Improvement in model predictability in the monsoon area of S. America: impact

of a simple super-model ensemble

Pedro L. Silva Dias Demerval S. Moreira.

Institute of Astronomy, Geophysics and Atmospheric Sciences University of São Paulo

VAMOS VPM8 Modeling Workshop – Mexico City, 09 to 11 March 2005

THORPEXA Global Atmospheric Research Programme

www.wmo.int/thorpex

Resumé of Science Plan• Research on weather forecasts from 1 to 14

days lead time• Four research Sub-programmes

– Predictability and dynamical processes– Observing systems– Data assimilation and observing strategies– Societal and economic applications

• Emphasis on ensemble prediction• Interactive forecast systems “tuned” for end

users – e.g. targeted observations and DA

• THORPEX Interactive Grand Global Ensemble • Emphasis on global-to-regional influences on

weather forecast skill

SALLJEX Intercomparison Program: 2003

GEF – Evaluation of Numerical Forecasts available in the Plata Basin

December 2004

Operational NWP and NCP at CPTEC Operational NWP and NCP at CPTEC

Weather Forecasting Operational Suite: (Weather Forecasting Operational Suite: (black 2003black 2003;;red2004red2004))

• Global Spectral Model T 215L42 up to 7 days, two times a Global Spectral Model T 215L42 up to 7 days, two times a day day NCEP analysis, GPSAS/DAO assimilation (6 hours)NCEP analysis, GPSAS/DAO assimilation (6 hours)

• Regional Eta Model (Regional Eta Model (4040) - 20kmL38, up to 5 days, two times ) - 20kmL38, up to 5 days, two times a day a day RPSAS/DAO CPTEC regional analysis CPTEC global RPSAS/DAO CPTEC regional analysis CPTEC global model BCmodel BC

• Global Ensemble T126L28, up to 15 days, twice a day, 15 Global Ensemble T126L28, up to 15 days, twice a day, 15 members;members;CPTEC/FSU ensemble principal components CPTEC/FSU ensemble principal components schemescheme  

Seasonal Prediction:Seasonal Prediction:

• Global Spectral Model T062L28 up to 4-6 months, once a month:Global Spectral Model T062L28 up to 4-6 months, once a month:

• 25 members each IRI mode (anomaly based on 25 members each IRI mode (anomaly based on (10)(10) 50 years); 50 years);

• now CPTEC is an IRI membernow CPTEC is an IRI member

• running two more sets of seasonal forecasting: running two more sets of seasonal forecasting: • DERF mode DERF mode • and alternative Cu Parameterizationand alternative Cu Parameterization

Boundary conditions: Boundary conditions: Monthly SST: persisted anomaly (observed) or Monthly SST: persisted anomaly (observed) or predicted (Tropical Atlantic (statistical) and Tropicalpredicted (Tropical Atlantic (statistical) and Tropical Pacific)Pacific) Initial climatological values: soil moisture;Initial climatological values: soil moisture; albedo and snow depth; albedo and snow depth; Sea ice: considered at grid points for which SST isSea ice: considered at grid points for which SST is below -2ºCbelow -2ºC

INMET

UFRJ

CPTEC

USP

FURGS

SIMEPAR

UFSC

SMACIMA

Investigação/ Operacional

Univ. Federal do Rio de Janeiro

Universidade de São Paulo

Fundação Universidade do Rio Grande do Sul

CIMA

Operacional/Pesquisa

Centro de Previsão de Tempo e Estudos Climáticos

Serviço Nacional

INMET - Brasil

SMA - Argentina

Institutições com atividade em modelagem/previsão Meteorológica Hidrológica

Instituto Nacional de Meteorologia – INMET – Brasil

Modelo Meteorológico

Sistema de Assimilação de dados

Divulgação

http://www.inmet.gov.br/•MBAR – Installed by the German weather service (DWD) through WMO agreement in 1999 (*)

•25km resolution, hydrostatic , 310 by 310 points

•Run twice a day 00 and 12 GMT

•Uses boundary conditions from DWD global model (internet)

•FORTRAN90 modular

•SGI cluster – limited parallelization (12 processors)

•INMET has 80 processors

•Data assimilation limited to conventional data update of DWD analysis •Large number of products available in

real time

•(*) Also runs at the Directorate for Hydrography and Navigation (DHN) - Brazil

Servicio Meteorologico Argentino SMN– Buenos Aires - Argentina

•ETA SMN, fue obtenida en el International Center for Theorietical Physics, Trieste, Italia y adaptada para el extremo sur de Sudamérica por el Grupo de Modelado Numérico del Departamento de Procesos Automatizados del Servicio Meteorológico Nacional.

