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Predictability of short-range forecasting: a multimodel approach Cover

Predictability of short-range forecasting: a multimodel approach

Open Access
|Jan 2011

Abstract

Numerical weather prediction (NWP) models (including mesoscale) have limitations when it comes to dealing with severe weather events because extreme weather is highly unpredictable, even in the short range. A probabilistic forecast based on an ensemble of slightly differentmodel runs may help to address this issue. Among other ensemble techniques, Multimodel ensemble prediction systems (EPSs) are proving to be useful for adding probabilistic value to mesoscale deterministic models. A Multimodel Short Range Ensemble Prediction System (SREPS) focused on forecasting the weather up to 72 h has been developed at the SpanishMeteorological Service (AEMET). The system uses five different limited area models (LAMs), namely HIRLAM (HIRLAM Consortium), HRM (DWD), the UM (UKMO), MM5 (PSU/NCAR) and COSMO (COSMO Consortium). These models run with initial and boundary conditions provided by five different global deterministic models, namely IFS (ECMWF), UM (UKMO), GME (DWD), GFS (NCEP) and CMC (MSC). AEMET-SREPS (AE) validation on the large-scale flow, using ECMWF analysis, shows a consistent and slightly underdispersive system. For surface parameters, the system shows high skill forecasting binary events. 24-h precipitation probabilistic forecasts are verified using an up-scaling grid of observations from European high-resolution precipitation networks, and compared with ECMWF-EPS (EC).

Language: English
Page range: 550 - 563
Submitted on: Apr 11, 2010
Accepted on: Nov 19, 2010
Published on: Jan 1, 2011
Published by: Stockholm University Press
In partnership with: Paradigm Publishing Services

© 2011 Jose-Antonio García-Moya, Alfons Callado, Pau Escribá, Carlos Santos, Daniel Santos-Muñoz, Juan Simarro, published by Stockholm University Press
This work is licensed under the Creative Commons Attribution 4.0 License.