干旱气象 ›› 2024, Vol. 42 ›› Issue (6): 976-986.DOI: 10.11755/j.issn.1006-7639-2024-06-0976

• 技术报告 • 上一篇    下一篇

基于CRA空间检验技术的甘肃河东汛期降水智能网格预报偏差特征分析

韩晶1(), 焦美玲1, 曹彦超1(), 王娟1, 贺涛1, 徐耕1, 周忠文1, 金满慧2   

  1. 1.甘肃省庆阳市气象局,甘肃 庆阳 745000
    2.甘南藏族自治州气象局,甘肃 甘南 747000
  • 收稿日期:2024-03-18 修回日期:2024-07-22 出版日期:2024-12-31 发布日期:2025-01-15
  • 通讯作者: 曹彦超(1985—),男,甘肃庆阳人,高级工程师,主要从事气候变化及灾害性天气研究。E-mail:646891024@qq.com
  • 作者简介:韩晶(1988—),女,甘肃庆阳人,工程师,主要从事气候变化及灾害性天气研究。E-mail:446843809@qq.com
  • 基金资助:
    甘肃省自然科学基金项目(22JRRM1045);甘肃省自然科学基金项目(24JRRM008);庆阳市科技计划项目(QY-STK-2022A-129);甘肃省气象局科研项目(ZcMs2022-33)

Deviation characteristics in intelligent grid forecast of flood season precipitation in Hedong area of Gansu based on CRA spatial forecast verification

HAN Jing1(), JIAO Meiling1, CAO Yanchao1(), WANG Juan1, HE Tao1, XU Geng1, ZHOU Zhongwen1, JIN Manhui2   

  1. 1. Qingyang Meteorological Bureau of Gansu Province, Qingyang 745000, Gansu, China
    2. Gannan Meteorological Bureau of Gansu Province, Gannan 747000, Gansu, China
  • Received:2024-03-18 Revised:2024-07-22 Online:2024-12-31 Published:2025-01-15

摘要:

研究甘肃河东地区汛期降水智能网格预报偏差特征,对提升区域降水预报预警的精准化水平和防灾减灾服务能力具有重要意义。利用甘肃河东1 766个自动气象观测站的汛期降水资料,筛选出2018—2020年264个降水个例,基于中央气象台发布的3 h智能网格降水预报数据,对预报场和实况场的连续雨区(Contiguous Rain Area,CRA)进行识别和配对,并按照命中、假警报和未命中3种情况分类分析,进一步对预报命中的CRA偏差特征进行研究。结果表明:对于预报命中的降水个例,智能网格降水预报的落区偏差以暖强迫类最大,斜压锋生类和冷强迫类次之;强度偏差以冷强迫类最大,暖强迫类和斜压锋生类次之;形态偏差以斜压锋生类最大,冷强迫类和暖强迫类次之。暖强迫类和斜压锋生类降水的落区预报偏东偏北,冷强迫类偏东偏南。3类降水预报均表现为对β及以下尺度的降水落区面积偏大,而对α尺度降水落区面积偏小。基于CRA空间检验结果,预报员可建立本地化的模式订正方案,提高智能网格降水预报的服务能力。

关键词: 天气形势, 空间检验, 降水预报, 偏差分析

Abstract:

It is of great significance to study the deviation characteristics in intelligent grid forecasting of precipitation during the flood season in Hedong area of Gansu for improving the accuracy level of regional precipitation forecasting and warning, and enhancing the ability of disaster prevention and reduction services. By using precipitation data in flood season from 1 766 automatic meteorological observation stations in Hedong area, 264 precipitation cases from 2018 to 2020 were selected. Based on the 3-hour interval intelligent grid precipitation forecasts issued by the Central Meteorological Observatory, the Contiguous Rain Area (CRA) of forecast field and the actual field are identified and matched, and classified according to hit, miss and false alarm, to further study the CRA deviation characteristics. The results show that, for the precipitation cases hit by the forecast, the falling area deviation of the intelligent grid forecast of the warm forcing precipitation is the largest, followed by precipitations of the oblique frontal generation and cold forcing categories. The intensity deviation of the cold forcing precipitation is the largest, followed by precipitations of the warm forcing and baroclinic frontogenetic categories. The maximum morphological deviation is found in baroclinic frontogenetic precipitation, followed by the cold forcing and warm forcing categories. The forecast area of the warm forcing and oblique frontal precipitations is biased towards the north and east, while the cold forcing precipitation is biased towards the south and east. The forecasted precipitation area of three types precipitation is larger for the beta scale and below, and smaller for the the alpha scale. Forecasters can establish localized model correction schemes based on the results of CRA spatial testing to improve the service capability of intelligent grid precipitation forecast.

Key words: weather situation, spatial verification, precipitation forecast, deviation analysis

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