This study presents a comprehensive evaluation of tropical cyclone(TC)forecast performance in the western North Pacific from 2013 to 2022,based on operational forecasts issued by the China Meteorological Administratio...This study presents a comprehensive evaluation of tropical cyclone(TC)forecast performance in the western North Pacific from 2013 to 2022,based on operational forecasts issued by the China Meteorological Administration.The analysis reveals systematic improvements in both track and intensity forecasts over the decade,with distinct error characteristics observed across various forecast parameters.Track forecast errors have steadily decreased,particularly for longer lead times,while error magnitudes have increased with longer forecast lead times.Intensity forecasts show similar progressive enhancements,with maximum sustained wind speed errors decreasing by 0.26 m/s per year for 120 h forecasts.The study also identifies several key patterns in forecast performance:typhoon-grade or stronger TCs exhibit smaller track errors than week or weaker systems;intensity forecasts systematically overestimate weaker TCs while underestimating stronger systems;and spatial error distributions show greater track inaccuracies near landmasses and regional intensity biases.These findings highlight both the significant advances in TC forecasting capability achieved through improved modeling and observational systems,and the remaining challenges in predicting TC changes and landfall behavior,providing valuable benchmarks for future forecast system development.展开更多
为进一步提高温度业务预报水平,本文采用美国国家环境预报中心环境模式中心(National Centers for Environmental Prediction-Environmental Modeling Center,NCEP-EMC)研发的基于递归贝叶斯模型过程(recursive Bayesian model process,...为进一步提高温度业务预报水平,本文采用美国国家环境预报中心环境模式中心(National Centers for Environmental Prediction-Environmental Modeling Center,NCEP-EMC)研发的基于递归贝叶斯模型过程(recursive Bayesian model process,RBMP)的多模式集合技术,开展了华东2 m温度预报试验。利用2016—2017年欧洲中期天气预报中心(European Centre for Medium-Range Weather Forecasts,ECMWF)、NCEP和加拿大气象中心(Canadian Meteorological Centre,CMC)3个具有代表性的全球集合预报系统产品,在对各模式进行偏差订正的基础上,开展了RBMP算法应用试验和评估,建立了华东地区应用方案,再利用2019年9月—2020年5月ECMWF、NCEP集合预报资料开展试运行,初步讨论了RBMP方法在冬春季节预报失败案例中的适用性。结果表明:RBMP方法能够提供更加可靠的概率预报分布并有效提高短期时效的预报技巧。其中,冬季改进最明显,集合平均的均方根误差比ECMWF订正预报和等权重多模式集合分别降低3.0%~10.5%和2.0%~5.0%,且对高温和低温事件均具有更优的分辨能力。此外,RBMP方法还能够提高大部分预报失败案例的预报准确率,为难报案例提供了有价值的不确定信息。总体而言,RBMP技术不仅保留了BMA(Bayesian model averaging)方法的优势,且能满足业务应用对资料存储和计算效率的需求,通过二阶矩调整可以有效校正集合离散度,为进一步提高短期温度预报技巧提供了一种思路。展开更多
本研究旨在提升湖南省盛夏(7、8月)高温过程的延伸期预报技巧。本文利用1999—2022年湖南省97个站点逐日最高气温资料以及次季节-季节(sub-seasonal to seasonal prediction,S2S)模式数据中欧洲中期天气预报中心(ECMWF)和美国国家环境...本研究旨在提升湖南省盛夏(7、8月)高温过程的延伸期预报技巧。本文利用1999—2022年湖南省97个站点逐日最高气温资料以及次季节-季节(sub-seasonal to seasonal prediction,S2S)模式数据中欧洲中期天气预报中心(ECMWF)和美国国家环境预报中心(NCEP)两种模式预报产品,并基于模式温度与环流预报产品提取物理因子,结合卷积神经网络(convolutional neural network,CNN)构建了湖南省盛夏高温过程的预报模型(high temperature prediction model,HTPM);对订正后的S2S模式和构建的预报模型结果进行集成,以实现对区域高温过程较为稳定的相对高技巧预报。结果表明:S2S模式的原始预报技巧较低,偏差订正能显著提高预报效果,但存在较高的空报率;基于ECMWF的S2S数据训练的高温预报模型(HTPM-ECS2S)和基于NCEP的S2S数据训练的高温预报模型(HTPM-NCEPS2S)能有效捕捉高温事件,在高温预报中具有较高的预报技巧;集成方案有效整合了多模型优点,可提升预报的准确性和可靠性。展开更多
