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Met Office Unified Model

Met Office Unified Model. Terry Davies Dynamics Research. UM Dynamical Core. Non-hydrostatic formulation Hybrid-height co-ordinate Semi-Lagrangian advection Semi-implicit predictor-corrector integration C grid horizontal staggering Charney- Phillips vertical staggering.

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Met Office Unified Model

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  1. Met Office Unified Model Terry Davies Dynamics Research

  2. UM Dynamical Core • Non-hydrostatic formulation • Hybrid-height co-ordinate • Semi-Lagrangian advection • Semi-implicit predictor-corrector integration • C grid horizontal staggering • Charney- Phillips vertical staggering Davies, Cullen, Malcolm, Mawson, Staniforth White, Wood 2005 Quart. Journal Roy. Met. Soc

  3. Ui- Ui+ ,j ,j i,j Vi,j+ W, Vi,j- U, W, Vertical and Horizontal Grid Staggering • Horizontal staggering - Awakawa c-grid • No grid decoupling • Better geostrophic adjustment for wavelengths of grid size less than Rossby radius of deformation • Vertical staggering - Charney-Phillips • No computational modes • More consistent with thermal wind balance • Can have complications in coupling with boundary layer parametrization    

  4. Edwards-Slingo Radiation (Edwards & Slingo 1996) Mixed phase precipitation (Wilson & Ballard 1999) New Boundary Layer + 38L (Lock et al 2000) New GWD scheme + GLOBE orography smoothed (Raymond filter) Modern spectral database for gaseous absorption in the atmosphere + new H2O continuum - flexible configuration Multiple scattering included Better optical properties for clouds inc. non-spherical ice parametrisation Physical Parametrizations

  5. Edwards-Slingo Radiation (Edwards & Slingo 1996) Mixed phase precipitation (Wilson & Ballard 1999) New Boundary Layer + 38L (Lock et al 2000) New GWD scheme + GLOBE orography smoothed (Raymond filter) Physically based transitions between vapour, liquid, ice and rain Ice content now a prognostic variable rather than diagnosed from cloud scheme Physical Parametrizations

  6. Edwards-Slingo Radiation (Edwards & Slingo 1996) Mixed phase precipitation (Wilson & Ballard 1999) New Boundary Layer + 38L (Lock et al 2000) New GWD scheme + GLOBE orography smoothed (Raymond filter) Allows for non-local mixing in unstable regimes Scheme diagnoses 6 different mixing regimes in order to represent stable, well mixed and cumulus processes Scheme includes boundary layer top entrainment parametrisation Improved interaction with the convection scheme Physical Parametrizations

  7. Edwards-Slingo Radiation (Edwards & Slingo 1996) Mixed phase precipitation (Wilson & Ballard 1999) New Boundary Layer + 38L (Lock et al 2000) New GWD scheme + GLOBE orography smoothed (Raymond filter) Simplified scheme Expression for linear 2D flow used to calculate total surface pressure drag Gravity wave amplitudes proportional to depth of sub-grid mountains above blocked layer Remainder of drag is attributed to flow blocking Physical Parametrizations

  8. Future Developments • Dynamical core improvements • More consistent treatment of moisture • Conserving semi-Lagrangian advection scheme • Variable resolution grid • Resolution increases - (70 levels, 40km) • New physical Parametrisations • New prognostic cloud scheme • New convection scheme

  9. Old UK 12 km Retiring eventually New UK 4 km 288x320x38 38 km top 35 million numbers UM 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

  10. MOGREPSMet Office Global and Regional Ensemble Prediction System Ken Mylne Ensemble Forecasting Manager

  11. ECMWF Ensemble (EPS) • 51 members • Control (unperturbed) + 25 pairs formed by adding and subtracting a perturbation • TL255 Resolution (approx 80km) • Designed for use beyond 48h • Perturbations are linear combinations of Forward and Evolved Singular Vectors • Includes Stochastic Perturbations to model physics

