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ECMWF Report 2010-2011 ... Slide 1 ECMWF report - WGNE 2011 Slide 1, ©ECMWF ECMWF Report 2010-2011 Jean-Noël Thépaut ECMWF October 2011 and many colleagues from the Research Department

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Text of ECMWF Report 2010-2011 ... Slide 1 ECMWF report - WGNE 2011 Slide 1, ©ECMWF ECMWF Report...

  • Slide 1

    ECMWF report - WGNE 2011 Slide 1, ©ECMWF

    ECMWF Report 2010-2011

    Jean-Noël Thépaut ECMWF

    October 2011

    and many colleagues from the Research Department

  • Slide 2

    Forecasting system upgrades 2010-11  Cycle 36R4 – 11 November 2010

      5-species prognostic cloud scheme   SEKF soil moisture and OI snow analysis   Convection entrainment, all-sky assimilation refinement, 2 outer loops in

    early-delivery 4D-Var, etc.

     Cycle 37R2 – 18 May 2011   Reduced AMSU-A radiance observation errors   Use of EDA variances in 4D-Var background error formulation   Cloud scheme modification, GPSRO obs operator improvement, etc.

     Cycle 37R3 – November 2011   Aircraft temperature bias correction   Combined UV-IR ozone observation assimilation   Cloud scheme & surface roughness modification, assimilation of Stage-IV,

    stratospheric model error cycling , convection detrainment, etc.

     Outlook ECMWF report - WGNE 2011 Slide 2, ©ECMWF

  • 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!

  • Slide 4

    New prognostic cloud microphysics scheme

    WATER VAPOUR

    CLOUD

    Liquid/Ice

    PRECIP Rain/Snow

    Evaporation

    CLOUD FRACTION

    CLOUD

    FRACTION

    Old Cloud Scheme New Cloud Scheme

    •  2 prognostic cloud variables + w.v. •  Ice/water diagnostic Fn(T)

    •  Diagnostic precipitation

    •  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!

    Slide 4, ©ECMWF ECMWF report - WGNE 2011

  • Slide 5

    New prognostic cloud microphysics Representation of mixed phase

    •  One of the most significant changes in the new scheme is the physical representation of the mixed phase.

    •  Old scheme: Single prognostic condensate and diagnostic fn(T) split between ice and liquid cloud

    •  New scheme: Prognostic liquid and prognostic ice and any fraction of supercooled liquid water allowed for a given T (determined by sources+sinks)

    PDF of liquid water fraction of cloud for

    the diagnostic mixed phase scheme

    (dashed line) and the prognostic ice/

    liquid scheme (shading)

    Slide 5, ©ECMWF ECMWF report - WGNE 2011

  • Slide 6

    Te m

    pe ra

    tu re

    Ice Water Content (g m-3)

    -80

    -60

    -40

    -20

    0 106 105 104 103 102 101 100

    -80

    -60

    -40

    -20

    0

    -80

    -60

    -40

    -20

    0 106 105 104 103 102 101 100 106 105 104 103 102 101 100 Ice Water Content (g m-3) Ice Water Content (g m-3)

    CloudSat/CALIPSO observations

    ECMWF old scheme without snow

    ECMWF new scheme with snow

    New scheme with prognostic ice and snow allows much higher ice water contents (seen by the radiation scheme)

    Relative frequency of occurrence of ice/snow for NH mid-latitudes in June 2006: ECMWF model vs. Cloudsat/Calipso retrievals

    CY36R4 highlights: New cloud scheme

    Slide 6, ©ECMWF ECMWF report - WGNE 2011

  • Slide 7

    Ice water content zonal cross-sections (1 year average)

    •  Previous scheme had unrealistic peaks of ice water content in 0 to -23°C temperature range.

    •  New scheme reduces cloud ice water content in closer agreement with CloudSat derived estimate.

    •  Careful comparison required at lower altitudes due to limitations of CloudSat observations.

    Previous scheme

    New scheme

    mg m-3

    CloudSat (non-convective, non-precipitating derived ice water)

    Slide 7, ©ECMWF ECMWF report - WGNE 2011

  • Slide 8

    M od

    el le

    ve l Cy37r3 super-cooled liquid (red) and ice (green)

    52°S 63°S

    Cy37r2 super-cooled liquid (red) and ice (green)

    M od

    el le

    ve l

    Southern Ocean SLW layer from CALIPSO

    Ice

    SLW

    50S 60S

    (4) Super-cooled liquid water Super-cooled liquid water

    •  Super-cooled liquid water (SLW) cloud frequently occurs in atmosphere down to -30°C and below (as seen in aircraft obs, lidar etc.)

