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© Crown copyright Met Office Met Office Experiences with Convection Permitting Models Humphrey Lean Reading, UK Nowcasting Workshop, Boulder, Oct 2011
© Crown copyright Met Office Timeline of MO convection permitting models Met Office has been experimenting with convection permitting versions of UM since 2001 (NH UM version). UK 4km model in operational suite since April On demand 1.5km model (9 domains) from Dec 2006 UKV 1.5km model from Nov Extended range UK 4km (global downscaler) from Dec 2010 Nowcasting Demonstration system from June 2012 Convective ensemble (2.2km) from June 2012
© Crown copyright Met Office UKV Model Runs out to T+36 4 times a day. 3 hour 3dvar assimilation cycle with nudging of radar reflectivity. 1.5km over most of UK, variable res to 4km at edge of domain Similar configuration to low res models except: No convection scheme Smagorinsky turbulence Prognostic rain
© Crown copyright Met Office UKV Domain 744(622) x 928( 810) points 1.5x x4 4x1.5 4x4 Variable zone Inner 1.5km domain covers most of UK. Gridlength increases to 4km at edge.
© Crown copyright Met Office UKV Model from 03UTC 19/11/2009
© Crown copyright Met Office Carlisle Flood - Observed and Forecast Accumulations Roberts, Forbes + EA 12 km 4 km 1 km Hand analysis of gauges and radar 12 km 1 km Model Orography Why High resolution? Benefits from more detailed orography 1: Orographic Rain
© Crown copyright Met Office Forecast visibility at 12 UTC 10/12/2003 from 18 UTC 09/12/ km L38 (part domain) 1km L76 Visibility (m) at station height, synoptic observations (km) Rachel Capon and Peter Clark Why High resolution? Benefits from more detailed orography 2: Fog in valleys
© Crown copyright Met Office Urban heat islands in UKV Heatwave temperature (00 UTC 19 th July 2006) Why High resolution? Benefits from more detailed land use: Urban Heat Islands
© Crown copyright Met Office However biggest benefit is expected to be in representation of Convection. Explicit convection means losing problems associated with parameterisation at these gridlengths. Also represent related features which are important (convergence lines etc).
© Crown copyright Met Office 3 rd May 2002 Scattered convection case History 1: No convection scheme Nigel Roberts
© Crown copyright Met Office History 2: Smagorinsky turbulence At early stage in research into km scale models found that horizontal diffusion was needed to reduce gridscale structure. Also discovered that applying too much uniform horizontal diffusion had detrimental effect on convective initiation (delay). This was motivation for using Smagorinsky turbulence (only apply once shear built up). Currently UKV uses 2D Smagorinsky with BL mixing in vertical.
© Crown copyright Met Office Example of rainfall forecasts Squall line southern England 14 UTC 1 st July 2003 T+7 forecast 12km4km 1kmRadar
© Crown copyright Met Office Rainfall Accumulations UTC 16 th August km 4 km NIMROD radar Forecasts from 03 UTC Peak Accumulations >60mm On 4 km grid Positional error and false alarm Boscastle Flood Peter Clark
© Crown copyright Met Office Snow Showers penetrating inland Well known problem with parameterised convection is showers not penetrating far enough inland. 25 th Nov 2010 Snow showers coming in on NE wind gave significant snow in NE England
© Crown copyright Met Office Snow Showers penetrating inland 24 hour precip accumulation (mm) 25 th Nov km radarUKV (1.5km) NAE (12km) Operational models
© Crown copyright Met Office How about objective verification? Need to take care with standard gridpoint scores! April to Oct 2010 Equitable Threat Score (ETS) Using gauges M Mittermaier, N Roberts & S Thompson submitted to Met Apps UKV 1.5 km UK 4 km NAE 12 km Global ~25 km
© Crown copyright Met Office Predictability Issues Use of 1.5km model does NOT automatically mean that we can issue forecasts with 1.5km accuracy. Small scales less predictable so individual showers not predictable more than a few hours ahead (unless driven by larger scale feature such as orography or convergence line). Consequences for: 1. Sensitivity testing of convective scale systems except in extreme cases one case is meaningless 2. Verification of models scale selective techniques 3. Interpretation/presentation of forecasts Avoid presenting unpredicable information. Move to probabalistic presentation.
