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
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
利用1999 2010年共12年NCEP CFSv2(NCEP Climate Forecast System version 2)每天4个时次对未来45天预测的回报数据,检验了CFSv2模式对北半球夏季(6 8月)中高纬乌拉尔山区域(10°E70°E)和贝加尔湖-鄂霍次克海区域(110°E 1...利用1999 2010年共12年NCEP CFSv2(NCEP Climate Forecast System version 2)每天4个时次对未来45天预测的回报数据,检验了CFSv2模式对北半球夏季(6 8月)中高纬乌拉尔山区域(10°E70°E)和贝加尔湖-鄂霍次克海区域(110°E 180°E)阻塞高压及其与之相联系的东亚气候的预测能力。分析结果显示,CFSv2可以较好的模拟夏季北半球阻塞高压发生频率的纬向分布特征,但随着预测时效的增加阻塞发生的频率不断降低。CFSv2对两个区域阻塞预测的命中率在7天时效内为50%左右,接近2周之后基本上没有技巧。CFSv2对区域阻塞事件的预测技巧要低于区域阻塞的技巧,贝加尔湖-鄂霍次克海区域阻塞事件的技巧略低于乌拉尔山区域。CFSv2对阻塞爆发和结束的预测超过7天左右,基本没有预测技巧,对乌拉尔山区域阻塞结束日的预测技巧要低于阻塞爆发日的预测技巧。CFSv2在可用的预测时效内可以较好再现与区域阻塞相联系的环流形势以及东亚地区气温、降水异常的分布特征,尤其是夏季乌拉尔山和鄂霍茨克海地区发生阻塞时我国长江流域及其以南地区降水容易偏多的特征。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
The boreal spring Antarctic Oscillation(AAO)has a significant impact on the spring and summer climate in China.This study evaluates the capability of the NCEP's Climate Forecast System,version 2(CFSv2),in predicti...The boreal spring Antarctic Oscillation(AAO)has a significant impact on the spring and summer climate in China.This study evaluates the capability of the NCEP's Climate Forecast System,version 2(CFSv2),in predicting the boreal spring AAO for the period 1983-2015.The results indicate that CFSv2 has poor skill in predicting the spring AAO,failing to predict the zonally symmetric spatial pattern of the AAO,with an insignificant correlation of 0.02 between the predicted and observed AAO Index(AAOI).Considering the interannual increment approach can amplify the prediction signals,we firstly establish a dynamical-statistical model to improve the interannual increment of the AAOI(DY AAOI),with two predictors of CFSv2-forecasted concurrent spring sea surface temperatures and observed preceding autumn sea ice.This dynamical-statistical model demonstrates good capability in predicting DY AAOI,with a significant correlation coeffcient of 0.58 between the observation and prediction during 1983-2015 in the two-year-out cross-validation.Then,we obtain an improved AAOI by adding the improved DY AAOI to the preceding observed AAOI.The improved AAOI shows a significant correlation coeffcient of 0.45 with the observed AAOI during 1983-2015.Moreover,the unrealistic atmospheric response to March-April-May sea ice in CFSv2 may be the possible cause for the failure of CFSv2 to predict the AAO.This study gives new clues regarding AAO prediction and short-term climate prediction.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。
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。
文摘利用1999 2010年共12年NCEP CFSv2(NCEP Climate Forecast System version 2)每天4个时次对未来45天预测的回报数据,检验了CFSv2模式对北半球夏季(6 8月)中高纬乌拉尔山区域(10°E70°E)和贝加尔湖-鄂霍次克海区域(110°E 180°E)阻塞高压及其与之相联系的东亚气候的预测能力。分析结果显示,CFSv2可以较好的模拟夏季北半球阻塞高压发生频率的纬向分布特征,但随着预测时效的增加阻塞发生的频率不断降低。CFSv2对两个区域阻塞预测的命中率在7天时效内为50%左右,接近2周之后基本上没有技巧。CFSv2对区域阻塞事件的预测技巧要低于区域阻塞的技巧,贝加尔湖-鄂霍次克海区域阻塞事件的技巧略低于乌拉尔山区域。CFSv2对阻塞爆发和结束的预测超过7天左右,基本没有预测技巧,对乌拉尔山区域阻塞结束日的预测技巧要低于阻塞爆发日的预测技巧。CFSv2在可用的预测时效内可以较好再现与区域阻塞相联系的环流形势以及东亚地区气温、降水异常的分布特征,尤其是夏季乌拉尔山和鄂霍茨克海地区发生阻塞时我国长江流域及其以南地区降水容易偏多的特征。
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.
基金supported by the National Key Research and Development Program of China (Grant No. 2016YFA0600703)the funding of the Jiangsu Innovation & Entrepreneurship Team and the Priority Academic Program Development of Jiangsu Higher Education Institutions
文摘The boreal spring Antarctic Oscillation(AAO)has a significant impact on the spring and summer climate in China.This study evaluates the capability of the NCEP's Climate Forecast System,version 2(CFSv2),in predicting the boreal spring AAO for the period 1983-2015.The results indicate that CFSv2 has poor skill in predicting the spring AAO,failing to predict the zonally symmetric spatial pattern of the AAO,with an insignificant correlation of 0.02 between the predicted and observed AAO Index(AAOI).Considering the interannual increment approach can amplify the prediction signals,we firstly establish a dynamical-statistical model to improve the interannual increment of the AAOI(DY AAOI),with two predictors of CFSv2-forecasted concurrent spring sea surface temperatures and observed preceding autumn sea ice.This dynamical-statistical model demonstrates good capability in predicting DY AAOI,with a significant correlation coeffcient of 0.58 between the observation and prediction during 1983-2015 in the two-year-out cross-validation.Then,we obtain an improved AAOI by adding the improved DY AAOI to the preceding observed AAOI.The improved AAOI shows a significant correlation coeffcient of 0.45 with the observed AAOI during 1983-2015.Moreover,the unrealistic atmospheric response to March-April-May sea ice in CFSv2 may be the possible cause for the failure of CFSv2 to predict the AAO.This study gives new clues regarding AAO prediction and short-term climate prediction.
