Nowcasting of Convective Rainfall Using Volumetric Radar Observations

Abstract

Short-range forecasts (nowcasts) of rainfall facilitate providing early warning of severe rainfall and flooding, which is particularly important in densely populated urban areas. Nowcasts are conventionally obtained by the extrapolation of radar echoes from a constant altitude plan position indicator (CAPPI) or lowest-angle plan position indicator (PPI). Lacking a model for growth or decay, this approach has a limited ability to forecast the summertime convective storms. In a previous attempt to address this shortcoming, called RadVil, the predicted surface rain rate is obtained from mass balance equations of vertically integrated liquid (VIL) retrieved from volumetric reflectivity measurements. Predicting the growth and decay has also been attempted by using an autoregressive (AR) model, which led to the development of spectral prognosis (S-PROG). A novel combination of these two methodologies, called ANVIL, is proposed. In this approach, the growth and decay of VIL are modeled by an autoregressive integrated (ARI) process. It is shown that the predictability of growth and decay is scale-dependent. Thus, the key idea of ANVIL is to decompose the VIL into multiple spatial scales and apply a separate ARI model to each scale. The operational feasibility of ANVIL is evaluated using the Next-Generation Radar (NEXRAD)/WSR-88D radar that covers the Dallas–Fort Worth metropolitan area. The evaluation is done using ten convective events in 2018 and 2019. Using several verification metrics, it is shown that ANVIL has up to 25% improved skill compared to conventional nowcasting techniques. The improvement is consistent for a wide range of spatial scales (1–11 km) and rain rate thresholds (5–20 mmh−1).