Transcript of The fundamentals of Seasonal Forecasting J.P. Céron – Météo-France.
- Slide 1
- The fundamentals of Seasonal Forecasting J.P. Cron
Mto-France
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- Some Vocabulary Long Range Forecasts and Climate Forecasts
Forecast Range, Forecast period and Lead time. Forecast Range Lead
TimeForecast Period Forecast issue time
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- Coupled Forecast : Range of 6 months LT - 1 month Forecast
issue time May June July Aug Sept Octo Nov Seasonal Forecast 1
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- Coupled Forecast : Range of 6 months Forecast issue time May
June July Aug Sept Octo Nov LT - 2 monthSeasonal Forecast 2
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- Coupled Forecast : Range of 6 months Seasonal Forecast 3
Forecast issue time May June July Aug Sept Octo Nov LT - 3
month
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- The Scientific bases The evolution of the atmosphere is partly
driven by the evolution of external forcing conditions (SST and
continental surfaces). The evolution of external forcings is often
slow and predictable. It gives a slow memory to the atmosphere ;
the evolution of the latter becoming partly predictable. The
successive instantaneous states of the atmosphere have a limited
predictability while the mean states of the atmosphere have a
greater predictability. The mean circulation in tropical regions is
strongly inflenced by the large scale organised convection.
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- Limitation of numerical forecast : Daily forecast Daily Scores
over Northern Hemisphere
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- Limitation of numerical forecast : Daily forecast Daily Scores
over Northern Hemisphere + Persistence Scores
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- Limitation of numerical forecast : Daily forecast Daily Scores
over Northern Hemisphere + Perfect model Scores
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- Limitation of numerical forecast : Monthly forecast Daily
Scores over Northern Hemisphere + Monthly running mean Scores
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- Limitation of numerical forecast : Seasonal forecast Daily
Scores over Northern Hemisphere + seasonal running mean Scores
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- Limitation of numerical forecast : Seasonal forecast Daily
Scores over Northern Hemisphere + Ensemble forecast, seasonal
running mean and SST forecast
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- The Predictability a Thunderstorm will be observed next Sunday
over the Toulouse Mtopole between 15h and 16h Irrealistic, the
confidence that one can have in this forecast is very low a rainy
system will cross the Toulouse region Sunday afternoon realistic,
one can be quite confident in this forecast
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- The Predictability The predictability depends on : The scale of
the forecasted phenomenum (Thunderstorm, Easterly Wave, Blocking
situation, ENSO, ) The Range of the forecast (NowCasting, Short,
Medium, Seasonnal, Climatic)
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- Predictability Space Scales Local 10-100 km Regional 100-1000
km Synoptic 1000-5000 km Supra-synoptic > 5000- km seasonal
Forecasting - supra-synoptic scales
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- Predictability Actors and Associated Scales
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- Predictability The different views of the Predictability
Through the observations Through the models
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- The evolution of external forcing conditions of Sea Surface
temperature (SST) Evolution of Sea Surface temperature (SST)
Interannual variability (like ENSO) Decadal variability (like PDO)
Evolution of continental surface conditions Influence of
continental surface conditions (snow, albedo,..), Intraseasonal
variability (notably soil moisture), Mutual influences Decadal/ENSO
ENSO/Intraseasonal Intraseasonal/Synoptic
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- The ENSO The planetary influence of El Nio (left) and La Nia
(right)
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- The ENSO Through the observations in Winter
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- The ENSO Through the observations in Summer
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- The ENSO Through the observations in Winter
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- The ENSO Through the observations in Summer
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- The fundamentals of seasonal Forecasting The climatic
variability The forecasting models Statistical models SST forced
Atmospheric General Circulation Models Ocean/Atmosphere Coupled
General Circulation Models The verifications Verification of the
forecasts Verification of the usefulness of the forecats The chaos
Link with the climatic variability Link with the ensemble
forecast
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- The fundamentals of Seasonal Forecasting The climatic
variability : slow variation in the Atmosphere NAO PNA mode PDO QBO
or TBO http://www.cpc.ncep.noaa.gov/data/teledoc/telecontents.html
and Barnston and Livezey 1987, Mon. Wea. Rev., 115, 1083- 1126)
East Atlantic (EA), East Atlantic Jet (EA-Jet), East