    •  Fine balance between turbulent production of water droplets, nucleation of ice, deposition growth and fallout.

    •  New cloud scheme represents microphysical processes in mixed- phase cloud rather than a diagnostic.

    •  Cy36r4/Cy37r2 had less SLW, Cy37r3 increases SLW, particularly at cloud top (as often observed).

    SLW layer (blue)

    Slide 8, ©ECMWF ECMWF report - WGNE 2011

  • Slide 9

    Land surface data assimilation evolution

    1999 2004 2009 2010/2011

    Optimum Interpolation (OI)

    screen level analysis

    Douville et al. (2000)‏ Mahfouf et al. (2000) ‏

    Soil moisture analysis based on Temperature and relative

    humidity analysis

    Revised snow analysis

    Drusch et al. (2004)‏ Cressman snow depth

    analysis using SYNOP data improved by using NOAA / NSEDIS Snow cover extend

    data

    Recent developments/implementations: •  SEKF (Simplified Extended Kalman Filter) surface analysis •  Use of active microwave data: ASCAT soil moisture product •  Use of passive microwave SMOS Brightness Temperature product •  New snow analysis and use of NOAA/NESDIS 4km snow cover product

    Structure Surface Analysis

    OI snow analysis and high resolution NESDIS data (4km)

    SEKF Soil Moisture analysis Simplified Extended Kalman Filter

    METOP-ASCAT SMOS

    Slide 9, ©ECMWF ECMWF report - WGNE 2011

  • Slide 10

    ECMWF report - WGNE 2011

    CY36R4 highlights: SEKF soil moisture analysis

      ECMWF land surface analysis:   Snow  depth    SYNOP     Snow  cover    NESDIS  (AVHRR,  SSM/I,  sta?ons)     2  m  T      SYNOP     2  m  RH      SYNOP     Soil  moisture    SYNOP  (T2m,  RH2m)  →  ASCAT  →  SMOS  →  SMAP     Soil  temperature  SYNOP  (T2m)   Land  and  atmospheric  analyses  are  performed  separately    indirect  coupling  

      OI:    computa?onally  cheap     simple  error  covariance  formula?on     only  based  on  screen-­‐level  observa?ons,  fixed  rela?onship  

      EKF:  computa?onally  expensive  (currently  w/  finite  differences  for  Jacobians)     physically  based,  i.e.  uses  land  surface  model     open  for  using  more  diverse  observa?ons  (at  correct  ?me)     (SEKF:  B  is  sta?c)   Slide 10, ©ECMWF

  • Slide 11

    EKF soil moisture analysis

    -  Dynamical estimates of the Jacobian Matrix that quantify accurately the

    physical relationship between observations and soil moisture

    -  Flexible to account for the land surface model H-TESSEL evolution

    -  Makes it possible to combine different sources of information

    -  Possible to investigate the use of new generation of satellite data:

    - Active microwave (C-Band MetOp/ASCAT) ‏

    - Passive microwave (L-band SMOS, SMAP)‏

    SYNOP ASCAT SMOS

    Slide 11, ©ECMWF ECMWF report - WGNE 2011

  • Slide 12

    Impact on 2-meter Temperature Forecasts

    Global mean RMS (against SYNOP)

    T2m 48h FC errors (OI-SEKF)  EKF improves T2m

    - EKF consistently improves SM & T2m - Makes it possible to assimilate satellite

    data to analyse soil moisture I m

    pr ov

    ed

    de gr

    ad ed

    Slide 12, ©ECMWF ECMWF report - WGNE 2011

  • Slide 13

    CY36R4 highlights: SEKF soil moisture analysis

    36R4 – 11/2010

    as 32R3 – 11/2007

    as ERA-Interim

    colder than ERA-I analysis warmer than ERA-I analysis

    Mean annual 2-metre temperature errors from 13-month model integrations

    Slide 13, ©ECMWF ECMWF report - WGNE 2011

  • Slide 14

    A new snow analysis Snow analysis uses SYNOP snow depth data and

    NOAA/NESDIS IMS snow cover

    2010 implementation: -  New Snow analysis based on the Optimum

    Interpolation with Brasnett 1999 structure functions

    - A new IMS 4km snow cover product to replace the 24km product

    - Improved QC (monitoring, Blacklisting)

    2011: - Assimilate additional snow data From Sweden (New Report Type

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