HIRLAM-6, development since last time

2. HIRLAM-6, development since last time • Strategy - ALADIN - MF - collaboration • Data assimilation, 3D/4D-VAR, surface • Observation Usage • Parameterisation – • turbulence and convection • Surface and radiation • Physics coupling - boundary conditions • Meso-scale modelling • EPS • Regular Cycle with the Reference (FMI)

3. HIRLAM-6 Memorandum of Understanding • Targets • achieve highest possible accuracy for severe weather and of wind, precipitation and temperature • develop 3D/4D-VAR further and its use of non-conventional data • maintain the regular analysis/forecasting cycle • continue development of synoptic model 10-20 km • develop meso-scale non-hydrostatic operational model with suitable physical parameterisation • Overhaul of complete System • develop methods for probabilistic forecasting • continue development of verification methods

4. HIRLAM strategy - synoptic • Synoptic model, 10-20 km, every 6 hours -> 2 (3) days, 4D-VAR and satellite data over a (fairly) large area • provides comprehensive set of forecast parameters for applications and driving other models • boundary conditions and tight coupling to meso-scale model • covers window between ECMWF forecasts - more recent observations and boundaries (frames)

5. HIRLAM strategy - meso-scale • Meso-scale data assimilation and model , 2-3 km non-hydrostatic model +3-12 (24 h) • physics for 2km, explicit convection • turbulence and radiation non-local (later, ~ 1 km ) • rapid update cycle, vast amount of regional data available, conv/non-conv, reflectivity, precipitation .. • 4D-VAR /3D-VAR FGAT - if in short time - spinup? • Boundary field impact, transparent boundary conditions !

6. HIRLAM strategy - meso-scale

7. HIRLAM strategy - meso-scale

8. HIRLAM research profile • Physics interfaces - combinations • HIRLAM physics / AROME physics • Synoptic physics HIRLAM/ALARO • Synoptic 4D-VAR - migrate to ALARO • Meso-scale 4D-VAR • Meso-scale basis functions - Jb - • Observations - radar winds, surface, refl. Cloud, • Large scale coupling - spectral - extension zone • Meso-scale validation • Probabilities with EPS and physical perturbations • Surface modelling and assimilation (SST)

9. HIRLAM meso-scale group • Learning - set up of ALADIN - climate - coupling • DMI-SMHI-FMI-INM - • Set up of domain(s) • Physics interface - temporary - general HIRLAM and AROME • First experiments • Coupling with HIRLAM outer model

10. Data assimilation -3D-VAR • 3D-VAR background constraint Jb : • (xb - H(y))TB-1 (xb - H(y)) , sigma-b, horizontal variation, new structure functions • => Background check, analysis increments • Analytical balance (enh) ->statistical balance

11. 3D-VAR (cont) • FGAT - First Guess at Appropriate Time

12. 4D-VAR Data Assimilation • Adjoints of semi-Lagrangian spectral model • Multi-incremental minimisation - low resolution • Optimisations of transforms • > significant gain in economy, feasible for operations

13. 4D-VAR single obs 3 Dec 99 06-12 3 Dec 06 ->3 Dec 12 3 Dec 06

14. 4D-VAR argument • Optimal solution in time including all information • Iterativ method enabels non-linear operators - • possible in 3D too, but : • Non-linear analysis can transfer a vortex • The model analyses non-observed quantaties • Possible to use integrated observations • Enables high time resolution of data and time sequence can be utilised - e.g. radar • Model generated structure functions • necessary for meso-scale

15. 4D-VAR Estimated cost of SL incremental 4D-VAR Estimated computer requirements of SL incremental 4D-VAR

16. 4D-VAR activity now • Jc DFI - control of noise - NNMI in iterations • Optimisation • Multi-incremental and real trials • 120 - 45 km minimisation, 22 - 17 km fcs • about 1 hour for very large area

17. Analysis of surface parameters • OI SST and Ice analysis • Ocean Sea Ice SAF data - • New OI snow analysis ready for implementation • QC and bias correction (due to height differences) • Tuning of 2m T och RH analysis (statistics) Old New

