Page 1© Crown copyright 2005 Progress with high resolution modelling with the Unified Model Peter Clark Group Leader Mesoscale Modelling Met Office Joint.

© Crown copyright 2005 Progress with high resolution modelling with the Unified Model Peter Clark Group Leader Mesoscale Modelling Met Office Joint Centre for Mesoscale Meteorology University of Reading

© Crown copyright 2005 Organisation JCMM (Reading) Mesoscale Modelling Peter Clark + 6 Mesoscale Data Assimilation Sue Ballard+3 General Parametrization Data Assimilation Satellite Applications Dynamics Research Evaluation and Diags UM Systems Met Office (Exeter)

© Crown copyright 2005 Met Office Regional NWP Strategy  North Atlantic and European (NAE) limited area model  Europe/Storm tracks, T+48….  12 km 4D Var Data Assimilation main forecast  24 km ensemble system embedded in global ensemble  New UK Model quasi-operational April 2005 T+36  4 km, UK weather especially surface impacts  ‘Spin-up’ from NAE analysis through summer 2005.  Full 3DVAR/MOPS assimilation cycle from end 2005 (tomorrow!).  Vertical resolution enhancement 2006  Experimenting with 1 km since 2002.  ‘On-demand’ small area 1.5 km model by 2007.  Expect UK model to move to 1.5 km in 2009.

© Crown copyright 2005 Other UM high resolution areas  S Africa and Ethiopia 4 km  New Zealand and Alps (MAP) 60km, 40km, 20km, 12km, 4km, 2km and 1km – studies of stress convergence (Stuart Webster) TOTAL RESOLVED SSO OR 2km 60km

© Crown copyright 2005 Motivation for high resolution forecasting  Severe convective storms can lead to flash flooding.  strong winds associated with storms.  Boscastle (SW England), 16th August 2004  ~£500,000,000 damage  Fortunately, no one killed  Even 2 hours warning useful Forecasting of convective precipitation is primary public safety need

© Crown copyright 2005 Emphasis for UK modelling  Main emphasis on ~1 km very short range model.  4 km very useful for temperature/visibility/wind forecasting. Uncomfortable about precipitation. Nevertheless, more useful than expected.  Much depends on intelligent upscaling in post- processing.

© Crown copyright 2005 Unified Model at 4 km and 1 km resolution  Non-hydrostatic, compressible, deep atmosphere, semi-Lagrangian, semi-implicit dynamics.  Arwakawa C horizontal rotated lat/long, Charney Philips vertical flexible terrain following height based.  Philosophy has been to start with existing UM physics and enhance only where evidence shows need.  Main physics developments are microphysics and turbulence  Still taking conservative approach  Additional developments:  Enhanced urban scheme  Surface slope in radiation

© Crown copyright 2005 Microphysics  Operational UM Wilson and Ballard microphysics– prognostic total ice+snow, diagnostic ice/snow split, diagnosic rain.  Since UM6.0 there is a prognostic representation of:  3D SL advection initially  Separate 3D advection by wind and 1D SL transport relative to air  Useful for single column  Cheaper and no significant impact on solution (we think!)  Developed from standard UM Wilson and Ballard microphysics  New microphysics fully flexible  Working towards (optional) convergence with Met Office CRM.  Working on improvements to numerics. Cloud liquid water Water vapour Graupel Ice crystals Snow aggregates Rain

© Crown copyright 2005 Quantifying Systematic and Local Impacts  Graupel → very small systematic increase in rainfall, small local impact.  Increasing rain evap. rate → small systematic decrease in rain, larger local impact.  Water loading → small systematic decrease in rainfall, significant local impact  Decreasing the snow fall speed → large systematic impact, significant local impact.

© Crown copyright 2005 Two alternative turbulence treatments  Standard UM 1D boundary layer (Lock et al, 2000)  Non local eddy diffusivity  Moist  Multiple regime  Not using shallow convection (future work)  Implicit solution  Fixed Horizontal hyper- diffusion (del-4).  Arbitrary chosen to give most reasonable power spectra.  Explicit solution  Smagorinsky-Lilly 3D with stability dependent length scale.  Stability functions same as local part of standard UM scheme.  Basic length scale proportional to horizontal grid length.  Same numerical solution method as standard scheme.

© Crown copyright 2005 Summary – Turbulent Mixing  3D sub-grid turbulent mixing parametrization introduced into the UM (based on Smagorinsky-Lilly).  Tested in idealised and real case studies and can have a very significant impact on convective initiation and evolution.  Reduces over-prediction of small convective cells at 1km. Reduces excessive rain rates in larger storms.  BUT not appropriate for all situations (e.g. very stable).  Work is ongoing into most appropriate formulation for different resolutions, and enhancing the scheme (e.g. stochastic backscatter).

© Crown copyright 2005 Convection at 4 km  We don’t know how to parametrize convection at 4 km.  We don’t all agree what a correct solution would look like!  We have decided not to try to develop a ‘4 km’ convection scheme.  Gregory-Rowntree ‘hands over’ to explicit smoothly but not correctly  Depends on parameter choice  Some modes of behaviour not necessarily physical  Behaviour different for boundary forced domain compared with periodic, homogeneous (CRM)  Nested has additional sink of small scale energy  Domain and problem dependent  Pragmatism (fudges!) necessary  CRM equilibrium behaviour used for guidance

 30 Convection scheme closure CAPE (J/Kg)Mass flux  Mass flux  CAPE /  CAPE closure timescale  3/9/02 Nigel Roberts, JCMM CAPE (J/Kg)

© Crown copyright 2005 Why (and when) 4 km can be useful for precipitation  Convergence lines that trigger new convection are quite well resolved by 4 km model.  This can give good spatial indication of rain.  Especially true with cold pool dynamics – (our) parametrized convection poor to useless. (Probably true of any quasi-equilibrium scheme).

