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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 in North Atlantic & European and UK models EWGLAM 2006
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km top 150 million numbers North Atlantic & European 12 km 720x432x38 38 km top 120 million numbers Old UK 12 km, withdrawn 26/09/06 New UK 4 km 288x320x38 38 km top 35 million numbers
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
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
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
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
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
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
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
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
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
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
Page 13 NAE 4DVAR Mar 2006 © Crown copyright 2006 Simple Var RH operator for cloud data Surface ob Satellite dataBoth MOPS cloud RH increment
Page 14 NAE 4DVAR Mar 2006 © Crown copyright 2006 Redesigned operator Surface ob Satellite dataBoth MOPS cloud RH increment
Page 15 NAE 4DVAR Mar 2006 © Crown copyright 2006 Camborne 00Z ascent01/02/2006
Page 16 NAE 4DVAR Mar 2006 © Crown copyright 2006 nudging scheme -----Camborne sonde -----model background -----model analysis
Page 17 NAE 4DVAR Mar 2006 © Crown copyright 2006 original 3DVAR scheme -----Camborne sonde -----model background -----model analysis
Page 18 NAE 4DVAR Mar 2006 © Crown copyright 2006 revised 3DVAR scheme -----Camborne sonde -----model background -----model analysis simple nudging is hard to beat!
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
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.
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
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
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
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
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.
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.
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
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) ′
Page 29 NAE 4DVAR Mar 2006 © Crown copyright 2006 Aerosol - advection of ( log m ) ′ v persistence better than persistence after 3 hours
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
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
Page 32 NAE 4DVAR Mar 2006 © Crown copyright 2006 Spring D-Var VIS v NO VIS
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
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
Page 35 NAE 4DVAR Mar 2006 © Crown copyright 2006 Ground GPS trial – impact on cloud cover
Page 1 Developments in regional DA Oct 2007 © Crown copyright 2007 Mark Naylor, Bruce Macpherson, Richard Renshaw, Gareth Dow Data Assimilation and Ensembles,
Page 1© Crown copyright 2004 SRNWP Lead Centre Report on Data Assimilation 2005 for EWGLAM/SRNWP Annual Meeting October 2005, Ljubljana, Slovenia.
Page 1 NAE 4DVAR Oct 2006 © Crown copyright 2006 Mark Naylor Data Assimilation, NWP NAE 4D-Var – Testing and Issues EWGLAM/SRNWP meeting Zurich 9 th -12.
© Crown copyright Met Office Review topic – Impact of High-Resolution Data Assimilation Bruce Macpherson, Christoph Schraff, Claude Fischer EWGLAM, 2009.
Page 1© Crown copyright 2005 DEVELOPMENT OF 1- 4KM RESOLUTION DATA ASSIMILATION FOR NOWCASTING AT THE MET OFFICE Sue Ballard, September 2005 Z. Li, M.
1 00/XXXX © Crown copyright Use of radar data in modelling at the Met Office (UK) Bruce Macpherson Mesoscale Assimilation, NWP Met Office EWGLAM / COST-717.
© Crown copyright Met Office Benefit of high resolution data assimilation and observing systems in the Met Office UK NWP model G.T. Dow and B. Macpherson.
1 10/2003 © Crown copyright Unified Model Developments 2003 Clive Wilson NWP Met Office.
© Crown copyright Met Office Impact experiments using the Met Office global and regional model Presented by Richard Dumelow to the WMO workshop, Geneva,
Global vs mesoscale ATOVS assimilation at the Met Office Global Large obs error (4 K) NESDIS 1B radiances NOAA-15 & 16 HIRS and AMSU thinned to 154 km.
Incrementing moisture fields with satellite observations Stefano Migliorini Met Office.
© Crown copyright Met Office Data Assimilation Developments at the Met Office Recent operational changes, and plans Andrew Lorenc, DAOS, Montreal, August.
COSMO General Meeting, Offenbach, 7 – 11 Sept Dependance of bias on initial time of forecasts 1 WG1 Overview
© Crown copyright Met Office Adaptive mesh method in the Met Office variational data assimilation system Chiara Piccolo and Mike Cullen Adaptive Multiscale.
Page 1© Crown copyright 2006 NWP in the Met Office.
Introduction to data assimilation in meteorology Pierre Brousseau, Ludovic Auger ATMO 08,Alghero, september 2008.
HIRLAM 3/4D-Var developments Nils Gustafsson, SMHI.
OSEs with HIRLAM and HARMONIE for EUCOS Nils Gustafsson, SMHI Sigurdur Thorsteinsson, IMO John de Vries, KNMI Roger Randriamampianina, met.no.
© Crown copyright Met Office Future Upper-Air Network Development (FUND)-Integration TECO 2008 St Petersburg Russia Catherine Gaffard, John Nash, Alec.
