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© 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?
© Crown copyright Met Office NEMOVAR status and plans Matt Martin, Dan Lea, Jennie Waters, James While, Isabelle Mirouze NEMOVAR SG, ECMWF, Jan 2012.
Ocean Data Variational Assimilation with OPA: Ongoing developments with OPAVAR and implementation plan for NEMOVAR Sophie RICCI, Anthony Weaver, Nicolas.
© Crown copyright Met Office Implementing a diurnal model at the Met Office James While, Matthew Martin.
© Crown copyright Met Office UK report for GOVST Matt Martin GOVST-V, Beijing, October 2014.
Page 1 of 26 A PV control variable Ross Bannister* Mike Cullen *Data Assimilation Research Centre, Univ. Reading, UK Met Office, Exeter, UK.
DYMECS: Dynamical and Microphysical Evolution of Convective Storms (NERC Standard Grant) University of Reading: Robin Hogan, Bob Plant, Thorwald Stein,
WCRP OSC 2011: Strategies for improving seasonal prediction © ECMWF Strategies for improving seasonal prediction Tim Stockdale, Franco Molteni, Magdalena.
HFIP Regional Ensemble Call Audio = Passcode = # 16 September UTC.
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.
Tropical Atlantic SST in coupled models; sensitivity to vertical mixing Wilco Hazeleger Rein Haarsma KNMI Oceanographic Research The Netherlands.
11 Background Error Daryl T. Kleist* National Monsoon Mission Scoping Workshop IITM, Pune, India April 2011.
Explicit Treatment of Model Error Simultaneous State and Parameter Estimation with an Ensemble Kalman Filter Altuğ Aksoy*, Fuqing Zhang, and John W. Nielsen-Gammon.
© Crown copyright 2007 Forecasting weeks to months ahead Dr. Alberto Arribas Monthly-to-Decadal area, Met Office Hadley Centre Exeter, April 2014.
© 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 The EN4 dataset of quality controlled ocean temperature and salinity profiles and monthly objective analyses Simon Good.
The operational Meteo-France ensemble 4D-Var (L. Berre, G. Desroziers, and co-authors) Ensemble assimilation (operational with 6 members…) :
M. Roberts, P. L. Vidale, D. Stevens, Ian Stevens, Len Shaffrey, UJCC team with help from many others at Met Office and NCAS-Climate and CCSR/NIES/FRCGC.
ECMWF flow dependent workshop, June Slide 1 of 14. A regime-dependent balanced control variable based on potential vorticity Ross Bannister, Data.
GOVST III, Paris Nov 2011 ECMWF ECMWF Activities on Coupled Forecasting Systems Status Ongoing research Needs for MJO Bulk formula in ocean models Plans.
Assimilation of T-TREC-retrieved wind data with WRF 3DVAR for the short-Term forecasting of Typhoon Meranti (2010) at landfall Xin Li 1, Yuan Wang 1, Jie.
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