基于站点观测资料和四个数值模式预报资料,以2011—2012年汛期(6—8月)为例,评估四个模式对淮河流域15个子单元客观面雨量预报效果。这四个模式为欧洲中期天气预报中心(European Centre for Medium-Range Weather Forecasts,简称ECMWF)...基于站点观测资料和四个数值模式预报资料,以2011—2012年汛期(6—8月)为例,评估四个模式对淮河流域15个子单元客观面雨量预报效果。这四个模式为欧洲中期天气预报中心(European Centre for Medium-Range Weather Forecasts,简称ECMWF)全球模式、日本气象厅(Japan Meteorological Agency,简称JMA)全球模式、安徽省气象台业务中尺度模式MM5(Mesoscale Model Version 5)和WRF(Weather Research&Forecasting)。15个子单元面雨量预报值采用网格算术平均法计算,面雨量实况值采用泰森多边形法计算。检验评估采用平均绝对误差、模糊评分、正确率以及TS评分。检验评估结果表明:1)ECMWF预报效果整体上优于其他模式,尤其是在小雨到大雨等级优势明显;JMA、MM5以及WRF的预报效果依次降低。2)各模式预报效果均表现出随降水等级(小雨、中雨、大雨、暴雨)增大而下降的趋势。3)随预报时效(24、48、72 h)延长,各模式预报效果逐渐下降。4)分析典型个例发现,ECMWF、JMA及WRF对于24 h预报时效的强、弱降水过程,预报效果存在较明显差异,对于强降水过程预报等级偏小;MM5对于强、弱过程预报等级均有所偏大。展开更多
为提升区域数值预报系统2 m气温预报性能,利用土壤温度和土壤湿度站点观测资料,对中国气象局陆面数据同化系统(CMA Land Data Assimilation System,CLDAS)陆面资料在浙江地区的精度进行评估,并将其融合应用于浙江省数值预报业务系统。...为提升区域数值预报系统2 m气温预报性能,利用土壤温度和土壤湿度站点观测资料,对中国气象局陆面数据同化系统(CMA Land Data Assimilation System,CLDAS)陆面资料在浙江地区的精度进行评估,并将其融合应用于浙江省数值预报业务系统。结果表明:CLDAS土壤温度、土壤湿度产品相对于美国全球预报系统(Global Forecast System,GFS)分析场,与观测相比具有更小的均方根误差和更高的相关系数,在浙江省有较好的适用性。个例分析表明区域数值模式2 m气温预报对陆面资料变化较敏感,融合CLDAS地表温度、土壤温湿度实时分析产品的初始场,可持续影响到模式预报后期,主要通过地表感热、潜热通量直接影响气温变化。从均方根误差来看,与基于GFS分析场作为陆面初始场的区域模式预报相比,应用了CLDAS陆面资料的模式预报改进了13.6%。2021年7月阶段性应用结果显示,模式初始场融合CLDAS陆面资料后有效提高了浙江省2 m气温预报水平,融合后的预报改进效果夜间较白天明显,且晴热高温天气背景下较梅雨期、台风期改进更佳。高温天气预报评估进一步表明,CLDAS陆面资料的应用对浙江省高温事件预报有较好的改进,尤其对金衢盆地等高温区改进明显。展开更多
Precipitable Water （PW） derived from Global Positioning System （GPS） measurements and numerical weather prediction （NWP） model analysis data were compared to further evaluate the effcacy of applying GPS-derived ...Precipitable Water （PW） derived from Global Positioning System （GPS） measurements and numerical weather prediction （NWP） model analysis data were compared to further evaluate the effcacy of applying GPS-derived PW to the NWP model. The spatial and temporal variations of GPS-derived PW during a rainfall event were also examined. GPS-derived PW measurements show good agreement with the behavior of water vapor at a high spatial resolution during the analysis period. Temporal anomalies of GPS-derived PW moving along with the front are successfully detected by the GPS array. Large positive anomalies of GPS-derived PW are indicated immediately before a rainfall event, and the intensity of these positive anomalies do not seem to decrease significantly as the precipitation system passes. These results indicate that the Korean GPS network may have great potential as a PW sensor over the Korean Peninsula. In contrast with GPS-derived PW, NWP-derived PW shows negative biases. These biases appear to stem mainly from the differences between modeled and actual GPS site elevations, as GPS sites were generally located at elevations lower than those employed by the NWP model. However, there still exists a discernable dry bias after a PW correction is applied to NWP-derived PW. GPS-derived PW better reflects the spatial and temporal moisture variations of precipitation systems, as compared to NWP-derived PW. These results provide entirely new information for improving the regional NWP system, since GPS-derived PW produced with data from the Korean GPS network may be incorporated into the NWP model to improve rainfall forecasts.展开更多
