Ingen bildrubrik

1
(No Transcript)
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 -gt 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))T B-1 (xb - H(y)) , sigma-b,
  horizontal variation, new structure functions
• gt Background check, analysis increments
• Analytical balance (enh) -gtstatistical 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
• gt significant gain in economy, feasible for
  operations

13
4D-VAR single obs 3 Dec 99 06-12
3 Dec 06
-gt3 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

20
(No Transcript)
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 gt1
• Increased roughness - vegetational - orografical
• Direction of surface stress vector
• gt 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 lt 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
Td snow
mixing of T in soil between timesteps
Tclim
32
(No Transcript)
33
(No Transcript)
34

• Soil moisture adapts in assimilation to different
  vegetation types

35
Radiation and snow cover

• Soil Freezing - implemented
• esat for ground lt0? 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
43
(No Transcript)
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 -gt
HR-FAR
47
SMHI HIRLAM - Dec -gt
HR-FAR
48
Effect from esat condensation och radiation