In this study, principal component analysis(PCA) and complex Morlet wavelet transform were used with daily rainfall in China for the period 1980-1993(1 May-31 Dec.) from observation and ECMWF reanalysis to study its v...In this study, principal component analysis(PCA) and complex Morlet wavelet transform were used with daily rainfall in China for the period 1980-1993(1 May-31 Dec.) from observation and ECMWF reanalysis to study its variability and evaluate the validation of reanalyzed precipitation. The results showed that northward movement of the summer rain belt was a wavelike propagation, which was always accompanied by rainfall breaks and could be treated as one event under time scale of about 1 month only. The first 4 EOFs accounted for 28% and 35% of total variance from observation and reanalysis, respectively, and were roughly consistent with each other. The first and third EOFs for observation mainly represented interweekly, interseasonal and interannual variations and contained some summer intraseasonal fluctuations also. The second and fourth ones mainly represented some rather strong summer intraseasonal fluctuations for a paticular year and contained interweekly, interseasonal and interannual variations also. Although there is still room for improvement, the ECMWF reanalysis is the best available dataset with global coverage and daily variability.展开更多
Due to long-term time series and many elements, reanalysis data of National Centers for Environmental Prediction （NCEP） and European Center for MediumRange Weather Forecasts （ECMWF） are widely used in present clim...Due to long-term time series and many elements, reanalysis data of National Centers for Environmental Prediction （NCEP） and European Center for MediumRange Weather Forecasts （ECMWF） are widely used in present climate studies. Even so, there are discrepancies between NCEP and ECMWF reanalysis. Some climate fields may be better reproduced by NCEP than by ECMWF. On the other hand, ECMWF may describe some climate characteristics more realistically than NCEP. Xu et al.pointed out that NCEP data are of uncertainty when used for studying long-term trends of climate change. By comparing temperatures and pressures from NCEP and observation, it can be seen that NCEP data show higher reliability in the east and lower-latitudes of China than in its west and higher latitudes, NCEP temperature is of more reality than pressure and NCEP data after 1979 are closer to the observations than before. Yang et al.also revealed some serious problems of NCEP data in the north of subtropical Asia. Regional differences of NCEP data in representation are also explored by other studiest. As for seasonal variability, NCEP simulates relatively real conditions of Chinese summer and annual mean but winter data are relatively bad, as in comparisons of NCEP data wity China surface station observations by Zhao et al.Moreover, Trenberth and Stepaniak showed that ECMWF data had better energy budgets than NCEP data for pure pressure coordinates are adopted by ECMWF. Renfrew et al. compared NCF, P data to ECMWF data in terms of surface fluxes and the results indicate that the time series of surface sensible and latent heating fluxes from ECMWF are 13% and 10% larger than the observations and those from NCEP would be 51% and 27% larger than the observations, respectively. So, Renfrew et al. suggested that it be more appropriate to drive ocean models by ECMWF data. Based on comparisons of multiple elements by some scientists, it seems that ECMWF data are better than NCEP data on global, hemispheric and regional scales. Whereas, reanalysis have big errors in some regions in contrast to observations, especially the variables related to humidity. Since that, researchers should compare the two sets of data and select a better one according to specific problems.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
利用欧洲中期天气预报中心(European Centre for Medium-Range Weather Forecasts,简称ECMWF)细网格10m风预报场和海南岛国家气象站地面风观测资料,基于天气学误差统计等方法对2019—2021年10m极大风速预报结果进行评估,以期为预报员更...利用欧洲中期天气预报中心(European Centre for Medium-Range Weather Forecasts,简称ECMWF)细网格10m风预报场和海南岛国家气象站地面风观测资料,基于天气学误差统计等方法对2019—2021年10m极大风速预报结果进行评估,以期为预报员更好地应用模式产品提供依据。结果表明:(1)海南岛四周地区10 m极大风速预报效果优于中部地区;预报误差与海拔高度密切相关,海拔较低站点与实况观测一致性更高;模式对海拔低且开阔地区的极大风速具有较好可预报性。(2)10 m极大风速预报误差随时效略有增长,昼夜误差呈现波峰特征,具有一定日变化。(3)海南岛6级风速预报效果最佳,5级及以下风速预报次之,7级及以上风速预报效果则最差;对于大风预报,ECMWF细网格预报量级具有偏小的特征。(4)基于机器学习方法,选取ECMWF细网格预报场,对海南岛极大风速预报进行订正,独立样本预报模型表明,该方法可以有效减小预报误差,改善效果显著,为海南岛大风预报的准确性提供可靠方法。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
针对ECMWF(European Centre for Medium-range Weather Forecasts)集合预报,融合降水产品在海河流域的偏差特征,进行基于频率匹配法的降水偏差订正,并对订正前后降水评分结果进行了系统检验。结果表明:经过2016年5—8月逐日试验分析表明...针对ECMWF(European Centre for Medium-range Weather Forecasts)集合预报,融合降水产品在海河流域的偏差特征,进行基于频率匹配法的降水偏差订正,并对订正前后降水评分结果进行了系统检验。结果表明:经过2016年5—8月逐日试验分析表明,改进后的ECMWF集合预报融合产品显著改善了原产品降水量和雨区范围偏大的特征,订正后降水预报的平均强度与实况更接近,且预报时效越长、降水量级越大、预报偏差越大改进效果越明显;改进后ECMWF的集合预报融合产品降水预报的TS评分均有一定程度的提高,降水预报的Bias评分更接近1,特别是对于小雨和暴雨、大暴雨量级的改进尤其明显,消除了大片降水虚报区;降水预报的空报率明显减小,但漏报率有所增加。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
文摘In this study, principal component analysis(PCA) and complex Morlet wavelet transform were used with daily rainfall in China for the period 1980-1993(1 May-31 Dec.) from observation and ECMWF reanalysis to study its variability and evaluate the validation of reanalyzed precipitation. The results showed that northward movement of the summer rain belt was a wavelike propagation, which was always accompanied by rainfall breaks and could be treated as one event under time scale of about 1 month only. The first 4 EOFs accounted for 28% and 35% of total variance from observation and reanalysis, respectively, and were roughly consistent with each other. The first and third EOFs for observation mainly represented interweekly, interseasonal and interannual variations and contained some summer intraseasonal fluctuations also. The second and fourth ones mainly represented some rather strong summer intraseasonal fluctuations for a paticular year and contained interweekly, interseasonal and interannual variations also. Although there is still room for improvement, the ECMWF reanalysis is the best available dataset with global coverage and daily variability.
