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Bruce Macpherson, Marek Wlasak, Mark Naylor, Richard Renshaw Data Assimilation, NWP

Assimilation developments in North Atlantic & European and UK models EWGLAM 2006. Bruce Macpherson, Marek Wlasak, Mark Naylor, Richard Renshaw Data Assimilation, NWP. Old UK 12 km, withdrawn 26/09/06. New UK 4 km 288x320x38 38 km top 35 million numbers.

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Bruce Macpherson, Marek Wlasak, Mark Naylor, Richard Renshaw Data Assimilation, NWP

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  1. Assimilation developments in North Atlantic & European and UK models EWGLAM 2006 Bruce Macpherson, Marek Wlasak, Mark Naylor, Richard Renshaw Data Assimilation, NWP

  2. Old UK 12 km, withdrawn 26/09/06 New UK 4 km 288x320x38 38 km top 35 million numbers Unified Model Operational Configurations Global 40 km N320L50 640x481x50 63 km top 150 million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

  3. This talk • 4km UK model • rainfall assimilation • cloud assimilation • NAE 4DVAR formulation • GPS IWV impact experiment

  4. 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

  5. 4km UK assimilation trial 4km forecast from 12km analysis mean error PMSL rms error 4km forecast from 4km analysis

  6. 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. 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 T+0 trial radar

  8. UK4 model – LHN changes -2 T+3 operational T+3 trial radar also ……T2m errors reduced at t+6 in several cases

  9. Impact of cloud and precipitation data 14UTC 25 August 2005 – CSIP IOP 18 T+2 forecast No cloud/rain data Radar 1 hour accumulation T+2 forecast 15min precip and hourly cloud

  10. 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. Cloud assimilation MOPS cloud data • impact of nudging scheme • significant benefit in Sc episodes (eg Feb ’06) rms cloud cover rms T2m NO MOPS cloud Control One week UK Mes Trial

  12. 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

  13. Simple Var RH operator for cloud data Surface ob Satellite data Both MOPS cloud RH increment

  14. Redesigned operator Surface ob Satellite data Both MOPS cloud RH increment

  15. Camborne 00Z ascent 01/02/2006

  16. nudging scheme ----- Camborne sonde ----- model background ----- model analysis

  17. original 3DVAR scheme ----- Camborne sonde ----- model background ----- model analysis

  18. revised 3DVAR scheme ----- Camborne sonde ----- model background ----- model analysis simple nudging is hard to beat!

  19. 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

  20. 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.

  21. 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. 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 5points

  23. 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

  24. 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. 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. 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. 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. 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. Aerosol - advection of (log m)′ v persistence • better than persistence after 3 hours

  30. 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

  31. 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

  32. Spring 2005 4D-Var VIS v NO VIS

  33. 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 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

  34. 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. Ground GPS trial – impact on cloud cover

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