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Some recent developments in the ECMWF model

Some recent developments in the ECMWF model. Mariano Hortal ECMWF Thanks to: A. Beljars (physics), E. Holm (humidity analysis). Noise in forecasts. H+12 from 26-09-2002 at 00z. H+12 Z 10 from 28-12-2002 at 12z. x. x. x. x. x. x. x. x. x. x. x. x. x. x. x. x. X. X. X. X.

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Some recent developments in the ECMWF model

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  1. Some recent developments in the ECMWF model Mariano Hortal ECMWF Thanks to: A. Beljars (physics), E. Holm (humidity analysis)

  2. Noise in forecasts H+12 from 26-09-2002 at 00z H+12 Z10 from 28-12-2002 at 12z

  3. x x x x x x x x x x x x x x x x X X X X -1 0 1 2 I x x x x Linear least square fit interpolation x

  4. SETTLS with LLSI at both departure and arrival in the vertical trajectory computation H+12 from 26-09-2002 at 00z H+12 Z10 from 28-12-2002 at 12z

  5. Radiation (aerosol climatology) Convection and clouds Clouds and boundary layer Land surface Simplified physics for linear and adjoint applications Orography (MAP reanalysis, turbulent orographic form drag) Recent developments in the ECMWF physics

  6. Radiation • 26R3: • Radiation on a separate grid to save costs (instead of 1 out of 4 points). In T511 model radiation is done on T255 grid. • New aerosol climatology • Post-processing of PAR and UV-B • Under development: • RRTM short wave • McICA: Monte Carlo Independent Column Approximation to represent cloud overlap and inhomogeneous clouds by using different samples of the clouds in the different computational intervals (140 g-points in 16 spectral intervals)

  7. Convection and clouds • 26R3: • Clean-up of code and improved numerics leading to better representation of ice fallout • New cloud base/top algorithm based on entraining plume • Convection from any layer in lowest 300 hPa • Revised initiation of convection with perturbed parcels (in T and q) starting from mixed layer properties • Reduced water load in updrafts through more efficient microphysics • Increased entrainment

  8. Convection and clouds old new

  9. Convection and clouds

  10. Clouds and boundary layer • A statistical cloud scheme based on variance of total water is under development • Moist boundary layer mixing scheme is nearly finished (better stratocumulus)

  11. Land surface • Fully implicit tile coupling with less noisy results for the tiles with small fraction • Tiles: • Water • Ice • Wet skin • Low vegetation • Exposed snow • High vegetation • Snow under vegetation • Bare soil

  12. Land surface • An Extended Kalman Filter (EKF) has been developed for soil moisture analysis (as part of the EU project ELDAS). • EKF can assimilate SYNOP-T/RH, Meteosat heating rates, and microwave brightness temperatures Single column simulation for MUREX (France), 1. Control with no data assimilation, 2. EKF with microwave Tb 3. EKF with SYNOP T/RH,4. EKF with surface heating rates

  13. Physics in relation to data assimilation • Linear and adjoint of radiation code has been developed and is currently under test • Simplified cloud and convection schemes have been developed for linear and adjoint applications • Experiments are under way to evaluate assimilation of microwave rain products and brightness T in rainy areas via 1DVAR of TCWV which is assimilated in 4DVAR • TRMM precipitation radar is used for verification

  14. Physics in relation to data assimilation TRMM-PR first guess assim. of TMI-rain rate assim. of TMI Tb

  15. TCWV from GPS 21-10-1999 Orography: MAP reanalysis • Reanalysis with all the additional MAP data is available TCWV from MAP reanalysis, T511 TCWV from operations 1999, T319

  16. New scheme for turbulent orographic form drag • Alternative to effective roughness length concept • Drag is distributed in vertical and implemented on model levels (Brown and Wood, 2003) • Scales between 5 km and 10 m are represented by this scheme • Universal orographic spectrum is assumed to account for scales smaller than 5 km • Standard deviation of orography at scales between about 10 to 2 km is used to drive the scheme (from 1 km data base) Comparison of orographic drag and turbulent surface drag (from vegetation) from new scheme with fine scale model results. Expressed as drag coefficient versus terrain slope.

  17. Nonlinearities in the humidity analysis • Humidity is bounded from below (>0) and restricted close to saturation by condensation. • Analysis increments behave asymmetrically at different levels of relative humidity. • A new humidity analysis accounts for this through nonlinear flow-dependent change of variable,

  18. Some humidity analysis results • With a better background error description, better use is made of humidity observations. • An example is given by HIRS-12 humidity sensitive radiances. • The new humidity analysis (bottom) has removed unrealistic outliers in the background error description. • This results in better humidity forecasts.

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