Based on the tropical cyclone data from the Central Meteorological Observatory of China, Japan Meteorological Agency, Joint Typhoon Warning Center and European Centre for Medium-Range Weather Forecasts （ECMWF） durin...Based on the tropical cyclone data from the Central Meteorological Observatory of China, Japan Meteorological Agency, Joint Typhoon Warning Center and European Centre for Medium-Range Weather Forecasts （ECMWF） during the period of 2004 to 2009, three consensus methods are used in tropical cyclone （TC） track forecasts. Operational consensus results show that the objective forecasts of ECMWF help to improve consensus skill by 2%, 3%-5% and 3%-5%, decrease track bias by 2.5 kin, 6-9 km and 10-12 km for the 24 h, 48 h and 72 h forecasts respectively over the years of 2007 to 2009. Analysis also indicates that consensus forecasts hold positive skills relative to each member. The multivariate regression composite is a method that shows relatively low skill, while the methods of arithmetic averaging and composite （in which the weighting coefficient is the reciprocal square of mean error of members） have almost comparable skills among members. Consensus forecast for a lead time of 96 h has negative skill relative to the ECMWF objective forecast.展开更多
In this study,an operational forecasting system of sea dike risk in the southern Zhejiang Province,South China was developed based on a coupled storm-surge and wave model.This forecasting system is important because o...In this study,an operational forecasting system of sea dike risk in the southern Zhejiang Province,South China was developed based on a coupled storm-surge and wave model.This forecasting system is important because of the high cost of storm-surge damage and the need for rapid emergency planning.A comparison with astronomical tides in 2016 and the validation of storm surges and high water marks of 20 typhoons verified that the forecast system has a good simulation ability.The system can forecast relatively realistic water levels and wave heights as shown under the parametric atmospheric forces simulated in a case study;the sea dikes in credible high risk were mainly located in the estuaries,rivers,and around the islands in the southern Zhejiang.Therefore,the forecast system is applicable in the southern Zhejiang with a support to the effective prevention from typhoon storm-surge damage.展开更多
Forecasts of the intense rainfall events are important for the disaster prevention and reduction in the Beijing-TianjinHebei region(BTHR). What are the common biases in the forecasts of intense rainfall in the current...Forecasts of the intense rainfall events are important for the disaster prevention and reduction in the Beijing-TianjinHebei region(BTHR). What are the common biases in the forecasts of intense rainfall in the current operational numerical models? What are the possible causes of model bias? In this study, intense rainfall events in the BTHR were categorized into two types: those mainly due to strong synoptic forcings(SSF) and those with weak synoptic forcings(WSF). The results showed that,the numerical forecasts tend to overestimate the frequency of intense rainfall events but underestimate the rainfall intensity. Of these, the overestimation of precipitation frequency mainly appeared in the mountainous areas in the afternoon. Compared with global models, high-resolution mesoscale models showed a notable improvement in forecasting the afternoon intense rainfall,while they all have an obvious bias in forecasting the nighttime rainfall. For the WSF type, both global model and mesoscale model have a low forecast skill, with large biases in subdaily propagation feature. The possible causes are related to a poor performance of the model in reproducing the local thermodynamical circulations and the dynamical processes in the planetary boundary layer. So, the biases in forecasting the WSF type intense rainfall showed notable features of nonlinearity, which made it really challenging to understand their physical processes and to improve the associated forecasts.展开更多
By utilizing operational forecast products from TIGGE(The International Grand Global Ensemble) during 2006 to 2015,the forecasting performances of the European Centre for Medium-Range Weather Forecasts(ECMWF), Nationa...By utilizing operational forecast products from TIGGE(The International Grand Global Ensemble) during 2006 to 2015,the forecasting performances of the European Centre for Medium-Range Weather Forecasts(ECMWF), National Centers for Environmental Prediction(NCEP), Japan Meteorology Agency(JMA) and China Meteorological Administration(CMA) for the onset of North Atlantic Oscillation(NAO) events are assessed against daily NCEP–NCAR reanalysis data. Twenty-two positive NAO(NAO+) and nine negative NAO(NAO-) events are identified during this time period. For these NAO events,control forecasts, one member of the ensemble that utilizes the currently most proper estimate of the analysis field and the best description of the model physics, are able to predict their onsets three to five days in advance. Moreover, the failure proportion for the prediction of NAO-onset is higher than that for NAO+ onset, which indicates that NAO-onset is harder to forecast. Among these four operational centers, ECMWF has performs best in predicting NAO onset, followed by NCEP,JMA, and then CMA.The forecasting performance of the ensemble mean is also investigated. It is found that, compared with the control forecast, the ensemble mean does not improve the forecasting skill with respect to the onset time of NAO events. Therefore,a confident forecast of NAO onset can only be achieved three to five days in advance.展开更多
基金National Natural Science Foundation of Ningbo City(2013A610124)Ningbo Planning Project of Science and Technology(2012C50044)Nanhai Disaster Mitigation Fund of Hainan Provincial Meteorological Bureau(NH2008ZY02)
文摘Based on the tropical cyclone data from the Central Meteorological Observatory of China, Japan Meteorological Agency, Joint Typhoon Warning Center and European Centre for Medium-Range Weather Forecasts （ECMWF） during the period of 2004 to 2009, three consensus methods are used in tropical cyclone （TC） track forecasts. Operational consensus results show that the objective forecasts of ECMWF help to improve consensus skill by 2%, 3%-5% and 3%-5%, decrease track bias by 2.5 kin, 6-9 km and 10-12 km for the 24 h, 48 h and 72 h forecasts respectively over the years of 2007 to 2009. Analysis also indicates that consensus forecasts hold positive skills relative to each member. The multivariate regression composite is a method that shows relatively low skill, while the methods of arithmetic averaging and composite （in which the weighting coefficient is the reciprocal square of mean error of members） have almost comparable skills among members. Consensus forecast for a lead time of 96 h has negative skill relative to the ECMWF objective forecast.
