干旱气象 ›› 2020, Vol. 38 ›› Issue (2): 329-338.DOI: 10.11755/j.issn.1006-7639(2020)-02-0329

• 业务技术应用 • 上一篇    下一篇

DERF2.0模式对甘肃省1月气温模拟的误差分析

卢国阳1,林纾1,姚瑞2,陈佩璇1,刘丽伟1,李丹华1,王鑫1   

  1. 1.兰州区域气候中心,甘肃 兰州 730020;
    2.甘肃省定西市气象局,甘肃 定西 743000
  • 出版日期:2020-04-28 发布日期:2020-04-28
  • 通讯作者: 林纾(1964— ),女,福建福州人,正研级高级工程师,主要从事短期气候预测业务及研究. E-mail: treewest@163.com。
  • 作者简介:卢国阳(1991— ),男,甘肃通渭人,工程师,主要从事短期气候预测工作. E-mail: lugy1991@126.com。
  • 基金资助:
    气象预报业务关键技术发展专项(YBGJXM(2018)04-06)和国家重点研发计划(SQ2018YFC150042-1-2)和甘肃省气象局气象科研项目(Ms2020-12)共同资助

Forecast Errors Analysis of January Temperature in Gansu Province Based on DERF2.0 Model

LU Guoyang1, LIN Shu1, YAO Rui2, CHEN Peixuan1,LIU Liwei1, LI Danhua1, WANG Xin1   

  1. 1. Lanzhou Regional Climate Center, Lanzhou 730020, China;
    2. Dingxi Meteorological Bureau of Gansu Province, Dingxi 743000, Gansu, China
  • Online:2020-04-28 Published:2020-04-28

摘要: 基于DERF2.0模式回报的2 m气温资料、甘肃省参与业务评分的69个气象站点观测资料,结合NCEP再分析资料和NOAA海表温度资料,分析了DERF2.0模式对甘肃省1992—2013年1月气温回报误差及其与外强迫之间的关系。结果表明:(1)DERF2.0模式对甘肃省河东1月气温的模拟效果优于河西大部,特别在甘南、临夏、兰州、定西、平凉、庆阳等地区模式回报的平均误差小、稳定性高,且与观测一致呈增暖趋势,而河西大部平均误差大,变化趋势与观测相反,且回报效果不稳定。(2)虽然模式对1月气温的年际变化及空间分布形态有较好的反映,但气温变化中心和数值与观测差异大。(3)误差场的第一模态反映了模式对气温一致的高估或低估;第二模态以黄河为界,河东和河西误差呈相反的分布型;第三模态则是甘南高原区与其余大部误差呈反位相分布。(4)回报误差的主要模态与关键区域的环流和海温存在显著的相关性,表明模式对环流和海温异常的响应能力存在缺陷,通过调整模式对关键区环流和海温的响应能力,在一定程度上可能减少模式对甘肃省1月气温的预报误差。

关键词: DERF2.0, 甘肃, 1月气温, 预报误差

Abstract: Based on 2-meter temperature data from the second generation monthly dynamic extended range forecast (DERF2.0) model, observational temperature data at 69 weather stations in Gansu Province, reanalysis data of NCEP/DOE and sea surface temperature data of NOAA,the forecast errors of January temperature in Gansu Province by DERF2.0 model from 1992 to 2013 and their relationship with external forcing were analyzed. The results are as follows: (1) The simulated effects of January temperature by DERF2.0 model in eastern Yellow River of Gansu (known as Hedong for short) were better than that in most regions of western Yellow River of Gansu (known as Hexi for short), especially  in Gannan, Linxia, Lanzhou, Dingxi, Pingliang and Qingyang, the average errors between forecast and observation were small and stable, and the linear tendency rates of forecasted temperature in January were consistent with observation from 1992 to 2013, while the average errors were bigger and unstable in most regions of Hexi, and the change trends of forecasted temperature were contrary to actual observation. (2) Although the model could well reflect the inter-annual variation and spatial distribution pattern of January temperature in Gansu, the abnormal centers and values of temperature change were significantly different from the observation. (3) The EOF1 of error field reflected consistent overestimate or underestimate to  January temperature, the EOF2 presented an opposite distribution pattern in Hedong and Hexi, while the EOF3 appeared a reverse phase distribution pattern in Gannan Plateau and other parts of Gansu Province. (4) The main modes of forecast error field were significantly correlated with circulation and sea surface temperature (SST) in key areas, which indicated that the response of model to circulation and SST anomalies was deficient. Therefore, it was partially possible to reduce forecast errors of January temperature in Gansu by adjusting the response ability of DERF2.0 model to circulation and SST in key areas.

Key words: DERF2.0, Gansu Province, January temperature, forecast errors

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