Outline Background Highlights of NCAR’s R&D efforts A proposed 5-year plan for CWB Final remarks.
Trials of a 1km Version of the Unified Model for Short Range Forecasting of Convective Events Humphrey Lean, Susan Ballard, Peter Clark, Mark Dixon, Zhihong.
Federal Department of Home Affairs FDHA Federal Office of Meteorology and Climatology MeteoSwiss The Latent Heat Nudging Scheme of COSMO EWGLAM/SRNWP Meeting,
KMD Consortium for Small-Scale Modeling (COSMO) Strengths and Weaknesses Vincent N. Sakwa RSMC, Nairobi.
© Crown copyright Met Office Recent [Global DA] Developments at the Met Office Dale Barker, Weather Science, Met Office THORPEX/DAOS Meeting, 28 June 2011.
Page 1© Crown copyright 2004 Unified Model Developments 2004 Mike Bush NWP Met Office.
Sensitivity Analysis of Mesoscale Forecasts from Large Ensembles of Randomly and Non-Randomly Perturbed Model Runs William Martin November 10, 2005.
Polly Smith, Alison Fowler, Amos Lawless School of Mathematical and Physical Sciences, University of Reading Exploring coupled data assimilation using.
Assimilation of GOES Hourly and Meteosat winds in the NCEP Global Forecast System (GFS) Assimilation of GOES Hourly and Meteosat winds in the NCEP Global.
© Crown copyright Met Office EURO4M Work Package 2 (chiefly WP2.1) Richard Renshaw EURO4M GA1, De Bilt, April 14 th 2010.
EWGLAM Oct Some recent developments in the ECMWF model Mariano Hortal ECMWF Thanks to: A. Beljars (physics), E. Holm (humidity analysis)
Progress in Radar Assimilation at MeteoSwiss Daniel Leuenberger 1, Marco Stoll 2 and Andrea Rossa 3 1 MeteoSwiss 2 Geographisches Institut, University.
Page 1© Crown copyright 2004 Unified Model Developments 2005 for EWGLAM/SRNWP Annual Meeting October 2005, Ljubljana, Slovenia Mike Bush NWP.
© Crown copyright Met Office Development of the Met Office's 4DEnVar System 6th EnKF Data Assimilation Workshop, May Andrew Lorenc, Neill Bowler,
1 ATOVS and SSM/I assimilation at the Met Office Stephen English, Dave Jones, Andrew Smith, Fiona Hilton and Keith Whyte.
© Crown copyright Met Office Mismatching Perturbations at the Lateral Boundaries in Limited-Area Ensemble Forecasting Jean-François Caron … or why limited-area.
The Impact of Data Assimilation on a Mesoscale Model of the New Zealand Region (NZLAM-VAR) P. Andrews, H. Oliver, M. Uddstrom, A. Korpela X. Zheng and.
Page 1© Crown copyright 2007SRNWP 8-11 October 2007, Dubrovnik Variable resolution or lateral boundary conditions Terry Davies Dynamics Research Yongming.
Toward a Real Time Mesoscale Ensemble Kalman Filter Gregory J. Hakim Dept. of Atmospheric Sciences, University of Washington Collaborators: Ryan Torn (UW)
© Crown copyright Met Office Development of NWP-based Nowcasting at the Met Office -The Nowcasting Demonstration Project Workshop on Use of NWP for Nowcasting.
© Crown copyright Met Office UK report for GOVST Matt Martin GOVST-V, Beijing, October 2014.
Page 1© Crown copyright 2005 Met Office Verification -status Clive Wilson, Presented by Mike Bush at EWGLAM Meeting October 8- 11, 2007.
Evaluation of the Latent heat nudging scheme for the rainfall assimilation at the meso- gamma scale Andrea Rossa* and Daniel Leuenberger MeteoSwiss *current.
Federal Department of Home Affairs FDHA Federal Office of Meteorology and Climatology MeteoSwiss High-resolution data assimilation in COSMO: Status and.
© Crown copyright Met Office Implementation of a new dynamical core in the Met Office Unified Model Andy Brown, Director of Science.
Weather forecasting by computer Michael Revell NIWA
EUMETSAT04 04/2004 © Crown copyright Use of EARS in Global and Regional NWP Models at the Met Office Brett Candy, Steve English, Roger Saunders and Amy.
June 20, 2005Workshop on Chemical data assimilation and data needs Data Assimilation Methods Experience from operational meteorological assimilation John.
© Crown copyright Met Office How will we COPE in Summer 2013? - The COnvective Precipitation Experiment Phil Brown.
Slide 1 Bilateral meeting 2011Slide 1, ©ECMWF Status and plans for the ECMWF forecasting System.
Page 1© Crown copyright D-VAR Retrieval of Temperature and Humidity Profiles from Ground-based Microwave Radiometers Tim Hewison and Catherine Gaffard.
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