Abstract ID: 134
An inter-comparison performance assessment of a Brazilian global sub-seasonal prediction model against four Sub-seasonal to Seasonal (S2S) prediction project models
Lead Author: Bruno dos Santos Guimarães
Center for Weather Forecast and Climate Studies, National Institute for Space Research, Brazil
Keywords: Sub-seasonal prediction, Forecast verification, Intraseasonal variability, Madden-Julian oscillation, Sub-seasonal to seasonal prediction project
Abstract: This work presents an inter-comparison performance assessment of the Centre for Weather Forecast and Climate Studies (CPTEC) model (the Brazilian Atmospheric Model version 1.2, BAM-1.2) against four Sub-seasonal to Seasonal (S2S) prediction project models from: Japan Meteorological Agency (JMA), Environmental and Climate Change Canada (ECCC), European Centre for Medium-range Weather Forecasts (ECMWF) and Australian Bureau of Meteorology (BoM). The inter-comparison was realized for weekly precipitation anomalies and the daily evolution of Madden-Julian Oscillation (MJO) during 12 extended austral summers (November–March, 1999/2000– 2010/2011), leading to a verification sample of 120 hindcasts for all models. The deterministic assessment of the prediction of precipitation anomalies revealed that CPTEC model presents similar performance to BoM, JMA and ECCC models, while ECMWF shows the highest (smallest) correlation (root mean squared error, RMSE) values among all examined models. The probabilistic assessment for the event “positive precipitation anomaly” revealed that ECMWF presents better discrimination, reliability and resolution when compared to CPTEC and BoM. For MJO predictions, CPTEC crosses the 0.5 bivariate correlation threshold at around 19 (20) days when using the mean of 4 (11) ensemble members, which is similar performance to BoM, JMA and ECCC. However, ECMWF crosses the 0.5 bivariate correlation at 30 days. Overall, CPTEC proved to be competitive compared to the S2S models investigated, but with respect to ECMWF there is scope to improve the prediction system, likely by a combination of including coupling to an interactive ocean, improving resolution and model parameterization schemes, and better methods for ensemble generation.
Co-authors:
Caio Coelho (Center for Weather Forecast and Climate Studies, National Institute for Space Research)
Steve Woolnough (National Centre for Atmospheric Science, Department of Meteorology, University of Reading, Reading)
Paulo Kubota (Center for Weather Forecast and Climate Studies, National Institute for Space Research)
Carlos Bastarz (Center for Weather Forecast and Climate Studies, National Institute for Space Research)
Silvio Figueroa (Center for Weather Forecast and Climate Studies, National Institute for Space Research)
José Bonatti (Center for Weather Forecast and Climate Studies, National Institute for Space Research)
Dayana Souza (Center for Weather Forecast and Climate Studies, National Institute for Space Research)
