june 16th, 2009 christian pagé, cerfacs laurent terray, cerfacs - ura 1875 julien boé, u...
TRANSCRIPT
June 16th, 2009
Christian Pagé, CERFACS
Laurent Terray, CERFACS - URA 1875
Julien Boé, U California
Christophe Cassou, CERFACS - URA 1875
Weather typing approachfor seasonal forecasts?
HEPEX09 - COST731 Workshop - Toulouse - 15-19 June 2009
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?
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Downscaling2. Background
Local fields(precipitations, temperature)
Local geographiccharacteristics
(topography, rugosity)Large-ScaleCirculation
Statistical downscaling
Build a statistical model linking the large-scale circulation and local
precipitation
Statistical Downscaling
From Global OR Regional
Models! (e.g. ARPEGE)
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Classification3. Methodology
Daily Mean Sea-Level Pressure
Clusters group #1
Clusters group #2
Cluster composite:
Average of the variable which is
classified withina group
Each cluster is defined by:- its composite- the days’ distribution within the cluster
Classification: main concepts as inBoe and Terray (2007) statistical downscaling methodology
Composite
Composite
Based on Michelangeli et al, 1995
• 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 consideredPictures by Julien Najac, Cerfacs
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Weather types3. Methodology
NCEP MSLP anomalies (hPa) Weather types examples Winter
Methodology produces Weather types discriminant for precipitation
Related precipitation anomaliesfrom Météo-France 8-kmmesoscale analysis SAFRAN (%)
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3. Methodology Validation
Weather types occurrence validation 1950-1999
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3. Methodology Validation
Downscaled NCEP reanalysisvs SAFRAN analysis
Downscaled ARPEGE V4 vsNCEP reanalysis
1981-2005 Validation Period
Annual total mean precipitation 1981-2005Differences in %
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3. Methodology Validation
Precipitation Time Tendencies Validation
=> Seasonal Cumulated Precipitation (NDJFM) reconstructed by multiple regression using weather types occurrence and clusters’ distances
Correlation observation
/reconstruction1900/2000
1 point=1 station, color: latitude=> blue=south, red=north
Time Tendencies Pr
1951-2000 observation
vs reconstruction
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The Météo-France SIM model for hydrological simulations(Habets et al., 2008)
SAFRAN : meteorological parameters: mesoscale analysis at 8-km resolution
ISBA : water flux andground surface energy fluxes
(evaporation, snow,runoff, water infiltration)
MODCOU : hydrological model(river flows)
Dailyriver flows
•
Latent
Sensible
Snow
Atmosphere
Source: Météo-France
3. Methodology Validation
Habets, F., et al. (2008), The SAFRAN-ISBA-MODCOU hydrometeorological model applied over France, J. Geophys. Res., 113, D06113, doi:10.1029/2007JD008548.
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3. Methodology Validation
River flow Validation using the SIM hydrometeorological model
Winter MeanOBSNCEP (0.85)SAFRAN (0.97)20101960
500
0
• 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
SIM simulations by Eric Martin, Météo-France
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 11
4. Perspectives
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Thanks for your attention!
Christian Pagé, CERFACSChristian Pagé, [email protected]
Laurent Terray, CERFACS - URA1875Laurent Terray, CERFACS - URA1875Julien BoJulien Boé, U Californiaé, U California
Christophe Cassou, CERFACS - URA1875Christophe Cassou, CERFACS - URA1875
HEPEX09 - COST731 Workshop - Toulouse - 15-19 June 2009
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4. Monthly/Seasonal Methodology Facts
BUT! Numerical models have forecasts performances at monthly timescales which are much better than at seasonal timescales(4 weeks lead time)
Ridge
• 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
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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 most probable weather type
Percentage of correct forecasts for the least probable weather type
days days30 30
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3. Methodology Validation
Flow Validation
Winter MeanOBSNCEP (0.85)SAFRAN (0.97)
Annual CycleOBSNCEP ARPEGE-VR
CDFOBSNCEP ARPEGE-VR
Jan Dec Jan Dec Jan Dec
0 1 0 1 0 1
ARIEGE (Foix)
ARIEGE (Foix)
LOIRE(Blois)
LOIRE (Blois)
SEINE (Poses)
SEINE (Poses)
VIENNE (Ingrandes)
0
2500
000
0 0
1200
2500250
150 800
20101960
500
0