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HIRLAM 3/4D-Var developments

HIRLAM 3/4D-Var developments. Nils Gustafsson, SMHI. HIRLAM for the synoptic scales: 3D-Var and 4D-Var Further developments during 2008-2009 To be phased out operationally 2010-2012. HARMONIE for the mesoscale: Based on ALADIN (IFS) 3D-Var mid 2008 4D-Var early 2009

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HIRLAM 3/4D-Var developments

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  1. HIRLAM 3/4D-Var developments Nils Gustafsson, SMHI

  2. HIRLAM for the synoptic scales: 3D-Var and 4D-Var Further developments during 2008-2009 To be phased out operationally 2010-2012 HARMONIE for the mesoscale: Based on ALADIN (IFS) 3D-Var mid 2008 4D-Var early 2009 To replace the synoptic scale HIRLAM (2010-2012) Parallel data assimilation work along 2 lines in HIRLAM

  3. HIRLAM 4D-Var components: • Tangent linear and adjoint of the semi-Lagrangian (SETTLS) spectral HIRLAM. • Simplified physics packages: Buizza vertical diffusion and Meteo France (Janiskova) package(vertical diffusion, large-scale condensation and convection). • Multi-incremental minimization (spectral or gridpoint HIRLAM in outer loops). • Weak digital filter constraint. • Control of lateral boundary conditions.

  4. Noise in assimilation cycles with the gridpoint model

  5. Comparison tests 3D-Var – 4D-Var • SMHI area, HIRLAM 7.1.1, KF/RK, SMHI area, statistical balance background constraint, reference system background error statistics (scaling 0.9), no ”large-scale mix”, LINUX cluster, 4.5 months, operational SMHI observations and boundaries • 3D-Var with FGAT, incremental digital filter initialization • 4D-Var, 6h assimilation window, weak digital filter constraint, no explicit initialization

  6. Summary of forecast scores

  7. Operationalization of 4D-Var • SMHI tests show positive impact of 4D-Var in comparison with 3D-Var • SMHI results need to be confirmed with the reference system (and new NL physics) • Improved parallel scaling is needed: (a) openMP within nodes & MPI between nodes; (b) Message passing for SL advection ”on demand” • To be included in HIRLAM 7.2 (late 2007)

  8. Pre-operational tests of 4D-Var at SMHI Cop - SMHI op. 22 km, Hirlam-6.3.5, KF/RK, 3DVAR FGAT Cnn - Hirlam-7.1.2, KF/RK, 4DVAR

  9. Illustration structure functions Impact of one single surface pressure observation 5 hPa less than the corresponding background equivalent (red: surface pressure, black: winds at lowest mod level) Analytical NMC (48-24) Statistical NMC (36-12) Statistical Ensemble

  10. Flow dependent background covariances through non-linear balance equations Non-linear balance equation on pressure levels: Tangent-linear version of balance equation on pressure levels: Tangent-linear version of omega equation on pressure levels: where

  11. Vertical crossection of T increments Statistical balance Weak constraints balance eq.

  12. A new moisture control variable and a new moisture balance Within the analytical balance formulation we follow Holm and use relative humidity as control variable and the TL RH definition for the balance: In addition, the background error variance depends on the background relative humidity (makes it more Gaussian). Within the statistical balance formulation (with q as control variable), we already have a statistical balance relation:

  13. In order to avoid double-counting of the temperature-moisture balance, we could try to improve the statistical balance relation by using coefficients from the analytical balance relation, for example: So far we have tried: In this case, we also used a background error variance depending on the background relative humidity

  14. New assimilation control variable for humidity (analytical balance version) q Old formulation New formulation: RH*= RH/σb(RHb+0.5RH)

  15. New assimilation control variable for humidity (statistical balance with multivariate humidity) Assimilation increments due 5 simulated specific humidity observations, 10 g/kg smaller than corresponding background equivalent (sigmao: 1 g/kg) q at 850 hPa (g/kg times 10) ps (hPa times 10)

  16. SEVIRI data coverage (At SMHI, we don’t store the raw-data for the full SEVIRI disc operationally)

  17. Example of impact of SEVIRI data on 3D-Var analysis • Difference of analysed 500hPa relative humidity (SEVIRI experiment minus Control) • Impact can be seen mainly in the southern part of the domain

  18. 3D-Var 4D-Var

  19. 3D-Var/4D-Var impact study • 3D-Var: • Positive impact on upper-troposhperic water vapour is found • Positive impact on MSLP forecast is found • 4D-Var: • Positive impact on upper-tropospheric water vapour is found • Also Temperature and Geopotential fields show some response (small positive impact) • Another impact study for December 2005 shows neutral impact of SEVIRI data in terms of forecast scores. Work is now continuing with a much more difficult problem, assimilation of cloudy SEVIRI radiances!

  20. What can we expect to achieve with the HIRLAM data assimilation before it will be phased out? • 4D-Var with several outer loops and improved moist physics • Control of lateral boundary conditions in 4D-Var • A new moisture control variable • Large scale mix vi a Jk cost function term • Background and large scale error statistics based on EnsAss • Tuning of screening and VarQC • Use of several new types of observations. (IASI?) Most development efforts should be finished during 2008! A synoptic scale HARMONIE should be comparable!!

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