A review of radar-based nowcasting of precipitation and applicable machine learning techniques

Heavy rainfall events can cause major disruption to human activities. It is desirable to predict these events ahead of time so that decision makers can take action to protect life, property and prosperity. Nowcasting, or short-term forecasting from observations, remains an important tool in predicting these events. The essential goals of nowcasting are identical to those of all weather forecasting, with the only difference being the spatial and temporal scales involved. The World Meteorological Organization (WMO, 2016) distinguishes among the various forecasting time horizons as: "Usually forecasts for the next 0-2 hours are called nowcasting, from 2-12 hours very short-range forecasting (VSRF), and short-range forecasting beyond that; but the capabilities of the different ranges can vary upon variables and weather situations." Radar-based nowcasting emerged in an era of mainly synoptic and mesoscale weather prediction. Predicting rainfall during that time was a challenge for numerical weather prediction (NWP) models, since computational restrictions limited the resolution at which NWP models could operate. As a result, NWP models were able to capture mesoscale weather patterns such as fronts, but not the smaller-scale convective patterns that occur within mesoscale systems. Thus, these models had limited utility in predicting rainfall in the early hours of the forecast because of its dependence on the unrepresented small scales.