Atlantic/Western Russia, Scandinavia (SCAND), Polar/Eurasia Asian
Summer, West Pacific (WP), East Pacific (EP), North Pacific (NP),
Tropical/Northern Hemisphere (TNH), Pacific Transition (PT)
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- The North Atlantic Oscillation
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- Rainfall in Winter Temperature in Winter
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- The Pacific Decadal Oscillation
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- The climatic variability
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- The fundamentals of Seasonal Forecasting The climate
variability
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- The climatic variability The fundamentals of Seasonal
Forecasting
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- The fundamentals of seasonal Forecasting The climatic
variability
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- The fundamentals of seasonal Forecasting The climatic
variability Atlantic El Nino Pirata buoy network
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- The fundamentals of seasonal Forecasting The climatic
variability JAS Observed Sahel Rainfall Vs JAS Observed THC index r
= 0.45
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- The fundamentals of seasonal Forecasting Forecasting models
Statistical models SST forced Atmospheric Global Circulation Models
Ocan/Atmosphre Coupled General Circulation Models
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- The fundamentals of seasonal Forecasting The Statistical models
East African Rainfall vs Nino3 Index Thanks to Simon Mason
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- The fundamentals of seasonal Forecasting The Statistical models
East African Rainfall vs Nino3 Index
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- The fundamentals of seasonal Forecasting The Statistical models
East African Rainfall vs Nino3 Index
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- The fundamentals of seasonal Forecasting The Statistical
models
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- The fundamentals of seasonal Forecasting The Statistical
models
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- The fundamentals of seasonal Forecasting The Statistical
models
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- The fundamentals of seasonal Forecasting The Statistical
models
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- The fundamentals of seasonal Forecasting The Statistical
models
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- The fundamentals of seasonal Forecasting Numerical models
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- The fundamentals of seasonal Forecasting The numerical
models
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- The fundamentals of seasonal Forecasting Coupled vs Forced
models
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- The fundamentals of seasonal Forecasting Verification in real
time (following up of the bias, pointing out and monitoring of the
errors, ), Verification in hindcast forecast mode, Verification of
the predictability of forecasting events, Verification of the
forecast value in a users point of view, Verification of the use
and impact of the forecast, Deterministic vs Probabilistic
Verifications Comparison with climatology and persistence (often
use as references by users), Problem of relevant and reliable
dataset for verification purpose. Forecast Verifications
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- Score/Skill and Value 2 complementary point of view : The
scientific point of view : Quality of the forecast = Scores
Interest of the use of the forecast = Skills The user point of view
: Usefulness of the forecast = Skills (using current forecast
strategy of the users e.g. Climatology) Value of the forecast =
Economonical evaluation of the use of the forecast (Cost/Lost
approach)
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- Score/Skill and Value Score point of view :
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- Cost/Lost approach 2 categories : e.g. dryer / wetter and ratio
Cost/Lost e.g. = 0.5 C1=Averaged cost using climatological forecast
C2 =Averaged cost using perfect model forecast C3= Averaged cost
using real model Score/Skill and Value Event obsnon obs forecast cc
non forcast L0 Event obsnon obs forecast n 11 n 12 non forecast n
21 n 22
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- Score/Skill and Value Value point of view :
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- The fundamentals of seasonal Forecasting Verifications WMO
Normes (parameters, scores, zones)
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- Verification in Hindcast mode retrospective forecasts European
research Projects PROVOST (CEPMMT, UKMO, EDF, LODYC, MPI, IMGA,
DMI, U. Alcala) : Perfect Ocan forecast, 4 different models, 15
years x 4 seasons x 9 membres Rsults : clear in Tropics, some skill
on North hemisphere in winter, but local uncertainties. Interest of
the Multi-model approach. ELMASIFA (SNM Maroc, Algrie Tunisie)
POTENTIALS (DMI, CINECA, LMD, MPI) DEMETER (CEPMMT, UKMO, LODYC,
CERFACS, MPI, ADGB, IMGA, DMI, JRC, U. Liverpool, INM) : Forecasted
Ocan using coupled models, 6 different models, 40 years x 4 seasons
x 6 month x 9 membres Rsults : Provost revisited, improvment in
Tropical regions and degradation in mid-latitude, extension of the
range of the forecast.