© Crown copyright 2005 Orographic features important to radiation Oliphant et. al. 2003, ‘Spatial variability of surface radiation fluxes in mountainous terrain’ Characteristics in order of importance: slope aspect, slope angle, elevation, albedo, shading, sky view factor, leaf area index The most important factor is the area presented by each grid-box to the incoming direct SW radiation

© Crown copyright 2005 Summary – Orography and Radiation  Included slope aspect and angle into the incoming direct short-wave radiation scheme.  Tested in UM with grid resolutions ranging from 60km (global) to 1km (over the southern UK).  At high resolution, small (0.5K) surface temperature changes resulting from the scheme can lead to differences in convective initiation and evolution.

© Crown copyright 2005 Variable Resolution An alternative approach to 1-way nesting. Grid varies from coarse resolution at the outer boundaries smoothly to a uniform fine resolution in the interior of the domain Benefits close to hires domain boundary, e.g. reduces spin-up of convection at inflow boundaries Uniform High Res zone Var-Res 2 Var-Res 1 Uniform Coarse Res 1 Uniform Coarse Res 2 Typically, there are 3 regions, and inflation ratio R 1 = R 2 = 5~10% R2R2 R1R1

© Crown copyright 2005 Summary – Variable Resolution  Variable resolution grid capability implemented in the UM.  Tested in idealised and real case studies with a nesting ratio of 1 : 4 and results look promising.  Currently working on the model parametrizations to make them depend more appropriately on the local grid-length in different parts of the domain (e.g. grid-length dependent convection scheme). (More fudges!)

© Crown copyright 2005 Known UM problems  4 km convective cells much too large, too few. (Expected and forecasters have adapted).  Latest physics produces acceptable area average precipitation but..  Non-fatal grid-point storms at 4 km. Solutions have run into problems. (No problems at 1 km).  Valley cooling problem at all scales. Caused by vertical non-interpolating SL advection. Fixed (we hope!) by selective fully interpolating mod.

© Crown copyright 2005 Model Physics (at present) 12 km/L384 km/L381 km/L76 Timestep300 s100 s30 s Convection Scheme Full Gregory- Rowntree Gregory-Rowntree with restricted mass flux None MicrophysicsPrognostic icePrognostic ice and rain Prognostic ice, rain. Two ice+graupel under test. Surface9 Tile MOSES DiffusionDel 4 theta + Targeted moisture Del 4 To be replaced by 3D turbulence. Boundary Layer / Turbulence Standard 1D (3D Local likely)

© Crown copyright 2005 Precipitation Verification Techniques  Philosophy based on assumption that small scales less skilful than large.  Rather than doing point by point verification use fractions (~probabilities) over a certain area surrounding each grid point.  Calculate various probability and categorical scores based on accumulation thresholds.  Basis of products and investigation of skill as function of scale.

© Crown copyright 2005 Summer 2004 Trial Seven cases from 2004 period (mostly convective) For each case run 4 forecasts at 3 hour intervals Run one suite with 4km, 1km assimilation and a second initialising 4km, 1km from 12km analyses. Forecasts out to T+7 for 1km model Aggregate statistics over forecasts and cases.

© Crown copyright 2005 Intensity/Scale verification  Barbara Casati PhD in conjunction with Met Office.  Similar ideas to Nigel Roberts – Haar wavelet transform similar to successive ‘box averages’  Summary methods useful for comparison between models.

Radar Model forecast from Casati (2004) Radar > 1 mmForecast > 1 mmBinary error image X > uX < u Y > u Hits a False Alarms ba+b Y < uMisses c Correct Rejections d c+d a+cb+da+b+c+d=n

wavelet decomposition of the binary error 1 0 Scale from Casati (2004)

12-18Z 4 km 00Z 6 hr rainfall 12 km 00Z 6 hr rainfall4 km 00Z avg 12 km 6 hr rainfall Error scale (km) 2x2x 16  x 1 mm 64 mm 2x2x 16  x Max radar = 44 mm 68 mm 7 mm 46 mm Rainfall threshold (mm)

© Crown copyright 2005  Distribution-free test as normality of errors can’t be assumed.  B = number of +ve skill scores for a given scale and intensity during a given time interval, e.g. 1 month.  Hypotheses:  H 0 : SS >= 0 (implicit positive and skillful)  H 1 : SS < 0 (less skill than a random forecast)  H 0 is rejected if b <= b n,   where B ~ bi(n, 0.5) for small samples (n < 40),  = 0.025  The value of (n – B) / n is shaded in intensity-phase space for each scale and intensity where H 0 is rejected. Modified sign-test statistic

© Crown copyright 2005 Added benefit: comparison of prevalent errors at the monthly time scale  (sub-)“grid” scale errors are more prevalent at trace rainfall totals for the 4 km model  prevalent errors at twice and four times the 12 km grid length for thresholds > 16 mm are less for the 4 km model (captures large totals better) May 2005 12 km vs radarMay 2005 4 km avg vs radar X X X X X X X X X X X X X X X X 48 km 32 mm

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Page 1© Crown copyright 2005 Progress with high resolution modelling with the Unified Model Peter Clark Group Leader Mesoscale Modelling Met Office Joint.

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