基于U-net神经网络的新疆10 m风速预报订正研究  

Forecast Calibration of 10-m Wind Speed over Xinjiang Based on a U-net Neural Network

作　　者：朱延和 朱寿鹏 夏平 孔扬 周林义[3] 吕阳 王宇浩 张殷宸 智协飞[4] 霍文[2] 艾力亚尔·艾海提 ZHU Yanhe;ZHU Shoupeng;XIA Ping;KONG Yang;ZHOU Linyi;LÜYang;WANG Yuhao;ZHANG Yinchen;ZHI Xiefei;HUO Wen;AILIYAER Aihaiti(Ningbo Meteorological Bureau,Ningbo 315012;Taklimakan National Field Scientific Observation and Research Station of Desert Meteorology/Xinjiang Key Laboratory of Desert Meteorology and Sandstorm/Taklimakan Desert Meteorology Field Experiment Station/Xinjiang Cloud Precipitation Physics and Cloud Water Resources Development Laboratory/Field Scientific Observation Base of Cloud Precipitation Physics in West Tianshan Mountains,Institute of Desert Meteorology,China Meteorological Administration,Ürümqi 830002;Key Laboratory of Transportation Meteorology of China Meteorological Administration/Nanjing Joint Institute for Atmospheric Sciences,Nanjing 210041;Key Laboratory of Meteorology Disaster,Ministry of Education(KLME)/Collaborative Innovation Center on Forecast and Evaluation of Meteorological Disasters(CIC-FEMD),Nanjing University of Information Science and Technology,Nanjing 210044)

机构地区：[1]宁波市气象局,宁波315012 [2]中国气象局乌鲁木齐沙漠气象研究所新疆塔克拉玛干沙漠气象国家野外科学观测研究站/新疆沙漠气象与沙尘暴重点实验室/塔克拉玛干沙漠气象野外科学试验基地/新疆云降水物理与云水资源开发实验室/西天山云降水物理野外科学观测基地,乌鲁木齐830002 [3]中国气象局交通气象重点开放实验室/南京气象科技创新研究院,南京210041 [4]南京信息工程大学气象灾害教育部重点实验室/气象灾害预报预警与评估协同创新中心,南京210044

出　　处：《大气科学》2024年第6期2300-2315,共16页Chinese Journal of Atmospheric Sciences

基　　金：江西省重点研发计划项目20223BBG71019;中国气象局创新发展专项CXFZ2023J008;中国气象局重点创新团队项目CMA2022ZD04;中国气象局能力提升联合研究专项22NLTSZ001;国家自然科学基金项目42105008。

摘　　要：本文以新疆地区1~7 d预报时效的10 m高度处风速为研究对象,基于2000~2019年NCEP全球集合预报系统(Global Ensemble Forecasting System,GEFS)新疆地区10 m风再预报资料,构建了基于U-net神经网络的深度学习预报订正模型,并以两种传统方法递减平均、分位数映射作为参考进行订正技巧对比分析。结果表明,原始GEFS风速预报误差呈不对称分布,表现出更多的正偏差特征,且在天山和昆仑山等海拔较高地区误差较大。与两种传统方法相比,U-net模型提高了整个新疆地区的风速预报技巧,有效改善了原始风速预报的正偏差情况,且对天山和昆仑山等原始预报误差较大区域改善效果尤为显著。此外,采用基于均方误差分解的误差分解方法来分析误差来源,结果表明,预报订正前后,序列误差项始终是主要误差来源,且随预报时效显著增长。三种订正方法对风速预报的偏差项、分布误差项和序列误差项都有不同程度的改进,其中U-net模型相较于两种参考预报的优势主要在于其对序列误差项的改进效果。经过U-net模型订正后序列误差项随预报时效增长缓慢,即使在7 d预报时效下,其序列误差项比原始预报减小60%。In this study,a U-net deep-learning neural network is utilized to calibrate a 10-m wind forecast,with forecast lead times of 1–7 days over Xinjiang based on the Global Ensemble Forecasting System(GEFS)reforecast dataset from 2000 to 2019.The two conventional postprocessing methods,namely the decaying averaging method and quantile mapping,are employed in parallel for comparison.Results show that raw GEFS exhibits an asymmetric error distribution tending toward positive biases in Xinjiang,with most conspicuous forecast biases distributed in high-altitude regions such as the Tianshan and Kunlun Mountains.Compared with two reference calibrations,the U-net model improves the forecast ability of wind speeds over the whole area,especially over areas where raw GEFS shows large biases such as the Tianshan and Kunlun Mountains.In particularly,the U-net model effectively reduces the positive biases.Furthermore,to analyze error sources,forecast errors are decomposed based on the mean squared errors.Results show that the sequence term remains the main error source after forecast calibrations,which obviously increases with increasing lead time.The multiple calibration methods display different capabilities of ameliorating different error terms.The advantage of the Unet model over two reference forecasts primarily lies in its improvement in terms of the sequence term.After U-net calibration,sequence term slowly grows with increasing lead time.Even for a 7-day lead time,the U-net model reduced the sequence error term by approximately 60%compared with raw GEFS forecasts.

关 键 词：新疆 10 M 风速 预报订正 U-net 神经网络 误差分解 

分 类 号：P456[天文地球—大气科学及气象学]