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© Crown copyright Met Office Implementing a diurnal model at the Met Office James While, Matthew Martin
© Crown copyright Met Office Overview Table of Contents The NEMOVAR SST bias correction system The diurnal model Diurnal data assimilation system The Python test system Future Plans – The diurnal analysis system
© Crown copyright Met Office NEMOVAR SST Bias correction system
© Crown copyright Met Office NEMOVAR SST Bias correction system Overview Before data assimilation we bias correct all SST data to a reference data set – AATSR, In-situ. We have recently updated our bias correction system to work within NEMOVAR Conceptually the system is similar to an SST analysis system, such as OSTIA, but with longer length scales (7º) and with matchups as observations. To perform the bias correction NEMOVAR is run in a 2-D configuration.
© Crown copyright Met Office SST Bias correction system Algorithm Matchup System Finds matchups between biased and unbiased reference observations. Matchups are found within specified time (1 day) and space (25 km) limits. Coded to NEMO standards. 2-D NEMOVAR Matchups are assimilated as if they are SST observations with long length scales. NEMO Bias is subtracted from the observations before they are passed into the observation operator. Ref obsBiased obs Matchups Bias field Bias background Relaxation to climatology This algorithm is applied individually to each biased data type.
© Crown copyright Met Office Bias for AVHRR after 3 days Correlation length scale = 7º SST Bias correction system Example field
© Crown copyright Met Office The Diurnal model
© Crown copyright Met Office Diurnal model Overview Ultimate aim is to produce a high resolution analysis of diurnal skin SST. For this we need a computationally cheap, accurate model that is also amiable to data assimilation. We chose to adapt the Takaya et al, 2010 warm layer model for this purpose. The model has been coded up in-house and has been adapted to use a 9 band light model (Gentermann et al, 2009) We do not fully exploit the wave parameterisation of the Takaya model – The Langmuir number is assumed constant at 0.3. To complete the skin SST analysis we are implementing the Artale, 2002 cool skin model.
© Crown copyright Met Office Diurnal Model Theory Based on the Takaya, 2010 bulk diurnal model. Implemented both as standalone system & within NEMO. T:- ΔSST t :- Time Q:- Thermal energy flux D T :- Layer depth ρ:- Water density c p :- Heat capacity ν:- Structure parameter u w *:- Friction velocity L a :- Langmuir number k:- Von Karmans constant g:- Acceleration due to gravity α w :- Thermal expansion coefficient Bulk thermal heating of a layer Turbulent damping These equations are solved using an implicit scheme
© Crown copyright Met Office Diurnal modelNEMO top level Diurnal Model Peak ΔSST in NEMO for Jan 07
© Crown copyright Met Office The Data Assimilation System
© Crown copyright Met Office Data assimilation system Overview We are designing a Data assimilation system to work with the Takaya model. The system will use a 1-D version of a strong constraint 4DVar algorithm. It is not sufficient to minimise with respect to the initial temperature, so we also constrain the heat and wind forcing. We now have working versions of the Tangent Linear and Adjoint of the Takaya model. The cool skin model will not be constrained by the data assimilation.
© Crown copyright Met Office Initial temperature Thermal energy flux at all timesteps Friction velocity at all timesteps Data assimilation system Control vector
© Crown copyright Met Office T, Q & u w * assumed uncorrelated with each other. Temporal correlations modelled as a Gaussian Diagonal, observations assumed uncorrelated y includes observations of T only. NOTE: The model is assumed perfect at night Data assimilation system Cost function (inner loop)
© Crown copyright Met Office The Python Test System
© Crown copyright Met Office Python Test system Overview A test system for our data assimilation algorithm has been written in Python using the numpy and scipy repositories. The full non-linear, The Tangent Linear, and the Adjoint are all FORTRAN subroutines accessed by the Python system. The system has been designed to be similar to NEMOVAR. Newton conjugate gradient minimiser Gaussian specification of error covariances The user can specify the obs error, model error, correlation scales, and the number of outer loops to perform.
