We think you have liked this presentation. If you wish to download it, please recommend it to your friends in any social system. Share buttons are a little bit lower. Thank you!
Presentation is loading. Please wait.
Published byKaylee Morrow
Modified over 2 years ago
© Crown copyright Met Office Length scales and anisotropy Isabelle Mirouze and NEMOVAR groups members NEMOVAR Steering Group meeting – 16 January 2012
© Crown copyright Met Office Length scales and anisotropy Zonal length scales estimated from an ensemble (Daget,2008) degree
© Crown copyright Met Office Length scales and anisotropy Horizontal correlations for SST errors in the AMM7 model (J. While)
© Crown copyright Met Office Length scales and anisotropy Questions: 1.How do the length scales vary in space and time? 2.How strong is the anisotropy, in which area? 3.How do the length scales vary according to the resolution of the model? Issues: How to estimate length scales and anisotropy Accounting for static / flow-dependent length scales Accounting for anisotropy
© Crown copyright Met Office Estimate and/or parameterise Parametrise: Rossby radius Depending on T/S gradient Depending on bathymetry Depending on the mixed layer depth … Estimate from an ensemble of unbalanced errors: Diagonal and off-diagonal terms of the tensor (Belo Pereira and Berre, 2006) Ratio of the length scales (main directions) and rotation angles (Chorti and Hristopulos, 2008)
© Crown copyright Met Office Estimate from an ensemble ECMWF: 5 members, 12/1957 to 02/2011, ORCA1, 10-day Unbalanced part of the errors Variances for every cycles and every 3 months Gradient of the unbalanced errors Met Office: NMC errors, 01/2007 to 12/2008, ORCA025, 1-day Options: Climatologic length scales, ~9500 / ~730 members Season length scales, ~2400 / ~180 members Cycle (or other) length scales 5 members filtering issues
© Crown copyright Met Office Accounting for the estimate Static isotropic length scales Climatologic estimate + related normalization factors Season estimate + related normalization factors => PrepIFS / SCS modifications Flow dependent isotropic length scales Parameterization Static estimate + parameterization Estimate from an ensemble at every cycles (possibly associated with static estimate) => Normalization issue
© Crown copyright Met Office Accounting for the estimate Anisotropy 1D implicit diffusion model Hexad and triad (Purser et al, 2003) Change of spatial coordinate (Yann Michel) C = D F F * D * F is a isotropic correlation model D is a deformation operator Full 2D and/or 3D implicit diffusion model Issues to handle…
© Crown copyright Met Office Suggesting plan Stage 1 Estimate climatologic length scales for ORCA1 using ensemble errors (NMC errors?) Stage 2 Estimate climatologic length scales for ORCA025, AMM7 and regional models using NMC errors Compare the different resolutions Stage 3 Take into account the diagonal elements of the tensor. What is the impact?
Slide 1 ECMWF Data Assimilation Training Course – May 2010 Ensemble Data Assimilation Massimo Bonavita ECMWF Acknowledgments: Lars Isaksen, Elias Holm,
© Crown copyright Met Office NEMOVAR status and plans Matt Martin, Dan Lea, Jennie Waters, James While, Isabelle Mirouze NEMOVAR SG, ECMWF, Jan 2012.
ECMWF Slide 1 ECMWF Data Assimilation Training Course - Background Error Covariance Modelling Elias Holm – slides courtesy Mike Fisher.
© Crown copyright Met Office Use of Ensembles in Variational Data Assimilation DAOS WG. Sept Andrew Lorenc.
© Crown copyright Met Office Implementing a diurnal model at the Met Office James While, Matthew Martin.
GEMS Kick-Off Meeting, Hamburg Aerosols: WP3 Jean-Jacques Morcrette, Olivier Boucher With contributions at ECMWF from: Angela Benedetti: background error.
Training Course 2009 – NWP-DA: Ocean Data Assimilation 1 Data assimilation in the ocean Magdalena A. Balmaseda.
Jennifer Catto Supervisors: Len Shaffrey – NCAS Climate and Kevin Hodges - ESSC The Representation of Extratropical Cyclones in HiGEM.
1 What constrains spread growth in forecasts initialized from ensemble filters? Tom Hamill (& Jeff Whitaker) NOAA Earth System Research Lab Boulder, Colorado,
ECMWF Stochastic representations of model uncertainty: Glenn Shutts March 2009 Stochastic representations of model uncertainty Glenn Shutts ECMWF/Met Office.
Page 1 of 26 A PV control variable Ross Bannister* Mike Cullen *Data Assimilation Research Centre, Univ. Reading, UK Met Office, Exeter, UK.
Page 1© Crown copyright 2007 CFMIP2: Options for SST-forced and slab experiments Mark Ringer, Brian Soden Hadley Centre,UK & RSMA/MPO, US CFMIP/ENSEMBLES.
DYMECS: Dynamical and Microphysical Evolution of Convective Storms (NERC Standard Grant) University of Reading: Robin Hogan, Bob Plant, Thorwald Stein,
© Crown copyright Met Office Investigating a perturbed physics scheme in a wave ensemble system Ray Bell (Line manager: Francois-Bocquet) Ocean Iced Tea.
Page 1© Crown copyright 2005 Use of EPS at the Met Office Ken Mylne and Tim Legg.
© Crown copyright Met Office Radiation Parametrisation Current development work with the UM James Manners, visit to Reading University on 19 th February.
© Crown copyright Met Office Recent & planned developments to the Met Office Global and Regional Ensemble Prediction System (MOGREPS) Richard Swinbank,
Page 1© Crown copyright 2004 Presentation to ECMWF Forecast Product User Meeting 16th June 2005.
1 The middle atmosphere and the parametrization of non-orographic gravity wave drag Peter Bechtold and Andrew Orr.
IB Design Cycle. Essential Question: How can we tell a story using photos? How can we tell a story using photos?
© Crown copyright Met Office Regional climate model formulation PRECIS Workshop, Reading University, 23 rd – 27 th April 2012.
Dynamical and Microphysical Evolution of Convective Storms (DYMECS) University: Robin Hogan, Bob Plant, Thorwald Stein, Kirsty Hanley, John Nicol Met Office:
Limitations and strengths of 4D-Var and Possible use of a variational analysis in the EnKF EnKF Internal Workshop CMC, Dorval Mark Buehner ASTD/MRD/ARMA.
Impact of EOS MLS ozone data on medium-extended range ensemble forecasts Jacob C. H. Cheung 1, Joanna D. Haigh 1, David R. Jackson 2 1 Imperial College.
Gravity wave drag Parameterization of orographic related momentum fluxes in a numerical weather processing model Andrew Orr Lecture 1:
ECMWF DA/SAT Training Course, May The Operational Data Assimilation System Lars Isaksen, Data Assimilation, ECMWF Overview of the operational data.
AES1310: Rock Fluid Interactions - Part 1 1 Susanne Rudolph Darcys law in heterogeneous medium - Introduction - Averages.
Turbulence and surface-layer parameterizations for mesoscale models Dmitrii V. Mironov German Weather Service, Offenbach am Main, Germany
Forecast Verification Research Beth Ebert, Bureau of Meteorology Laurie Wilson, Meteorological Service of Canada WWRP-JSC, Geneva, April 2012.
Page 1© Crown copyright 2007 Initial tendencies of cloud regimes in the Met Office Unified Model Keith Williams and Malcolm Brooks Met Office, Hadley Centre.
© 2016 SlidePlayer.com Inc. All rights reserved.