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Transcript of Training Course 2009 – NWP-PR: Initialization and Ensemble Generation in Seasonal Forecasting...
Training Course 2009 – NWP-PR: Initialization and Ensemble Generation in Seasonal Forecasting
Initialization and Ensemble generation for Seasonal Forecasting
Magdalena A. Balmaseda
Training Course 2009 – NWP-PR: Initialization and Ensemble Generation in Seasonal Forecasting
• The importance of the ocean initial conditions in seasonal forecasts A well established case: ENSO in the Equatorial Pacific
A tantalizing case: NAO forecasts
• Ocean Model initialization Ocean initialization: requirements
Standard practice: assessment
Other initialization strategies: assessment
Role of ocean initialization into context
• Ensemble Generation: Sampling Uncertainty Seasonal forecasts versus Medium range: different problems, different
solutions? The ECMWF ensemble generation system. Other ensemble generation strategies
Outline
Training Course 2009 – NWP-PR: Initialization and Ensemble Generation in Seasonal Forecasting
The Basis for Seasonal Forecasts
•Atmospheric point of view: Boundary condition problem Forcing by lower boundary conditions changes the PDF of the
atmospheric attractor
“Loaded dice”
The lower boundary conditions (SST, land) have longer memoryo Higher heat capacity (Thermodynamic argument)o Predictable dynamics
•Oceanic point of view: Initial value problem Prediction of tropical SST: need to initialize the ocean subsurface. Examples:
o A well established case is ENSOo A more tantalizing case is the importance subsurface temperature in the
North Subtropical Atlantic for seasonal forecasts of NAO and European Winters.
Training Course 2009 – NWP-PR: Initialization and Ensemble Generation in Seasonal Forecasting
Need to Initialize the subsurface of the ocean
Training Course 2009 – NWP-PR: Initialization and Ensemble Generation in Seasonal Forecasting
A tantalizing case: SF of NAO and European Winters
80OW 70OW 60OW 50OW 40OW 30OW 20OW 10OW 0O 10OE 20OE
Longitude
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Latit
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Plot resolution is 1.5 in x and 1 in y
Horizontal section at 5.0 metres depthPotential temperature contoured every 0.25 deg CASSIM (S3)
Interpolated in x and y
20051016
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MAGICS 6.10 hyrokkin - neh Fri Apr 21 17:35:41 2006
SST anomaly Autumn 2005
80OW 70OW 60OW 50OW 40OW 30OW 20OW 10OW 0O 10OE 20OE
Longitude
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0O
20ON
40ON
60ON
80ON
Latit
ude
20OS
0O
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Plot resolution is 1.5 in x and 1 in y
Horizontal section at 5.0 metres depthPotential temperature contoured every 0.25 deg CASSIM (S3)
Interpolated in x and y
20060115
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MAGICS 6.10 hyrokkin - neh Fri Apr 21 17:36:24 2006
SST anomaly Winter 2005/6
T anomaly@ 90m: Autumn 2005
Anomalies below the mixed layer re-emerge and
Do they force the Atmosphere?
Training Course 2009 – NWP-PR: Initialization and Ensemble Generation in Seasonal Forecasting
Pro
bab
ilist
ic
fore
cast
calib
ratio
n Rel
iab
le p
rob
abili
ty f
ore
cast
sT
ailo
red
pro
du
cts
End to End Forecasting System
atmosDA
atmos obs
SST analysis
oceanDA
ocean obs
ocean reanalysis
atmos reanalysis
land,snow…?
sea-ice?
initialconditions
initialconditions
AGCM
OGCM
ensemble
generation
Training Course 2009 – NWP-PR: Initialization and Ensemble Generation in Seasonal Forecasting
Coupled Hindcasts, needed to estimate climatological PDF, require a historical ocean reanalysis
Real time Probabilistic Coupled Forecast
time
Ocean reanalysis
Quality of reanalysis affects the climatological PDF
Consistency between historical and real-time initial initial conditions is required
Main Objective: to provide ocean Initial conditions for coupled forecasts
Training Course 2009 – NWP-PR: Initialization and Ensemble Generation in Seasonal Forecasting
Creation of Ocean Initial conditions
• Ocean model driven by surface fluxes:
Daily fluxes of momentum, Heat (short and long wave), fresh water flux
From atmospheric reanalysis ( and from NWP for the real time).
but uncertainty in surface fluxes is large.