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- The fundamentals of seasonal Forecasting Chaos and ensemble
forecast Differences between analysis and real initial state
Assimilation system Imperfection Lack of observations Model Errors
(both Oceanic and/or atmospheric) Natural variability of the
climate system Interpretation of the forecast Uncertainty Sources
:
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- The fundamentals of seasonal Forecasting Chaos and ensemble
forecast Model errors (Ocanic and/or atmospheric)
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- Butterfly effect (JFM 2003)
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- The fundamentals of seasonal Forecasting Chaos and ensemble
forecast To sample the initial state uncertainty analysis
disturbances Corresponding to the most unstable modes Compatible
with analysis errors Methods : Singular vectors, breeding To sample
the modelisation uncertainty model disturbances Using several
models Using stochastic physics Modifying some physical parameters
To sample all the possible solutions for the Ocean Atmosphere
system
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- The fundamentals of seasonal Forecasting Chaos and climatic
variability COLD & WETHOT & DRY
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- The fundamentals of seasonal Forecasting Chaos and Climate
variability
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- The fundamentals of seasonal Forecasting The forecast sytem The
description of the initial state of the Ocean/Atmosphere system
Atmospheric Data Oceanic Data Data from the continental surface
Assimilation data scheme Elaboration of products Direct Methods
(Deterministic vs Probablilistic products) Indirect Methods
(notably PPM or MOS) Adaptation of the products (notably
downscaling) Interpretation of the forecast Transformation of the
forecast to the benefit of the user Following-up of the process
Update of the forecast Users Evaluation of the forecast (value, use
and impact)
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- The fundamentals of seasonal Forecasting The forecasting
suite
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- The fundamentals of seasonal Forecasting Description of the
initial state of the Ocean/Atmosphere system
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- The fundamentals of seasonal Forecasting Description of the
initial state of the Ocean/Atmosphere system
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- The fundamentals of seasonal Forecasting Description of the
initial state of the Ocean/Atmosphere system
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- The fundamentals of seasonal Forecasting The Oceanic data
assimilation
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- The fundamentals of seasonal forecasting Assimilation of the
surface wind
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- The fundamentals of seasonal Forecasting Description of the
initial state of the Ocean/Atmosphere system
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- The fundamentals of seasonal forecasting Elaboration of
products Direct Methods (deterministic vs Probabilistic products)
Indirect Methods (Statistical adaptations notably) Adaptation of
the products (downscaling)
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- The fundamentals of seasonal forecasting Deterministic vs
Probabilistic product Is the product usefull? Is the product well
adapted to the user? If no, can we do something? Can the quality,
the value and the use of the product be checked and verified?
Require a Forecaster/User discussion through Multidisciplinary
groups (Experts in forecasting, communication, userss needs)
Elaboration of products
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- Elaboration of numerical products Direct Methods (deterministic
and probabilistic products) formulation as Indices or Anomalies
Model Forecast : raw information not usefull !
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- Elaboration of numerical products Direct Methods (deterministic
and probabilistic products) formulation as Indices or Anomalies
Debiased model forecast : better formulation
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- Elaboration of numerical products Direct Methods (deterministic
and probabilistic products) formulation as Indices or Anomalies
Normalized model forecast : Model forecats compared to its own
climatology
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- Elaboration of numerical products Direct Methods (deterministic
and probabilistic products) formulation as Indices or Anomalies
Indices : Model forecats compared to its own climatology Anomalies
: Adaptation to local observation properties
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- Downscaling Problem Seasonal predictability and associated
scales adaptation to the user G l o b a l L o c a l statistical
methods : Observations, Downscaling models numerical methods :
Numerical models using GCM simulations as boundary conditions
(single column, LAM, )
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- Dowscaling Problem Seasonal predictability and associated
scales adaptation to the user SeasonalSeasonal intra seasonal
statistical methods : Observations, Downscaling models numerical
methods : Numerical models using GCM simulations as boundary
conditions (single column, LAM, )
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- The fundamentals of seasonal forecasting Interpretation of the
forecast transformation to the benefit of the user Translation in
terms of actions, risks, scenario, and associated probabilities
Following_up of the process Update of the forecast continuous
process Evaluation on a user point of view Processs Experience
Feedbacks
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- Highlights of seasonal forecasting Basically Probabilistic
forecast, Forecast of the Mean State and not of the Weather, Not
usefull elsewhere neither for everything, Confidence in the
forecast depending of the year and the parameter, Evaluation of
both aspects quality (scientific) and usefulness (economical value,
use), Usefull in a decision making context and in meteo sensitive
activities (in an economical sense), Since a few years better
knowledge of the limits of the seaonnal predictability and its
potential uses, Operational forecast systems aiming to provide
targeted products, Improvements : ERA40, coupled models,
donwscalling, intraseasonal forecasts, Observation system,