Status and plans for the ecmwf forecasting system
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Status and plans for the ECMWF forecasting System. Overview. Performance of the forecasting system Research highlights: CY36R2 (22 June 2010): GRIB API and Ensemble Data Assimilation (to initiate the EPS) CY36R4 (9 November 2010): New physics package, surface EKF, snow analysis,…

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Overview
Overview

  • Performance of the forecasting system

  • Research highlights:

    • CY36R2 (22 June 2010): GRIB API and Ensemble Data Assimilation (to initiate the EPS)

    • CY36R4 (9 November 2010): New physics package, surface EKF, snow analysis,…

    • CY37R2 (in the pipeline): Ensemble Data Assimilation to provide flow-dependent variances to 4D-Var, reduction of observation error for AMSU-A, GRIB-2 for model level fields

Bilateral meeting 2011


Overview1
Overview

  • Performance of the forecasting system

  • Research highlights:

    • CY36R2 (22 June 2010): GRIB API and Ensemble Data Assimilation (to initiate the EPS)

    • CY36R4 (9 November 2010): New physics package, surface EKF, snow analysis,…

    • CY37R2 (in the pipeline): Ensemble Data Assimilation to provide flow-dependent variances to 4D-Var, reduction of observation error for AMSU-A, GRIB-2 for model level fields

Bilateral meeting 2011



Comparison with other centres autumn nh
Comparison with other centres:autumn, NH

Bilateral meeting 2011


Precipitation skill europe
Precipitation skill Europe

D+2

D+4

Bilateral meeting 2011


Comparison of tc forecasts from hko 2008 2009 western north pacific
Comparison of TC forecastsfrom HKO, 2008-2009, western North Pacific

Bilateral meeting 2011


Russian heat wave
Russian heat wave

Bilateral meeting 2011


Overview2
Overview

  • Performance of the forecasting system

  • Research highlights:

    • CY36R2 (22 June 2010): GRIB API and Ensemble Data Assimilation (to initiate the EPS)

    • CY36R4 (9 November 2010): New physics package, surface EKF, snow analysis…

    • CY37R2 (in the pipeline): Ensemble Data Assimilation to provide flow-dependent variances to 4D-Var, reduction of observation error for AMSU-A, GRIB-2 for model level fields

    • others

Bilateral meeting 2011


November 2010 ifs cycle 36r4

Selected contents

Prognostic rain and snow with more comprehensive cloud microphysics

EKF for soil moisture analysis

New snow analysis (O-I)

Enhancement of all-sky radiance assimilation

November 2010 IFS cycle 36r4


New prognostic cloud microphysics scheme
New prognostic cloud microphysics scheme

WATER VAPOUR

Evaporation

Condensation

CLOUD FRACTION

CLOUD

Liquid/Ice

Evaporation

CLOUD FRACTION

Autoconversion

PRECIP Rain/Snow

Current Cloud Scheme

New Cloud Scheme

  • 5 prognostic cloud variables + water vapour

  • Ice and water now independent

  • More physically based, greater realism

  • Significant change to degrees of freedom

  • Change to water cycle balances in the model

  • More than double the lines of “cloud” code!

  • 2 prognostic cloud variables + w.v.

  • Ice/water diagnostic Fn(T)

  • Diagnostic precipitation


New prognostic cloud microphysics representation of mixed phase
New prognostic cloud microphysicsRepresentation of mixed phase

  • The most significant change in the new scheme is the improved physical representation of the mixed phase.

  • Current scheme: diagnostic fn(T) split between ice and liquid cloud(a crude approximation of the wide range of values observed in reality).

  • New scheme: wide range of supercooled liquid water for a given T.

PDF of liquid water fraction of cloud for the diagnostic mixed phase scheme (dashed line) and the prognostic ice/liquid scheme (shading)


A new snow analysis i

For snow SYNOP reports an satellite based snow cover are assimilated

A new snow analysis (I)

A new 4 km IMS snow cover is assimilated into a new OI analysis replacing Cressmaninterpolation

Here shown isthe analysedsnow cover

Cressman and IMS_24km OI and IMS 4km


Impact of cycle 36r4
Impact of Cycle 36r4 assimilated

Bilateral meeting 2011


Seeps impact of 36r4
SEEPS: impact of 36r4 assimilated

Bilateral meeting 2011


Overview3
Overview assimilated

  • Performance of the forecasting system

  • Research highlights:

    • CY36R2 (22 June 2010): GRIB API and Ensemble Data Assimilation (to initiate the EPS)

    • CY36R4 (9 November 2010): New physics package, surface EKF, snow analysis…

    • CY37R2 (in the pipeline): Ensemble Data Assimilation to provide flow-dependent variances to 4D-Var, reduction of observation error for AMSU-A, GRIB-2 for model level fields

    • others

Bilateral meeting 2011


In the pipeline ifs cycle 37r2

Selected contents assimilated

Increased weight to AMSU-A data

Direct use of EDA in 4D-Var

Retuning of new physics

GRIB-2 for model level fields

In the pipeline: IFS cycle 37R2


Ensemble of data assimilations eda
Ensemble of Data Assimilations (EDA) assimilated

  • Perturbed observations

  • Perturbed SSTs

  • Stochastic physics

Δx2

Δx1

Ensemble initial

perturbations

Δx3

Δx4

X0

4DVAR

X+12h


Eda flow dependent variances
EDA – flow dependent variances assimilated

9h forecasts

23/1 2009 21 UTC

Standard deviation of zonal wind component at ~850hPa and pmsl

ms-1

24/1 2009 21 UTC


Impact of cycle 37r2
Impact of Cycle 37R2 assimilated

NH

SH

Z

VW

Bilateral meeting 2011


Overview4
Overview assimilated

  • Performance of the forecasting system

  • Research highlights:

    • CY36R2 (22 June 2010): GRIB API and Ensemble Data Assimilation (to initiate the EPS)

    • CY36R4 (9 November 2010): New physics package, surface EKF, snow analysis…

    • CY37R2 (in the pipeline): Ensemble Data Assimilation to provide flow-dependent variances to 4D-Var, reduction of observation error for AMSU-A, GRIB-2 for model level fields

    • Others (small selection)

Bilateral meeting 2011


Main research development topics 2011
Main assimilatedresearch/development topics 2011:

  • Ensemble data assimilation methods (EDA, EKF)

  • Weak constraint, long window 4D-Var

  • Vertical resolution increase

  • Numerical experimentation into the “grey zone”

  • Improved physical parameterizations

  • Implement NEMO ocean model and NEMOVAR in EPS

  • Seasonal forecasting system 4

  • ERA-CLIM

  • MACC in Near-Real-Time

  • IFS maintenance and optimisation (cycles, code, scripts)

  • Object Oriented Prediction System

Research Department Annual Plan 2011



Ensemble kalman filter development
Ensemble Kalman filter development assimilated

Bilateral meeting 2011