阵风的预报误差检验对实际工作中的精细化预报订正具有一定的指导意义,同时对精细化预报中如何消除误差日变化的影响提供了借鉴。选取2017—2019年3~72 h逐日逐3 h欧洲中期天气预报中心(European Centre for Medium-Range Weather Forec...阵风的预报误差检验对实际工作中的精细化预报订正具有一定的指导意义,同时对精细化预报中如何消除误差日变化的影响提供了借鉴。选取2017—2019年3~72 h逐日逐3 h欧洲中期天气预报中心(European Centre for Medium-Range Weather Forecast,ECMWF)细网格10 m阵风和10 m平均风预报资料,基于大连地区9个国家气象观测站实况逐3 h极大风资料进行预报误差检验分析。结果表明:按预报风级和实况风级分类的预报误差对比检验均表明ECMWF细网格预报整体偏大,平均误差为0.96 m·s-1,但具体到各风级时两种分类的预报误差统计结论并不一致,按预报风级分类的检验更符合基于模式预报开展的实际预报工作。以预报为基准统计,各风向、各风级、各站的预报误差均差异明显,风级越大预报偏大的程度越高,风向也表现出随风级增大误差增大的趋势。阵风预报的平均误差具有明显日变化,08:00(北京时,下同)前后误差最大,20:00前后误差最小,主要由10 m平均风的平均误差日变化所致。全部预报个例与实况各时效预报相关系数均在0.7以上,具体到各风级、风向时,各风向相关性均较好,而各风级的相关系数则明显降低,8级及以上风力预报的可信度大幅下降。展开更多
[目的/意义]在全球气候变暖的大背景下,准确确定冬小麦的适宜播种期对于提高小麦产量、保障国家粮食安全具有重要意义。本研究旨在对县级镇在气候变暖长时间序列影响下冬小麦适宜播种期进行分析。[方法]本研究以山东省齐河县为研究区域...[目的/意义]在全球气候变暖的大背景下,准确确定冬小麦的适宜播种期对于提高小麦产量、保障国家粮食安全具有重要意义。本研究旨在对县级镇在气候变暖长时间序列影响下冬小麦适宜播种期进行分析。[方法]本研究以山东省齐河县为研究区域,基于1997—2022年的欧洲中期天气预报中心(European Centre for Medium-Range Weather Forecasts,ECMWF)再分析数据,首先,采用温度阈值法确定稳定通过18、16、14和0℃终日的日期,并从不同小麦品种的适宜播种温度、不同日期播种至越冬前≥0℃的积温、适播期历年日平均气温等关键播期指标对冬小麦适宜播种期进行统计分析;其次,利用叶龄积温法对冬前壮苗所需合适积温的日期进行测算;最后,结合实际生产实践情况,确定气候变暖趋势下齐河县各乡镇冬小麦的适宜播种期。[结果和讨论]从小麦适宜播种温度、播种至小麦越冬停止生长0℃的积温等农业气象指标,以及考虑齐河县种植的冬小麦品种,得出齐河县冬小麦适宜播种期为10月3日—10月16日,最佳播种期为10月5日—10月13日。但具体年份的适播期还需要依据当年的具体情况灵活播种。[结论]研究结果证明了温度阈值法和叶龄积温法在确定冬小麦适宜播种期研究中的可行性,通过温度变化趋势可判断冷冬或暖冬,及时调整播种时间以提高小麦产量,减少温度过高或过低对冬小麦的影响。本研究不仅可以为齐河县冬小麦产量评估提供决策参考,还可以为科学安排农业生产提供重要的理论依据。展开更多
This study examines the spatio-temporal characteristics of heavy precipitation forecasts in eastern China from the European Centre for Medium-Range Weather Forecasts(ECMWF) using the time-domain version of the Method ...This study examines the spatio-temporal characteristics of heavy precipitation forecasts in eastern China from the European Centre for Medium-Range Weather Forecasts(ECMWF) using the time-domain version of the Method for Object-based Diagnostic Evaluation(MODE-TD). A total of 23 heavy rainfall cases occurring between 2018 and 2021 are selected for analysis. Using Typhoon “Rumbia” as a case study, the paper illustrates how the MODE-TD method assesses the overall simulation capability of models for the life history of precipitation systems. The results of multiple tests with different parameter configurations reveal that the model underestimates the number of objects’ forecasted precipitation tracks, particularly at smaller radii. Additionally, the analysis based on centroid offset and area ratio tests for different classified precipitation objects indicates that the model performs better in predicting large-area, fast-moving, and longlifespan precipitation objects. Conversely, it tends to have less accurate predictions for small-area, slow-moving, and shortlifespan precipitation objects. In terms of temporal characteristics, the model overestimates the forecasted movement speed for precipitation objects with small-area, slow movement, or both long and short lifespans while underestimating it for precipitation with fast movement. In terms of temporal characteristics, the model tends to overestimate the forecasted movement speed for precipitation objects with small-area, slow movement, or both long and short lifespans while underestimating it for precipitation with fast movement. Overall, the model provides more accurate predictions for the duration and dissipation of precipitation objects with large-area or long-lifespan(such as typhoon precipitation) while having large prediction errors for precipitation objects with small-area or short-lifespan. Furthermore, the model’s simulation results regarding the generation of precipitation objects show that it performs relatively well in simulating the generation of large-area and fast-moving precipitation objects. However, there are significant differences in the forecasted generation of small-area and slow-moving precipitation objects after 9 hours.展开更多
