Transcript Bruce Macpherson - c

Assimilation developments in
North Atlantic & European and UK models
EWGLAM 2006
Bruce Macpherson, Marek Wlasak, Mark Naylor, Richard Renshaw
Data Assimilation, NWP
NAE 4DVAR Mar 2006 © Crown copyright 2006
Page 1
Unified Model Operational Configurations
Global 40 km
N320L50
Old UK 12 km,
withdrawn 26/09/06
640x481x50 63 km top
150 million numbers
North Atlantic &
European 12 km
New UK 4 km
288x320x38 38 km top
35 million numbers
720x432x38 38 km top
120 million numbers
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Page 2
This talk
4km UK model
rainfall assimilation
cloud assimilation
NAE 4DVAR formulation
GPS IWV impact experiment
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Page 3
4km UK model assimilation
3DVAR as for old 12km Mesoscale model
operational since December 2005
eight 3-hourly cycles per day
same forecast error covariances
 explore ‘lagged’ covariance statistics in future
same nudging scheme for cloud & rainfall assimilation
forecasts from 03, 09, 15, 21 UTC
lateral boundaries from hh-3 run of 12km NAE
slight advantage over forecast from interpolated
12km analysis
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Page 4
4km UK assimilation trial
4km forecast from
12km analysis
mean error
PMSL
rms error
4km forecast from
4km analysis
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Page 5
Operational trial of 4km assimilation
Spurious rain area due to spin up effects reduced.
4km t+5 forecast from
12km analysis
4km assimilation and
t+5 forecast
Image courtesy of Camilla Mathison
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Page 6
UK4 model – Latent Heat Nudging changes
• remove use of evaporative part of latent
heating profile (cf Leuenberger 2005)
• reduce filter scale for LHN theta increments
from 20km  6km
T+0 operational
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T+0 trial
radar
Page 7
UK4 model – LHN changes -2
T+3
operational
T+3 trial
radar
also ……T2m errors reduced at t+6 in several cases
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Page 8
Impact of cloud and precipitation data
14UTC 25 August 2005 – CSIP IOP 18
T+2 forecast
No cloud/rain data
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T+2 forecast
15min precip and hourly cloud
Radar
1 hour accumulation
Page 9
Impact of data frequency
currently use:
hourly rain rate data
3-hourly cloud data
tests with
 15-min rain rate data &
 hourly cloud data
show benefit only up to ~t+2 hours in convective cases
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Page 10
Cloud assimilation
MOPS cloud data
impact of nudging scheme
 significant benefit in Sc episodes
rms cloud cover
(eg Feb ’06)
rms T2m
NO MOPS
cloud
Control
One week
UK Mes
Trial
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Page 11
3DVAR assimilation of MOPS cloud data
Simplify system, remove old AC nudging
code
Combine MOPS cloud with other ob types
Integrate with future variational precipitation
assimilation
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Page 12
Simple Var RH operator for cloud data
Surface ob
Satellite data
Both
MOPS cloud
RH increment
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Page 13
Redesigned operator
Surface ob
Satellite data
Both
MOPS cloud
RH increment
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Page 14
Camborne 00Z ascent
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01/02/2006
Page 15
nudging scheme
-----
Camborne sonde
-----
model background
-----
model analysis
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Page 16
original 3DVAR scheme
-----
Camborne sonde
-----
model background
-----
model analysis
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Page 17
revised 3DVAR scheme
-----
Camborne sonde
-----
model background
-----
model analysis
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simple nudging is hard to beat!
Page 18
NAE 4DVAR Project
Oct 04 - Global 4DVAR operational
Nov 04 - NAE project initiated
Sept 05 - 2-week low resolution trial completed
Dec 05 – full resolution real-time trial begins
Feb 06 – Parallel Suite trial begins
Operational 14th March 06
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Page 19
Formulation
Global system baseline:
6-hourly cycle
Similar science (including covariance statistics)
Latest additions eg JC term.
Observations specific to regional models:
visibility
hourly T2m, RH2m, V10m
MOPS cloud and rainfall data.
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Page 20
Formulation - 2
MOPS cloud and rainfall data
3D-Var & nudging interface
 nudge during IAU  ‘over-correction’
4D-Var & nudging interface
 nudge during forecast after Var
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Page 21
Perturbation Forecast (PF) Model
PF model
 the Met Office’s linear model, (+ adjoint), to extend 3D4D-Var.
 semi-implicit semi-Lagrangian integration scheme as in UM.
Limited-Area PF model:
need to enforce zero increments around the boundary
relaxation zone: 8-point rim with zero increments on first 5 points
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Page 22
Limited-Area PF model – 2
Physics (as global version)
Micro-physics scheme - large-scale latent heating
Vertical diffusion of momentum in the boundary layer
Moisture (as global version)
PF model: advect q′ & qC′  VAR: qT′ control variable
Advection of qc′ now has option to include u '  q c
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Page 23
PF Model – Linearisation Tests
linearisation test
To see how different PF model output is to difference of 2
nonlinear UM NAE runs.( nonlinear increment)
use same lateral boundary data.
use a settled UM NAE nonlinear increment to start the PF run.
Solution error = || UM_incs – PF_incs||2/||UM_incs||2A
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Page 24
PF Model – linearisation tests
12km UM / 36km PF
Evolution of the solution error after
1 (blue), 2 (purple), 4 (green), 6 (red) hours of a PF model run.
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Page 25
PF Model – linearization tests & resolution
impact of increasing
resolution
(483624km)
improvement for
pressure, density,
temperature, humidity
 reducing with time
slight detriment for wind
 increasing with
time