基金Natural Science Foundation of China (40505019) Natural Science Foundation of GuangdongProvince (5300001) Open Foundation of Guangzhou Institute of Tropical and Marine Meteorology,CMA
文摘Due to long-term time series and many elements, reanalysis data of National Centers for Environmental Prediction （NCEP） and European Center for MediumRange Weather Forecasts （ECMWF） are widely used in present climate studies. Even so, there are discrepancies between NCEP and ECMWF reanalysis. Some climate fields may be better reproduced by NCEP than by ECMWF. On the other hand, ECMWF may describe some climate characteristics more realistically than NCEP. Xu et al.pointed out that NCEP data are of uncertainty when used for studying long-term trends of climate change. By comparing temperatures and pressures from NCEP and observation, it can be seen that NCEP data show higher reliability in the east and lower-latitudes of China than in its west and higher latitudes, NCEP temperature is of more reality than pressure and NCEP data after 1979 are closer to the observations than before. Yang et al.also revealed some serious problems of NCEP data in the north of subtropical Asia. Regional differences of NCEP data in representation are also explored by other studiest. As for seasonal variability, NCEP simulates relatively real conditions of Chinese summer and annual mean but winter data are relatively bad, as in comparisons of NCEP data wity China surface station observations by Zhao et al.Moreover, Trenberth and Stepaniak showed that ECMWF data had better energy budgets than NCEP data for pure pressure coordinates are adopted by ECMWF. Renfrew et al. compared NCF, P data to ECMWF data in terms of surface fluxes and the results indicate that the time series of surface sensible and latent heating fluxes from ECMWF are 13% and 10% larger than the observations and those from NCEP would be 51% and 27% larger than the observations, respectively. So, Renfrew et al. suggested that it be more appropriate to drive ocean models by ECMWF data. Based on comparisons of multiple elements by some scientists, it seems that ECMWF data are better than NCEP data on global, hemispheric and regional scales. Whereas, reanalysis have big errors in some regions in contrast to observations, especially the variables related to humidity. Since that, researchers should compare the two sets of data and select a better one according to specific problems.
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。
文摘利用欧洲中期天气预报中心(European Centre for Medium-Range Weather Forecasts,简称ECMWF)细网格10m风预报场和海南岛国家气象站地面风观测资料,基于天气学误差统计等方法对2019—2021年10m极大风速预报结果进行评估,以期为预报员更好地应用模式产品提供依据。结果表明:(1)海南岛四周地区10 m极大风速预报效果优于中部地区;预报误差与海拔高度密切相关,海拔较低站点与实况观测一致性更高;模式对海拔低且开阔地区的极大风速具有较好可预报性。(2)10 m极大风速预报误差随时效略有增长,昼夜误差呈现波峰特征,具有一定日变化。(3)海南岛6级风速预报效果最佳,5级及以下风速预报次之,7级及以上风速预报效果则最差;对于大风预报,ECMWF细网格预报量级具有偏小的特征。(4)基于机器学习方法,选取ECMWF细网格预报场,对海南岛极大风速预报进行订正,独立样本预报模型表明,该方法可以有效减小预报误差,改善效果显著,为海南岛大风预报的准确性提供可靠方法。
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.
文摘针对ECMWF(European Centre for Medium-range Weather Forecasts)集合预报,融合降水产品在海河流域的偏差特征,进行基于频率匹配法的降水偏差订正,并对订正前后降水评分结果进行了系统检验。结果表明:经过2016年5—8月逐日试验分析表明,改进后的ECMWF集合预报融合产品显著改善了原产品降水量和雨区范围偏大的特征,订正后降水预报的平均强度与实况更接近,且预报时效越长、降水量级越大、预报偏差越大改进效果越明显;改进后ECMWF的集合预报融合产品降水预报的TS评分均有一定程度的提高,降水预报的Bias评分更接近1,特别是对于小雨和暴雨、大暴雨量级的改进尤其明显,消除了大片降水虚报区;降水预报的空报率明显减小,但漏报率有所增加。
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。
