Multi-model operational seasonal forecasts for SADC

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Multi-model operational seasonal forecasts for SADC. Willem A. Landman Asmerom Beraki Cobus Olivier Francois Engelbrecht. Conformal-Cubic Atmospheric Model (CCAM). Runs performed on a computer cluster at the University of Pretoria Climatological ensemble runs - 12hr LAF (5 members) - PowerPoint PPT Presentation

Text of Multi-model operational seasonal forecasts for SADC

  • Multi-model operational seasonal forecasts for SADCWillem A. LandmanAsmerom BerakiCobus OlivierFrancois Engelbrecht

  • Conformal-Cubic Atmospheric Model (CCAM)Runs performed on a computer cluster at the University of Pretoria

    Climatological ensemble runs - 12hr LAF (5 members)

    Atmospheric initial conditions for climatological runs obtained from NCEP reanalysis data

    Climatological simulations performed for the period: 1979-2003. Lower boundary forcing from AMIP SST and sea-ice

  • ECHAM4.5 at the SAWSAll runs performed on NEC SX-8Climatological (6 members) and operational ensemble runs - 24hr LAFAtmospheric initial conditions from ECMWF (1979 to 1996) analysisClimatological dataset (1979-2003) constructed using AMIP physics; model constrained by lower boundary conditions generated from a high resolution AMIP2 dataset for SST and sea-iceOperational set-up: persisted and forecast SSTs obtained from a high resolution observed SST (optimum interpolation v-2) and IRI (mean) respectively (6 members each)12-member ensemble operational runs on 18th of each month for 6 consecutive months (i.e., 0-5 months lead-time)

  • First objective multi-model forecastOld subjective consensus forecast

  • Combining algorithm:1. CPT downscaling2. Equal weightsMulti-model ensemble Ensemble 1 (ECHAM4.5 at SAWS)12 membersEnsemble 2 (CCAM at UP)5 membersEnsemble 3(CCM3.6 at IRI)24 membersEnsemble 4 (CFS at CPC)40 membersThe current long-range forecast multi-model ensemble system of the South African Weather Service

  • New forecasting systemUEA CRU data (0.5 resolution)PrecipitationMinimum temperaturesMaximum temperatures MOS using 850 hPa geopotential height fieldsDomain: 10N-50S; 0-70EProduction date: from July 2008

  • DJF rainfall simulation skill

  • DJF 1999/2000 precip & max temp PROBABILITY forecastsPrecipMax TA typical example of the format of the forecasts

  • Rainfall forecast issued in December

  • DMC and VACSDMCSAWS to compile draft document on modernizing the SARCOF processDMC has been receiving MM forecasts from SAWS since August 2008MM work to be linked with VACSWorkshop in 2009 (will introduce product)

  • ENSO forecastCCA (antecedent SST)ECHAM4.5-MOM3 (from Dave DeWitt)CFS (NCEP)

  • Combining algorithm:1. CPT downscaling2. Equal weightsMulti-model ensemble(& verification statistics) Ensemble 1 (ECHAM4.5 at SAWS)12 membersEnsemble 2 (CCAM at UP)5 membersEnsemble 3(CCM3.6 at IRI)24 membersEnsemble 4 (CFS at CPC)40 membersThe planned long-range forecast multi-model ensemble system of the South African Weather ServiceEnsemble 5+6 (+7) (GloSea4 at UKMOandCPTEC/COLA at INPE(ECMWF?))

    The multiple-model is produced by combining ensembles from two or more individual models. The benefits of the multiple-model system derive potentially from;1) Exploitation of complementary predictive skill: Different models generally have different strengths and weakness. Forecast skill will vary among models depending on the variable, season and region. Moreover, skill is often complementary, e.g. one model may perform particularly well over Indonesia while for another model best skill may be over South America. Research has shown that by combining ensembles from different models the strengths of each individual model can be exploited to optimise global skill (see later and e.g. Graham et al. 2000)2) Increase in ensemble size: In general, the greater number of ensemble members will in itself provide skill improvements. (However the benefits provided by multiple-models derive mainly from the use of more than one model formulation, see e.g. Evans et al. 2000).

    Combining algorithmMultiple-model ensembles have been shown to provide improved performance with simple, unweighted combination of the individual ensembles (e.g. Graham et al 2000; Brankovic and Palmer 2000). However, more sophisticated methods of combining the ensembles, e.g. by weighting each individual ensemble according to its track record of skill, have been proposed (e.g. Krishnamurti et al. 1999)

    The multiple-model is produced by combining ensembles from two or more individual models. The benefits of the multiple-model system derive potentially from;1) Exploitation of complementary predictive skill: Different models generally have different strengths and weakness. Forecast skill will vary among models depending on the variable, season and region. Moreover, skill is often complementary, e.g. one model may perform particularly well over Indonesia while for another model best skill may be over South America. Research has shown that by combining ensembles from different models the strengths of each individual model can be exploited to optimise global skill (see later and e.g. Graham et al. 2000)2) Increase in ensemble size: In general, the greater number of ensemble members will in itself provide skill improvements. (However the benefits provided by multiple-models derive mainly from the use of more than one model formulation, see e.g. Evans et al. 2000).

    Combining algorithmMultiple-model ensembles have been shown to provide improved performance with simple, unweighted combination of the individual ensembles (e.g. Graham et al 2000; Brankovic and Palmer 2000). However, more sophisticated methods of combining the ensembles, e.g. by weighting each individual ensemble according to its track record of skill, have been proposed (e.g. Krishnamurti et al. 1999)