Abstract

Short-term high-resolution precipitation forecasting has important implications for navigation, flood forecasting, and other hydrological and meteorological concerns. This article introduces a pixel-based algorithm for Short-term Quantitative Precipitation Forecasting (SQPF) using radar-based rainfall data. The proposed algorithm called Pixel- Based Nowcasting (PBN) tracks severe storms with a hierarchical mesh-tracking algorithm to capture storm advection in space and time at high resolution from radar imagers. The extracted advection field is then extended to nowcast the rainfall field in the next 3hr based on a pixel-based Lagrangian dynamic model. The proposed algorithm is compared with two other nowcasting algorithms (WCN: Watershed-Clustering Nowcasting and PER: PERsistency) for ten thunderstorm events over the conterminous United States. Object-based verification metric and traditional statistics have been used to evaluate the performance of the proposed algorithm. It is shown that the proposed algorithm is superior over comparison algorithms and is effective in tracking and predicting severe storm events for the next few hours.

Highlights

  • Introduction and literature reviewNowcasting is referred as forecasting the future state of the atmosphere within a very short time (e.g., 0 ~ 3 hr) at a given location

  • These approaches are: (1) the application of storm-scale Numerical Weather Prediction (NWP) models which explicitly model the initiation, growth, and dissipation of storms based on the physical modeling of the related atmospheric processes, and (2) “data-driven” extrapolationbased approaches which are storm-tracking and advectionbased techniques, with an attempt to predict the evolution of the observed storms (Li et al, 1995; Golding, 1998; Ganguly and Bras, 2003; Bowler et al, 2004; Wilson et al, 2004; Vila et al, 2008; Liang et al, 2010; Liguori et al, 2012; Sokol and Pesice, 2012; Zahraei et al, 2011a, 2011b)

  • The Pixel-Based Nowcasting (PBN) algorithm forecasts storms associated with intensive rainfall more accurately using a pixel-based storm-tracking process to catch each storm dynamic advection process using radar imagery, and an extrapolation/nowcasting step that provides the dynamic evolution of pixel position and precipitation intensity from the current to the future time steps

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Summary

Introduction

Introduction and literature reviewNowcasting is referred as forecasting the future state of the atmosphere within a very short time (e.g., 0 ~ 3 hr) at a given location. Two primary approaches are used frequently for storm nowcasting depending on the length of prediction and the forecast skill These approaches are: (1) the application of storm-scale Numerical Weather Prediction (NWP) models which explicitly model the initiation, growth, and dissipation of storms based on the physical modeling of the related atmospheric processes, and (2) “data-driven” extrapolationbased approaches which are storm-tracking and advectionbased techniques, with an attempt to predict the evolution of the observed storms (Li et al, 1995; Golding, 1998; Ganguly and Bras, 2003; Bowler et al, 2004; Wilson et al, 2004; Vila et al, 2008; Liang et al, 2010; Liguori et al, 2012; Sokol and Pesice, 2012; Zahraei et al, 2011a, 2011b). For storm-scale prediction, the shorter terms are most likely to be forecasted using extrapolated observations, while the relatively longer-term forecasts (e.g., > 3 hr) will likely need to incorporate more dynamics contained in storm-scale NWP models (Ganguly and Bras, 2003)

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