challenges in sub-seasonal prediction · re-forecast strategy re-forecasts are used for model...
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Slide 1 © ECMWF
Challenges in Sub-seasonal Prediction
Frédéric Vitart
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Slide 2 © ECMWF
10 years ago, few operational centres produced sub-seasonal
forecasts. Now most operational centres have forecasting
systems targeting this time range.
Increased interest in sub-seasonal prediction by operational
centres triggered by:
Growing demand from applications (e.g. agriculture, health,
hydrology,..)
Progress in medium-range forecasting (1 day of predictive skill
per decade). Weeks 3 and 4 are seen as the new prediction
frontier.
Progress in prediction of key sources of predictability
Introduction
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Slide 3 © ECMWF
A particularly difficult time range: Is it an atmospheric initial condition problem as medium-range forecasting or is it a boundary condition problem as seasonal forecasting? Is it a predictability desert? Some sources of predictability – Windows of opportunity :
Sea surface temperatures
Land surface conditions: snow, soil moisture
The Madden Julian Oscillation
Stratospheric variability
Atmospheric dynamical processes (Rossby wave propagations, weather regimes…)
Sea ice cover
Bridging the gap between Climate and weather prediction
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Slide 4 © ECMWF
1st Challenge: to predict the predictors
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Slide 5 © ECMWF
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Slide 6 © ECMWF
MJO Forecasts have improved
NCEP ECMWF
Zhang and Van den Dool 2012 Vitart, 2014
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Slide 7 © ECMWF
Year Of Maritime Continent - 2017
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Slide 8 © ECMWF
Sudden Stratospheric Warming
SSW index: Difference of temperature at 50hPa between 90N and 60N averaged over
all the longitudes
62 levels
91 levels
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Slide 9 © ECMWF 9
Koster et al, GRL 2011
Impact of soil moisture
Soil moisture/temperature initialization is a challenge. Snow
depth, sea-ice thickness are also difficult to initialize due to
lack of observations.
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Slide 10 © ECMWF
2nd Challenge: to predict the impact of the predictors
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Slide 11 © ECMWF
Impact of MJO on Euro-Atlantic weather regimes
Cassou (2008)
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Slide 12 © ECMWF
CFS.V2
Cold March 2013 – 14 Feb 2013 -Day 26-32
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Slide 13 © ECMWF
MJO teleconnections Z500 anomalies
10 days after an MJO in Phase 3
Evolution of NAO skill scores-Day 19-25 NAO Index: projection of Z500 on pre-computed EOF
Vitart, 2014
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Slide 14 © ECMWF
Analysis ECMWF JMA
NCEP CAWCR
Z500 anomalies
10 days after an
MJO in Phase 3
MJO Teleconnections (S2S re-forecasts)
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Slide 15 © ECMWF
Molteni et al, 2014
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Slide 16 © ECMWF
16
From Om Tripathi, 2015
Impact of SSWs on forecast skill scores
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Slide 17 © ECMWF
Zonal Wind Anomaly at 60N
over Europe (15 Dec 2012-15 Feb 2013)
SSW: Downward propagation
too weak in the model?
NAO/SSW lag correlation
Sudden Stratospheric Warming
Analysis
15-day Forecast
Forecast ERA-I
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Slide 18 © ECMWF
Design of forecasting systems
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Slide 19 © ECMWF
Modelling issues
Impact of resolution (Ocean, atmosphere)
Role of ocean-atmosphere coupling
Systematic errors
Initialisation strategies for subseasonal prediction – (Coupled data assimilation)
Ensemble generation (Burst of lag ensemble? Coupled ocean-atmosphere perturbations?)
Spread/skill relationship
Verification (flow dependent verification, verification of precipitation…)
Benefit of Multi-model forecasting
Re-forecast size and length
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Slide 20 © ECMWF
Impact of resolution
T1259 T159 ERA-40
Blocking Index. 13 month integrations of ECMWF model (at
T159 and T1259). DJFM 1960-2003- Project ATHENA
Jung et al. (2012), J. Climate
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Slide 21 © ECMWF
Ocean-Atmosphere coupling
U50
T850
RPSS over NH
Obs SSTs Coupled
80 case, starting on 1st Feb/May/Aug/Nov 1989-2008
WEEK1 WEEK2 WEEK3 WEEK4
MJO Bivariate Correlation
Coupled
Obs SSTs
Pers SSTs
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Slide 22 © ECMWF
Re-forecast strategy
Re-forecasts are used for model calibration and also for skill assessment.
A large reforecast database is needed for calibration to distinguish between random
error and systematic errors and also to estimate flow dependent errors.