This study used the China Meteorological Administration(CMA)three-source fusion gridded precipitation analysis data as a reference to evaluate the precipitation forecast performance of the European Centre for Medium-R...This study used the China Meteorological Administration(CMA)three-source fusion gridded precipitation analysis data as a reference to evaluate the precipitation forecast performance of the European Centre for Medium-Range Weather Forecasts(ECMWF)model for China from 2017 to 2022.The main conclusions are as follows.The precipitation forecast capability of the ECMWF model for China has gradually improved from 2017 to 2022.Various scores such as bias,equitable threat score(ETS),and Fractions Skill Score(FSS)showed improvements for different categories of precipitation.The bias of light rain forecasts overall adjusted towards smaller values,and the increase in forecast scores was greater in the warm season than in the cold season.The ETS for torrential rain more intense categories significantly increased,although there were large fluctuations in bias across different months.The model exhibited higher precipitation bias in most areas of North China,indicating overprediction,while it showed lower bias in South China,indicating underprediction.The ETSs indicate that the model performed better in forecasting precipitation in the northeastern part of China without the influence of climatic background conditions.Comparison of the differences between the first period and the second period of the forecast shows that the precipitation amplitude in the ECMWF forecast shifted from slight underestimation to overestimation compared to that of CMPAS05,reducing the likelihood of missing extreme precipitation events.The improvement in ETS is mainly due to the reduction in bias and false alarm rates and,more importantly,an increase in the hit rate.From 2017 to 2022,the area coverage error of model precipitation forecast relative to observations showed a decreasing trend at different scales,while the FSS showed an increasing trend,with the highest FSS observed in 2021.The ETS followed a parabolic trend with increasing neighborhood radius,with the better ETS neighborhood radius generally being larger for moderate rain and heavy rain compared with light rain and torrential rain events.展开更多
为做好ECMWF(European Centre for Medium-Range Weather Forecasting)模式本地化释用,提高四川省降水预报准确率,对四川省2020—2021年7—9月模式各量级降水预报系统性偏差规律分析发现,该模式预报的雨日较实况偏多,尤其是攀西地区和...为做好ECMWF(European Centre for Medium-Range Weather Forecasting)模式本地化释用,提高四川省降水预报准确率,对四川省2020—2021年7—9月模式各量级降水预报系统性偏差规律分析发现,该模式预报的雨日较实况偏多,尤其是攀西地区和川西高原;预报的大雨日数盆地西南部及攀西地区多于实况,而盆地南部少于实况。然后,基于分位数映射法对模式预报的24 h累积降水开展大量级降水订正试验与检验。基于分位数映射法订正后,暴雨及以上量级TS(Threat Score)提高7%~15%,且各量级降水TS均高于多模式集成客观预报产品2%~4%,大雨及以上、暴雨及以上量级命中率提高10%~20%,订正后雨带位置特别是暴雨落区与实况更接近。展开更多
