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June 16th, 2009

Weather typing approach for seasonal forecasts?. June 16th, 2009. Christian Pagé, CERFACS Laurent Terray, CERFACS - URA 1875 Julien Boé, U California Christophe Cassou, CERFACS - URA 1875. HEPEX09 - COST731 Workshop - Toulouse - 15-19 June 2009. 1. Motivations.

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June 16th, 2009

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  1. Weather typing approachfor seasonal forecasts? June 16th, 2009 Christian Pagé, CERFACS Laurent Terray, CERFACS - URA 1875 Julien Boé, U California Christophe Cassou, CERFACS - URA 1875 HEPEX09 - COST731 Workshop - Toulouse - 15-19 June 2009

  2. 1. Motivations • Difficult to forecast precipitation adequately at long range and at monthly/seasonal timescales • Even more at higher spatial resolution (hydrological applications) • Numerical Models and Ensemble Forecast Systems have more abilities to forecast Large-Scale Circulation than fine-scale local variables at these timescales • Downscaling techniques based on statistical relationships between the Large-Scale Circulation and local scale fields have proven significant abilities in climate sciences (Boe and Terray, 2007) • Weather-typing approach • A sort of extended analog methodology with dynamical and local variable constraints • Can process a large number of simulations, such as large ensemble forecasts systems of atmospheric and/or hydrological models (low CPU cost) • Monthly/Seasonal forecasts applications?

  3. 2. Background Downscaling Statistical Downscaling Local geographic characteristics (topography, rugosity) Large-Scale Circulation Local fields(precipitations, temperature) From Global OR Regional Models! (e.g. ARPEGE) Statistical downscaling Build a statistical model linking the large-scale circulation and local precipitation

  4. 3. Methodology Classification Classification: main concepts as inBoe and Terray (2007) statistical downscaling methodology Clusters group #1 Composite Cluster composite: Average of the variable which isclassified withina group Daily Mean Sea-Level Pressure Composite Clusters group #2 • Precipitation observations are used in the classification learning phase (multi-variate): discriminant • Temperature (model AND observations) is also used when selecting analog day • Distances to all clusters (inter-types) are also considered • Each cluster is defined by: • its composite • the days’ distribution within the cluster Based on Michelangeli et al, 1995 Pictures by Julien Najac, Cerfacs

  5. 3. Methodology Weather types Methodology produces Weather types discriminant for precipitation Winter Weather types examples NCEP MSLP anomalies (hPa) Related precipitation anomalies from Météo-France 8-kmmesoscale analysis SAFRAN (%)

  6. 3. Methodology Validation Weather types occurrence validation 1950-1999

  7. 3. Methodology Validation 1981-2005 Validation Period Downscaled NCEP reanalysisvs SAFRAN analysis Downscaled ARPEGE V4 vsNCEP reanalysis Annual total mean precipitation 1981-2005Differences in %

  8. 3. Methodology Correlation observation /reconstruction 1900/2000 Validation Precipitation Time Tendencies Validation => Seasonal Cumulated Precipitation (NDJFM) reconstructed by multiple regression using weather types occurrence and clusters’ distances Time Tendencies Pr 1951-2000 observation vs reconstruction 1 point=1 station, color: latitude => blue=south, red=north

  9. 3. Methodology Validation The Météo-France SIM model for hydrological simulations (Habets et al., 2008) Atmosphere SAFRAN : meteorological parameters: mesoscale analysis at 8-km resolution ISBA : water flux and ground surface energy fluxes (evaporation, snow, runoff, water infiltration) MODCOU : hydrological model (river flows) Latent Sensible Snow • Dailyriver flows Habets, F., et al. (2008), The SAFRAN-ISBA-MODCOU hydrometeorological model applied over France, J. Geophys. Res., 113, D06113, doi:10.1029/2007JD008548. Source: Météo-France 9

  10. 3. Methodology Validation River flow Validation using the SIM hydrometeorological model SIM simulations by Eric Martin, Météo-France 500 Winter Mean OBS NCEP (0.85) SAFRAN (0.97) 0 1960 2010 • Precipitation and other meteorological variables reconstructed at 8-km using: • NCEP reanalysis data (Large-Scale Circulation and Temperature) • Statistical downscaling methodology (SAFRAN analysis used for analog daily data) • Good agreement of downscaled NCEP data vs SAFRAN and observations

  11. 4. Perspectives Could this kind of statistical downscaling weather typing methodology be used for Monthly/Seasonal forecasts? • Predictability of Weather Regimes at Monthly/Seasonal scales • Very preliminary and exploratory studies have already been done (Chabot et al., 2008, 2009) • 4 Standard weather regimes, large North Atlantic Domain • Many questions still to be addressed ! • Weather types • Are some weather types more predictable than others at monthly/seasonal scale ? Increase in predictability ? • If yes, what would be the forcings responsible for the most predictable weather types ? • Which region and large-scale variable(s) to use ? How many weather types to use ? • Some questions should be explored by doing a hindcast experiment

  12. Thanks for your attention!  Christian Pagé, CERFACS christian.page@cerfacs.fr Laurent Terray, CERFACS - URA1875 Julien Boé, U California Christophe Cassou, CERFACS - URA1875 HEPEX09 - COST731 Workshop - Toulouse - 15-19 June 2009

  13. 4. Monthly/Seasonal Methodology Facts • A previous preliminary and exploratory study (Chabot et al., 2008) showed that: • Weather regimes predictability at seasonal timescales is low • Except when strong oceanic forcing (ENSO, Tropical Atlantic) • This study used: • Geopotential Height at 500 hPa (Z500) for Large-Scale Circulation classification (tendencies problems) • A Large North Atlantic Domain • Four Standard Weather Types Blocking Ridge BUT! Numerical models have forecasts performances at monthly timescales which are much better than at seasonal timescales (4 weeks lead time)

  14. 4. Monthly/Seasonal Methodology Facts • A monthly extension to the Chabot et al., 2008 study shows (Chabot et al., 2009) : • Good predictability for weather types anomaly sign (60 to 80 % of correct forecasts) Percentage of correct forecasts for the least probable weather type Percentage of correct forecasts for the most probable weather type 30 30 days days 14

  15. 3. Methodology Validation 1200 150 800 Flow Validation LOIRE(Blois) SEINE (Poses) ARIEGE (Foix) Annual Cycle OBS NCEP ARPEGE-VR 0 0 0 Jan Dec Jan Dec Jan Dec 2500 2500 LOIRE (Blois) 250 SEINE (Poses) ARIEGE (Foix) CDF OBS NCEP ARPEGE-VR 0 0 0 0 1 0 1 0 1 VIENNE (Ingrandes) 500 Winter Mean OBS NCEP (0.85) SAFRAN (0.97) 0 1960 2010

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