Training Course 2009 – NWP-PR: Initialization and Ensemble Generation in Seasonal Forecasting
Equatorial Atlantic: Taux anomalies
Equatorial Atlantic upper heat content anomalies. No assimilation
Equatorial Atlantic upper heat content anomalies. Assimilation
ERA15/OPS
ERA40
Uncertainty in Surface Fluxes:
Need for Data Assimilation
• Large uncertainty in wind products
lead to large uncertainty in the
ocean subsurface
• The possibility is to use additional
information from ocean data
(temperature, others…)
• Questions:
1. Does assimilation of ocean data constrain the ocean state?
2. Does the assimilation of ocean data improve the ocean estimate?
3. Does the assimilation of ocean data improve the seasonal forecasts
Training Course 2009 – NWP-PR: Initialization and Ensemble Generation in Seasonal Forecasting
Real Time Ocean Observations
ARGO floatsXBT (eXpandable BathiThermograph)
Moorings
Satellite
SST
Sea Level
Training Course 2009 – NWP-PR: Initialization and Ensemble Generation in Seasonal Forecasting
Time evolution of the Ocean Observing System
XBT’s 60’s Satellite SST Moorings/Altimeter ARGO
1982 1993 2001
1998-1999 PIRATA
TRITON
Training Course 2009 – NWP-PR: Initialization and Ensemble Generation in Seasonal Forecasting
Data coverage for Nov 200560°S 60°S
30°S30°S
0° 0°
30°N30°N
60°N 60°N
60°E
60°E
120°E
120°E
180°
180°
120°W
120°W
60°W
60°W
0°
0°
X B T p r o b e s : 9 3 7 6 p r o f i l e sOBSERVATION MONITORING Changing observing
system is a challenge for consistent reanalysis
Today’s Observations will be used in years to
come
60°S 60°S
30°S30°S
0° 0°
30°N30°N
60°N 60°N
60°E
60°E
120°E
120°E
180°
180°
120°W
120°W
60°W
60°W
0°
0°
▲Moorings: SubsurfaceTemperature
◊ ARGO floats: Subsurface Temperature and Salinity
+ XBT : Subsurface Temperature
Data coverage for June 1982
Ocean Observing System
Training Course 2009 – NWP-PR: Initialization and Ensemble Generation in Seasonal Forecasting
Impact of data assimilation on the mean
Assim of mooring data
CTL=No data
Large impact of data in the mean state: Shallower thermocline
PIRATA
EQATL Depth of the 20 degrees isotherm
1993 1994 1995 1996 1997 1998 1999 2000 2001 2002Time
-95
-90
-85
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-75
-70ega8 omona.assim_an0edp1 omona.assim_an0
Training Course 2009 – NWP-PR: Initialization and Ensemble Generation in Seasonal Forecasting
ORA-S3• Main Objective: Initialization of seasonal forecasts
Historical reanalysis brought up-to-date (11 days behind real time) Source of climate variability
Main Features
•ERA-40 daily fluxes (1959-2002) and NWP thereafter
•Retrospective Ocean Reanalysis back to 1959
•Multivariate on-line Bias Correction (pressure gradient)
•Assimilation of temperature, salinity, altimeter sea level anomalies an global sea level trends.