利用2021年10月至2022年3月(2021年冬半年)欧洲中期天气预报中心(European centre for medium-range weather forecasts,ECMWF)细网格阵风预报数据和河南省国家级地面气象站阵风观测资料,基于一元线性回归(linear regression,LR)方法,...利用2021年10月至2022年3月(2021年冬半年)欧洲中期天气预报中心(European centre for medium-range weather forecasts,ECMWF)细网格阵风预报数据和河南省国家级地面气象站阵风观测资料,基于一元线性回归(linear regression,LR)方法,对河南省ECMWF阵风预报进行订正,并对其检验评估。结果表明:(1)2021年冬半年,河南省多出现6级以下的阵风天气。ECMWF模式对于7级及以下的阵风预报存在整体高估的现象,对于7级以上的阵风预报存在低估的现象。(2)LR订正后准确率、均方根误差在所有预报时效均有明显的改善。订正后准确率较高、均方根误差较小的站点主要分布在京广线以东大部分地区、焦作、南阳南部;豫西山区订正效果一般,其复杂的地形易导致阵风偏高,而6级以上阵风样本数较少,预报订正值稳定性相对较差。(3)阵风预报与起报时次的关系不大。LR方法针对6级以下阵风预报有一定的优势,技巧评分(T s评分)较ECMWF预报高,预报偏差(B ias评分)更接近1。展开更多
文摘[目的/意义]在全球气候变暖的大背景下,准确确定冬小麦的适宜播种期对于提高小麦产量、保障国家粮食安全具有重要意义。本研究旨在对县级镇在气候变暖长时间序列影响下冬小麦适宜播种期进行分析。[方法]本研究以山东省齐河县为研究区域,基于1997—2022年的欧洲中期天气预报中心(European Centre for Medium-Range Weather Forecasts,ECMWF)再分析数据,首先,采用温度阈值法确定稳定通过18、16、14和0℃终日的日期,并从不同小麦品种的适宜播种温度、不同日期播种至越冬前≥0℃的积温、适播期历年日平均气温等关键播期指标对冬小麦适宜播种期进行统计分析;其次,利用叶龄积温法对冬前壮苗所需合适积温的日期进行测算;最后,结合实际生产实践情况,确定气候变暖趋势下齐河县各乡镇冬小麦的适宜播种期。[结果和讨论]从小麦适宜播种温度、播种至小麦越冬停止生长0℃的积温等农业气象指标,以及考虑齐河县种植的冬小麦品种,得出齐河县冬小麦适宜播种期为10月3日—10月16日,最佳播种期为10月5日—10月13日。但具体年份的适播期还需要依据当年的具体情况灵活播种。[结论]研究结果证明了温度阈值法和叶龄积温法在确定冬小麦适宜播种期研究中的可行性,通过温度变化趋势可判断冷冬或暖冬,及时调整播种时间以提高小麦产量,减少温度过高或过低对冬小麦的影响。本研究不仅可以为齐河县冬小麦产量评估提供决策参考,还可以为科学安排农业生产提供重要的理论依据。
基金National Key Research and Development Program of China (2021YFC3000802)National Natural Science Foundation of China (41875059)The Open Research Program of the State Key Laboratory of Severe Weather (2021LASW-A04)。
文摘This study examines the spatio-temporal characteristics of heavy precipitation forecasts in eastern China from the European Centre for Medium-Range Weather Forecasts(ECMWF) using the time-domain version of the Method for Object-based Diagnostic Evaluation(MODE-TD). A total of 23 heavy rainfall cases occurring between 2018 and 2021 are selected for analysis. Using Typhoon “Rumbia” as a case study, the paper illustrates how the MODE-TD method assesses the overall simulation capability of models for the life history of precipitation systems. The results of multiple tests with different parameter configurations reveal that the model underestimates the number of objects’ forecasted precipitation tracks, particularly at smaller radii. Additionally, the analysis based on centroid offset and area ratio tests for different classified precipitation objects indicates that the model performs better in predicting large-area, fast-moving, and longlifespan precipitation objects. Conversely, it tends to have less accurate predictions for small-area, slow-moving, and shortlifespan precipitation objects. In terms of temporal characteristics, the model overestimates the forecasted movement speed for precipitation objects with small-area, slow movement, or both long and short lifespans while underestimating it for precipitation with fast movement. In terms of temporal characteristics, the model tends to overestimate the forecasted movement speed for precipitation objects with small-area, slow movement, or both long and short lifespans while underestimating it for precipitation with fast movement. Overall, the model provides more accurate predictions for the duration and dissipation of precipitation objects with large-area or long-lifespan(such as typhoon precipitation) while having large prediction errors for precipitation objects with small-area or short-lifespan. Furthermore, the model’s simulation results regarding the generation of precipitation objects show that it performs relatively well in simulating the generation of large-area and fast-moving precipitation objects. However, there are significant differences in the forecasted generation of small-area and slow-moving precipitation objects after 9 hours.
