Elsevier

Atmospheric Research

Volume 118, 15 November 2012, Pages 418-434
Atmospheric Research

Quantitative Precipitation Nowcasting: A Lagrangian Pixel-Based Approach

https://doi.org/10.1016/j.atmosres.2012.07.001Get rights and content

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 3 hr 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

► We describe Pixel-Based Nowcasting (PBN) algorithm. ► The algorithm has hydrological and meteorological applications. ► It has shown promising performance in severe storms forecasting.

Section snippets

Introduction and literature review

Nowcasting is referred as forecasting the future state of the atmosphere within a very short time (e.g., 0 ~ 3 hr) at a given location. For such short forecast lead times, an effective estimation and extrapolation of existing storms from the current observations (radar and satellite images) is critical (Golding, 1998).

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

Pixel-Based Nowcasting (PBN) algorithm

Thunderstorms usually have relatively small-scale high-rainfall cores that should be predicted accurately. Regardless of their sizes and relatively short lifetimes, the advection-based nowcasting algorithm should enhance the prediction of the storms’ future positions. Therefore, the 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 then an

Data and Case Studies

The next step involves the application and testing (verification) of the proposed PBN algorithm presented above. For this purpose, radar observations are used. Radar images have been used frequently in detecting severe storms. For this study, the Q2 radar-based quantitative precipitation estimation data set with 0.01o spatial and 2.5-5-min temporal resolution over the entire conterminous U.S. (CONUS) produced by the NOAA-NSSL is used (Vasiloff et al., 2007). This study focuses on the

Verification and Results

The proposed PBN approach is compared with two nowcasting algorithms that have been presented in the literature (Montanari et al., 2006). Both of these algorithms are based on Eq. (1) and are described below.

Summary and Conclusions

In this manuscript, we introduce a new nowcasting algorithm named Pixel-Based Nowcasting (PBN) to improve the predictability of severe thunderstorms. The proposed PBN algorithm is particularly suitable for very short-duration forecasts useful for hydrological modeling applications, such as flash-flood forecasting. In testing the PBN prediction capabilities, ten severe storms were selected for their features, including relatively short lifetime, smaller-scale, damaging winds, and rainfall. The

Acknowledgments

This research was supported by the Center for Hydrometeorology and Remote Sensing (CHRS) at the University of California, Irvine. Partial financial support was provided by NOAA/NESDIS/NCDC (prime award NA09NES4400006, NCSU CICS subaward 2009-1380-01), ARO (grant W911NF-11-1-0422) and NASA NEWS (grant NNX06AF93G). The graduate fellowship support provided by the Hydrologic Research Lab of the U.S. National Weather Service (HRL-NWS) is also greatly appreciated. Part of the research was carried out

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