Correction method by introducing cloud cover forecast factor in model temperature forecast

Frontiers in Earth Science

Earth Science

Han Mei¹  Yin Siyu¹  Wei Guofei¹  Wu Qishu¹  Liu Huijun¹ 

[1] Fujian Meteorological Observatory, Fuzhou, China;Fujian Provincial Key Laboratory of Disaster Weather, Fuzhou, China;

关键词: the maximum temperature;    the minimum temperature;    cloud cover;    MOS method;    forecast accuracy;    mean absolute deviation;   

DOI  :  10.3389/feart.2023.1099344

 received in 2022-11-15, accepted in 2023-03-31,  发布年份 2023

来源: Frontiers

【 摘 要 】

Objective temperature forecast products can achieve better forecast quality by using one-dimensional regression correction directly based on the present model temperature forecast product, and the forecast accuracy can be further improved by adding appropriate auxiliary factors. In this paper, ECMWF forecast products and ground observation data from Fujian are used to revise the surface temperature at 2 m by introducing a cloud cover forecast factor based on the model temperature forecast correction method. Analysis shows that the forecast deviation of daily maximum and minimum temperature after the revision of a single-factor forecast is obviously correlated with cloud cover. A variety of prediction schemes are designed, and the final scheme is determined through comparative testing. The following conclusions are drawn: all schemes based on cloud cover grouping can improve forecast performance, and the total cloud cover scheme is generally better than the low cloud cover scheme. There is a good positive correlation between the forecast deviation of maximum temperature and the mean total cloud cover; that is, the more cloud cover, the bigger the deviation. The minimum temperature is negatively correlated with cloud cover when the cloud cover is less than 40% and positively correlated for the rest. The absolute forecast deviations of the maximum and minimum temperatures are larger when the total cloud cover is less. Whether for Tmax or Tmin forecast, the binary regression scheme after grouping consistently showed the best performance, with the lowest MAE. The final scheme was used to forecast the maximum and minimum temperature in 2021, and most verification indicators showed improvement in most forecast periods. The forecast accuracy for the 36-h daily maximum and minimum temperature is 81.312% and 91.480%, respectively, which is 2.4%–2.6% higher than the single-factor regression scheme. The forecast skill scores (FSS) reach 0.065 and 0.086, indicating that the method can effectively improve forecast quality in a stable manner and can be used for practical forecasting.

【 授权许可】

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Copyright © 2023 Mei, Qishu, Huijun, Siyu and Guofei.