基于时间滞后集合和实时偏差订正的气温精细化预报研究
Refined Air Temperature Forecast Based on Time-lagged Ensemble and Real-time Bias Correction
张武龙 1陈朝平 1杨康权 1周威 1银航1
作者信息
- 1. 四川省气象台,成都 610072;高原与盆地暴雨旱涝灾害四川省重点实验室,成都 610072
- 折叠
摘要
基于SWC-WINGS模式 0.01°×0.01°分辨率的小时 2 m气温产品,利用时间滞后集合和实时偏差订正,得到新的逐小时滚动更新的 1 km网格气温预报,并基于预报准确率、平均误差、平均绝对误差等指标,对 2022年 7-8月逐日逐时气温预报结果进行检验分析.结果表明:时间滞后集合预报准确率在各时效均高于SWC-WINGS模式最新时次预报,实时偏差订正可明显提升临近时效准确率,1～6 h时效的平均提高率为 17.3%.SWC-WINGS模式对于四川地区高、低温预报存在明显的系统性偏差,时间滞后集合对于系统性偏差的改进能力有限,而实时偏差订正可将 1 h时效上四川大部分地区低温和高温预报的平均绝对误差分别控制在 1℃和 2℃以内.针对 2022年 8月 13日四川地区高温天气,时间滞后集合与实时偏差订正集成预报对SWC-WINGS模式预报有较好的订正效果.
Abstract
Based on hourly 2m air temperature product of SWC-WINGS model with a resolution of 0.01°×0.01°,a new 1km grid air temperature forecast with hourly rolling update was obtained by using time-lagged ensemble and real-time bias correction.The hourly air temperature forecast from July to August 2022 was verified by prediction accuracy,average error,and average absolute error.The results showed the accuracy of time-lagged ensemble forecast was higher than that of the up-to-date forecast,and the real-time bias correction leaded to obvious improvement with 17.3%in 1～6 h forecasts.The SWC-WINGS model had obvious systematic bias in the prediction of high and low temperature in Sichuan,and the time-lagged ensemble reduced the deviation weakly.The real-time bias correction could con-trol the average absolute error of low and high temperature forecast in most areas of Sichuan within 1℃ and 2℃,respectively.For the high temperature weather in Sichuan on 13 August 2022,the integration forecast of time-lagged ensemble and real-time bias correction had a high positive skill compared with SWC-WINGS model.
关键词
1公里网格/SWC-WINGS/时间滞后/偏差订正Key words
1km grid/SWC-WINGS/Time lag/Bias correction引用本文复制引用
基金项目
国家重点研发计划项目(2021YFC3000900)
四川智能网格预报创新团队项目(SCQXCXTD-202201)
中国气象局气象能力提升联合研究专项重点专项(22NLTSZ006)
四川省重点实验室项目(SCQXKJZD202101)
四川省重点实验室项目(SCQXKJYJXMS202112)
四川省重点研发项目(2022YFS0542)
四川省重点研发项目(2022YFS0540)
西南区域创新团队项目(XNQYCXTD-202202)
中国气象局创新发展专项(CXFZ2023J016)
中国气象局创新发展专项(CXFZ2024J013)
出版年
2024
NETL
NSTL
万方数据