A novel rainfall forecast model using GNSS observations and CAPE

Abstract

Precipitable water vapor (PWV) and zenith total delay (ZTD) are highly correlated indicators used for forecasting rainfall effectively. These two factors are widely used when establishing qualitative and quantitative rainfall forecast models. Another indicator, however, convective available potential energy (CAPE), is also highly correlated with the occurrence of rainfall, but has not been combined with PWV or ZTD in previous studies. Therefore, a novel rainfall forecast model based on support vector regression that combines CAPE and Global Navigation Satellite System (GNSS)-derived PWV is proposed in this paper. Moreover, annual, seasonal scales, and hourly autocorrelation for predictors were also considered, and wavelet coherence (WTC) was introduced to further reveal the correlation relationships between CAPE, PWV and rainfall in both time and frequency domains. Hourly PWV, CAPE, temperature (T) and rainfall over a time span of three years were collected from four stations in Singapore and Taiwan to evaluate the performance of the proposed model. The root mean square error (RMSE) and mean absolute error (MAE) between the measured and forecasted rainfall were calculated for each year and averaged. Subsequently, the average RMSE and MAE were determined for the annual seasonal rainfall. The annual average RMSE value was 0.71 mm and the average annual seasonal RMSE value was 0.69 mm. The annual average MAE value was 0.21 mm and the annual seasonal was 0.22 mm. The average correlation coefficient (R2) for the annual value was 0.94 and the annual seasonal R2 value was 0.93. These results indicate that the forecast accuracy at the seasonal scale is basically consistent with annual scale. Additionally, a measure called comparable RMSE (CRMS) was introduced to evaluate the forecasting accuracy across all grades of rainfall. This analysis showed that the accuracy of moderate rainfall (MR) and heavy rainfall (HR) forecasts is similar, but slight rainfall (SR) forecasts are the most accurate among seasons and schemes.