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

THORPEX A Global Atmospheric Research Programme

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

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 (10) 50 years); 25 members each IRI mode (anomaly based on (10) 50 years); now CPTEC is an IRI member now 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 Parameterization and alternative Cu Parameterization Boundary conditions: Monthly SST: persisted anomaly (observed) or Monthly SST: persisted anomaly (observed) or predicted (Tropical Atlantic (statistical) and Tropical predicted (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 is Sea ice: considered at grid points for which SST is below -2ºC below -2ºC

INMET UFRJ CPTE C USP FURGS SIMEPAR UFSC SMA CIMA 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

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 – BRAMS – 16 proc. PC-Cluster ARPS – Origin 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 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

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

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 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: 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:

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

Data assimilation: the art of “inventing” data… Objective: combine a forecast with observations. First step is to perform a forecast of, e.g., temperature T b (initial guess) and then an observation is combined T o through the optimization problem based on the cost function: The analysis is given by where and the variance of the analysis error is smaller than the forecast and the observation

T= ∑ (T i -B i )/MSE i Where T i is the forecast provided by the i th model B i is the ith model bias MSE i is the i th 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/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) 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!!!!