Abarca el área definida entre 14 y 65º latitud Sur y 30 y 91º longitud Oeste, y utiliza como campo inicial y de borde los análisis y pronósticos cada 12 horas producidos por el modelo global GFS (NCEP).

•ETA SMN pronostica a 120 horas a intervalos de 3 horas para 38 niveles de presión en la vertical con una resolución horizontal de 0.25º.

•El modelo corre en una Origin 2000 (sgi) con 7 procesadores R10000 en paralelo. Las salidas están disponibles dos veces al día y corresponden a las corridas de 00Z y 12Z

Laboratório MASTER - Universidade de São Paulo – São Paulo SP Brasil•BRAMS - Brazilian Regional Atmospheric Modelling System (RAMS) - version of RAMS (CSU/ATMET) – partnership since 1989 with FINEP/FAPESP support.

•Air pollution module (urban and biomass burning)/ photochemistry of ozone, convective parameterization and transport,surface processes, dynamical vegetation – validation studies with field experiments.

•Weather forecasting up to 3 days, 20km resolution, 2X/day; BC from CPTEC or NCEP

•Surface data assimilation cycle

•PC Cluster 18 processadores PC (aprox. 2 h)

•Downscaling of the CPTEC seasonal prediction – 3 mo (2-3 members/month)

•Operational System implemented at other institutions (FURGS and SIMEPAR)

•Validation against surface metrics

SIMEPAR – Sistema Meteorológico do Paraná – Curitiba/PR – Brasil – www.simepar.br

•BRAMS – 16 proc. PC-Cluster

•ARPS – Origin 2000 16 processors

•Surface data assimilation cycle

•Nesting op. system: 64 km and 16 km resolution

•Products not available in public homepage

LPM - Universidade Federal do Rio de Janeiro –Rio de Janeiro RJ

http://www.lpm.meteoro.ufrj.br/ - SIMERJ (Meteorological System of the State of Rio de Janeiro)

•Model: MM5 and BRAMS; 2 grades; configuração de 30km e 10 km; 2 X/dia 00 e 12 GMT;

•BC and IC from AVN/NCEP

•Data assimilation not in operational work but experimenting with MM5 system.

•Products for the Civil Defense and available in open homepage

•Model: ARPS.

•FORTRAN-90.

ARPS configured with 3 nested grids based on AVN IC and BC (NCEP)

•60 hour forecast at 40 and 12 km e up to 36 hr with 4 km, 2X day.

•PC Cluster PC 14 processors

Universidade Federal de Santa Catarina – Florianópolis/SC – Brasil

http://www.eps.ufsc.br/servico/meteoro.htm

Fundação Universidade Federal de Rio Grande - Rio Grande/RS

•BRAMS – 64 km, 16 km e pequena grade de 4 km sobre Porto Alegre

•60 horas 2X/dia

•Condição inicial e de fronteira do CPTEC

•Não assimila dados de superfície ou altitude

•Cluster de 32 processadores PC

http://www.gepra.furg.br/

•Versión adaptada en el CIMA del Limited Area Hibu Model, con los paquetes físicos del Geophysical Fluid Dynamics Laboratory -Orlanski y Katzfey, 1987)

•La resolución horizontal es de 65 km. en cada dirección) y la vertical es de 18 niveles up to 10mb.

•2 veces/dia 00 y 12 GMT from NCEP analysis

• Este diseño requiere aproximadamente de 4 horas en una SGI-Indigo 2 para completar un pronóstico a 72 horas. Este sistema de pronóstico se encuentra funcionando en forma experimental desde Agosto de 1998.

•Malla E de Arakawa (1972) horiz. Y coordenada sigma vert.

Centro de Investigaciones del Mar y la Atmósfera - CIMA

Buenos Aires - Argentina

The Eta model

Settings:

Large domain for seasonal simulations

Intermediate domain for routine daily runs

Higher resolution (22 km) domain for studies of hydrologic impacts

- Initial and boundary conditions: AVN; NCEP Reanalyses- Further online information and forecasts: http://www.atmos.umd.edu/~berbery/etasam

University of Maryland – Dr Hugo Berbery - ETA model

72 hr forecasts -

Other models:

•FURNAS – Belo Horizonte MG – Brasil – MM5 15 km (CI e CF do AVN); operational for internal purposes (partnership with UFRJ).

