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Blending a probabilistic nowcasting method with a high‐resolution numerical weather prediction ensemble for convective precipitation forecasts
Author(s) -
Kober K.,
Craig G. C.,
Keil C.,
Dörnbrack A.
Publication year - 2011
Publication title -
quarterly journal of the royal meteorological society
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 1.744
H-Index - 143
eISSN - 1477-870X
pISSN - 0035-9009
DOI - 10.1002/qj.939
Subject(s) - nowcasting , probabilistic logic , quantitative precipitation forecast , numerical weather prediction , meteorology , extrapolation , precipitation , probabilistic forecasting , radar , weighting , forecast skill , computer science , ensemble forecasting , environmental science , consensus forecast , mathematics , statistics , artificial intelligence , geography , medicine , telecommunications , radiology
A seamless prediction of convective precipitation for a continuous range of lead times from 0–8 h requires the application of different approaches. Here, a nowcasting method and a high‐resolution numerical weather prediction ensemble are combined to provide probabilistic precipitation forecasts. For the nowcast, an existing deterministic extrapolation technique was modified by the local Lagrangian method to calculate the probability of exceeding a threshold value in radar reflectivity. Numerical forecasts were obtained from an experimental high‐resolution ensemble that provides 20 different deterministic forecasts of synthetic radar reflectivity. Probabilistic information was calculated by different approaches from the ensemble output. The probabilistic forecasts based on the ensemble were calibrated with the reliability diagram statistics method. The skill of the probabilistic nowcasts and forecasts was evaluated using three quality measures. Finally, a seamless probabilistic forecast was generated as an additive combination of nowcast and forecast, using a weighting function based on their relative skills. The skill of the seamless forecast was greater than or equal to that of the nowcast or ensemble forecast in all quality measures and at all lead times. Copyright © 2011 Royal Meteorological Society