  12. NAE MOGREPS – The Met Office short-range ensemble • Ensemble designed for short-range forecasting • Global ensemble (~90km resolution, 38 levels) • ETKF used within global ensemble to determine initial condition perts • Regional ensemble over N. Atlantic and Europe (NAE) at 24km resolution, 38 levels. Nested within global ensemble for initial and lateral boundary conditions • Stochastic physics • T+72 global, T+36 regional • Global run at 0Z and 12Z. Regional run at 6Z & 18Z MOGREPS is on Operational Trial for 1 year from September 2005

  13. Ensemble Creation – Analysis Perturbations To achieve these desirable properties: • Not sufficient to sample randomly • models have ~107 degrees of freedom - too many slow-growing directions! • Look for rapidly growing perturbations • Singular vectors (ECMWF) • Error breeding (NCEP) • New! Ensemble Transform Kalman Filter (Met Office) Thanks to Craig Bishop and colleagues.

  14. Singular Vectors (SVs) • SVs use linear adjoint of ECMWF model to identify fastest-growing directions in phase-space over the next 48 hours. • SV perturbations scaled by forecast error statistics at 48h - fast-growing so very small at initial time • Perturbations also include Evolved SVs from 48h previously • identify areas of greatest analysis uncertainty (where model background is likely to be in error)

  15. SV Perturbations • Each perturbation is a linear combination of: • 25 NHem SVs • 25 SHem SVs • 25 Tropical moist SVs targetted on • Caribbean • TCs

  16. Error Breeding Start with random perturbation - allow to grow in forecast Rescale bred mode to analysis errors (fixed climatological rescaling factor) Use for perturbation in next cycle Cycle “breeds” the rapidly growing modes in the analysis cycle Toth and Kalnay (1997), MWR 125, 3297-3319

  17. Ensemble Transform Kalman Filter (ETKF) 0.9 Pert 1 -0.1 Pert 2 -0.1 Pert 3 -0.1 Pert 4 -0.1 Pert 5 ( - ) + = ( - ) + = ( - ) + = ( - ) + = ( - ) + = T+12 perturbed forecast T+12 ensemble mean forecast Transform matrix Control analysis Perturbed analysis

  18. Perturbation Structures – Mean and spread PMSL • Spread tends to be concentrated around fronts and sharp gradients • Perturbation is non-zero everywhere (in contrast to SVs)

  19. Stochastic physics …. the quest to increase spread! Buizza et al., MWR, 2004 All three systems are under-dispersive!!

  20. Stochastic physics in MOGREPS MOGREPS employs three schemes to address different sources of model error: • Random Parameters (RP) • Error due to approximations in parameterisation • Stochastic Convective Vorticity (SCV) • Unresolved impact of organised convection (MCSs) • Stochastic Kinetic Energy Backscatter (SKEB) • Excess dissipation of energy at small scales Impact is propagated to next cycle through the ETKF

  21. Stochastic scheme for the UM The Random Parameters

  22. RP+SCV in MOGREPS

  23. SKEB Stochastic Kinetic Energy Backscatter (SKEB) • Based on original idea and previous work by Shutts (2004) • Related to new scheme for ECMWF EPS • Aim: To backscatter (stochastically) into the forecast model some of the energy excessively dissipated by it at scales near the truncation limit • In the case of the UM, a total dissipation of 0.75 Wm-2 has been estimated from the Semi-lagrangian and Horizontal diffusion schemes. (Dissipation from Physics to be added later on) • Each member of the ensemble is perturbed by a different realization of this backscatter forcing

  24. SKEB • Streamfunction forcing: K.- Kinetic En.; R.- Random field; D.- Dissipated en. in a time-step R is designed to reproduce some statistical properties found with CRMs Example: u increments at H500 • Largest at the jets/storm track

  25. SKEB Preliminary results: • Positive increase in spread (comparable to that seen at ECMWF) Increase in spread respect to an IC-only ensemble 500 hPa geopotential height SKEB RP+SCV

