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Accession Number ADA586506
Title Quantitative Precipitation Nowcasting: A Lagrangian Pixel-Based Approach.
Publication Date 2012
Media Count 63p
Personal Author A. Zahraei J. Vallippa J. J. Gourley K. Hsu S. Sorooshian
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.
Keywords Advection
Lagrangian functions
Mathematical models
Pbn(Pixel-based nowcasting)
Severe rainfall prediction
Short range(Time)
Sqpf(Short-term quantitative precipitation forecasting)
Storm tracking
United states
Weather forecasting

Source Agency Non Paid ADAS
NTIS Subject Category 55C - Meteorological Data Collection, Analysis, & Weather Forecast
72B - Algebra, Analysis, Geometry, & Mathematical Logic
Corporate Author California Univ., Irvine.
Document Type Journal article
Title Note Journal article.
NTIS Issue Number 1405
Contract Number W911NF-11-1-0422

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