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Page 1 NAE 4DVAR Mar 2006 © Crown copyright 2006 Bruce Macpherson, Marek Wlasak, Mark Naylor, Richard Renshaw Data Assimilation, NWP Assimilation developments.

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Presentation on theme: "Page 1 NAE 4DVAR Mar 2006 © Crown copyright 2006 Bruce Macpherson, Marek Wlasak, Mark Naylor, Richard Renshaw Data Assimilation, NWP Assimilation developments."— Presentation transcript:

1 Page 1 NAE 4DVAR Mar 2006 © Crown copyright 2006 Bruce Macpherson, Marek Wlasak, Mark Naylor, Richard Renshaw Data Assimilation, NWP Assimilation developments in North Atlantic & European and UK models EWGLAM 2006

2 Page 2 NAE 4DVAR Mar 2006 © Crown copyright 2006 Unified Model Operational Configurations Global 40 km N320L50 640x481x50 63 km top 150 million numbers North Atlantic & European 12 km 720x432x38 38 km top 120 million numbers Old UK 12 km, withdrawn 26/09/06 New UK 4 km 288x320x38 38 km top 35 million numbers

3 Page 3 NAE 4DVAR Mar 2006 © Crown copyright 2006 This talk  4km UK model  rainfall assimilation  cloud assimilation  NAE 4DVAR formulation  GPS IWV impact experiment

4 Page 4 NAE 4DVAR Mar 2006 © Crown copyright km 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

5 Page 5 NAE 4DVAR Mar 2006 © Crown copyright km UK assimilation trial 4km forecast from 12km analysis 4km forecast from 4km analysis mean error PMSL rms error

6 Page 6 NAE 4DVAR Mar 2006 © Crown copyright 2006 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

7 Page 7 NAE 4DVAR Mar 2006 © Crown copyright 2006 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

8 Page 8 NAE 4DVAR Mar 2006 © Crown copyright 2006 UK4 model – LHN changes -2 T+3 operational T+3 trialradar also ……T2m errors reduced at t+6 in several cases

9 Page 9 NAE 4DVAR Mar 2006 © Crown copyright 2006 Impact of cloud and precipitation data Radar 1 hour accumulation T+2 forecast 15min precip and hourly cloud T+2 forecast No cloud/rain data 14UTC 25 August 2005 – CSIP IOP 18

10 Page 10 NAE 4DVAR Mar 2006 © Crown copyright 2006 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

11 Page 11 NAE 4DVAR Mar 2006 © Crown copyright 2006 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 week UK Mes Trial

12 Page 12 NAE 4DVAR Mar 2006 © Crown copyright DVAR 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

13 Page 13 NAE 4DVAR Mar 2006 © Crown copyright 2006 Simple Var RH operator for cloud data Surface ob Satellite dataBoth MOPS cloud RH increment

14 Page 14 NAE 4DVAR Mar 2006 © Crown copyright 2006 Redesigned operator Surface ob Satellite dataBoth MOPS cloud RH increment

15 Page 15 NAE 4DVAR Mar 2006 © Crown copyright 2006 Camborne 00Z ascent01/02/2006

16 Page 16 NAE 4DVAR Mar 2006 © Crown copyright 2006 nudging scheme -----Camborne sonde -----model background -----model analysis

17 Page 17 NAE 4DVAR Mar 2006 © Crown copyright 2006 original 3DVAR scheme -----Camborne sonde -----model background -----model analysis

18 Page 18 NAE 4DVAR Mar 2006 © Crown copyright 2006 revised 3DVAR scheme -----Camborne sonde -----model background -----model analysis simple nudging is hard to beat!

19 Page 19 NAE 4DVAR Mar 2006 © Crown copyright 2006 NAE 4DVAR Project  Oct 04 - Global 4DVAR operational  Nov 04 - NAE project initiated  Sept week low resolution trial completed  Dec 05 – full resolution real-time trial begins  Feb 06 – Parallel Suite trial begins  Operational 14 th March 06

20 Page 20 NAE 4DVAR Mar 2006 © Crown copyright 2006 Formulation  Global system baseline:  6-hourly cycle  Similar science (including covariance statistics)  Latest additions eg J C term.  Observations specific to regional models:  visibility  hourly T 2m, RH 2m, V 10m  MOPS cloud and rainfall data.

21 Page 21 NAE 4DVAR Mar 2006 © Crown copyright 2006 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

22 Page 22 NAE 4DVAR Mar 2006 © Crown copyright 2006 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

23 Page 23 NAE 4DVAR Mar 2006 © Crown copyright 2006 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′ & q C ′  VAR: q T ′ control variable  Advection of q c ′ now has option to include

24 Page 24 NAE 4DVAR Mar 2006 © Crown copyright 2006 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

25 Page 25 NAE 4DVAR Mar 2006 © Crown copyright 2006 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.

26 Page 26 NAE 4DVAR Mar 2006 © Crown copyright 2006 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.

27 Page 27 NAE 4DVAR Mar 2006 © Crown copyright 2006 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

28 Page 28 NAE 4DVAR Mar 2006 © Crown copyright 2006 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) ′

29 Page 29 NAE 4DVAR Mar 2006 © Crown copyright 2006 Aerosol - advection of ( log m ) ′ v persistence  better than persistence after 3 hours

30 Page 30 NAE 4DVAR Mar 2006 © Crown copyright 2006 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 J C term  retuned minimisation option for weakly nonlinear penalty function Mark Naylor, Richard Renshaw

31 Page 31 NAE 4DVAR Mar 2006 © Crown copyright 2006 Cost - 2  options to allow ‘main run’ cut-off to move from 3.5  ~1.5 hours (operational since 26 th 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

32 Page 32 NAE 4DVAR Mar 2006 © Crown copyright 2006 Spring D-Var VIS v NO VIS

33 Page 33 NAE 4DVAR Mar 2006 © Crown copyright 2006 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’: a and b are constants, p and p w 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

34 Page 34 NAE 4DVAR Mar 2006 © Crown copyright 2006 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

35 Page 35 NAE 4DVAR Mar 2006 © Crown copyright 2006 Ground GPS trial – impact on cloud cover


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