•3D OI, Salinity along isotherms
•Balance constrains (T/S and geostrophy)
•Sequential, 10 days analysis cycle, IAUBalmaseda etal 2007/2008
Training Course 2009 – NWP-PR: Initialization and Ensemble Generation in Seasonal Forecasting
The Assimilation corrects the ocean mean state
50OE 100OE 150OE 160OW 110OW 60OW 10OW
Longitude
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0Plot resolution is 1.4063 in x and 10 in yZonal section at 0.00 deg NICODE=178 contoured every 0.0002 XXXHOPE gcm:: 0001
Interpolated in y 0 ( 31 day mean)
difference from20020101 ( 31 day mean)
-0.0008
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MAGICS 6.9.1 hyrokkin - neh Tue Jul 25 19:19:38 2006
Mean Assimation Temperature Increment
Free model
Data Assimilation
z
(x)Equatorial Pacific
Data assimilation corrects the slope and mean depth of the equatorial thermocline
Training Course 2009 – NWP-PR: Initialization and Ensemble Generation in Seasonal Forecasting
The Assimilation corrects the ocean mean state
-1.5 -1.2 -0.9 -0.6 -0.3 0temperature
-400
-200
De
pth
(m
) S3-a S3-c
Mean(199301-200201) of Model minus Observationseq3-All in situ data
-0.4 -0.2 0 0.2 0.4 0.6temperature
-400
-200
De
pth
(m
)
S3-a S3-cMean(199301-200201) of Model minus Observations
eqind-All in situ dataWestern Pacific Equatorial Indian
Analysis minus Observations
DATA ASSIM
NO DATA ASSIM
Training Course 2009 – NWP-PR: Initialization and Ensemble Generation in Seasonal Forecasting
50O E 100OE 150OE 160OW 110OW 60OW 10OW
Longitude
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40ON
Latit
ude
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Plot resolution is 1 in x and 1 in ySurface fieldSea level contoured every 0.1 mCorrel: Control
Interpolated in y
20020101 ( 9 year mean)
0.5 0.5
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MAGICS 6.9 hyrokkin - neh Thu Sep 9 12:12:36 2004
CNTL:No data (1993-2001)
50OE 100OE 150OE 160OW 110OW 60OW 10OW
Longitude
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ude
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Plot resolution is 1 in x and 1 in ySurface fieldSea level contoured every 0.1 mCorrel: Assim
Interpolated in y 0 ( 9 year mean)
difference from20020101 ( 9 year mean)
0.5
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MAGICS 6.9 hyrokkin - neh Thu Sep 9 12:12:37 2004
Assimilation of T (1993-2001)
Data Assimilation Improves the
Interannual variability of the Analysis
Correlation of SL from System2 with altimeter data (which was not assimilated)
Training Course 2009 – NWP-PR: Initialization and Ensemble Generation in Seasonal Forecasting
Correlation with OSCAR currents
Monthly means, period: 1993-2005
Seasonal cycle removed
No Data Assimilation Assimilation:T+S
Assimilation:T+S+Alt
Data Assimilation improves the interannual variability of the ocean analysis
Training Course 2009 – NWP-PR: Initialization and Ensemble Generation in Seasonal Forecasting
Ocean data assimilation also improves the forecast skill
(Alves et al 2003)
Data Assimilation
Data Assimilation
No
Da
ta A
ss
imil
ati
on
No
Da
t a A
ss
imi l
at i
on
Impact of Data Assimilation
Forecast Skill
Training Course 2009 – NWP-PR: Initialization and Ensemble Generation in Seasonal Forecasting
•Progress is not monotonic: Need good coupled models to gauge the quality of initial conditions
Skill can be limited by the quality of the coupled model
So far so good, but:
0 1 2 3 4 5 6Forecast time (months)
0.4
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1
An
om
aly
co
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wrt NCEP adjusted OIv2 1971-2000 climatology
NINO3 SST anomaly correlation
0 1 2 3 4 5 6Forecast time (months)
0
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0.8
1
1.2
Rm
s e
rro
r (d
eg
C)
Ensemble sizes are 5 (0001) and 1 (0001) 60 start dates from 19870501 to 20010201
NINO3 SST rms errors
Fc S2 /m1 Fc S2 /m0 Persistence
MAGICS 6.11 verhandi - neh Fri Jun 1 16:49:20 2007
a) ERA15/OPS fluxes S2 NO Asim S2 Assim
b) ERA40/OPS fluxes DEM NO Assim DEM Assim
Training Course 2009 – NWP-PR: Initialization and Ensemble Generation in Seasonal Forecasting
Impact on ECMWF-S3 Seasonal Forecast Skill
0 1 2 3 4 5 6 7Forecast time (months)
0.4
0.5
0.6
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0.8
0.9
1
An
om
aly
co
rre
latio
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wrt NCEP adjusted OIv2 1971-2000 climatology
NINO4 SST anomaly correlation
0 1 2 3 4 5 6 7Forecast time (months)
0
0.2
0.4
0.6
0.8
Rm
s e
rro
r (d
eg
C)
Ensemble sizes are 3 (esj6) and 3 (esj6) 76 start dates from 19870101 to 20051001
NINO4 SST rms errors
Fc esj6/m1 Fc esj6/m0 Persistence Ensemble sd
MAGICS 6.10 hyrokkin - neh Thu Sep 7 19:11:46 2006
S3 Nodata S3 Assim
Training Course 2009 – NWP-PR: Initialization and Ensemble Generation in Seasonal Forecasting
Initialization into context
Ocean Initial Conditions
Versus
Coupled Model
S2 S2ic_S3model S3
Training Course 2009 – NWP-PR: Initialization and Ensemble Generation in Seasonal Forecasting
Some general considerations on initialization
Training Course 2009 – NWP-PR: Initialization and Ensemble Generation in Seasonal Forecasting
Initialization Problem: Production of Optimal I.C.