A large reforecast database is also needed for verification and for flow dependant
skill assessment, like assessing the concurrent impact of ENSO and specific phases
of the MJO on the forecast skill scores. Signal to noise ration is also improved in long
reforecast datasets (Shi et al, 2014)
Large ensemble size is also important for skill assessment , since some probabilistic
skill scores are impacted by the ensemble size.
However
Large re-forecast datasets with large ensemble size are often not affordable. Not
clear what is more important: ensemble size, number of years?
Long re-forecasts suffer from inconsistent quality in the initial conditions (pre-satellite
period).
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Slide 23 © ECMWF
23
Problem with re-forecast initial conditions
Probability of T2m to be in lowest tercile
100 % 0
Forecast of week 1
Start: 11-05-2006
1994 1995 1996 1997 1998 1999 2000 2001 2002 2003 2004 2005 2006
YEAR0
0.005
0.01
0.015
Sn
ow
de
pth
in
me
ters
SNOW DEPTH (m)
0-10E, 40N-50N
Snow ANALYSIS 11 MAY
Observations
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Slide 24 © ECMWF
The sub-seasonal to seasonal prediction project (S2S)
●“To improve forecast skill and understanding on the sub-seasonal to seasonal timescale with special emphasis on high-impact weather events”
●“To promote the initiative’s uptake by operational centres and exploitation by the applications community”
●“To capitalize on the expertise of the weather and climate research communities to address issues of importance to the Global Framework for Climate Services”
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Slide 25 © ECMWF
Madden-Julian Oscillation
Monsoons
Africa
Extremes
Verification
Su
b-P
roje
cts
S2S Database
Interactions and teleconnections between midlatitudes and tropics
Sub-seasonal to Seasonal (S2S) Prediction Project
Research Issues
• Predictability
• Teleconnection
• O-A Coupling
• Scale interactions
• Physical processes
Modelling Issues
• Initialisation
• Ensemble generation
• Resolution
• O-A Coupling
• Systematic errors
• Multi-model combination
Needs & Applications
Liaison with SERA
(Working Group on
Societal and Economic
Research Applications)
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Slide 26 © ECMWF
● Daily real-time forecasts + re-forecasts
● 3 weeks behind real-time
● Common grid (1.5x1.5 degree)
● Variables archived: about 80 variables including ocean variables, stratospheric levels and soil moisture/temperature
● Archived in GRIB2 – NETCDF conversion available
●Database to open in 2015, initially with 3 models (ECMWF, NCEP and JMA)
Database Description
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Slide 27 © ECMWF
27
CAWCR
NCEP
EC HMCR
JMA KMA
CMA
ECMWF
Météo
France
UKMO
Data provider Archiving centre
S2S Database
11 data providers and 2 archiving centres
CNR
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Slide 28 © ECMWF
S2S partners
Time-
range
Resol. Ens. Size Freq. Hcsts Hcst length Hcst Freq Hcst Size
ECMWF D 0-32 T639/319L91 51 2/week On the fly Past 20y weekly 5
UKMO D 0-60 N216L85 4 daily On the fly 1996-2009 4/month 3
NCEP D 0-45 N126L64 4 4/daily Fix 1999-2010 4/daily 1
EC D 0-35 0.6x0.6L40 21 weekly On the fly Past 15y weekly 4
CAWCR D 0-60 T47L17 33 2/week Fix 1981-2013 6/month 33
JMA D 0-34 T159L60 25 2/week Fix 1979-2009 3/month 5
KMA D 0-60 N216L85 4 daily On the fly 1996-2009 4/month 3
CMA D 0-45 T106L40 4 daily Fix 1992-now daily 4
Met.Fr D 0-60 T127L31 51 monthly Fix 1981-2005 monthly 11
CNR D 0-32 0.75x0.56 L54 40 weekly Fix 1981-2010 6/month 1
HMCR D 0-63 1.1x1.4 L28 20 weekly Fix 1981-2010 weekly 10
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Slide 29 © ECMWF
Footer-text Slide 29
Day 12-18 2-m temp anomalies - Forecasts starting on 15/01 Verification ECMWF
JMA NCEP
S2S Database products
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Slide 30 © ECMWF
MJO forecast – 26/02/2015 CAWCR NCEP ECMWF
JMA UKMO
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Slide 31 © ECMWF
SSWs - 1st January 2015
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Slide 32 © ECMWF
Conclusion
Increased interest for this time range – New frontier for prediction
Progress in the prediction of main sources of predictability over the past decade
Next challenge is the accurate simulation of their impact and better understand their interaction (e.g. MJO-ENSO)
Real-time and re-forecast configurations are very different in operational centres. It is not clear yet what is the best strategy.
WWRP/WRCP S2S project to address some of these challenges