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THE ECMWF Seasonal Forecasting system - PowerPoint PPT Presentation


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THE ECMWF Seasonal Forecasting system. Overview. OCEANOBS 09 & EUROBRISA Applications most welcome for the concept of End To End Seasonal Forecasting Systems The ECMWF S4 Better skill in the Equatorial and South Atlantic Mixed results everywhere else (large biases) EUROBRISA PROJECT

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Presentation Transcript
overview
Overview
  • OCEANOBS 09 & EUROBRISA
    • Applications most welcome for the concept of End To End Seasonal Forecasting Systems
  • The ECMWF S4
    • Better skill in the Equatorial and South Atlantic
    • Mixed results everywhere else (large biases)
  • EUROBRISA PROJECT
    • Subseasonal time scales (WCRP workshop in Exeter)
    • Decadal time scales (Paco’s talk)
    • NCEP part of EUROSIP
    • Corean Centre?
end to end seasonal forecasting system

COUPLED MODEL

Atmosphere model

Atmosphere model

Atmosphere model

Ocean model

Ocean model

Ocean model

PROBABILISTIC CALIBRATED FORECAST

ENSEMBLE GENERATION

Forward Integration

Forecast Calibration

Initialization

End-To-End Seasonal forecasting System

Forecast PRODUCTS

OCEAN

a decade of progress on enso prediction

Half of the gain on forecast skill is due to improved ocean initialization

S1 S2 S3

A decade of progress on ENSO prediction
  • Steady progress: ~1 month/decade skill gain
  • How much is due to the initialization, how much to model development?

OceanObs09 plenary paper

what is the value of a long historical record example from the medium range weather forecasts tiggi
What is the value of a long historical record? Example from the Medium Range Weather Forecasts (TIGGI)

Impact of Increased ensemble size versus longer calibration period

(Continuous Rank Probability Skill Score, T-2m Europe)

A longer calibration period has larger impact than increasing the ensemble size. From Hagerdorn 2008

slide6

Prediction of Dengue Risk transmission:

5 month lead time

5-month

lead fcst

Obs

Corr. skill

Forecast issued in Nov 1997, valid for Apr 1998

From EUROBRISA

http://eurobrisa.cptec.inpe.br/

Numerical Model+ Calibration + Dengue model

slide7

ECMWF S4

  • NEMO (ORCA1)+CY36R4
  • Increased atmos resolution (to T255 + 91 levels) [S3 was T159+62 levels)
  • Initial conditions with NEMOVAR, ERA-INTERIM, and…
ecwmf combine ocean re analysis used to initialized ec earth decadal forecasts
ECWMF: COMBINE Ocean Re-AnalysisUsed to initialized EC-EARTH decadal forecasts

It uses NEMO/NEMOVAR, ORCA1 configuration, 42 levels (ORCA1_Z42_v2)

NEMO V3.0 + Local Modifications

.

Forced by ERA40 (until 1989) + ERA Interim (after 1989)

Assimilates Temperature/Salinity from EN3 (corrected XBT’s).

Strong relaxation to SST (OI_v2)

Offline+Online model bias correction scheme (T/S and pressure gradient):

Offline bias term estimated from Argo Period

Latitudinal dependence of the P/T/S bias: P strong at the Eq, weak at mid latitudes. Viceversa with T/S

5 ensemble members (perturbations to wind, initial deep ocean, observation coverage)

assessment of the combine re analysis
Assessment of the COMBINE re-analysis
  • Compared with the CONTROL (e.i., no data assim)
    • Better fit to T/S profiles
    • No degraded Equatorial Currents
    • Spread in the deep ocean
    • Improvement in ENSO forecasts
    • Correlation with altimeter data as a measure of interannual variability: Improvements in the tropics, slight degradation at mid latitudes (especially North East Atlantic)
    • Atlantic MOC?

Further developments for the next operational system (due end of this year):

Altimeter, revised assimilation parameters, partition of bias,SST,…

slide10

RMSE of 10 days forecast

EQ Central Pacific

EQ Indian Ocean

CONTROL ASSIM: T+S ASSIM: T+S+Alti

TROPICAL Pacific

GLOBAL

Altimeter Improves the fit to InSitu Temperature Data

correlation with altimeter
Correlation with Altimeter

COMBINE ASSIM

T+S+Alti

impact of ocean assim in sst forecasts prototype of s4 latest nemovar 36r4
Impact of Ocean Assim in SST forecasts Prototype of S4: latest NEMOVAR+36r4

ASSIMCONTROL

NEMOVAR consistent improves the forecast skill of SST at different lead times and different regions, at SEASONAL TIME SCALES. See Later for Decadal

slide13
Combine project –Strategies for dealing with systematic errors in a coupled ocean-atmosphere forecasting systemProject concept

Nature climate

Flux correction

Normal initialisation

Anomaly initialisation

Model climate

Linus Magnusson et al.

momentum flux correction rationale
Momentum flux correction - rationale

Systematic wind error (example October)

experiments
Experiments

Seasonal (14-month forecasts), 1989-1999, Start dates November and May

Decadal (10-year), 1960-2005, Start dates November every 5th year

Control forecast

Anomaly initialisation

Momentum flux correction

Heat and momentum flux correction

Model cycle 36r1, Nemo version 3, sampled sea-ice

3 ensemble members

sst bias in decadal integrations fc year 2 10
SST bias in decadal integrations (fc year 2-10)

Control (“or” Anomaly initialisation)

U-flux correction

U- and H-flux correction

t bias cross section equatorial pacific fc year 2 10
T bias cross section, equatorial Pacific (fc year 2-10)

Control (“or” Anomaly initialisation)

U-flux correction

U- and H-flux correction

nino3 4 sst forecasts november 1995 november 1998
Nino3.4 SST forecasts November 1995 – November 1998

Control

Anomaly Initialisation

U-flux correction

U- and H-flux correction

99

96

97

98

99

96

97

98

enso statistics seasonal cycle year 2 10
ENSO statistics – seasonal cycle (year 2-10)

Re-analysis

Control

Anomaly Init.

U- flux corr.

U- and H-flux corr.

Nino 3.4 SST mean

Nino 3.4 SST st. dev.

opinions
Opinions
  • At WCRP there is “thirst” for examples of applications:
    • EUROBRISA is very well placed!
    • Should continue
  • The FORECAST ASSIMILATION project is very powerful
    • THERE is a lot of science to do.