• Optimal Initial Conditions: those that produce the best forecast.
Need of a metric: lead time, variable, region (i.e. subjective choice) In complex non linear systems there is no “objective searching algorithm” for
optimality. The problem is solved by subjective choices.
• Theoretically: I.C. should represent accurately the state of the real world. I.C. should project into the model attractor, so the model is able to evolve them.
In case of model error the above 2 statements may seem contradictory
• Practical requirements: If forecasts need calibration, the forecast I.C. should be “consistent” with the I.C. of
the calibrating hindcasts. Need for historical ocean reanalysis
• Current Priorities:o Initialization of SST and ocean subsurface. o Land/ice/snow potentially important. Not much effort so far …o Atmospheric initial conditions play a secondary role.
We choose a metric, forecasts of SST from 1-6 months.
Training Course 2009 – NWP-PR: Initialization and Ensemble Generation in Seasonal Forecasting
Perceived Paradigm for initialization of coupled forecasts
Real world Model attractor
Medium rangeBeing close to the real world is perceived as advantageous. Model retains information for these time scales.
Model attractor and real world are close?
Decadal or longerNeed to initialize the model attractor on the relevant time and spatial scales.
Model attractor different from real world.
•Experiments:
•Uncoupled SST + Wind Stress + Ocean Observations (ALL)
•Uncoupled SST + Wind Stress (NO-OCOBS)
•Coupled SST (SST-ONLY) (Keenlyside et al 2008, Luo et al 2005)
Seasonal?Somewhere in the middle?
At first sight, this paradigm would not allow a seamless prediction system.
Training Course 2009 – NWP-PR: Initialization and Ensemble Generation in Seasonal Forecasting
Impact of “real world” information on skill:
NINO3.4 RMS ERROR
ALL NO-OCOBS SST-ONLY
Adding information about the real world improves ENSO forecasts
From Balmaseda and Anderson 2009
Training Course 2009 – NWP-PR: Initialization and Ensemble Generation in Seasonal Forecasting
NINO-W
EQATL
EQ3
STIO
WTIO
Reduction (%) in SST forecast error Range 1-3 months In Central/Western
Pacific, up to 50% of forecast skill is due to atmos+ocean observations.
Sinergy: > Additive contribution
Ocean~20%
Atmos ~25%
OC+ATM~55%
Impact of “real world” information on skill:
Training Course 2009 – NWP-PR: Initialization and Ensemble Generation in Seasonal Forecasting
•Relation between drift and Amplitude of Interannual variability.
•Upwelling area penetrating too far west leads to stronger IV than desired.
Western Pacific
•Relation between drift and Amplitude of Interannual variability.
•Possible non linearity: is the warm drift interacting with the amplitude of ENSO?
Impact of Initialization
•Drift and Variability depend on Initialization !!
•More information corrects for model error, and the information is retained during the fc.
•Need “more balanced” initialization methods to prevent initialization shock hitting non linearities
Eastern Pacific ALL
NO-OCOBS
SST-ONLY
DRIFT
VARIABILITY
Training Course 2009 – NWP-PR: Initialization and Ensemble Generation in Seasonal Forecasting
Initialization Shock and non linearities
Forecast lead time
pha
se s
pace
Model Attractor (MA)
non-linear interactions important
Real World (RW)
Initialization shock
a
b
c
Training Course 2009 – NWP-PR: Initialization and Ensemble Generation in Seasonal Forecasting
Initialization shock and non linearities
Forecast lead time
pha
se s
pace
Model Attractor (MA)
non-linear interactions important
Real World (RW)
Empirical Flux Corrections
Training Course 2009 – NWP-PR: Initialization and Ensemble Generation in Seasonal Forecasting
Initialization
• Advantages:
It is possible
The systematic error during the initialization is small(-er)
It can be used in a seamless system
• Disadvantages: Model is different during the
initialization and forecast
Possibility of initialization shock
No synergy between ocean and atmospheric observations
•Full Coupled Initialization: No clear path for implementation
in operational systems
Need of a good algorithm to treat systematic error. Problem with different time scales
•Simplified coupled models? Initialization of slow time scales
only, limited number of modes.
