Correction method by introducing cloud cover forecast factor in model temperature forecast - Mei, Han; Qishu, Wu; Huijun, Liu; Siyu, Yin; Guofei, Wei (Frontiers in Earth Science, 2023)
The temperature objective forecast products can achieve better forecast quality by using the one-dimensional regression correction directly based on the model temperature forecast, but the forecast accuracy can be further improved by adding auxiliary factors appropriately. In this paper, the ECMWF forecast products and the ground observation data of Fujian are used to revise the surface temperature at 2m by introducing the cloud cover forecast factor by correction method. Through the analysis, it is found that the forecast deviation of daily maximum temperature and daily minimum temperature after the revision of single factor forecast is obviously correlated with cloud cover. Then a variety of prediction schemes are designed, and the final scheme is determined through comparative test. The following conclusions are drawn in the process of analysis and verification. All the schemes based on cloud cover grouping can improve the forecast performance, and the total cloud cover scheme is better than the low cloud cover scheme in general. There is a good positive correlation between the deviation of maximum temperature forecast and the mean total cloud cover, that is, the more cloud cover, the bigger deviation. The minimum temperature is bounded by 40% cloud cover, which is negative correlation when lower than 40%, and the rest is positive correlation. The absolute deviation of forecast for maximum temperature and minimum temperature is large when the total cloud cover is less. For maximum temperature forecast, the absolute deviation of the binary regression scheme after grouping is slightly lower than that of the binary regression scheme. For minimum temperature forecast, the binary regression scheme after grouping has significantly improved compared with no grouping. The improved scheme was used to forecast the maximum and minimum temperature in 2021, each test indicator has a certain improvement in most forecast time period. The forecast accuracy of maximum temperature and minimum temperature is 91.480% and 81.312% respectively at 12-36h, which is 2.4~2.6% higher than the single factor regression scheme, and the forecast skill reaches 0.086 and 0.065, indicating that the method can effectively improve the forecast quality stable, which can be used for practical forecasting.