A systematic quantitative review on the performance of some of the recent short-term rainfall forecasting techniques

Rainfall forecasting is a high-priority research problem due to the complex interplay of multiple factors. Despite extensive studies, a systematic quantitative review of recent developments in rainfall forecasting is lacking in the literature. This study conducted a systematic quantitative review of statistical, numerical weather prediction (NWP) and machine learning (ML) techniques for rainfall forecasting. The review adopted the preferred reporting items for systematic reviews and meta-analyses (PRISMA) technique for screening keywords and abstracts, leading to 110 qualified papers from multiple databases. The impact of rainfall threshold, meteorological parameters, topography, algorithm techniques, geographic location, the horizontal resolution of the model, and lead time on rainfall forecast was examined. The review shows the importance of precipitable water vapor (PWV) along with other meteorological parameters for accurate nowcasting in coastal and mountainous regions. An increase in rainfall forecast uncertainty with an increase in the lead time makes the NWP model less popular for the short-term forecast. The pre-processing techniques increased the accuracy of ML techniques by considering extreme values and detecting the irregularly distributed multi-scale features of rainfall in space and time. Future research can focus on hybrid models with improved accuracy for nowcasting. The output from the hybrid model serves as input for the decision support system required for urban flood risk management. HIGHLIGHTS The PRISMA method is applied for a systematic quantitative review.; The Global Navigation Satellite System-derived precipitable water vapor (PWV) is found to be capable of analyzing the real-time profile of water vapor content.; Forecast can be improved by considering additional meteorological parameters along with the PWV.; A longer lead time in the NWP model enhances the forecast uncertainty.; There is a significant improvement in the forecast by machine learning models after pre-processing.;