  26. 100% Prob 0% Products for the Risk Manager • Plot of ensemble spread • Range of uncertainty • Probability graph for multiple severity thresholds • Example of use for risk management in offshore oil industry

  27. 2006-2009 plans • Forecast uncertainty information derived from EPS (July 2007) • Report on public understanding of probabilistic forecast information based on experiments at Exeter University (July 2007) • Ensemble surge prediction system trials (October 2007) • Report on predicting extreme deviations from ensemble mean using singular vector perturbations (March 2008)

  28. 2006-2009 plans • Probabilistic short-range first-guess warning system for severe weather (March 2008) • Verification report on first-guess warnings (March 2009) • 50 km global ensemble, 12km regional ensemble (November 2009) • Verification report on the enhanced resolution ensembles (March 2010) • Report on potential benefit of a convection-resolving EPS (March 2010)

  29. HIGH RESOLUTION DATA ASSIMILATION Sue Ballard Z. Li, M. Dixon, S.Swarbrick, O.Stiller and H. Lean Met Office, JCMM, ReadingUniversity

  30. Contents • Aim of high resolution convective scale system • Prediction of flood risk, replace nowcasting system • Detailed local weather • 4km UK 2005, 1.5km ~ 2008-2010 • Trial system – small domains • Data assimilation options • Assimilation issues • Impact of data assimilation • Impact of relative humidity and latent heat nudging • 4DVar of cloud and precipitation • Assimilation of radar doppler radial winds • Conclusions • Exploitation of high resolution observations

  31. 4 km 38 levels Mass-limited convection 1 km 76 levels Resolved convection High Resolution Trial Model

  32. Original HRTM Assimilation Options 12 km 3D-Var Data Assimilation With or without moisture and Latent Heat Nudging (LHN) using AC scheme (referred to as MOPS data – moisture observation processing system) i.e. spin up 4km, 1km from 12km T+1 each cycle. 4km 3D-VAR with continuous cycles with or without MOPS 1km with nudged reconfigured 4km increments using IAU With or without LHN and moisture nudging using AC scheme • IAU – increments output from 3D-Var and fixed over time window • AC scheme – increments depend on latest model fields so vary with timestep through weighting factor and model evolution/impact of data

  33. Plans - ongoing • 4km • MOPS - Hourly cloud, 15min precipitation, filtering, weights • Background errors – lagged/unlagged, lengthscales • Operational doppler radar winds – superobbing, errors, monitoring • Salford Univ and COST 731 • Observations – Satellite Applications + Radar Group + Obs • Radar reflectivity – observation operators, compare model and obs • Reading Univ • Geostationary imagery – low level moisture, cloud top • Radar refractivity – low level moisture (Reading Univ) • Wind profiler humidity, ground based radiometer, cloud radar • Development of 3D-Var and 4D-Var for direct assimilation of cloud and precipitation • 3D-Var MOPS cloud cover, precipitation rate – currently not resourced • Cloudy radiance, PF physics, infrastructure

  34. Moisture Observation Preprocessing • Resolution: 15km, 3 hours (Testing 1 hour) Precipitation 5km smoothed to 15km Hourly Testing 15min Surface reports Satellite data Radar data Nudge model state 3D Cloud fraction 3D Relative humidity

  35. 3D-Var system including MOPS RH and LH nudging via AC scheme Conventional observations 3D-Var (FGAT) Obs window 3 hour f/c: background Hourly ModelOb IAU T-2 T+2 T-3 T+3 T-1 T+0 T+1 Previous analysis Next analysis NudgingRH & Latent heat AC scheme/UM 3D cloud fraction Surface rainrate

  36. 6hr accumulations from13Z to 19Z 16/8/04 from 12UTC analysis With 4km 3D-Var +MOPS radar Spun-up from 12km T+1 Rain rates at 14.30 UTC from 12UTC analysis

