Analysis on the Error Correction Method of 2m Temperature Hourly Forecast Based on CMA-GD Model

The most significant meteorological component is temperature， and weather forecasting relies heavily on how accurately temperatures are predicted.This study uses a linear non-graded regression method to rectify the inaccuracies in temperature forecasts induced by terrain variation in the 2 m temperature hourly forecast product of the mesoscale numerical model （China Meteorological Administration Guangdong， CMA-GD）， and use the one-dimensional Kalman filtering method and the double-weighted moving average method to correct the results.The accuracy of the hourly distribution exhibits a diurnal variation feature， and the model terrain height deviation is linearly negatively connected with the temperature error mean value， according to the results.The daytime correction impact is superior than the nighttime correction effect following the ungraded regression method.recorrecting using the best time frame for mathematical correction techniques （15 days for the Kalman method and 20 days for the mean method）.It is discovered that the mean method's re-correction effect outperforms the Kalman methods， and that the correction effect is more pronounced during the day than at night.Summer and autumn have a better re-correction impact than winter and spring， with some negative correction effects in spring and little difference between the two techniques in the latter.In the former， the mean value method outperforms the Kalman method.There are eight stations with negative correction following the ungraded regression method， but no negative correction stations follow the mathematical correction methods.Therefore the northern region typically experiences a better corrective impact than the southern region.The fraction of correction magnitude for both MAE and ACC is positively correlated with a binomial connection.The terrain deviation correction method has the least slope and restricted correction effect， while the mean value approach has the best correlation and largest slope.An error assessment was conducted in the middle part of Poyang Lake Plain and the south Zhejiang-Fujian hilly region.The peak error value in the former was lower than that in the latter， and the correction amplitude at the peak was smaller.After correction， the MAE decreased by 25.1% and 19.8%， respectively.From November 2022 to January 2023， during frequent cold air intrusions， the MAE in the middle part of the Poyang Lake Plain decreased by 13.5%.With corrected forecast errors oscillating around the zero axis and a noticeable improvement in systematic positive errors， the model significantly overestimates the temperature forecast for high mountain areas.The temperature forecast errors oscillate with the smallest amplitude from August to October and the largest amplitude in spring and winter.Taking the warming process （May 1-6， 2022） and the strong cooling process （November 28-December 3， 2022） as examples， the corrected MAE decreased by 18.2% and 16.0%， respectively， indicating that the method has achieved stable correction effects during transitional weather.This composite method has good stability， strong forecast correction ability， easy to promote.