Abstract:

Using the MWP967KV ground-based microwave radiometer data at Shanghai Pudong International Airport Ground Meteorological Observation Station from 2018 to 2019, the civil aviation surface observation data, and the conventional sounding data of Baoshan Station, we analyzed the reliability of the microwave radiometer detection data.On this basis, we calculated the atmospheric parameters related to the occurrence of thunderstorms, selected the appropriate parameters as the forecasting factors, established the BP (Back Propagation) artificial neural network model for airport thunderstorm forecast, and evaluated the forecasting effect of the model.The results show that the mean absolute deviations of temperature, relative humidity, and water vapor density obtained by the MWP967KV ground-based microwave radiometer with the corresponding sounding data are 1.94 ℃, 16.05%, and 0.82 g·m^-3, respectively, the root mean square errors are 1.41 ℃, 20.14%, 1.90 g·m^-3, respectively, and the correlation coefficients are 0.99, 0.66, and 0.85, respectively.The established BPNN model can predict the occurrence of thunderstorms accurately.The forecast accuracy rates of 2 h, 3 h, and 6 h reach 93.27%, 93.33%, and 89.47%, respectively, and the missing rates are 6.73%, 6.67% and 10.53%, respectively.The reporting rates reach 4.90%, 4.78%, and 2.86%, and the critical success indices reach 89.99%, 80.33% and 81.18%, respectively.Therefore, this study realizes the intelligent forecast of thunderstorms to some extent, and the model can be applied to the forecasting and early warning of thunderstorm weather at airports and single stations.

Key words: Ground-based microwave radiometer, Back-propagation neural network, Thunderstorm forecast, Warning