•Weakly-coupled initialization? Atmosphere initialization with a
coupled model
Ocean initialization with a coupled model.
Ocean initialization in anomaly mode with a coupled model (DePreSyS)
Uncoupled: Most common Other Strategies
Training Course 2009 – NWP-PR: Initialization and Ensemble Generation in Seasonal Forecasting
Delayed Ocean Analysis ~12 days
Real Time Ocean Analysis ~Real time
ECMWF:
Weather and Climate Dynamical Forecasts
ECMWF:
Weather and Climate Dynamical Forecasts
18-Day Medium-Range
Forecasts
18-Day Medium-Range
Forecasts
Seasonal Forecasts
Seasonal Forecasts
Monthly Forecasts
Monthly Forecasts
Ocean model
Atmospheric model
Wave model
Atmospheric model
Ocean model
Wave model
Training Course 2009 – NWP-PR: Initialization and Ensemble Generation in Seasonal Forecasting
D1
Time (days)
11 1210987654321
BRT ocean analysis: D1-12 NRT ocean analysis: D1
Assimilation at D1-12
Assimilation at D1-5
Operational Ocean Analysis Schedule
•BRT ( Behind real time ocean analysis): ~12 days delay to allow data reception
For seasonal Forecasts.
Continuation of the historical ocean reanalysis
•NRT (Near real time ocean analysis):~ For Var-EPS/Monthly forecasts
Training Course 2009 – NWP-PR: Initialization and Ensemble Generation in Seasonal Forecasting
ENSEMBLE GENERATION
•Representing Uncertainty without disrupting
Predictability
•Seasonal versus Medium Range
•Source of Uncertainty
•Different Strategies
Training Course 2009 – NWP-PR: Initialization and Ensemble Generation in Seasonal Forecasting
0 0M M x t x t
Initial pdf
forecast pdf
0, ttM
x t
0x t
Tangent propagator
Medium Range: Singular Vectors
Ensemble Generation
Are Singular Vectors a valid approach for Seasonal Forecasts?
We need the TL& Adjoint of the full coupled model is required. BUT…
1. The linear assumption would fail for the atmosphere at lead times relevant for seasonal (~>1month).
Besides
2. Uncertainty in the initial conditions may not be the dominant source of error (See later)
Training Course 2009 – NWP-PR: Initialization and Ensemble Generation in Seasonal Forecasting
Ensemble Generation
Sampling Uncertainty in Initial Conditions:
•Random sampling of initial uncertainty (as opposed to optimal)•ECMWF burst mode ensemble•Lag ensemble (NCEP)
•Simplified problem (Moore et al 2003) (academic, non operational)•Full Ocean GCM and a simplified atmosphere •Measure growth only on SST
•Breeding techniques
Sampling Uncertainty in Model Formulation:•Stochastic physics (operational)•Stochastic optimals (academic)•Perturbed parameters (climate projections)•Multimodel ensemble (EUROSIP)
Training Course 2009 – NWP-PR: Initialization and Ensemble Generation in Seasonal Forecasting
Ensemble Generation In the ECMWF Seasonal Forecasting System
1. Uncertainty in initial conditions:Burst ensemble: (as opposed to lag-ensemble)
40-member ensemble forecast first of each monthUncertainty in the ocean surface
40 SST perturbations Uncertainty in the Ocean Subsurface
5 different ocean analysis generated with wind perturbations+ SV for atmospheric initial conditions
Impact during the first month
2. Uncertainty in model formulation:Stochastic physicsMulti-model ensemble (EUROSIP)
Training Course 2009 – NWP-PR: Initialization and Ensemble Generation in Seasonal Forecasting
-Create data base with errors of weekly SST anomalies,arranged by calendar week:
Error in SST product: (differences between OIv2/OI2dvar)
Errors in time resolution: weekly versus daily SST
-Random draw of weekly perturbations, applied at the beginning of the coupled forecast. Over the mixed layer (~60m)
-A centred ensemble of 40 members
1.1 Uncertainties in the SST
SST Perturbations
Training Course 2009 – NWP-PR: Initialization and Ensemble Generation in Seasonal Forecasting
1-3 months decorrelation time in wind
~6-12 months decorrelation time in the thermocline
-Create data base with errors in the monthly anomalous wind stress, arranged by calendar month:
(differences between ERA40-CORE)
-Random draw of monthly perturbations, applied during the ocean analyses.