基金supported by the National Key R&D Program of China [grant number 2023YFC3008004]。
文摘This study presents a comprehensive evaluation of tropical cyclone(TC)forecast performance in the western North Pacific from 2013 to 2022,based on operational forecasts issued by the China Meteorological Administration.The analysis reveals systematic improvements in both track and intensity forecasts over the decade,with distinct error characteristics observed across various forecast parameters.Track forecast errors have steadily decreased,particularly for longer lead times,while error magnitudes have increased with longer forecast lead times.Intensity forecasts show similar progressive enhancements,with maximum sustained wind speed errors decreasing by 0.26 m/s per year for 120 h forecasts.The study also identifies several key patterns in forecast performance:typhoon-grade or stronger TCs exhibit smaller track errors than week or weaker systems;intensity forecasts systematically overestimate weaker TCs while underestimating stronger systems;and spatial error distributions show greater track inaccuracies near landmasses and regional intensity biases.These findings highlight both the significant advances in TC forecasting capability achieved through improved modeling and observational systems,and the remaining challenges in predicting TC changes and landfall behavior,providing valuable benchmarks for future forecast system development.
文摘为进一步提高温度业务预报水平,本文采用美国国家环境预报中心环境模式中心(National Centers for Environmental Prediction-Environmental Modeling Center,NCEP-EMC)研发的基于递归贝叶斯模型过程(recursive Bayesian model process,RBMP)的多模式集合技术,开展了华东2 m温度预报试验。利用2016—2017年欧洲中期天气预报中心(European Centre for Medium-Range Weather Forecasts,ECMWF)、NCEP和加拿大气象中心(Canadian Meteorological Centre,CMC)3个具有代表性的全球集合预报系统产品,在对各模式进行偏差订正的基础上,开展了RBMP算法应用试验和评估,建立了华东地区应用方案,再利用2019年9月—2020年5月ECMWF、NCEP集合预报资料开展试运行,初步讨论了RBMP方法在冬春季节预报失败案例中的适用性。结果表明:RBMP方法能够提供更加可靠的概率预报分布并有效提高短期时效的预报技巧。其中,冬季改进最明显,集合平均的均方根误差比ECMWF订正预报和等权重多模式集合分别降低3.0%~10.5%和2.0%~5.0%,且对高温和低温事件均具有更优的分辨能力。此外,RBMP方法还能够提高大部分预报失败案例的预报准确率,为难报案例提供了有价值的不确定信息。总体而言,RBMP技术不仅保留了BMA(Bayesian model averaging)方法的优势,且能满足业务应用对资料存储和计算效率的需求,通过二阶矩调整可以有效校正集合离散度,为进一步提高短期温度预报技巧提供了一种思路。
文摘本研究旨在提升湖南省盛夏(7、8月)高温过程的延伸期预报技巧。本文利用1999—2022年湖南省97个站点逐日最高气温资料以及次季节-季节(sub-seasonal to seasonal prediction,S2S)模式数据中欧洲中期天气预报中心(ECMWF)和美国国家环境预报中心(NCEP)两种模式预报产品,并基于模式温度与环流预报产品提取物理因子,结合卷积神经网络(convolutional neural network,CNN)构建了湖南省盛夏高温过程的预报模型(high temperature prediction model,HTPM);对订正后的S2S模式和构建的预报模型结果进行集成,以实现对区域高温过程较为稳定的相对高技巧预报。结果表明:S2S模式的原始预报技巧较低,偏差订正能显著提高预报效果,但存在较高的空报率;基于ECMWF的S2S数据训练的高温预报模型(HTPM-ECS2S)和基于NCEP的S2S数据训练的高温预报模型(HTPM-NCEPS2S)能有效捕捉高温事件,在高温预报中具有较高的预报技巧;集成方案有效整合了多模型优点,可提升预报的准确性和可靠性。
