ECMWF cloud scheme: Validation and Direction Adrian Tompkins

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ECMWF cloud scheme Validation and
DirectionAdrian Tompkins

• The MP Question What have ECMWF ever done for
  us?
• ECMWFs minor role in Cloudnet To provide data
  and await feedback?
• Due to my lack of time, this puts the data in the
  slow feedback loop

Model parametrization
2. Validation
1. Development
Data
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Validation

• Example Validation of model versus Meteosat
  Brightness Temperatures
• Expensive (human resources) validation for a
  fixed period
• But what if t (validation) gtgt t (model cycle
  updates) ?
• i.e. When results arrive they refer to old
  cycle

Courtesy of F. Chevallier
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Uses of ARM

• ARM data has been used as a validation tool
• Cloud cover, Cloud ice retrievals from radar
  (Janiskova)
• Simulated Z (Morcrette)
• Surface radiative fluxes and liquid water paths
  (JJM)
• 2D-Var assimilation of radar data to test future
  cloudsat use (Bennedetti and Lopez)
• SGP data used to validate new turbulence model
  (Neggers and Koehler)
• Cases studies and one-offs, no routine use in
  model cycle development

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Development

• Development can mean using the data to derive /
  develop / tune a parametrization
• e.g. Tompkins and Di Giuseppe use cloudnet data
  to tune and test a new SW cloud overlap
  parametrization for solar zenith angle effects on
  cloud geometry

ECMWF SW albedo error with respect to a TIPA
benchmark calculation using over 100 cloud scenes
taken over Chilbolton
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Development

• Hogan Length-scale tuned to give correct Cloud
  Cover over Chilbolton, then used for 600
  Palaiseau scenes as independent test
• Experience Data extremely easy to use
• Reprocessing of ARM site data extremely welcome!!!

ECMWF SW albedo error with respect to a benchmark
calculation using over 600 cloud scenes taken
over Palaiseau
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Development

• Can also mean a validation tool fast and
  efficient enough to be included in
  parametrization tests
• ECMWF T799 L91 medium-range scores
• RMS, AC of Z,T,U
• Parametrization Group climate suite
• 3 member 13 month atmosphere only T159L91
• Validation seasons against MODIS, ISCCP,
  Quikscat, SSMI, TRMM, GPCP, Xie-Arkin, Da-Silva,
  CERES, ERBE
• For parameters of LWP, TCWV, TCC, 10m winds,
  rainfall, TOA radn fluxes, surface heat fluxes

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Example ISCCP Total cloud cover model cycle
29r1operational early 2005
Issue Cloudnet in slower feedback loop, but
independent and comprehensive validation (also
over points) extremely important
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Validation and tuning
Model parametrization
Fast validation tuned metric
Slow validation Independent source
Data error
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ECMWF Validation needs Ice!

• Information from cloudnet regarding glaciated
  clouds is useful
• e.g. First comparison of ice water content
  comparison with microwave limb sounder (Frank Li
  et al.)

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ECMWF validation needs Higher order moments

• Information on subgridscale variability of ice,
  liquid and water vapour is paramount to
  developments of statistical cloud cover schemes
• Much emphasis has been placed on this, and the
  Cloudnet results will be central to efforts at
  ECMWF

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ECMWF Directions, Short term

• Numerics have been revised to reduce sensitivity
  to vertical resolution (moving from T511L60 to
  T799L91 soon)
• Ice sedimentation now a pure advection term
• Ice-to-Snow autoconversion added to model
• Simple diagnostic parametrization to allow
  supersaturation with respect to ice
• Final testing for implementation early 2006

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ECMWF Directions, Medium term

• Prognostic ice mass mixing ratio
• Prognostic ice number concentration
• Prognostic moments of total water, with cloud
  cover derived from a statistical cloud scheme
• Interaction between aerosols and microphysics
  (GEMS)
• Attention to numerics

Reduction in ice water path in response to 3x
dust aerosols over Africa