-A centered ensemble of 5 analysis is constructed with:
-p1-p20
+p1 +p2
1.2 Uncertainties in the ocean Subsurface
Wind perturbations +p1/-p1
EQ3 Depth of the 20 degrees isotherm
1986 1988 1990 1992 1994 1996 1998 2000Time
-20
-10
0
10
20
EQPAC Depth of the 20 degrees isotherm
1986 1988 1990 1992 1994 1996 1998 2000Time
-10
-5
0
5
10
Effect on Ocean Subsurface (D20)
Training Course 2009 – NWP-PR: Initialization and Ensemble Generation in Seasonal Forecasting
1.2 Uncertainty in the ocean subsurface: wind perturbationsERA40-CORE (1958-1979) ERA40-CORE (1980-2000)
Ocean Subsurface: Data assimilation
C.I=0.2 C
Ocean Subsurface: No Data assimilation
Training Course 2009 – NWP-PR: Initialization and Ensemble Generation in Seasonal Forecasting
1.3 Uncertainty in the Atmospheric initial conditions
• The atmosphere model is also perturbed using singular vectors (SV):
Same as for the medium range and monthly forecasting system The SV affect the spread of the seasonal forecasts:
Mainly during the first monthMainly in mid-latitudes
It makes the medium-range, monthly and seasonal forecasting systems more integrated
Training Course 2009 – NWP-PR: Initialization and Ensemble Generation in Seasonal Forecasting
) i X D P P ECMWF stochastic physics scheme:
is a stochastic variable, constant over time intervals of 6hrs and over 10x10 lat/long boxesBuizza, Miller and Palmer, 1999; Palmer 2001
Stochastic forcing
2.1) Uncertainties in deterministic atmospheric physics?
The Stochastic Physics is a probabilistic parameterization, but it does not intend to sample uncertainty in the parameters, nor model error
Training Course 2009 – NWP-PR: Initialization and Ensemble Generation in Seasonal Forecasting
•The spread by different methods converge to the same asymptotic value after after 5-6 months.
•SST and Lag-averaged perturbations dominate spread at ~1month lead time.
•With DA, the wind perturbations grow slowly, and notably influence the SST only after 3m.
•Without DA, the initial spread (<3m) is larger. The asymptotic value is slightly larger
Is the level of spread sufficient?
SP
ST
From Vialard et al, MWR 2005
Wind Perturbations (WP)
SST Perturbations(ST)
Stochastic Physics (SP)
Ensemble Spread
Wind Perturbations No DA (WPND)
All(SWT)
Lag-averaged(LA)
Training Course 2009 – NWP-PR: Initialization and Ensemble Generation in Seasonal Forecasting
Forecast System is not reliable:
RMS > Spread
To improve the ensemble generation we need to sample other sources of error:
a) Model error: multi-model, physical parameterizations
b) To design optimal methods: Stochastic Optima, Breeding Vectors, …
A. Can we reduce the error? How much? (Predictability limit)
Is the ensemble spread sufficient? Are the forecast reliable?
B. Can we increase the spread by improving the ensemble generation?
Training Course 2009 – NWP-PR: Initialization and Ensemble Generation in Seasonal Forecasting
Persistence
ECMWF
ensemble spread
RMS error of Nino3 SST anomalies
EUROSIP
EUROSIP
ECMWF-UKMO-MeteoFrance
2.1) Sampling model error: The Real Time Multimodel
Training Course 2009 – NWP-PR: Initialization and Ensemble Generation in Seasonal Forecasting
2.2) Sampling model error: The Real Time Multimodel
Persistence
ECMWF
ensemble spread
RMS error of Nino3 SST anomalies
Bayesian Calibration
EUROSIP
EUROSIP
ECMWF-UKMO-MeteoFrance