% difference in solution
error 24km  48 km.
+ve where 48km grid
performs better.
comparisons at 1, 2, 4, 6
hours into run.
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Page 26
PF Model – aerosol advection
UM aerosol
single aerosol mass mixing ratio m
tracer advection
boundary layer mixing
sources
removal by precipitation
visibility diagnosis
humidity
aerosol
temperature
precipitation rate
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Page 27
PF model: aerosol advection (2)
PF aerosol
do we need to advect aerosol?
Persistence?
assume advection dominates sources/sinks
advect m′
 m + m′ >0 when m′  (logm)′ gave poor convergence
advect m′ in terms of (logm)′
 more gaussian error pdf
 first step: approximate linearized advection of m′ by
linearized advection of (logm)′
 (log m ) '
t
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 u   (log m ) ' u '  (lo g m )  0
Page 28
Aerosol - advection of (log m)′ v persistence
better than persistence
after 3 hours
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Page 29
Cost
Computational cost
extra time per run ~15-18min on 4 nodes of SX-8
max VAR iterations set at 85 (mean ~80)
existing cost reduced by:
 retuned representativeness error for visibility obs
 reduced weight to JC term
 retuned minimisation option for weakly nonlinear penalty
function
Mark Naylor, Richard Renshaw
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Page 30
Cost - 2
options to allow ‘main run’ cut-off to move from
3.5~1.5 hours (operational since 26th Sept 2006)
 reduce time window from 6 to 4.5 hours for ‘main run’ with
90min cut-off (and include update cycles for late data)
 omit visibility obs (save ~25%)?
 advance cut-off a few minutes
 small degradation in PF resolution
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Page 31
Spring 2005 4D-Var
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VIS v NO VIS
Page 32
Ground based GPS
 As signals from GPS satellites travel
to a ground station they are slowed
by the presence of the atmosphere.
 Expressed as ‘zenith total delay’:
z
Z T D  10
6
z0

ap
T

bpW
T
2
dz
a and b are constants,
p and pw are pressure & WV pressure,
 Near Real-Time GPS network
T is temperature, z is height above the
shown above.
ground receiver.
 Obs frequency often several
NB water vapour dependence.
per hour - potential in 4D-Var
(No profile information).
 1 per 6-hrs used initially
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Adrian Jupp
Page 33
Ground based GPS – trial results
 3 week real-time 4DVAR trial v operational run (July 2006)
UK index based on 5 variables

+0.5% (Mes area)

+0.3% (UK area)
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Adrian Jupp
Page 34
Ground GPS trial – impact on cloud cover
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Page 35