•Serviço Meteorológico de Paraguay – WRF installed by a private consultant (off the shelve)- (– operational problems – not yet fully operational);

•National Laboratory of Scientific Computation– Petrópolis RJ. Model : ETA-Workstation – 10km – research and operation for local civil defense.

•Universidade do Chile – Santiago: Modelo MM5 (CI e CF do AVN); http://www.dgf.uchile.cl/~rgarreau/MM5/

NoAVN00 and 1280 to 2272hrMost of S. America

ETASemi-opResearch

UMD

NoAVN00 and 126572hrS.S.AmericaLAHMSemi-opResearch

CIMA

NoAVN/NCEP00 and 1264,16.460hrS/Bral/N.Arg

BRAMSSemi-opresearch

FURGS

No(possible)

AVN/NCEP00 and 1236,12,460hrSE/SBraN. Arg

ARPSIrregular op.research

UFSC

Surface CPTECAVN/NCEP

00 and 1264,1660hrSE/SBraN. Arg.

BRAMSARPS

Operational/research

SIMEPAR

Surface only

CPTECAVN/NCEP

00 and 1220,472hrCentral/SES. America

BRAMSSemi-opresearch

USP

NoAVN/NCEP00 and 1230,1060 hrSE S. Bra America

MM5Semi-op/ researchUFRJ

NoYes

CPTEC/GLOBALRPSAS

00 and 12407 daysS. AmericaETA/CPTECOper/researchCPTEC

YesNCEPGPSAS

00 and 1210015 daysglobalGlobal/CPTECOper/researchCPTEC

NoDWD00 and 122572hr S. AmericaDWD regionalNational ServiceINMET

Data Assim.

Initial/ BoundCond.

FrequencyResolutionkm

Forecast time

DomainModelMain CharacterInstituition

Integration of models:

Concept of Super Model Ensemble

•Several models are available:

• global, (CPTEC, NCEP, JMA, ECMWF, UKMO, CMS etc…) ;

•Regional models in S. America: CPTEC(ETA), INMET (DWD), MASTER (BRAMS), SIMEPAR (ARPS, BRAMS), UFRJ (MM5, RAMS), UFSC(ARPS), FURGS (BRAMS), CEMIG (MM5), LNCC (ETA), UBA (ETA, LMD, RAMS), Univ. Chile (MM5), aprox. 14 models !…

•Differences in physical processes parameterization, data assimilation, data source …

•Brazilian Marine Services

•NCEP

•To be included: ECMWF, JMA, BMRC, UKMO

•Project financed by FINEP/Brazil (BRAMSNET).

How can we combine several forecasts in an optimal way???

•Simple solution based on concepts of data assimilation

T= ∑ (Ti-Bi)/MSEi

Where Ti is the forecast provided by the ith model

Bi is the ith model bias

MSEi is the ith model mean square error

Optimal Forecast

Problem:

•Bias and MSE need an averaging period

•How long?

•2 years??? – typical sample for MOS

•Practical choice: 10, 15, 20, 30 … days?

•Intraseasonal signal in model bias suggests shorter period

Choose the model:

RAMSC_25km_/MASTER-Univ.Sao Paulo (init. CPTEC )RAMSA_25km_/MASTER-Univ.São Paulo (init. AVN)RAMSP_25km_/MASTER-Univ.São Paulo(init. with assimilation cycle)CATT-BRAMS_40km_g2/CPTECCATT-BRAMS_20km_g3/CPTECETA_40km/CPTEC (init. CPTEC global)ETA_20km/CPTEC (init. CPTEC global)ETA_40km/CPTEC (regional assimilation cycle)ETA-80km_Workstation Univ. of MarylandETA_17km_SE_Workstation CATO/LNCCETA_10km_LNRJ_Workstation CATO/LNCCMM5_30km_g1/LPM-Fed.Univ.Rio de JaneiroMM5_10km_g2/LPM-Fed.Univ. Rio de JaneiroHRM_30km_DWD regional model at Brazilian Hydrographic CenterMRF/NCEP-globalAVN/NCEP-globalCPTEC_T126-globalMean CPTEC ensemble_T126/CPTEC Mean NCEP Ensemble

PSTAT (Optimal combination of all forecasts)

Multi model Ensemble Homepage at the MASTER Laboratory/University of São Paulo

Conclusions

•Simple procedure based on data assimilation principles: quite successful;

•Future: optimal choice of the averaging period for computing bias and MSE;

•Include longer time scales impact on model error (e.g., interannual);

•Probably 70% of the potential result need to improve 30%: work done so far is 3% of the immediate target….

•Collaborative work!!! Quite a progress!!!!