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© Crown copyright Met Office Radar data from cold air outbreak during Constrain Kirsty McBeath, Paul Field
© Crown copyright Met Office Table of Contents Introduction Overview of radar observations Variations applied to UM Cluster analysis of data
© Crown copyright Met Office Looking at cases of cold air outbreak in the Northwest approaches 4 flights during Constrain examined these conditions during January 2010 Radar data from these cases used for comparisons with UKV model This data is from a case on January 31 st 2010 which coincides with flight b507 of the BAe-146 research aircraft MODIS 31 st Jan 2010 Introduction
© Crown copyright Met Office Overview of Radar Observations
© Crown copyright Met Office Composite radar data Data available every 5 minutes for 24 hour period Scans performed at a range of elevation angles: 0.5°, 1.0°, 1.5°, 2.5° 4 scan angles intercept cells at different distances from radar Data from 4 scan angles combined to produce one dataset which captures all cells 0.5°: 75-150km 1.0°: 54-85km 1.5°: 32-64km 2.5°: 30-42km 150km 1.91 ±0.36km
© Crown copyright Met Office Reflectivity and rain rate Conversion from radar reflectivity (dBz) in to rainrate (mm/h), is typically approximated by: Z = 200R 1.6 This relation includes assumptions about drop size distributions and phase of precipitation This conversion can introduce additional uncertainties into the radar data 10dBz4mm/day 20dBz16mm/day 30dBz66mm/day 5dBz1mm/day
© Crown copyright Met Office Model data
© Crown copyright Met Office Model data Region examined is too far North for UKV (edge effects close to boundaries) Nested version of UKV model set up to cover the region without any edge effects Covered region from 10.16W,48.45N to 3.54E,59.18N
© Crown copyright Met Office Model Reflectivity Reflectivity values for UKV model computed using model microphysics data Reduces processing done on radar data Removes assumptions used when converting reflectivity to rain- rate
© Crown copyright Met Office Radar Model Reflectivity values for UM computed using model microphysics data, this reduces processing done on radar data and removes assumptions used when converting reflectivity to rain-rate
© Crown copyright Met Office Shear dominated boundary layer Local Richardson number used as indicator of shear dominating convection: if so then boundary layer diagnoses stratocumulus topped boundary layer (see Bodas-Salcedo et al. 2011) Reducing ice nucleation temperature ( T nuc =-18°C ) Changing the primary hetrogemeous ice nucleation temperature from -10 ° C to -18 ° C. This inhibits ice production until the boundary layer top approaches 4km Reducing autoconversion efficiency ( AcE = 0.1 ) The autoconversion efficiency is usually set to 0.55 (using the Cotton formulation of autoconversion), this is reduced to 0.1 to reduce the transfer if cloud water to precipitation Changes made to model
© Crown copyright Met Office No ice All ice processes switched off by setting T nuc =-50°C and converting any existing ice to liquid Field PSD Snow representation changed from standard exponential ( Wilson and Ballard 1999 ) to representation of Field et al. (2007) 3D Smagorinsky Vertical mixing done explicitly using 3D Smagorinsky approach rather than boundary layer scheme Changes made to model
© Crown copyright Met Office Model variations JobSh. Dom. B.L. T nuc = -18CAcE = 0.1No IceField P.S.D3D Smag. dimsh dimsp X dimsq X dimsn X dimsk X dimsi XX dimsz XX dimsy XXX dimsu XXXX dimsw XXX
© Crown copyright Met Office Cluster analysis
© Crown copyright Met Office Cluster Analysis 10dBz (~4mm/day) threshold used to select regions of precipitation in both datasets
© Crown copyright Met Office Cluster Analysis Identified cells tracked in time for both datasets Whole frame advected to find overlap between cells Fractional overlap for calculated for each overlapping pair of cells Overlap threshold used to determine if identified cells are the same Cells excluded from dataset if they moved outside the region of interest, or were only seen to decay 147 cells tracked in radar data 62 cells tracked in model data
