Characteristics and Model Error Analysis of Persistent Extreme Heat of Sichuan Basin in August 2022
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Abstract:
Based on the hourly 2 m temperature and precipitation data of 104 stations in the Sichuan Basin in August 2022 and the historical data of the same period from 1971 to 2021, and the 2 m temperature forecast data of the EC, CMAGFS, and CMAMESO models, the temporal and spatial distribution characteristics and mode prediction deviation of this extreme heat weather process are analysed by statistical correlation method. The results show that: (1) In August 2022, the extreme heat weather process in the Sichuan Basin has a wide range, strong intensity, and long duration. There are 87.5% of stations in the basin that have broken the extreme value in the same period, and the peak period of the high temperature is obviously delayed compared with the same period in history. (2) The distribution of the maximum temperature in August 2022 is higher in the east and lower in the west, and the distribution of the difference between the maximum temperature and the extreme value in the same period is opposite, in which the maximum temperature decreases with the increase of the altitude of the station, while the extreme value difference first increases and then decreases with the altitude of the station. In addition, affected by the heat island effect, the stations with large extreme difference are mainly concentrated near the Longquan Mountain range. (3) During the high temperature period, the average temperature of the maximum and minimum are high, and the anomaly is large. In addition, the cumulative precipitation and rainy days are also far lower than the historical period. (4) In comparison, the prediction advantage of the EC model is mainly in the low elevation area of the basin, while the average absolute error of the CMAMESO model is smaller in the steep terrain area around the basin. In addition, the EC model predicts the peak temperature time closer to the reality, while the CMAMESO model predicts the number of hot days closer to the reality.
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Supported by:Beijing E-Tiller Technology Development Co., Ltd.
Supported by:Beijing E-Tiller Technology Development Co., Ltd.