© Crown copyright Met Office Andrew Colman presentation to EuroBrisa Workshop July 21-23 2009 Met...

© Crown copyright Met Office

Andrew Colman

presentation to EuroBrisa Workshop July 21-23 2009

Met Office combined statistical and dynamical forecasts for NE Brazil:method, skill and prediction for MAM 2009.


CONTENTS

1. Forecast method and Performance (measured using hindcasts)

2. Forecast for 2009

3. Two Issues arising:

1.1 Data uncertainty

1.2 Effect of changes in S Atlantic SST

4. Future Developments


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


Sea surface temperature patterns used in empirical regression forecasting method

Atlantic SST

pattern

Pacific El Nino SST pattern


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 (www.metoffice.gov.uk)


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

mean

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


Linear discriminant combination/calibration:

2

2

1,3

exp[ 0.5 ( )]( )

exp[ 0.5 ( )]t

tu

u

D xP x

D x

( )tP x

2 ( )tD x

- probability of category ‘t’

- generalised squared ‘distance’ from hindcast predictor values when ‘t’ is observed (takes account of skill)

x -vector of predictor values

-Eg Nino 3.4 SST index

-Dynamical model gridpoint forecast

2 1( ) ( ) ( )t ttD x x m S x m mt -predictor mean prior to each category t

S-1 –Covariance matrix of predictor variables


2 Variable Discriminant analysis

Variable 1

Variable 2


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 1959-2001 produced as part of the DEMETER project ( www.ecmwf.int/demeter) 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)

http://www.ecmwf.int/demeter


GloSea model skill maps verified against GPCP


GloSea + statistical model skill maps 1959-2001


GloSea+statistical model ROC skill maps 1959-2001


Plots of forecast v Observed series for SNEBR


Real-time forecast performance


2009 Forecast

1. Recent SST patterns

2. EuroSip model output

3. Statistical Forecast for Terciles

4. Combined Stats/Dynamical Forecast for terciles

5. Verification


SST anomalies for Nov, Dec 08 and Jan 09


Global SST anomaly forecast from GloSea


Ensemble mean forecast from GloSea, ECMWF and EuroSIP


Statistical forecast probabilities for terciles (with respect to 1961-1990 climatology)


Multi-variate Statistical/Dynamical forecast probabilities for terciles (with respect to 1961-1990 climatology)


Verification of 2009 forecast


ISSUE 1

DATA UNCERTAINTY

Differences between NCEP Gauge data and GPCP blended gauge and satellite data


Comparison between GPCP AND NCEP


ISSUE 2

CHANGES IN S ATLANTIC SST

ANOMALIES

Look at incorrect wet forecast for 2004 as example.


Observed SST Anomaly For January,

February, March and April 2004


Glosea forecast SST Anomaly For MAM 2004 from December and February


ECMWF forecast SST Anomaly For MAM 2004 from February, January & December


Future Developments

1. GloSea4 upgrade

2. KMA/NCEP multi-model system

3. Daily event predictions


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?) 09 0.25º x 0.25º (?)

IC Perturbations Climatological errors in SSTs and wind stress

Lagged approach

Model Perturbations

None Perturbed physics

Stochastic Kinetic Energy Backscatter (sub-grid scale uncertainties)


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)

http://www.wmolc.org/

9 WMO GPCs designated in 2006





WMO GPC ensemble-mean precipitation forecasts for JJA 2008

Melbourne Montreal Moscow

Seoul Tokyo Washington


Performance of 2 examples of rain-days predictions 1987-2004 compared with seasonal mean skill (predict number of days with rainfall > R)


Performance of 2 examples of rain-days predictions 1987-2004 compared with seasonal mean skill


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


Questions and answers


Discriminant Probabilities

Discriminant equations are calculated from historicaldata 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

© Crown copyright Met Office Andrew Colman presentation to EuroBrisa Workshop July 21-23 2009 Met...

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