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
Vector winds play a crucial role in weather and climate,as well as the effective utilization of wind energy resources.However,limited research has been conducted on treating the wind field as a vector field in the eva...Vector winds play a crucial role in weather and climate,as well as the effective utilization of wind energy resources.However,limited research has been conducted on treating the wind field as a vector field in the evaluation of numerical weather prediction models.In this study,the authors treat vector winds as a whole by employing a vector field evaluation method,and evaluate the mesoscale model of the China Meteorological Administration(CMA-MESO)and ECMWF forecast,with reference to ERA5 reanalysis,in terms of multiple aspects of vector winds over eastern China in 2022.The results show that the ECMWF forecast is superior to CMA-MESO in predicting the spatial distribution and intensity of 10-m vector winds.Both models overestimate the wind speed in East China,and CMA-MESO overestimates the wind speed to a greater extent.The forecasting skill of the vector wind field in both models decreases with increasing lead time.The forecasting skill of CMA-MESO fluctuates more and decreases faster than that of the ECMWF forecast.There is a significant negative correlation between the model vector wind forecasting skill and terrain height.This study provides a scientific evaluation of the local application of vector wind forecasts of the CMA-MESO model and ECMWF forecast.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.
基金primarily supported by the National Key R&D Program of China[grant number 2021YFC3000904]the Jiangsu Provincial Key Technology R&D Program[grant number BE2022851]National Natural Science Foundation of China[grant number 42405035]。
文摘Vector winds play a crucial role in weather and climate,as well as the effective utilization of wind energy resources.However,limited research has been conducted on treating the wind field as a vector field in the evaluation of numerical weather prediction models.In this study,the authors treat vector winds as a whole by employing a vector field evaluation method,and evaluate the mesoscale model of the China Meteorological Administration(CMA-MESO)and ECMWF forecast,with reference to ERA5 reanalysis,in terms of multiple aspects of vector winds over eastern China in 2022.The results show that the ECMWF forecast is superior to CMA-MESO in predicting the spatial distribution and intensity of 10-m vector winds.Both models overestimate the wind speed in East China,and CMA-MESO overestimates the wind speed to a greater extent.The forecasting skill of the vector wind field in both models decreases with increasing lead time.The forecasting skill of CMA-MESO fluctuates more and decreases faster than that of the ECMWF forecast.There is a significant negative correlation between the model vector wind forecasting skill and terrain height.This study provides a scientific evaluation of the local application of vector wind forecasts of the CMA-MESO model and ECMWF forecast.
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。