基金Special Innovation and Development Program of China Meteorological Administration(CXFZ2022J023)Projects in Key Areas of Social Development in Shaanxi Province(2024SF-YBXM-556)Shaanxi Province Basic Research Pro-gram of Natural Science(2023-JC-QN-0285)。
文摘This study used the China Meteorological Administration(CMA)three-source fusion gridded precipitation analysis data as a reference to evaluate the precipitation forecast performance of the European Centre for Medium-Range Weather Forecasts(ECMWF)model for China from 2017 to 2022.The main conclusions are as follows.The precipitation forecast capability of the ECMWF model for China has gradually improved from 2017 to 2022.Various scores such as bias,equitable threat score(ETS),and Fractions Skill Score(FSS)showed improvements for different categories of precipitation.The bias of light rain forecasts overall adjusted towards smaller values,and the increase in forecast scores was greater in the warm season than in the cold season.The ETS for torrential rain more intense categories significantly increased,although there were large fluctuations in bias across different months.The model exhibited higher precipitation bias in most areas of North China,indicating overprediction,while it showed lower bias in South China,indicating underprediction.The ETSs indicate that the model performed better in forecasting precipitation in the northeastern part of China without the influence of climatic background conditions.Comparison of the differences between the first period and the second period of the forecast shows that the precipitation amplitude in the ECMWF forecast shifted from slight underestimation to overestimation compared to that of CMPAS05,reducing the likelihood of missing extreme precipitation events.The improvement in ETS is mainly due to the reduction in bias and false alarm rates and,more importantly,an increase in the hit rate.From 2017 to 2022,the area coverage error of model precipitation forecast relative to observations showed a decreasing trend at different scales,while the FSS showed an increasing trend,with the highest FSS observed in 2021.The ETS followed a parabolic trend with increasing neighborhood radius,with the better ETS neighborhood radius generally being larger for moderate rain and heavy rain compared with light rain and torrential rain events.
文摘为做好ECMWF(European Centre for Medium-Range Weather Forecasting)模式本地化释用,提高四川省降水预报准确率,对四川省2020—2021年7—9月模式各量级降水预报系统性偏差规律分析发现,该模式预报的雨日较实况偏多,尤其是攀西地区和川西高原;预报的大雨日数盆地西南部及攀西地区多于实况,而盆地南部少于实况。然后,基于分位数映射法对模式预报的24 h累积降水开展大量级降水订正试验与检验。基于分位数映射法订正后,暴雨及以上量级TS(Threat Score)提高7%~15%,且各量级降水TS均高于多模式集成客观预报产品2%~4%,大雨及以上、暴雨及以上量级命中率提高10%~20%,订正后雨带位置特别是暴雨落区与实况更接近。
文摘利用2021年10月至2022年3月(2021年冬半年)欧洲中期天气预报中心(European centre for medium-range weather forecasts,ECMWF)细网格阵风预报数据和河南省国家级地面气象站阵风观测资料,基于一元线性回归(linear regression,LR)方法,对河南省ECMWF阵风预报进行订正,并对其检验评估。结果表明:(1)2021年冬半年,河南省多出现6级以下的阵风天气。ECMWF模式对于7级及以下的阵风预报存在整体高估的现象,对于7级以上的阵风预报存在低估的现象。(2)LR订正后准确率、均方根误差在所有预报时效均有明显的改善。订正后准确率较高、均方根误差较小的站点主要分布在京广线以东大部分地区、焦作、南阳南部;豫西山区订正效果一般,其复杂的地形易导致阵风偏高,而6级以上阵风样本数较少,预报订正值稳定性相对较差。(3)阵风预报与起报时次的关系不大。LR方法针对6级以下阵风预报有一定的优势,技巧评分(T s评分)较ECMWF预报高,预报偏差(B ias评分)更接近1。