© Crown copyright Met Office The Python Test system Example output ForcingΔSST
© Crown copyright Met Office Future Plans – The diurnal analysis system
© Crown copyright Met Office The diurnal analysis system Overview We plan to create a high resolution (~1/20º) diurnal model based within NEMOVAR. This will include our warm layer & cool skin models, which will be coded within NEMO. We will use a 1 layer configuration, similar to the SST bias correction, with all ocean physics turned off. The model will include horizontal as well as temporal correlations to allow the spreading of observational data. Diurnal analysis system SST skin OSTIA SST found Analysis SST found ΔSST
© Crown copyright Met Office Summary
© Crown copyright Met Office Summary We have developed a SST bias correction system that uses NEMOVAR in a 2-D configuration. We are developing an analysis system for skin SST that uses the Takaya, 2010 and Artale,2002 models. Stand alone versions of the full non-linear, Tangent Linear, and Adjoint of the Takaya model have been coded. The non-linear model has been incorporated into NEMO. We have developed a 1-D test data assimilation system based upon a 4DVar methodology. We plan to develop a high resolution analysis of skin SST using OSTIA, and the Takaya & Artale models incorporated into NEMOVAR.
© Crown copyright Met Office The End
© Crown copyright Met Office NEMOVAR status and plans Matt Martin, Dan Lea, Jennie Waters, James While, Isabelle Mirouze NEMOVAR SG, ECMWF, Jan 2012.
© Crown copyright Met Office UK report for GOVST Matt Martin GOVST-V, Beijing, October 2014.
Slide 1 NEMOVAR-LEFE Workshop 22/ Slide 1 Current status of NEMOVAR Kristian Mogensen.
The Inverse Regional Ocean Modeling System: Development and Application to Data Assimilation of Coastal Mesoscale Eddies. Di Lorenzo, E., Moore, A., H.
Polly Smith, Alison Fowler, Amos Lawless School of Mathematical and Physical Sciences, University of Reading Exploring coupled data assimilation using.
Coupled Model Data Assimilation: Building an idealised coupled system Polly Smith, Amos Lawless, Alison Fowler* School of Mathematical and Physical Sciences,
Introduction to Data Assimilation NCEO Data-assimilation training days 5-7 July 2010 Peter Jan van Leeuwen Data Assimilation Research Center (DARC) University.
Exploring strategies for coupled 4D-Var data assimilation using an idealised atmosphere-ocean model Polly Smith, Alison Fowler & Amos Lawless School of.
Ocean Data Variational Assimilation with OPA: Ongoing developments with OPAVAR and implementation plan for NEMOVAR Sophie RICCI, Anthony Weaver, Nicolas.
PreSAC Progress on NEMOVAR. Overview of NEMOVAR status First NEMOVAR experiments Use of NEMOVAR analyses to initialize ocean only forecasts Missing.
Page 1© Crown copyright 2004 SRNWP Lead Centre Report on Data Assimilation 2005 for EWGLAM/SRNWP Annual Meeting October 2005, Ljubljana, Slovenia.
Improved NCEP SST Analysis Xu Li NCEP/EMC. Project Objective: To Improve SST Analysis Use satellite data more effectively Resolve diurnal variation Improve.
1 Data assimilation developments for NEMO A working group was created at the 2006 Developers Meeting with the objective of standardizing for NEMO certain.
Federal Department of Home Affairs FDHA Federal Office of Meteorology and Climatology MeteoSwiss High-resolution data assimilation in COSMO: Status and.
Center for Satellite Applications and Research (STAR) Review 09 – 11 March 2010 MAP (Maximum A Posteriori) x is reduced state vector [SST(x), TCWV(w)]
The I nverse R egional O cean M odeling S ystem Development and Application to Variational Data Assimilation of Coastal Mesoscale Eddies. Di Lorenzo, E.
An Assimilating Tidal Model for the Bering Sea Mike Foreman, Josef Cherniawsky, Patrick Cummins Institute of Ocean Sciences, Sidney BC, Canada Outline:
Use of high resolution global SST data in operational analysis and assimilation systems at the UK Met Office. Matt Martin, John Stark,
1 NOAA’s National Climatic Data Center April 2005 Climate Observation Program Blended SST Analysis Changes and Implications for the Buoy Network 1.Plans.
Weak Constraint 4DVAR in the R egional O cean M odeling S ystem ( ROMS ): Development and application for a baroclinic coastal upwelling system Di Lorenzo,
1 NGGPS Dynamic Core Requirements Workshop NCEP Future Global Model Requirements and Discussion Mark Iredell, Global Modeling and EMC August 4, 2014.
