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THORPEX A Global Atmospheric Research Programme wmot /thorpex

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

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THORPEX A Global Atmospheric Research Programme wmot /thorpex

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  1. 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

  2. THORPEXA Global Atmospheric Research Programmewww.wmo.int/thorpex

  3. 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

  4. SALLJEX Intercomparison Program: 2003 GEF – Evaluation of Numerical Forecasts available in the Plata Basin December 2004

  5. Operational NWP and NCP at CPTEC • Weather Forecasting Operational Suite: (black 2003;red2004) • Global Spectral Model T 215L42 up to 7 days, two times a day NCEP analysis, GPSAS/DAO assimilation (6 hours) • Regional Eta Model (40) - 20kmL38, up to 5 days, two times a day RPSAS/DAO CPTEC regional analysis CPTEC global model BC • Global Ensemble T126L28, up to 15 days, twice a day, 15 members;CPTEC/FSU ensemble principal components scheme

  6. Seasonal Prediction: • Global Spectral Model T062L28 up to 4-6 months, once a month: • 25 members each IRI mode (anomaly based on (10) 50 years); • now CPTEC is an IRI member • running two more sets of seasonal forecasting: • DERF mode • and alternative Cu Parameterization • Boundary conditions: • Monthly SST: persisted anomaly (observed) or • predicted (Tropical Atlantic (statistical) and Tropical • Pacific) • Initial climatological values: soil moisture; • albedo and snow depth; • Sea ice: considered at grid points for which SST is • below -2ºC

  7. Institutições com atividade em modelagem/previsão Meteorológica Hidrológica Investigação/ Operacional Univ. Federal do Rio de Janeiro Universidade de São Paulo Fundação Universidade do Rio Grande do Sul CIMA INMET UFRJ USP CPTEC SIMEPAR Operacional/Pesquisa Centro de Previsão de Tempo e Estudos Climáticos UFSC FURGS CIMA SMA Serviço Nacional INMET - Brasil SMA - Argentina

  8. Instituto Nacional de Meteorologia – INMET – Brasil Modelo Meteorológico Sistema de Assimilação de dados Divulgação

  9. 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

  10. 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

  11. 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

  12. 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

  13. 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

  14. Universidade Federal de Santa Catarina – Florianópolis/SC – Brasil http://www.eps.ufsc.br/servico/meteoro.htm • 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

  15. 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/

  16. Centro de Investigaciones del Mar y la Atmósfera - CIMA Buenos Aires - Argentina • 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.

  17. University of Maryland – Dr Hugo Berbery - ETA model 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 72 hr forecasts - - Initial and boundary conditions: AVN; NCEP Reanalyses - Further online information and forecasts:http://www.atmos.umd.edu/~berbery/etasam

  18. 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/

  19. Instituition Main Character Model Domain Forecast time Resolution km Frequency Initial/ Bound Cond. Data Assim. INMET National Service DWD regional S. America 72hr 25 00 and 12 DWD No CPTEC Oper/research Global/CPTEC global 15 days 100 00 and 12 NCEP GPSAS Yes CPTEC Oper/research ETA/CPTEC S. America 7 days 40 00 and 12 CPTEC/GLOBAL RPSAS No Yes UFRJ Semi-op/ research MM5 SE S. Bra America 60 hr 30,10 00 and 12 AVN/NCEP No USP Semi-op research BRAMS Central/SE S. America 72hr 20,4 00 and 12 CPTEC AVN/NCEP Surface only SIMEPAR Operational/ research BRAMS ARPS SE/SBra N. Arg. 60hr 64,16 00 and 12 CPTEC AVN/NCEP Surface UFSC Irregular op. research ARPS SE/SBra N. Arg 60hr 36,12,4 00 and 12 AVN/NCEP No (possible) FURGS Semi-op research BRAMS S/Bral/ N.Arg 60hr 64,16.4 00 and 12 AVN/NCEP No CIMA Semi-op Research LAHM S.S.America 72hr 65 00 and 12 AVN No UMD Semi-op Research ETA Most of S. America 72hr 80 to 22 00 and 12 AVN No

  20. 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 …

  21. Brazilian Marine Services • NCEP • To be included: ECMWF, JMA, BMRC, UKMO • Project financed by FINEP/Brazil (BRAMSNET).

  22. How can we combine several forecasts in an optimal way??? • Simple solution based on concepts of data assimilation

  23. Optimal Forecast 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

  24. 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

  25. Multi model Ensemble Homepage at the MASTER Laboratory/University of São Paulo 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/CPTEC CATT-BRAMS_20km_g3/CPTEC ETA_40km/CPTEC (init. CPTEC global) ETA_20km/CPTEC (init. CPTEC global) ETA_40km/CPTEC (regional assimilation cycle) ETA-80km_Workstation Univ. of Maryland ETA_17km_SE_Workstation CATO/LNCC ETA_10km_LNRJ_Workstation CATO/LNCC MM5_30km_g1/LPM-Fed.Univ.Rio de Janeiro MM5_10km_g2/LPM-Fed.Univ. Rio de Janeiro HRM_30km_DWD regional model at Brazilian Hydrographic Center MRF/NCEP-global AVN/NCEP-global CPTEC_T126-global Mean CPTEC ensemble_T126/CPTEC Mean NCEP Ensemble PSTAT (Optimal combination of all forecasts)

  26. 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!!!!

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