© Crown copyright Met Office Skill depends on the scale you look at Nigel Roberts Roberts and Lean MWR 2008
© Crown copyright Met Office Summary FSS scores UKV vs NAE Forecast Ranges Percentage of times UKV better minus percentage of times its worse. Background colour gives indication of statistical significance (green >95%). Marion Mittermaier and Matthew Trueman
© Crown copyright Met Office Summary FSS scores UKV vs UK4 Forecast Ranges Percentage of times UKV better minus percentage of times its worse. Background colour gives indication of statistical significance (Green >95%). Marion Mittermaier and Matthew Trueman
© Crown copyright Met Office Problems with representation of convection UKV does improve on representation of convection in lower resolution models with convective parameterisations. However problems remain: Peak rain rates often too great. w too large (up to 15m/s in UK) Cell properties very dependent on mixing settings. Too much gridscale structure (esp in w). Know in principle that deep convection very under-resolved at 1.5km. Evidence that behaviour at 1.5km still dominated by gridlength (convection permitting rather than resolving)
© Crown copyright Met Office Gridscale structure in 750 hPa w 13UTC 12/05/2010 4km 1.5km 500m Emilie Carter
© Crown copyright Met Office Compare gridlengths down to 100m 4km1.5km 500m 100mRadar (1km) Emilie Carter Features continue to get smaller 12 UTC from 06 UTC run 7 th Aug 2011 Areas shown are 80x80km (whole domain of 100m)
© Crown copyright Met Office Need to find out how to do best we can at 1.5km. Mixing (horizontal and vertical) Shallow convection scheme Standard convection scheme (as in UK4)? Stochastic backscatter Microphysics Other?
© Crown copyright Met Office Effect of vertical mixing at 1.5km Radar ControlSmag+Vert mixing Vertical Mixing has big effect on no of cells
© Crown copyright Met Office Need to constrain model set up with observations DYnamical and Microphysical Evolution of Convective Storms (DYMECS). Hogan et. al. U of Reading Track cells with Chilbolton research radar and build up statistics of properties of convection. (runs from now through summer 2012). COnvective Precipiatation Experiment (COPE). Blythe et. al. with Met Office. Development of convection in SW England. (Peninsular convergence lines) (field programme summer 2013). For both of these will compare to UM at gridlengths between 100m and 4km.
© Crown copyright Met Office Rainfall (mm/hr) Reflectivity (dBZ) Chilbolton Radar Radar CompositePPI Composite Grey: 5dBZ isosurface Red: 30dBZ isosurface Thorwald Stein
© Crown copyright Met Office Conclusions Met Office has gathered much experience with convection permitting versions of the UM. Current models are used for longer time ranges than nowcasting. Many benefits seen for representation of convection and other phenomena Problems still remain with representation of convection which are being addressed Need to consider predictability issues for verification and interpretation of forecasts.
© Crown copyright Met Office Thank you for listening. Any questions?
Dynamical and Microphysical Evolution of Convective Storms (DYMECS) University: Robin Hogan, Bob Plant, Thorwald Stein, Kirsty Hanley, John Nicol Met Office:
DYMECS: Dynamical and Microphysical Evolution of Convective Storms (NERC Standard Grant) University of Reading: Robin Hogan, Bob Plant, Thorwald Stein,
Page 1© Crown copyright 2005 Use of EPS at the Met Office Ken Mylne and Tim Legg.
Regional Models Lake Victoria Model Prepared by C. Tubbs, P. Davies, Met Office UK Revised, delivered by P. Chen, WMO Secretariat SWFDP-Eastern Africa.