18. New Snow analysis • SSM/I will help – LAND SAF data -

19. Observation Usage • Conventional data • radiosonde launch times • radiosonde drift • comparing observation availability • Remote sensing data • AMSU-A • AMSU-B • QuikScat • Radar doppler winds • GPS ZTD • WINDPROFILER

21. DMI Jan/Feb 2003

22. Reference case GPS included Radar 20020712_06 (analysis time)

23. HIRLAM EWP feasibility study

24. Forecast Model - parameterisation • Turbulence (CBR TKE-l) • Much attention to stable case - more mixing at high stability - modified - cut - smooth Ri >1 • Increased roughness - vegetational - orografical • Direction of surface stress vector • => filling of lows, reduce 10 m wind • Moist conservative and moist stability version • effect of condensation on stability

25. Stable stratification - increased mixing

26. Increased vegetational roughness

27. Turning of wind stress

28. Turning of wind stress II

29. Turning of stress and smooth mixing (Tijm, 2004)

30. Snow scheme in ISBA main modifications to original code: • Only new snow scheme on fractions 3 and 4 and now 5 • Force-restore formulation replaced by heat conduction • Heat capacity of uppermost layer replaced by 1 cm • moist soil. • A second soil layer (7.2 cm) • Forest area decreased so that at least 10% of area • is low-vegetation • At present (temporarily!) no soil freezing • Forest tile, being developed - canopy snow and ground

31. ISBA: snow covering parts of fractions 3 and 4 snow in beginning of timestep Snow change • Features of the snow scheme: • move the snow from fractions 3 and 4 to fraction 6 every timestep • one layer of the snow, with a thermally active layer < 15 cm • water in the snow, which can refreeze • varying albedo and density • mirroring of temperature profile in the ground to assure correct memory Thermally active layer Ts snow T snow Ts 3 and 4 Ts2 3 and 4 Td 3 and 4 Ts2 snow Tdsnow mixing of T in soil between timesteps Tclim

34. Soil moisture adapts in assimilation to different vegetation types

35. Radiation and snow cover • Soil Freezing - implemented • esat for ground <0 for ice implemented • esat over water and ice following K-I Ivarsson • distribution water - ice in clouds to be consistent - large effect on emissivity - implemented • radiation for sloping ground calculated - for HR

36. Radiation and condensation

37. Convection - condensation • Kain-Fritsch Rash-Kristjanson • extensive tests and verification at 22 km • better humidity • 11 km indicates better results • Expensive, and very much so, on vector systems • Possible vectorised version

38. Model dynamics and embedding • Coupling between SL advection and physics • Semi-Lagrangian mods for orography (T eq.) • Boundary relaxation (Host orography, interp.) • Development of transparent boundary conditions • Incremental Digital Filter Initialisisation • Ensemble forecasts with HIRLAM • Verification methods - meso-scale - Workshop • Climate system developments • System - upgrades - Reference test - RCR • Communication - HeXNeT - RCR monitoring

39. Tanguy-Ritchie SL T-equation, SL extr

40. Transparent Boundary conditions

41. Transparent LBC progress • 2D-shallow water model - several results • 3D-simplest 2 layer baroclinic • 3D-multilevel Z - • eigenvalues - Laplace transform • demonstrated • 3D-mulitlevel eta - to be done • Spectral LAM - extension zone - programming ?

42. New HR rotated climate data sets 0.025 0.0125

44. Conclusions • Systematic near surface errors adressed and worked on • turbulence, surface scheme, radiation-clouds • New orientation towards Meso-scale • Collaboration with ALADIN • 4D-VAR for synoptic scales • More remote sensing • Lateral Boundary conditions developing - necessary • Monitoring and quality of Reference system

45. DMI Jan/ Feb 2003 Bias corrected

46. SMHI HIRLAM - 11 km -> HR-FAR

47. SMHI HIRLAM - Dec -> HR-FAR

48. Effect from esat condensation och radiation