Download - HIRLAM-6, development since last time
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Europaav Kolumn B
ALADIN (12)COSMO (5)HIRLAM (8)UK (1)
Europa
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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)
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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
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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)
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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 !
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HIRLAM strategy - meso-scale
HIRLAM-ALADIN cooperation
ALADIN
SLV
PORSLK
CZ
A
CRO
HUN
ROM
MOR
PL
MF
HIRLAM
FI
IC
NL
IR
NO
SP
DK
SW BEL
MOLTUNBUL
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HIRLAM strategy - meso-scale
HIRLAM - ALADIN Code development
BEL
Météo-France
ARPEGE HIRALDECMWF
CZ
A
SL
CRO
POR
SLO
SLK
PL
MOR
IFS/ARPEGE code
HUN
SPNL
DKFISWNO
IR
IC
Shared ALADIN/HIRLAM code
BUL
ROMTUN
MOL
HIRLAM synoptic code
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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)
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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
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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
• => Background check, analysis increments
• Analytical balance (enh) ->statistical balance
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3D-VAR (cont)
• FGAT - First Guess at Appropriate Time
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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
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4D-VAR single obs 3 Dec 99 06-12
3 Dec 06 3 Dec 06 ->3 Dec 12
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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
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4D-VAR
Estimated computer requirements of SL incremental 4D-VAR
Estimated cost of SL incremental 4D-VAR
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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
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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
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New Snow analysis
•SSM/I will help – LAND SAF data -
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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
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Reference case GPS included Radar
20020712_06 (analysis time)
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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
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Stable stratification - increased mixing
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Increased vegetational roughness
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Turning of wind stress
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Turning of wind stress II
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Turning of stress and smooth mixing (Tijm, 2004)
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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
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Tclim
ISBA: snow covering parts of fractions 3 and 4
Td snow
Td 3 and 4Ts2 snow
Ts2 3 and 4
Ts snow Ts 3 and 4T
snow
Thermally active layer
snow in beginning of timestep Snow change
mixing of T in soilbetween timesteps
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
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• Soil moisture adapts in assimilation to different vegetation types
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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
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Radiation and condensation
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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
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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
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Tanguy-Ritchie SL T-equation, SL extr
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Transparent Boundary conditions
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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 ?
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New HR rotated climate data sets
0.025 0.0125
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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
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Bias corrected
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SMHI HIRLAM - 11 km -> HR-FAR
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SMHI HIRLAM - Dec ->
HR-FAR
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Effect from esat condensation och radiation