Accurate short-range rainfall forecasts, known as nowcasts, are crucial for providing early warningsof extreme precipitation and flooding, especially in urban areas with high population density.Traditional nowcasting techniques, such as extrapolating radar echoes from constant altitudeplan position indicator (CAPPI) or lowest-angle plan position indicator (PPI), often struggleto predict the development and dissipation of convective storms effectively. To address theselimitations, methods like RadVIL, which uses mass balance equations of Vertically IntegratedLiquid (VIL), and Spectral Prognosis (SPROG), which employs an autoregressive (AR) model,have been developed. Recent advancements include Autoregressive Nowcasting using VIL(ANVIL), which models the growth and decay of VIL using an autoregressive integrated (ARI)process, decomposing VIL into multiple spatial scales and applying a separate ARI model toeach scale. Another advancement called SPROG-Localized (SPROG-LOC) extends the SPROGapproach, which is the deterministic version of Short-Term Ensemble Prediction System (STEPS),by estimating spatially localized parameters of the AR process, improving the accuracy of rainfallforecasts. Building on these recent methods, we propose a novel approach that combines ANVILand SPROG-LOC, termed SLANVIL (SPROG-Localized Autoregressive Nowcasting using VIL).This integrated method leverages the strengths of both techniques, aiming to improve nowcastingperformance, particularly in scenarios with large, non-uniformly distributed precipitation areasand isolated convective features. While we are currently in the process of obtaining preliminaryresults and its probabilistic extension, we will present a first validation of our method for leadtimes up to 2 hours, comparing its forecast skill at various precipitation thresholds and spatialscales to established and operational nowcasting methods, such as pySTEPS-BE.
Villarreal-Jaime, DE, Willems, P, De Cruz, L & Reinoso-Rondinel, R 2025, 'Localized Radar-Based Nowcasting of Convective Rainfall', Precipitation Processes - Estimation and Prediction, Bonn, Germany, 16/03/25 - 21/03/25 pp. 93. https://doi.org/10.5281/zenodo.15735778
Villarreal-Jaime, D. E., Willems, P., De Cruz, L., & Reinoso-Rondinel, R. (2025). Localized Radar-Based Nowcasting of Convective Rainfall. 93. Abstract from Precipitation Processes - Estimation and Prediction, Bonn, Germany. https://doi.org/10.5281/zenodo.15735778
@conference{c1808a56ee204ea8997ce62890e20f5a,
title = "Localized Radar-Based Nowcasting of Convective Rainfall",
abstract = "Accurate short-range rainfall forecasts, known as nowcasts, are crucial for providing early warningsof extreme precipitation and flooding, especially in urban areas with high population density.Traditional nowcasting techniques, such as extrapolating radar echoes from constant altitudeplan position indicator (CAPPI) or lowest-angle plan position indicator (PPI), often struggleto predict the development and dissipation of convective storms effectively. To address theselimitations, methods like RadVIL, which uses mass balance equations of Vertically IntegratedLiquid (VIL), and Spectral Prognosis (SPROG), which employs an autoregressive (AR) model,have been developed. Recent advancements include Autoregressive Nowcasting using VIL(ANVIL), which models the growth and decay of VIL using an autoregressive integrated (ARI)process, decomposing VIL into multiple spatial scales and applying a separate ARI model toeach scale. Another advancement called SPROG-Localized (SPROG-LOC) extends the SPROGapproach, which is the deterministic version of Short-Term Ensemble Prediction System (STEPS),by estimating spatially localized parameters of the AR process, improving the accuracy of rainfallforecasts. Building on these recent methods, we propose a novel approach that combines ANVILand SPROG-LOC, termed SLANVIL (SPROG-Localized Autoregressive Nowcasting using VIL).This integrated method leverages the strengths of both techniques, aiming to improve nowcastingperformance, particularly in scenarios with large, non-uniformly distributed precipitation areasand isolated convective features. While we are currently in the process of obtaining preliminaryresults and its probabilistic extension, we will present a first validation of our method for leadtimes up to 2 hours, comparing its forecast skill at various precipitation thresholds and spatialscales to established and operational nowcasting methods, such as pySTEPS-BE.",
author = "Villarreal-Jaime, {Daniel Eduardo} and Patrick Willems and {De Cruz}, Lesley and Ricardo Reinoso-Rondinel",
year = "2025",
month = jun,
day = "25",
doi = "10.5281/zenodo.15735778",
language = "English",
pages = "93",
note = "Precipitation Processes - Estimation and Prediction, PrePEP ; Conference date: 16-03-2025 Through 21-03-2025",
url = "https://indico.kit.edu/event/4015/page/1131-schedule",
}