© Crown copyright Met Office Cell Size Radar mean size = 10.82±0.26km dimsq ( AcE=0.1 ) and dimsi ( Sh. Dom. B.L. and AcE=0.1 ) produce mean sizes within 1 of radar mean
© Crown copyright Met Office Cell Size with lifetime Radar RMSE (from std dev) = 0.617 km dimsi fails to capture growth/decay of cells very well dimsz captures cell growth/decay quite well (has low RMSE value)
© Crown copyright Met Office Cell lifetime Radar mean lifetime = 69±3mins dimsu ( Sh. Dom. B.L., T nuc =-18°C, AcE=0.1 & Field P.S.D. ) has mean lifetime within 1 of radar Other runs with all do worse than control run for mean cell lifetime values
© Crown copyright Met Office Cell reflectivity Radar mean reflectivity = 16.9±1.4 dBz dimsh (ctrl), dimsq ( AcE=0.1 ) and dimsz ( Sh. Dom. B.L. and T nuc =-18°C ) produce mean reflectivity within 1 of radar mean None of the variation runs produce mean cell reflectivity values closer to the radar mean than the control run
© Crown copyright Met Office Cell reflectivity with lifetime Radar RMSE (from std dev) = 1.42 dBz Runs which do well for mean reflectivity, also do well when examining reflectivity with cell lifetime dimsq and dimsz both out-perform control when looking at RMSE over cell lifetime
© Crown copyright Met Office Size and reflectivity Symbol sizes increase with cell lifetime
© Crown copyright Met Office JobBetter than controlWorse than control dimsp mean cluster size size with lifetime mean cluster lifetime mean cluster reflectivity reflectivity with lifetime dimsq mean cluster size* size with lifetime reflectivity with lifetime mean cluster reflectivity mean cluster lifetime mean cluster reflectivity dimsi mean cluster size* size with lifetime mean cluster lifetime mean cluster reflectivity reflectivity with lifetime dimsz mean cluster size size with lifetime reflectivity with lifetime mean cluster reflectivity mean cluster lifetime mean cluster reflectivity dimsy reflectivity with lifetime mean cluster lifetime mean cluster reflectivity size with lifetime dimsu mean cluster lifetime*mean cluster reflectivity reflectivity with lifetime mean cluster size size with lifetime dimsq (AcE = 0.1) and dimsz (Sh. Dom. B.L. and Tnuc= -18°C) out-perform the control run over 3 variables dimsk (Sh. Dom. B.L.), dimsn (3D Smag.) and dimsw (T nuc = -18°C) all perform worse than the control run across all variables examined here * Within 1 of radar mean and better than control Within 1 of radar mean but worse than control
© Crown copyright Met Office Effect of Changing Cluster Threshold
© Crown copyright Met Office Impact of threshold on Cell Size Cluster analysis repeated using a range of reflectivity thresholds from 5-30dBz (1mm/day - 66mm/day) Both radar and model data show a decrease in cell size as reflectivity threshold increases seen in enclosing circle diameter and in pixel area
© Crown copyright Met Office Impact of reflectivity threshold on fill fraction Model fill fraction shows little variation with reflectivity threshold compared to radar fill fraction
© Crown copyright Met Office Next steps
© Crown copyright Met Office Questions
© Crown copyright Met Office High resolution COPE simulations Kirsty Hanley, Humphrey Lean UK.
1 00/XXXX © Crown copyright Carol Roadnight, Peter Clark Met Office, JCMM Halliwell Representing convection in convective scale NWP models : An idealised.
DYMECS: Dynamical and Microphysical Evolution of Convective Storms (NERC Standard Grant) University of Reading: Robin Hogan, Bob Plant, Thorwald Stein,
The three-dimensional structure of convective storms Robin Hogan John Nicol Robert Plant Peter Clark Kirsty Hanley Carol Halliwell Humphrey Lean Thorwald.
Dynamical and Microphysical Evolution of Convective Storms (DYMECS) University: Robin Hogan, Bob Plant, Thorwald Stein, Kirsty Hanley, John Nicol Met Office:
Page 1© Crown copyright 2005 Damian Wilson, 12 th October 2005 Assessment of model performance and potential improvements using CloudNet data.
Evaluation of three-dimensional cloud structures in DYMECS Robin Hogan John Nicol Robert Plant Peter Clark Kirsty Hanley Carol Halliwell Humphrey Lean.
© Crown copyright Met Office Radiation developments Latest work on the radiation code in the Unified Model James Manners, Reading collaboration meeting.
