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 ( ) 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 q c, q r, q i, q s, q h 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 increment u increment 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 convergence 1)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 Domain and single observation experiments central close to boundary

Joint research – important work-packages (1) WP1: Basic 3D-Var ( ) WP2: Further development of 3D-Var ( ) 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 ( ) 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 error at the lowest vertical level: from data (left) and from wavelet- B (right).

Wavelet transform for J b Variable substitution Fourier/Wavelet transform

Correct treatment of the border – lifted wavelets

Joint research – important work-packages (2) WP5: Assimilation of ground-based remote sensing data ( ) Radar reflectivity and wind GPS delays WP6: Assimilation of high resolution satellite data ( ) IASI SSM/I Clear and cloudy SEVIRI radiances Binary cloudiness information WP9: Surface data assimilation ( ) – talk by J.-F. Mahfouf

Assimilation of radar reflectivities Reference: Haase, G., J. Bech, E. Wattrelot, U. Gjertsen and M. Jurasek, 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) M j 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 ( , 09 UTC) Difference of simulated reflectivities (VIS - CTL)

Summary + = HARMONIE !