page 1 nae 4dvar mar 2006 © crown copyright 2006 bruce macpherson, marek wlasak, mark naylor,...

NAE 4DVAR Mar 2006 © Crown copyright 2006

Bruce Macpherson, Marek Wlasak, Mark Naylor, Richard RenshawData Assimilation, NWP

Assimilation developments in

North Atlantic & European and UK modelsEWGLAM 2006


Unified Model Operational Configurations

Global 40 kmN320L50640x481x50 63 km top150 million numbers

North Atlantic & European 12 km720x432x38 38 km top120 million numbers

Old UK 12 km,withdrawn 26/09/06

New UK 4 km288x320x38 38 km top35 million numbers


This talk

4km UK model rainfall assimilation

cloud assimilation

NAE 4DVAR formulationGPS IWV impact experiment


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


4km UK assimilation trial

4km forecast from 12km analysis

4km forecast from 4km analysis

mean error PMSL rms error


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


UK4 model – Latent Heat Nudging changes

T+0 operational T+0 trial radar

• remove use of evaporative part of latent heating profile (cf Leuenberger 2005)

• reduce filter scale for LHN theta increments from 20km 6km


UK4 model – LHN changes -2

T+3 operational T+3 trial radar

also ……T2m errors reduced at t+6 in several cases


Impact of cloud and precipitation data

Radar1 hour accumulation

T+2 forecast 15min precip and hourly cloud

T+2 forecast No cloud/rain data

14UTC 25 August 2005 – CSIP IOP 18


Impact of data frequency

currently use: hourly rain rate data

3-hourly cloud data

tests with15-min rain rate data &

hourly cloud data

show benefit only up to ~t+2 hours in convective cases


Cloud assimilation

MOPS cloud data impact of nudging scheme

significant benefit in Sc episodes (eg Feb ’06)

NO MOPS cloud

Control

rms T2mrms cloud cover

One weekUK MesTrial


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


Simple Var RH operator for cloud data

Surface ob Satellite data Both

MOPS cloud

RH increment


Redesigned operator

Surface ob Satellite data Both

MOPS cloud

RH increment


Camborne 00Z ascent 01/02/2006


nudging scheme

----- Camborne sonde

----- model background

----- model analysis


original 3DVAR scheme





revised 3DVAR scheme




simple nudging is hard to beat!


NAE 4DVAR Project

Oct 04 - Global 4DVAR operationalNov 04 - NAE project initiated

Sept 05 - 2-week low resolution trial completedDec 05 – full resolution real-time trial beginsFeb 06 – Parallel Suite trial begins

Operational 14th March 06


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.


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


Perturbation Forecast (PF) Model

PF model the Met Office’s linear model, (+ adjoint), to extend 3D4D-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


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 ' cu q


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


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.


PF Model – linearization tests & resolution

impact of increasing resolution (483624km)

improvement for pressure, density, temperature, humidity

reducing with timeslight 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.


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


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 ) '(log ' (lo) g )' 0u

mm

tmu


Aerosol - advection of (log m)′ v persistence

better than persistence after 3 hours


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


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


Spring 2005 4D-Var VIS v NO VIS


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’:

62

0

10z

W

z

bpapZTD dz

T T

a and b are constants,

p and pw are pressure & WV pressure,

T is temperature, z is height above the ground receiver.

(No profile information).

Near Real-Time GPS network shown above.

Obs frequency often several per hour - potential in 4D-Var

1 per 6-hrs used initially

NB water vapour dependence.

Adrian Jupp


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)

Adrian Jupp


Ground GPS trial – impact on cloud cover

page 1 nae 4dvar mar 2006 © crown copyright 2006 bruce macpherson, marek wlasak, mark naylor,...

Documents

crown copyright

hourly cloud t

impact of cloud

analysis slide

uk model assimilation

hourly cloud data tests

uk assimilation trial

cloudrain data