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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°: km 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) = 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
Dynamical and Microphysical Evolution of Convective Storms (DYMECS) University: Robin Hogan, Bob Plant, Thorwald Stein, Kirsty Hanley, John Nicol Met Office:
1 00/XXXX © Crown copyright Carol Roadnight, Peter Clark Met Office, JCMM Halliwell Representing convection in convective scale NWP models : An idealised.
Clouds and their turbulent environment Robin Hogan, Andrew Barrett, Natalie Harvey Helen Dacre, Richard Forbes (ECMWF) Department of Meteorology, University.
Reading, UK Parametrizations and data assimilation © ECMWF 2012 Marta JANISKOVÁ ECMWF Parametrizations and data assimilation.
© Crown copyright Met Office Met Office Experiences with Convection Permitting Models Humphrey Lean Reading, UK Nowcasting Workshop,
Robin Hogan Anthony Illingworth, Ewan OConnor, Jon Shonk, Julien Delanoe, Andrew Barratt University of Reading, UK And the Cloudnet team: D. Bouniol, M.
1 All Rights Reserved. No part of this document may be reproduced, stored in a retrieval system, or transmitted, in any form or by any means, electronic,
Cloud Resolving Models: Their development and their use in parametrization development Richard Forbes, Adrian Tompkins.
Robin Hogan Ewan OConnor Julien Delanoe Anthony Illingworth Use of ground-based radar and lidar to evaluate model clouds.
Numerical Weather Prediction Parametrization of Diabatic Processes Clouds (1) Cloud Microphysics Richard Forbes and Adrian Tompkins
Reading, UK Parametrizations and data assimilation © ECMWF 2010 Marta JANISKOVÁ ECMWF Parametrizations and data assimilation.
DYMECS: Dynamical and Microphysical Evolution of Convective Storms (NERC Standard Grant) University of Reading: Robin Hogan, Bob Plant, Thorwald Stein,
Robin Hogan Julien Delanoe Department of Meteorology, University of Reading, UK Variational methods for retrieving cloud, rain and hail properties combining.
Parametrizations in Data Assimilation ECMWF Training Course May 2012 Philippe Lopez Physical Aspects Section, Research Department, ECMWF (Room 113)
Page 1© Crown copyright 2005 Use of EPS at the Met Office Ken Mylne and Tim Legg.
Ewan OConnor, Anthony Illingworth, Robin Hogan and the Cloudnet team Cloudnet.
From Research to Real-Time: Modeling and Forecasting the Ring Current Paul OBrien UCLA/IGPP
Robin Hogan, Julien Delanoe and Nicola Pounder University of Reading Towards unified retrievals of clouds, precipitation and aerosols.
© 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 2006Page 1 The Cloud Feedback Model Intercomparison Project (CFMIP) Progress and future plans Mark Webb (Hadley Centre) and CFMIP contributors.
Parameterizations in Data Assimilation Philippe Lopez Physical Aspects Section, Research Department, ECMWF (Room 113) ECMWF Training Course May 2010.
© 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.
Grand Unified Algorithms How to retrieve radiatively consistent profiles of clouds, precipitation and aerosol from radar, lidar and radiometers Robin Hogan,
Page 1© Crown copyright Operational Use of ECMWF products at the Met Office: Current practice, Verification and Ideas for the future Tim Hewson 17 th June.
Fundamentals of Weather Modification The purpose of these lectures can be enounced in a simple sentence: to help pilots to become good cloud physics observers.
Data Assimilation Training Course, Reading, 5-14 May 2010 Observation Operators in Variational Data Assimilation David Tan, Room 1001
Corporate Finance/Restructuring Forensic and Litigation Consulting Economic Consulting Strategic Communications Technology Sound recordings used as specially.
1 Recent (selected) results from CloudSat and the A-Train Graeme L Stephens Co-op Institute for Res. Atmosphere (CIRA) and Dept Atmospheric Sciences Colorado.
Chapter 6 Three Simple Classification Methods The Naïve Rule Naïve Bayes k-Nearest Neighbor 1.
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