HIRLAM 3/4D-Var developments

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HIRLAM 3/4D-Var developments

• Nils Gustafsson, SMHI

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Parallel data assimilation work along 2 lines in
HIRLAM

• HIRLAM for the synoptic scales
• 3D-Var and 4D-Var
• Further developments during 2008-2009
• To be phased out operationally 2010-2012

• HARMONIE for the mesoscale
• Based on ALADIN (IFS)
• 3D-Var mid 2008
• 4D-Var early 2009
• To replace the synoptic scale HIRLAM (2010-2012)

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HIRLAM 4D-Var components

• Tangent linear and adjoint of the semi-Lagrangian
  (SETTLS) spectral HIRLAM.
• Simplified physics packages Buizza vertical
  diffusion and Meteo France (Janiskova)
  package(vertical diffusion, large-scale
  condensation and convection).
• Multi-incremental minimization (spectral or
  gridpoint HIRLAM in outer loops).
• Weak digital filter constraint.
• Control of lateral boundary conditions.

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Noise in assimilation cycles with the gridpoint
model
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Comparison tests 3D-Var 4D-Var

• SMHI area, HIRLAM 7.1.1, KF/RK, SMHI area,
  statistical balance background constraint,
  reference system background error statistics
  (scaling 0.9), no large-scale mix, LINUX
  cluster, 4.5 months, operational SMHI
  observations and boundaries
• 3D-Var with FGAT, incremental digital filter
  initialization
• 4D-Var, 6h assimilation window, weak digital
  filter constraint, no explicit initialization

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Summary of forecast scores
Period Surface pressure Upper air
April 2004 Neutral Positive impact of 4D-Var
Jan 2005 Positive impact of 4D-Var Positive impact of 4D-Var
June 2005 Neutral Positive impact of 4D-Var
Jan 2006 (11 days) Positive impact of 4D-Var Small positive impact of 4D-Var
Jan 2007 Positive impact of 4D-Var Small negative impact of 4D-Var on 300 and 200 hPa heights
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Operationalization of 4D-Var

• SMHI tests show positive impact of 4D-Var in
  comparison with 3D-Var
• SMHI results need to be confirmed with the
  reference system (and new NL physics)
• Improved parallel scaling is needed (a) openMP
  within nodes MPI between nodes (b) Message
  passing for SL advection on demand
• To be included in HIRLAM 7.2 (late 2007)

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Pre-operational tests of 4D-Var at SMHI Cop -
SMHI op. 22 km, Hirlam-6.3.5, KF/RK, 3DVAR
FGAT Cnn - Hirlam-7.1.2, KF/RK, 4DVAR
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Illustration structure functions
Impact of one single surface pressure observation
5 hPa less than the corresponding background
equivalent (red surface pressure, black winds
at lowest mod level)
Analytical NMC (48-24)
Statistical NMC (36-12)
Statistical Ensemble
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Flow dependent background covariances through
non-linear balance equations
Non-linear balance equation on pressure levels
Tangent-linear version of balance equation on
pressure levels

Tangent-linear version of omega equation on
pressure levels
where
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Vertical crossection of T increments
Statistical balance
Weak constraints balance eq.
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A new moisture control variable and a new
moisture balance
Within the analytical balance formulation we
follow Holm and use relative humidity as control
variable and the TL RH definition for the balance
In addition, the background error variance
depends on the background relative humidity
(makes it more Gaussian). Within the statistical
balance formulation (with q as control variable),
we already have a statistical balance relation
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In order to avoid double-counting of the
temperature-moisture balance, we could try to
improve the statistical balance relation by using
coefficients from the analytical balance
relation, for example
So far we have tried
In this case, we also used a background error
variance depending on the background relative
humidity
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New assimilation control variable for
humidity (analytical balance version)
?q
Old formulation
New formulation
?RH ?RH/sb(RHb0.5?RH)
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New assimilation control variable for
humidity (statistical balance with multivariate
humidity)
Assimilation increments due 5 simulated specific
humidity observations, 10 g/kg smaller than
corresponding background equivalent (sigmao 1
g/kg)
q at 850 hPa (g/kg times 10)
ps (hPa times 10)
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SEVIRI data coverage
(At SMHI, we dont store the raw-data for the
full SEVIRI disc operationally)
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Example of impact of SEVIRI data on 3D-Var
analysis

• Difference of analysed 500hPa relative humidity
  (SEVIRI experiment minus Control)
• Impact can be seen mainly in the southern part
  of the domain

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3D-Var
4D-Var
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3D-Var/4D-Var impact study

• 3D-Var
• Positive impact on upper-troposhperic water
  vapour is found
• Positive impact on MSLP forecast is found
• 4D-Var
• Positive impact on upper-tropospheric water
  vapour is found
• Also Temperature and Geopotential fields show
  some response (small positive impact)
• Another impact study for December 2005 shows
  neutral impact of SEVIRI data in terms of
  forecast scores.

Work is now continuing with a much more difficult
problem, assimilation of cloudy SEVIRI radiances!
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What can we expect to achieve with the HIRLAM
data assimilation before it will be phased out?

• 4D-Var with several outer loops and improved
  moist physics
• Control of lateral boundary conditions in 4D-Var
• A new moisture control variable
• Large scale mix vi a Jk cost function term
• Background and large scale error statistics based
  on EnsAss
• Tuning of screening and VarQC
• Use of several new types of observations. (IASI?)
• Most development efforts should be finished
  during 2008!
• A synoptic scale HARMONIE should be comparable!!