文摘基于站点观测资料和四个数值模式预报资料,以2011—2012年汛期(6—8月)为例,评估四个模式对淮河流域15个子单元客观面雨量预报效果。这四个模式为欧洲中期天气预报中心(European Centre for Medium-Range Weather Forecasts,简称ECMWF)全球模式、日本气象厅(Japan Meteorological Agency,简称JMA)全球模式、安徽省气象台业务中尺度模式MM5(Mesoscale Model Version 5)和WRF(Weather Research&Forecasting)。15个子单元面雨量预报值采用网格算术平均法计算,面雨量实况值采用泰森多边形法计算。检验评估采用平均绝对误差、模糊评分、正确率以及TS评分。检验评估结果表明:1)ECMWF预报效果整体上优于其他模式,尤其是在小雨到大雨等级优势明显;JMA、MM5以及WRF的预报效果依次降低。2)各模式预报效果均表现出随降水等级(小雨、中雨、大雨、暴雨)增大而下降的趋势。3)随预报时效(24、48、72 h)延长,各模式预报效果逐渐下降。4)分析典型个例发现,ECMWF、JMA及WRF对于24 h预报时效的强、弱降水过程,预报效果存在较明显差异,对于强降水过程预报等级偏小;MM5对于强、弱过程预报等级均有所偏大。
文摘为提升区域数值预报系统2 m气温预报性能,利用土壤温度和土壤湿度站点观测资料,对中国气象局陆面数据同化系统(CMA Land Data Assimilation System,CLDAS)陆面资料在浙江地区的精度进行评估,并将其融合应用于浙江省数值预报业务系统。结果表明:CLDAS土壤温度、土壤湿度产品相对于美国全球预报系统(Global Forecast System,GFS)分析场,与观测相比具有更小的均方根误差和更高的相关系数,在浙江省有较好的适用性。个例分析表明区域数值模式2 m气温预报对陆面资料变化较敏感,融合CLDAS地表温度、土壤温湿度实时分析产品的初始场,可持续影响到模式预报后期,主要通过地表感热、潜热通量直接影响气温变化。从均方根误差来看,与基于GFS分析场作为陆面初始场的区域模式预报相比,应用了CLDAS陆面资料的模式预报改进了13.6%。2021年7月阶段性应用结果显示,模式初始场融合CLDAS陆面资料后有效提高了浙江省2 m气温预报水平,融合后的预报改进效果夜间较白天明显,且晴热高温天气背景下较梅雨期、台风期改进更佳。高温天气预报评估进一步表明,CLDAS陆面资料的应用对浙江省高温事件预报有较好的改进,尤其对金衢盆地等高温区改进明显。
基金funded by the Korea Meteorological Administration Research and Development Program under Grant GATER 2006-2201 supported by the Brain Korea 21 Project
文摘Precipitable Water （PW） derived from Global Positioning System （GPS） measurements and numerical weather prediction （NWP） model analysis data were compared to further evaluate the effcacy of applying GPS-derived PW to the NWP model. The spatial and temporal variations of GPS-derived PW during a rainfall event were also examined. GPS-derived PW measurements show good agreement with the behavior of water vapor at a high spatial resolution during the analysis period. Temporal anomalies of GPS-derived PW moving along with the front are successfully detected by the GPS array. Large positive anomalies of GPS-derived PW are indicated immediately before a rainfall event, and the intensity of these positive anomalies do not seem to decrease significantly as the precipitation system passes. These results indicate that the Korean GPS network may have great potential as a PW sensor over the Korean Peninsula. In contrast with GPS-derived PW, NWP-derived PW shows negative biases. These biases appear to stem mainly from the differences between modeled and actual GPS site elevations, as GPS sites were generally located at elevations lower than those employed by the NWP model. However, there still exists a discernable dry bias after a PW correction is applied to NWP-derived PW. GPS-derived PW better reflects the spatial and temporal moisture variations of precipitation systems, as compared to NWP-derived PW. These results provide entirely new information for improving the regional NWP system, since GPS-derived PW produced with data from the Korean GPS network may be incorporated into the NWP model to improve rainfall forecasts.
