Progress in the joint HIRLAM/ALADIN mesoscale data assimilation activities Nils Gustafsson and...
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Transcript of Progress in the joint HIRLAM/ALADIN mesoscale data assimilation activities Nils Gustafsson and...
Progress in the joint HIRLAM/ALADIN mesoscale data assimilation activities
Nils Gustafsson and Claude Fischer
Mesoscale data assimilation –some initial considerations (1)
• During a planning meeting in Zürich (October 2006) a strong consensus among the participants from the 2 communities for development of A common HIRLAM/ALADIN mesoscale data assimilation system was established.
• 4 year planning horizon (2007-2010)• The question of dynamical/physical “balances” lies in
the heart of the data assimilation problem. We must be able to project the observed information on structures that will survive initial oscillations (adjustment processes).
• The present state of knowledge on adjustment processes and “balances” (including moisture) relevant for the mesoscale data assimilation is very limited.
Example: HIRLAM 4D-Var dependence on condensation/convection in the non-linear model
Some initial considerations (2)
• Variational techniques should continue to be the core of our strategy, in order to select challenges with the most likely substantial return of investments. However, we should be very open to improve the variational techniques by utilizing ensemble prediction input information.
• We need to pay more attention to surface and soil data assimilation, and for this purpose we need to utilize all available remote sensing data, in particular satellite data that will become available in the near future. Furthermore, as is the case with the surface parameterisation, it will (in the long run) be beneficial with an externalisation of the surface and soil assimilation
Example: Implicit flow-dependent structure functions
through 4D-Var
Retrieval of Microphysical Variables at T=65 min
Truth
Vr + Z
qc, qr, qi, qs, qh
RESULTS TOO GOOD! From Dale Barker 2007
Hybrid DA (Global) Single Observation Test• Temperature observation (O-B, =1K) at 50N, 150E, 500hPa.• Worst case scenario: Ensemble size N=1 (taken from KMA’s error breeding system).
T in
crem
ent
u in
crem
ent
Pure 3D-Var Pure Ensemble,No Localization
Pure Ensemble,With Localization
From Dale Barker 2007
Ensemble « climatological » statistics for very high resolution
AROME
ALADIN-FR
AROME
ALADIN-FR
List of actions to obtain code convergence1) Installation and testing of ALADIN 3D-VAR within the
HIRLAM (so far at met.no and SMHI)2) Possible adaptation of the extension zone treatment in ALADIN
(needed for efficiency of 4D-VAR) 3) Observation operator recoding and convergence (documentation
done and some comparison actions initiated)4) Comparison and validation of ALADIN and HIRLAM 3D-VAR
for synoptic scales. 5) Coding of ALADIN semi-Lagrangian TL and AD models (done)6) ALADIN 4D-Var “in a nutshell”7) Basic ALADIN 4D-Var
January 2007 – December 2008
Norwegian Domainand
single observation experiments
central close to boundary
Joint research – important work-packages (1)
WP1: Basic 3D-Var (2007-2008)
WP2: Further development of 3D-Var (2007-2010)• Wavelets • Water vapor control variable • Jb and Jk based on ensemble assimilations • Flow-dependency from ensembles • Improved balance constraints• Control of total water
WP3: 4D-Var (2007-2010)• ALADIN 4D-Var in “a nutshell”• Initial 4D-Var• TL and AD physics• “Moist physics” (research program)
Complex wavelets for LAM (A. Deckmyn)
• Wavelets are (partially) localised in both grid point space and Fourier space.
• Diagonalization of B in wavelet space can reproduce local variations in the structure functions and standard deviations.
• Current work is focussing on reproducing 3D structure functions for different variables (Alex Deckmyn, Tomas Landelius, Loik Berre)
Standard deviations of Temperature errorat the lowest vertical level: from data (left)and from wavelet-B (right).
Wavelet transform for Jb
• Variable substitution
• Fourier/Wavelet transform
)(
/ , /
)( )( )()(
**
1**
1
bo
b
obT
b
xuTJuu
ITBTuTxx
xJxxBxxxJ
*21* FDFBFDT
Correct treatment of the border – lifted wavelets
Joint research – important work-packages (2)
WP5: Assimilation of ground-based remote sensing data (2007-2010)
• Radar reflectivity and wind • GPS delays
WP6: Assimilation of high resolution satellite data (2007-2010)
• IASI• SSM/I• Clear and cloudy SEVIRI radiances• Binary cloudiness information
WP9: Surface data assimilation (2007-2010) – talk by J.-F. Mahfouf
Assimilation of radar reflectivities
Reference:
Haase, G., J. Bech, E. Wattrelot, U. Gjertsen and M. Jurasek, 2007. Towards the assimilation of radar
reflectivities: improving the observation operator by applying beam blockage information. Proc. 33rd
Conf. on Radar Meteorology. CD-ROM.
Antenna’s radiation pattern: 3d Gaussian power distribution (main lobe only)
Ray path: standard beam propagation (4/3 Earth’s radius)
Scattering by hydrometeors: Rayleigh scattering (attenuation neglected)
Mj hydrometeor contents (rain water, snow, graupel, pristine ice)
Observation operator for radar reflectivities
Courtesy O. Caumont
From model space to observation space
Interpolation on radar path:
part of the observation operator
horizontal bilinear interpolation from model grid to radar grid
vertical Gaussian-weighted interpolation from model levels to beam centre
Topographical beam blockage:
currently, not considered in the
observation operator
a beam propagation model (BPM)
simulates visibility maps for each
radar and each elevation angle
Radar challenges
Results
simulate visibility maps for three
French radars assuming standard
propagation conditions
integrate visibility maps into AROME
run an AROME experiment with the
improved observation operator for Z
and compare the results with a
control run
Results:
decrease of the absolute mean
departure (simulations are closer
to observations)
less rejected pixels after screening
Topography (radar Bollène) Visibility (BPM)
Observed reflectivities(14.05.2007, 09 UTC)
Difference of simulated reflectivities (VIS - CTL)
Summary
+
= HARMONIE !