Radar/lidar observations of boundary layer clouds
© Crown copyright 2006Page 1 CFMIP II sensitivity experiments Mark Webb (Met Office Hadley Centre) Johannes Quaas (MPI) Tomoo Ogura (NIES) With thanks.
Convection Initiative discussion points What info do parametrizations & 1.5-km forecasts need? –Initiation mechanism, time-resolved cell size & updraft.
R. Forbes, 17 Nov 09 ECMWF Clouds and Radiation University of Reading ECMWF Cloud and Radiation Parametrization: Recent Activities Richard Forbes, Maike.
DYnamical and Microphysical Evolution of Convective Storms Thorwald Stein, Robin Hogan, John Nicol DYMECS.
© Crown copyright Met Office How will we COPE in Summer 2013? - The COnvective Precipitation Experiment Phil Brown.
Page 1© Crown copyright 2006 Precipitating Shallow Cumulus Case Intercomparison For the 9th GCSS Boundary Layer Cloud Workshop, September 2006, GISS.
Lidar observations of mixed-phase clouds Robin Hogan, Anthony Illingworth, Ewan OConnor & Mukunda Dev Behera University of Reading UK Overview Enhanced.
Robin Hogan Ewan OConnor Anthony Illingworth Department of Meteorology, University of Reading UK PDFs of humidity and cloud water content from Raman lidar.
© Crown copyright Met Office Convection Permitting Modelling Humphrey Lean et al. Reading, UK Leeds April 2014.
Robin Hogan & Anthony Illingworth Department of Meteorology University of Reading UK Parameterizing ice cloud inhomogeneity and the overlap of inhomogeneities.
Robin Hogan Anthony Illingworth Marion Mittermaier Ice water content from radar reflectivity factor and temperature.
Algebra 2 Chapter 2.
© Crown copyright Met Office Cloudier Evaluating a new GCM prognostic cloud scheme using CRM data Cyril Morcrette, Reading University, 19 February 2008.
Schematic diagram of the convective system life cycle size evolution Lifetime= (A e Initiation ) Mass flux or condensation process in the initiation.
The University of Reading Helen Dacre AGU Dec 2008 Boundary Layer Ventilation by Convection and Coastal Processes Helen Dacre, Sue Gray, Stephen Belcher.
Robin Hogan, Richard Allan, Nicky Chalmers, Thorwald Stein, Julien Delanoë University of Reading How accurate are the radiative properties of ice clouds.
Polygon Scan Conversion – 11b
Initial 3D isotropic fractal field An initial fractal cloud-like field can be generated by essentially performing an inverse 3D Fourier Transform on the.
Introduction The 2014 Report contains an analysis of 1,483 cases of occupational fraud that occurred in more than 100 countries. Figure 1 illustrates the.
The DYMECS project A statistical approach for the evaluation of convective storms in high-resolution models Thorwald Stein, Robin Hogan, John Nicol, Robert.
7. Radar Meteorology References Battan (1973) Atlas (1989)
Solving a Sudoku Puzzle
Precipitation and albedo variability in marine low clouds
© Crown copyright Met Office Overview of Cloud Scheme Developments at the Met Office Cyril Morcrette November 2009, Reading University.
Robin Hogan, Chris Westbrook University of Reading Lin Tian NASA Goddard Space Flight Center Phil Brown Met Office Why it is important that ice particles.
© Crown copyright Met Office Systematic Biases in Microphysics: observations and parametrization Ian Boutle, Steven Abel, Peter Hill, Cyril Morcrette QJ.
© Crown copyright Met Office Experiences with a 100m version of the Unified Model over an Urban Area Humphrey Lean Reading, UK WWOSC.
Robin Hogan, Chris Westbrook University of Reading Lin Tian NASA Goddard Space Flight Center Phil Brown Met Office The importance of ice particle shape.
Modified NAM Microphysics for Use in High-Resolution Forecasts
High-Resolution Simulation of Hurricane Bonnie (1998). Part II: Water Budget Braun, S. A., 2006: High-Resolution Simulation of Hurricane Bonnie (1998).
Ed Maurer Partitioning precipitation into rain and snow for event-based hydrologic modeling in the Pacific Northwest U.S. Edwin Maurer Civil Engineering.
Before Between After.
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