Nowcasting of Storms Using Predicted Integrated Water Vapor with a Machine Learning Technique and Satellite Brightness Temperature

A hybrid model for nowcasting of storms has been developed by employing predicted Integrated Water Vapor (IWV) with light Gradient Boosting Machine (GBM) on Global Navigation Satellite System (GNSS) receiver data collected at Gadanki (13.46°N, 79.17°E) and estimated thresholds for three storm predictors. Utilization of predicted IWV allows more lead time for disaster preparedness. The efficacy of light GBM technique in predicting IWV has been tested on 54 stormy days (from the year 2020), identified with a collocated polarimetric weather radar observations. The predicted IWV agrees very well with observed IWV with rms errors <1.2 mm (correlation coefficient >0.85) for predictions with a lead time up to 2 hours. Among several predictors considered for nowcasting, IWV is found to have a great predictive potential, as the moisture buildup is seen few hours (1-4 hours) prior to the occurrence of storm/rainfall. Thresholds for chosen predictors (magnitude of IWV, change in IWV and change in brightness temperature (
T_b
)) are finalized using the data from known stormy days (65 days from the years 2018 and 2019). The sensitivity analysis of the predictors independently and in combination in predicting storms reveals that 1 and 2 parameter-based predictions detects storms accurately but produce large false alarm rates. The three-parameter scheme reduced the false alarm rate drastically to 5% and improved the model accuracy to 97%, which is much better than the existing methods.