50 Years of the Monin-Obukhov Similarity Theory Thomas Foken University of Bayreuth, Bayreuth, Germany.
Page 1 of 26 A PV control variable Ross Bannister* Mike Cullen *Data Assimilation Research Centre, Univ. Reading, UK Met Office, Exeter, UK.
Assimilation of Sea Surface Temperature in OPAVAR State of the art: Relaxation to Reynolds SST (daily, interpolated to ORCA grid, ENACT) - strong relaxation.
In collaboration with: J. S. Allen, G. D. Egbert, R. N. Miller and COAST investigators P. M. Kosro, M. D. Levine, T. Boyd, J. A. Barth, J. Moum, et al.
Assimilation Algorithms: Tangent Linear and Adjoint models Yannick Trémolet ECMWF Data Assimilation Training Course March 2006.
Infrared and Microwave Remote Sensing of Sea Surface Temperature Gary A. Wick NOAA Environmental Technology Laboratory January 14, 2004.
Yoichi Ishikawa 1, Toshiyuki Awaji 1,2, Teiji In 3, Satoshi Nakada 2, Tsuyoshi Wakamatsu 1, Yoshimasa Hiyoshi 1, Yuji Sasaki 1 1 DrC, JAMSTEC 2 Kyoto University.
Mesoscale Assimilation of Rain-Affected Observations Clark Amerault National Research Council Postdoctoral Associate - Naval Research Laboratory, Monterey,
Initialization Issues of Coupled Ocean-atmosphere Prediction System Climate and Environment System Research Center Seoul National University, Korea In-Sik.
Chelle L. Gentemann & Peter J. Minnett Introduction to the upper ocean thermal structure Diurnal models M-AERI data Examples of diurnal warming Conclusions.
© Crown copyright Met Office Length scales and anisotropy Isabelle Mirouze and NEMOVAR groups members NEMOVAR Steering Group meeting – 16 January 2012.
DMI-OI analysis in the Arctic DMI-OI processing scheme or Arctic Arctic bias correction method Arctic L4 Reanalysis Biases (AATSR – Pathfinder) Validation.
© Crown copyright Met Office The EN4 dataset of quality controlled ocean temperature and salinity profiles and monthly objective analyses Simon Good.
Wind Driven Circulation I: Planetary boundary Layer near the sea surface.
Experiments with Monthly Satellite Ocean Color Fields in a NCEP Operational Ocean Forecast System PI: Eric Bayler, NESDIS/STAR Co-I: David Behringer, NWS/NCEP/EMC/GCWMB.
Testing of the Zeng and Beljaars scheme in the TWP Michael Brunke and Xubin Zeng Department of Atmospheric Sciences The University of Arizona Tucson, Arizona.
Parametrization of PBL outer layer Martin Köhler Overview of models Bulk models local K-closure K-profile closure TKE closure.
Page 1© Crown copyright HadISST2: progress and plans Nick Rayner, 14 th March 2007.
1 JRA-55 the Japanese 55-year reanalysis project - status and plan - Climate Prediction Division Japan Meteorological Agency.
Sophie RICCI CALTECH/JPL Post-doc Advisor : Ichiro Fukumori The diabatic errors in the formulation of the data assimilation Kalman Filter/Smoother system.
Regional Modelling Prepared by C. Tubbs, P. Davies, Met Office UK Revised, delivered by P. Chen, WMO Secretariat SWFDP-Eastern Africa Training Workshop.
Methane inversion from satellite, TRANSCOM meeting, Jena,12-15 May 2003 Inversion strategy 4D-var method adjoint model of TM3 CH4-only version has been.
Balance and 4D-Var Saroja Polavarapu Meteorological Research Division Environment Canada, Toronto WWRP-THORPEX Workshop on 4D-Var and EnsKF intercomparisons.
© Crown copyright Met Office Radiation developments Latest work on the radiation code in the Unified Model James Manners, Reading collaboration meeting.
© Crown copyright Met Office Does the order of the horizontal and vertical transforms matter in the representation of an operational static covariance.
Methane inversion from satellite, TRANSCOM workshop, Jena, May 2003 Inverse modelling of methane sources and sinks using satellite observations Jan.
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