效能较好,研究其成因发现11月西伯利亚高压强度主要受该地区热力、动力过程以及西伯利亚地区积雪状况的影响。在热力过程方面,NCEP-CFSv2模式可以较好地再现11月西伯利亚高压强度及其相联的该地区表层土壤温度、对外长波辐射等热力因素;在动力过程方面,模式能较好地再现11月西伯利亚高压强度及其相联的该地区对流层低层辐散环流、中高层下沉运动;同时,模式也能较好地再现11月西伯利亚高压强度与该地区积雪覆盖率之间的相互作用。因此,与11月西伯利亚高压相联的热力、动力过程和该地区积雪状况可能是11月西伯利亚高压强度的可预测来源,且NCEP-CFSv2模式能较好地再现这些可预测来源。
基金jointly supported by the Guangdong Major Project of Basic and Applied Basic Research(Grant No.2020B0301030004)the National Natural Science Foundation of China(Grant Nos.42088101,41975074 and 42175023)+2 种基金the Strategic Priority Research Program of the Chinese Academy of Sciences(XDA20100304)the Second Comprehensive Scientific Investigation on the Tibetan Plateau of China(2019QZKK0208)the Guangdong Province Key Laboratory for Climate Change and Natural Disaster Studies(Grant No.2020B1212060025)。
文摘This study depicts the sub-seasonal prediction of the South China Sea summer monsoon onset(SCSSMO)and investigates the associated oceanic and atmospheric processes,utilizing the hindcasts of the National Centers for Environmental Prediction(NCEP)Climate Forecast System version 2(CFSv2).Typically,the SCSSMO is accompanied by an eastward retreat of the western North Pacific subtropical high(WNPSH),development of the cross-equatorial flow,and an increase in the east-west sea surface temperature(SST)gradient.These features are favorable for the onset of westerlies and strengthening of convection and precipitation over the South China Sea(SCS).A more vigorous SCSSMO process shows a higher predictability,and vice versa.The NCEP CFSv2 can successfully predict the onset date and evolution of the monsoon about 4 pentads(20 days)in advance(within 1–2 pentads)for more forceful(less vigorous)SCSSMO processes.On the other hand,the climatological SCSSMO that occurs around the 27th pentad can be accurately predicted in one pentad,and the predicted SCSSMO occurs 1–2 pentads earlier than the observed with a weaker intensity at longer leadtimes.Warm SST biases appear over the western equatorial Pacific preceding the SCSSMO.These biases induce a weaker-thanobserved WNPSH as a Gill-type response,leading to weakened low-level easterlies over the SCS and hence an earlier and less vigorous SCSSMO.In addition,after the SCSSMO,remarkable warm biases over the eastern Indian Ocean and the SCS and cold biases over the WNP induce weaker-than-observed westerlies over the SCS,thus also contributing to the less vigorous SCSSMO.
文摘利用代表南海夏季风季节内振荡特征的850 hPa纬向风EOF分解的前两个主成分,定义南海夏季风季节内振荡指数,并利用美国国家环境预测中心第2代气候预报系统(NCEP Climate Forecast System Version 2,NCEP/CFSv2)提供的1982 2009年逐日回算预报场计算了南海夏季风季节内振荡指数的预报值,用于我国南方地区持续性强降水的预报试验。试验结果表明:利用南海夏季风季节内振荡实时监测指数与模式直接预报降水量相结合的统计动力延伸预报方法,能够有效提高季节内降水分量的预报效果。同时,该方法能够避免末端数据损失,修正了对模式预报降水直接进行带通滤波而导致的负相关现象,并起到消除模式系统误差的作用。
文摘基于国家气候中心气候系统模式(Beijing Climate Center Climate System Model,BCC_CSM1.1m)和美国NCEP/NCAR的气候预测模式(The NCEP Climate Forecast System Version 2,CFSv2)分别建立针对长江流域汛期降水的动力与统计相结合的降尺度预测模型,并比较两模式对应模型的预报技巧和差异来源。分别选择两模式2月起报的500 hPa及200 hPa全球位势高度场为预报因子,结合年际增量及经验正交分解(EOF)迭代法建立降尺度模型(分别简称DY_CSM1.1m和DY_CFSv2),研究发现:(1) EOF迭代法中截断解释方差的递增增加了预报因子的协同性和稳定性,从而显著提高预报技巧,并由此确定98%的截断解释方差为模型的最优参数。(2)两模型基于最优参数的预测效果均优于模式原始的降水预测,其中DY_CSM1.1m预测技巧更高,对应29 a距平相关系数(ACC)平均评分可达0.43,尤其在长江干流区域预报效果显著提高。将两模型预测的降水年际增量百分率转换为降水距平百分率时,ACC多年平均评分降为0.27和0.22,仍高于模式原始预测。(3) DY_CSM1.1m的ACC历年评分和长江流域汛期降水年际增量均与西太平洋副热带高压的一系列指数具有高相关性(以西太平洋副高脊线位置指数为例,DY_CFSv2则无此关系),因此BCC_CSM1.1m在西太平洋地区模拟性能优于CFSv2是导致该模式降尺度后预报技巧更高的重要原因,这一点在典型洪涝年1998和2020年中得以佐证。
