© Crown copyright Met Office Andrew Colman presentation to EuroBrisa Workshop July Met Office combined statistical and dynamical forecasts for NE Brazil: method, skill and prediction for MAM 2009.

© Crown copyright Met Office CONTENTS 1.Forecast method and Performance (measured using hindcasts) 2. Forecast for Two Issues arising: 1.1 Data uncertainty 1.2 Effect of changes in S Atlantic SST 4. Future Developments

© Crown copyright Met Office NE Brazil areas Forecasts for all regions are produced using statistical methods and GloSea model output Region 8 is our Standard NE Brazil Region (SNEBR) Statistical forecasts use indices of 2 SST patterns as predictors

© Crown copyright Met Office Sea surface temperature patterns used in empirical regression forecasting method Atlantic SST pattern Pacific El Nino SST pattern

© Crown copyright Met Office Global Seasonal Forecast (GloSea) model Enhanced version of the Hadley Centre climate model (HadCM3) 2.5° x 3.75° x 19Level AGCM (1.25° to 0.3°) x 1.25° x 40Level OGCM Six month real-time 41-member ocean atmosphere global forecast ensemble 5 ocean analyses from perturbed wind stresses Ocean analyses further perturbed with instantaneous SST perturbations Forecasts of temperatures and rainfall produced monthly and published on our website (

© Crown copyright Met Office Prob(abv) 1/d Prob(avg) 1/d Prob(blw) 1/d Principles of linear discriminant calibration (a skillful system example) Historical predictions eg rainfall at nearest grid- point to predicted location mean d d d real-time prediction (e.g. single ensemble member) Predicted values when above-normal category observed Predicted values when near-normal category observed Predicted values when below-normal category observed Corresponding observations

© Crown copyright Met Office Linear discriminant combination/calibration: - probability of category ‘t’ - generalised squared ‘distance’ from hindcast predictor values when ‘t’ is observed (takes account of skill) -vector of predictor values -Eg Nino 3.4 SST index -Dynamical model gridpoint forecast m t -predictor mean prior to each category t S -1 –Covariance matrix of predictor variables

© Crown copyright Met Office 2 Variable Discriminant analysis Variable 1 Variable 2

© Crown copyright Met Office Combined Statistical- Dynamical Forecasts Statistical forecasts are produced using discriminant equations of the following form Probability of rainfall category for gridbox g = f(b x Atlantic SST predictor + c x Pacific SST predictor + constant) Dynamical (GloSea) forecasts are calibrated using discriminant equations of the following form Probability of rainfall category for gridbox g = f(GloSea forecast precipitation for gridbox g + constant) Glosea Hindcasts for produced as part of the DEMETER project ( are used to create the discriminant equations. Combined Statistical/Dynamical forecasts are produced using discriminant equations of the form Probability of rainfall category for gridbox g = f(a x GloSea forecast precipitation for gridbox g + b x Atlantic SST predictor + c x Pacific SST predictor + constant)

© Crown copyright Met Office GloSea model skill maps verified against GPCP

© Crown copyright Met Office GloSea + statistical model skill maps

© Crown copyright Met Office GloSea+statistical model ROC skill maps

© Crown copyright Met Office Plots of forecast v Observed series for SNEBR

© Crown copyright Met Office Real-time forecast performance

© Crown copyright Met Office 2009 Forecast 1.Recent SST patterns 2. EuroSip model output 3.Statistical Forecast for Terciles 4.Combined Stats/Dynamical Forecast for terciles 5.Verification

© Crown copyright Met Office SST anomalies for Nov, Dec 08 and Jan 09

© Crown copyright Met Office Global SST anomaly forecast from GloSea

© Crown copyright Met Office Ensemble mean forecast from GloSea, ECMWF and EuroSIP

© Crown copyright Met Office Statistical forecast probabilities for terciles (with respect to climatology)

© Crown copyright Met Office Multi-variate Statistical/Dynamical forecast probabilities for terciles (with respect to climatology)

© Crown copyright Met Office Verification of 2009 forecast

© Crown copyright Met Office ISSUE 1 DATA UNCERTAINTY Differences between NCEP Gauge data and GPCP blended gauge and satellite data

© Crown copyright Met Office Comparison between GPCP AND NCEP

© Crown copyright Met Office Comparison between GPCP AND NCEP

© Crown copyright Met Office Comparison between GPCP AND NCEP

© Crown copyright Met Office ISSUE 2 CHANGES IN S ATLANTIC SST ANOMALIES Look at incorrect wet forecast for 2004 as example.

© Crown copyright Met Office Observed SST Anomaly For January, February, March and April 2004

© Crown copyright Met Office Glosea forecast SST Anomaly For MAM 2004 from December and February

© Crown copyright Met Office ECMWF forecast SST Anomaly For MAM 2004 from February, January & December

© Crown copyright Met Office Future Developments 1.GloSea4 upgrade 2.KMA/NCEP multi-model system 3.Daily event predictions

© Crown copyright Met Office Current (GloSea3) and next (GloSea4) seasonal forecast models GloSea3 (present model) GloSea4 (as of May 2009) Atmosphere Resolution HadCM3 N48 (~250km),19 lev. HadGAM3 May 09: N96 (~150 km), 60 lev. Oct 09: N144 (~90km), 60 lev. or N216 (~50km), 85 lev. (?) Ocean Resolution HadCM3 1.25ºx1.25º (0.3º tropics) NEMO 1º x1º (0.3º tropics) May(Oct?) º x 0.25º (?) IC PerturbationsClimatological errors in SSTs and wind stress Lagged approach Model Perturbations NonePerturbed physics Stochastic Kinetic Energy Backscatter (sub-grid scale uncertainties)

© Crown copyright Met Office KMA/NCEP: developing Lead Centre for Long-Range Forecast Multi-model Ensembles (LC-LRFMME) collect LRF data from GPCs (Global Producing Centres of Long-range Forecasts), display GPC forecasts and MME forecasts in standard formats research MME techniques and products Lead Centre is active, WMO designation expected March 2009 Dr Won-Tae Yun (KMA), Dr Arun Kumar (NCEP CPC) 9 WMO GPCs designated in 2006

© Crown copyright Met Office WMO GPC ensemble-mean precipitation forecasts for JJA 2008 MelbourneMontrealMoscow SeoulTokyoWashington

© Crown copyright Met Office Performance of 2 examples of rain-days predictions compared with seasonal mean skill (predict number of days with rainfall > R)

© Crown copyright Met Office Performance of 2 examples of rain-days predictions compared with seasonal mean skill

© Crown copyright Met Office Summary The Met Office has been issuing seasonal rainfall forecasts for the NE Brazil wet season (March-May) since 1987 based on an established physical link between rainfall and SST Useful predictions can be made as far ahead as December Problems include uncertainty in rainfall observations and predicting month to month changes in SST in the Gulf of Guinea Introduction of newer higher resolution models will hopefully lead to better skill

© Crown copyright Met Office Questions and answers

© Crown copyright Met Office Discriminant Probabilities Discriminant equations are calculated from historical data like regression equations but the output is probabilities for a set of forecast categories Linear Discriminant Analysis is our principal tool for combining and calibrating forecasts