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

  38. Development of 4D-Var • 4D-Var operational in global and 12km NAE models • 3D-Var operational in 12 and 4km UK models • 3D-Var being set-up/run for 1.5km model • 4D-Var NAE has MOPS RH and latent heat nudging in outer loop • Developing • direct assimilation of surface precipitation rates (accumulations?) • Cloudy radiances • Cloud top pressure • Starting to set up research 4D-Var at 4km resolution

  39. Trial Results - NAE summer rainfall t+9 3DVAR t+9 4DVAR radar

  40. Exploitation of high resolution data • Radar radial doppler winds • Initial development using Chilbolton research radar – single elevation • Now winds available from 2-4 radars in operational network – multiple elevations • Need to modify background errors to exploit high resolution information

  41. Impact of S-band radial radar wind data- radial wind on 1deg scan elevation Analysis ½ Length scale 12km Back- ground Super- obbed Radar Doppler wind Reduced background wt

  42. Impact of S-band radial radar wind data- radial wind on scan elevation Analysis ½ Length scale 4km Back- ground Super- obbed Radar Doppler wind Reduced background wt

  43. Need to combine synoptic scale and high resolution analysis • 3D-Var analysis in small area cannot capture synoptic scales • Need to somehow get synoptic scale information from larger area coarser resolution analysis • Can analyse different scales in different areas • However won’t always have an up-to-date coarser resolution analysis eg 6 hourly with high resolution hourly • Needs further research

  44. theta inc “new” 4km analysis /12km back 12km analysis standard 4km analysis doesn’t see obs outside domain Zero at boundary Scale selective analysis Problem of small domain “new” 4km analysis /4km back & half length 12km long waves 4km short wave analysis

  45. Conclusions • Need 1-4km to capture small scale severe events • Need DA to overcome spin-up of explicit convection • MOPS RH & LHN improves locations • Amounts sensitive to model formulation, weights, frequency of data • Ideally want to use assimilation of cloud and precipitation in 3D-Var and 4D-Var • Need to combine synoptic scale and convective analysis • Need improved background error covariances • Need to move towards full analysis for 1km model

  46. Plans - Future • Aim: 1hrly forecasts 0-6hours 1-1.5km NOWCASTS • also 36 hour UK forecasts ? • Need to move towards full analysis for 1km model • Ideally want 4D-Var and Ensembles - Start with 3D-Var +MOPS • need to build on experience with 4km and NAE • Use reduced vertical resolution (and horizontal?) for analysis? • High resolution data • Balance and background errors • Variable resolution model • Surface analysis – SST and soil mositure

  47. Other Collaborations • DARC (Reading University) • PhD – combining small scale and large scale in LAM analysis • Post-doc/lecturer – surface friction in control variable • Post-doc/lecturer – non-linear evolution of Gaussian pdfs • Post-doc – balance at convective scale • Post-doc – wavelet transforms • Post-doc and PhD – Ensemble Kalman Filter • EPSRC project – Peter Clark, Surrey, Aston • FREE (Flood Risk from Extreme Events) – Reading University, Met Office and CEH, Wallingford – assimilation of clear air doppler winds and humidity from refractivity

  48. Current/planned data sources include: • Surface observations : now SYNOPs hourly, 69 stations every 10mins, full network every 10 mins by 2006-2009 • AMDAR : now every 3 hours take off and landing can request hourly at extra cost • Geostationary imagery: every 15mins • Radar VAD profile: every hour user requirement, every 15mins potentially • Radar radial doppler winds: every 15mins, 5 elevations range 125km now 2 radars • Rainfall rate analysis : now every 15minutes 12 radars potentially every 5 mins • Radar reflectivity : 12 radars every 5 mins , 5 elevations range 255km • Cloud cover analysis: every hour (potentially every 15mins) • Wind profiler: 5 sites every 30mins (poss 15mins) • GPS: every 15mins 70-150 sites in GB end 2006 • Integrated system – wind profiler, microwave radiometer (1+2), cloud radar, ceilometer, GPS

  49. Radar Network Coverage – 2006 1km resolution 2km 5km

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