Europaav Kolumn B

ALADIN (12)COSMO (5)HIRLAM (8)UK (1)

Europa

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)

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

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)

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 !

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

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

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)

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

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

3D-VAR (cont)

• FGAT - First Guess at Appropriate Time

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

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

3 Dec 06 3 Dec 06 ->3 Dec 12

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

4D-VAR

Estimated computer requirements of SL incremental 4D-VAR

Estimated cost of SL incremental 4D-VAR

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

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

New Snow analysis

•SSM/I will help – LAND SAF data -

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

Reference case GPS included Radar

20020712_06 (analysis time)

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

Stable stratification - increased mixing

Increased vegetational roughness

Turning of wind stress

Turning of wind stress II

Turning of stress and smooth mixing (Tijm, 2004)

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

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

• Soil moisture adapts in assimilation to different vegetation types

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

Radiation and condensation

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

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

Tanguy-Ritchie SL T-equation, SL extr

Transparent Boundary conditions

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 ?

New HR rotated climate data sets

0.025 0.0125

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

Bias corrected

SMHI HIRLAM - 11 km -> HR-FAR

SMHI HIRLAM - Dec ->

HR-FAR

Effect from esat condensation och radiation