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Improvement in Model Predictability in the Monsoon Area of S. America: Impact of a Simple Super-Model Ensemble

This research study focuses on improving the predictability of monsoon weather patterns in South America through the use of a simple super-model ensemble. The study examines different sub-programs, such as predictability and dynamical processes, observing systems, data assimilation and observing strategies, and societal and economic applications. The goal is to enhance ensemble prediction and interactive forecast systems for end-users.

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Improvement in Model Predictability in the Monsoon Area of S. America: Impact of a Simple Super-Model Ensemble

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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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