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

EWGLAM Oct Noise in forecasts H+12 Z 10 from at 12z H+12 from at 00z

EWGLAM Oct Linear least square fit interpolation xxxx X X X X I xxxx xxxx xxxx xxxx x

EWGLAM Oct SETTLS with LLSI at both departure and arrival in the vertical trajectory computation H+12 from at 00z H+12 Z 10 from at 12z

EWGLAM Oct Recent developments in the ECMWF physics  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)

EWGLAM Oct 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)

EWGLAM Oct 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

EWGLAM Oct Convection and clouds newold

EWGLAM Oct Convection and clouds

EWGLAM Oct 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)

EWGLAM Oct 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

EWGLAM Oct 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

EWGLAM Oct 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

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

EWGLAM Oct Orography: MAP reanalysis Reanalysis with all the additional MAP data is available TCWV from GPS TCWV from MAP reanalysis, T511 TCWV from operations 1999, T319

EWGLAM Oct 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.

EWGLAM Oct 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,

EWGLAM Oct 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.