© 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 Met Office progress report Andy Brown WGNE, Tokyo, October 2010.
Page 1© Crown copyright Some Strengths and Weaknesses of ECMWF Forecasts for the UK Tim Hewson 15 th June 2006 Contributors include: Eleanor Crompton,
Cliquez pour modifier le style du titre Cliquez pour modifier le style des sous-titres du masque 1 Nowcasting strategies : Rapid analysis refresh and high.
Robin Hogan Ewan OConnor, Anthony Illingworth University of Reading, UK Clouds radar collaboration meeting 17 Nov 09 Ground based evaluation of cloud forecasts.
© Crown copyright Met Office Scientific background and content of new gridded products Bob Lunnon, Aviation Outcomes Manager, Met Office WAFS Workshop.
Problems With Model Physics in Mesoscale Models Clifford F. Mass, University of Washington, Seattle, WA.
Numerical Weather Prediction Parametrization of diabatic processes Clouds (4) Cloud Scheme Validation Richard Forbes and Adrian Tompkins
Ingredients to improve rainfall forecast in very short-range: Diabatic initialization and microphysics Eunha Lim 1, Yong-Hee Lee 2, and Jong-Chul Ha 2.
Cloud Resolving Models: Their development and their use in parametrization development Richard Forbes, Adrian Tompkins.
Robin Hogan Ewan OConnor, Anthony Illingworth University of Reading, UK Chris Ferro, Ian Jolliffe, David Stephenson University of Exeter, UK Verifying.
Robin Hogan Julien Delanoe, Ewan OConnor, Anthony Illingworth, Jonathan Wilkinson University of Reading, UK Quantifying the skill of cloud forecasts from.
Parametrizations in Data Assimilation ECMWF Training Course May 2012 Philippe Lopez Physical Aspects Section, Research Department, ECMWF (Room 113)
1 00/XXXX © Crown copyright Carol Roadnight, Peter Clark Met Office, JCMM Halliwell Representing convection in convective scale NWP models : An idealised.
Training Course 2009 – NWP-PR: The Seasonal Forecast System at ECMWF 1 The Seasonal Forecast System at ECMWF Tim Stockdale European Centre for Medium-Range.
ECMWF DA/SAT Training Course, May The Operational Data Assimilation System Lars Isaksen, Data Assimilation, ECMWF Overview of the operational data.
Data Assimilation Strategies for Operational NWP at Meso-scale and Implication for Nowcasting Thibaut Montmerle CNRM-GAME/GMAP WMO/WWRP Workshop on Use.
Research and Development Project Improving the prediction of heavy precipitating systems over La Plata Basin LPB-ReD Presented by Alice M. Grimm Based.
Reading, UK Parametrizations and data assimilation © ECMWF 2012 Marta JANISKOVÁ ECMWF Parametrizations and data assimilation.
© Crown copyright 2007 Impact studies with satellite observations at the Met Office John Eyre and Steve English Met Office, UK 4th WMO Workshop on "The.
Robin Hogan Anthony Illingworth, Ewan OConnor, Jon Shonk, Julien Delanoe, Andrew Barratt University of Reading, UK And the Cloudnet team: D. Bouniol, M.
Some questions on convection that could be addressed through another UK field program centered at Chilbolton Dan Kirshbaum 1.
© Crown copyright Met Office UM 4D-Var Regional Reanalysis Progress Richard Renshaw, Stephen Oxley, Adam Maycock, Peter Jermey, Dale Barker, DingMin Li.
Clouds and their turbulent environment Robin Hogan, Andrew Barrett, Natalie Harvey Helen Dacre, Richard Forbes (ECMWF) Department of Meteorology, University.
© Crown copyright Met Office Recent & planned developments to the Met Office Global and Regional Ensemble Prediction System (MOGREPS) Richard Swinbank,
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