基金Supported by the National Key Research and Development Program of China(No.2016YFC1402000)
文摘In this study,an operational forecasting system of sea dike risk in the southern Zhejiang Province,South China was developed based on a coupled storm-surge and wave model.This forecasting system is important because of the high cost of storm-surge damage and the need for rapid emergency planning.A comparison with astronomical tides in 2016 and the validation of storm surges and high water marks of 20 typhoons verified that the forecast system has a good simulation ability.The system can forecast relatively realistic water levels and wave heights as shown under the parametric atmospheric forces simulated in a case study;the sea dikes in credible high risk were mainly located in the estuaries,rivers,and around the islands in the southern Zhejiang.Therefore,the forecast system is applicable in the southern Zhejiang with a support to the effective prevention from typhoon storm-surge damage.
基金supported by the National Key R&D Project (Grant No.2018YFC1507606)the National Natural Science Foundation of China (Grant Nos.41505079, 42075154, 41475051 and 42030611)。
文摘Forecasts of the intense rainfall events are important for the disaster prevention and reduction in the Beijing-TianjinHebei region(BTHR). What are the common biases in the forecasts of intense rainfall in the current operational numerical models? What are the possible causes of model bias? In this study, intense rainfall events in the BTHR were categorized into two types: those mainly due to strong synoptic forcings(SSF) and those with weak synoptic forcings(WSF). The results showed that,the numerical forecasts tend to overestimate the frequency of intense rainfall events but underestimate the rainfall intensity. Of these, the overestimation of precipitation frequency mainly appeared in the mountainous areas in the afternoon. Compared with global models, high-resolution mesoscale models showed a notable improvement in forecasting the afternoon intense rainfall,while they all have an obvious bias in forecasting the nighttime rainfall. For the WSF type, both global model and mesoscale model have a low forecast skill, with large biases in subdaily propagation feature. The possible causes are related to a poor performance of the model in reproducing the local thermodynamical circulations and the dynamical processes in the planetary boundary layer. So, the biases in forecasting the WSF type intense rainfall showed notable features of nonlinearity, which made it really challenging to understand their physical processes and to improve the associated forecasts.
基金supported by the National Natural Science Foundation of China (Grant Nos. 41230420 and 41775001)
文摘By utilizing operational forecast products from TIGGE(The International Grand Global Ensemble) during 2006 to 2015,the forecasting performances of the European Centre for Medium-Range Weather Forecasts(ECMWF), National Centers for Environmental Prediction(NCEP), Japan Meteorology Agency(JMA) and China Meteorological Administration(CMA) for the onset of North Atlantic Oscillation(NAO) events are assessed against daily NCEP–NCAR reanalysis data. Twenty-two positive NAO(NAO+) and nine negative NAO(NAO-) events are identified during this time period. For these NAO events,control forecasts, one member of the ensemble that utilizes the currently most proper estimate of the analysis field and the best description of the model physics, are able to predict their onsets three to five days in advance. Moreover, the failure proportion for the prediction of NAO-onset is higher than that for NAO+ onset, which indicates that NAO-onset is harder to forecast. Among these four operational centers, ECMWF has performs best in predicting NAO onset, followed by NCEP,JMA, and then CMA.The forecasting performance of the ensemble mean is also investigated. It is found that, compared with the control forecast, the ensemble mean does not improve the forecasting skill with respect to the onset time of NAO events. Therefore,a confident forecast of NAO onset can only be achieved three to five days in advance.
