VERIFICATION STRATEGY FOR OCEANS, WAVES & SEA-ICE Robert Grumbine, Avichal Mehra and Arun Chawla NWS/NCEP/EMC.

Slides:



Advertisements
Similar presentations
2. The WAM Model: Solves energy balance equation, including Snonlin
Advertisements

Mercator Ocean activity
WP4 Task T4.2 WP4-T4.2 : Establishment of validation criteria of multidisciplinary information products
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.
Assimilation of Sea Surface Temperature into a Northwest Pacific Ocean Model using an Ensemble Kalman Filter B.-J. Choi Kunsan National University, Korea.
1 Evaluation of two global HYCOM 1/12º hindcasts in the Mediterranean Sea Cedric Sommen 1 In collaboration with Alexandra Bozec 2 and Eric Chassignet 2.
Maria Valdivieso Department of Meteorology, University of Reading, UK ▶ Focus on surface heat fluxes ▶ Timeseries comparison at buoy sites
INTRODUCTION Although the forecast skill of the tropical Pacific SST is moderate due to the largest interannual signal associated with ENSO, the forecast.
THE BEST ANALYZED AIR- SEA FLUXES FOR SEASONAL FORECASTING 2.12 Glenn H. White, W. Wang, S. Saha, D. Behringer, S. Nadiga and H.-L. Pan Global Climate.
The NCEP operational Climate Forecast System : configuration, products, and plan for the future Hua-Lu Pan Environmental Modeling Center NCEP.
© Crown copyright Met Office UK report for GOVST Matt Martin GOVST-V, Beijing, October 2014.
Global Real Time Ocean Forecast System (RTOFS- Global) Team: Avichal Mehra, Ilya Rivin, Bhavani Balasubramaniam, Todd Spindler, Zulema Garaffo, Hendrik.
UMAC data callpage 1 of 28Operational wave modeling suite EMC Operational Models NCEP WAVE MODELING SUITES Arun Chawla and Jose-Henrique Alves NCEP Wave.
Ensemble-variational sea ice data assimilation Anna Shlyaeva, Mark Buehner, Alain Caya, Data Assimilation and Satellite Meteorology Research Jean-Francois.
The Centre for Australian Weather and Climate Research A partnership between CSIRO and the Bureau of Meteorology Oscar Alves, Li Shi, Yonghong Yin, Robin.
MERSEA IP WP 5 “Integrated System Design and Assessment” Internal Metrics for the MERSEA Global Ocean: Specifications for Implementation Planning Workshop.
GOV Technical Workshop OSE-IV - Santa Cruz, USA, June 2011 Fabrice Hernandez (Mercator Océan, Fr) and Matt Martin (Met Office, UK) GODAE OceanView.
Using Physics to Generate Tropical Cyclone Event Catalogs Kerry Emanuel and Sai Ravela Massachusetts Institute of Technology.
GOVST III, Paris Nov 2011 ECMWF ECMWF Activities on Coupled Forecasting Systems Status Ongoing research Needs for MJO Bulk formula in ocean models Plans.
The role of gliders in sustained observations of the ocean Deliverable 4.1 or WP 4.
UMAC data callpage 1 of 17Ocean modeling/RTOFS EMC Operational Models Ocean Modeling/RTOFS Avichal Mehra Lead-Ocean Modeling, Environmental Modeling Center.
Ocean and sea-ice data assimilation and forecasting in the TOPAZ system L. Bertino, K.A. Lisæter, I. Kegouche, S. Sandven NERSC, Bergen, Norway Arctic.
1 Global Ocean Modeling Strategy Presented by: Avichal Mehra (NWS/NCEP/EMC) Contributors: Hendrik Tolman (NWS/NCEP/EMC) Carlos Lozano (NWS/NCEP/EMC)
The Centre for Australian Weather and Climate Research A partnership between CSIRO and the Bureau of Meteorology Summary/Future Re-anal.
IGST Meeting June 2-4, 2008 The GMAO’s Ocean Data Assimilation & SI Forecasts Michele Rienecker, Christian Keppenne, Robin Kovach Jossy Jacob, Jelena Marshak.
Inter-comparison and Validation Task Team Breakout discussion.
1 Precipitation verification Precipitation verification is still in a testing stage due to the lack of station observation data in some regions
IICWG 5 th Science Workshop, April Sea ice modelling and data assimilation in the TOPAZ system Knut A. Lisæter and Laurent Bertino.
Global, Basin and Shelf Ocean Applications of OPA An Inter-Agency Canadian Initiative EC-DFO-DND + Universities + Mercator-Ocean  CONCEPTS -- Canadian.
Automated Weather Observations from Ships and Buoys: A Future Resource for Climatologists Shawn R. Smith Center for Ocean-Atmospheric Prediction Studies.
TOPAZ the Arctic TEP and the Arctic GOOS L. Bertino, G. Evensen, K.A. Lisæter, I. Keghouche Arctic GOOS opening, Bergen, 12 th Sept
Developments within FOAM Adrian Hines, Dave Storkey, Rosa Barciela, John Stark, Matt Martin IGST, 16 Nov 2005.
Validation of US Navy Polar Ice Prediction (PIPS) Model using Cryosat Data Kim Partington 1, Towanda Street 2, Mike Van Woert 2, Ruth Preller 3 and Pam.
2nd GODAE Observing System Evaluation Workshop - June Ocean state estimates from the observations Contributions and complementarities of Argo,
Course Evaluation Closes June 8th.
1 Arun Kumar Climate Prediction Center 27 October 2010 Ocean Observations and Seasonal-to-Interannual Prediction Arun Kumar Climate Prediction Center NCEP.
Ocean Analysis and Reanalysis: Phil Arkin, ESSIC, University of Maryland Background Concept and Implementation Issues.
Evapotranspiration Estimates over Canada based on Observed, GR2 and NARR forcings Korolevich, V., Fernandes, R., Wang, S., Simic, A., Gong, F. Natural.
Page 1© Crown copyright 2004 WP5.3 Assessment of Forecast Quality ENSEMBLES RT4/RT5 Kick Off Meeting, Paris, Feb 2005 Richard Graham.
F. Hernandez, Mercator Océan – IGST XII – St Johns 8/08/2007 Intercomparisons Working Groupe activities Definition of metrics at the global level: where.
Evaluation of the Real-Time Ocean Forecast System in Florida Atlantic Coastal Waters June 3 to 8, 2007 Matthew D. Grossi Department of Marine & Environmental.
AOMIP status Experiments 1. Season Cycle 2. Coordinated - Spinup Coordinated - Analysis Coordinated 100-Year Run.
GODAE OceanView-GSOP-CLIVAR workshop June Monitoring the Ocean State from the Observations Stéphanie Guinehut Sandrine Mulet Marie-Hélène.
One-year re-forecast ensembles with CCSM3.0 using initial states for 1 January and 1 July in Model: CCSM3 is a coupled climate model with state-of-the-art.
Assimilation of Sea Ice Concentration Observations in a Coupled Ocean-Sea Ice Model using the Adjoint Method.
Assimilating Satellite Sea-Surface Salinity in NOAA Eric Bayler, NESDIS/STAR Dave Behringer, NWS/NCEP/EMC Avichal Mehra, NWS/NCEP/EMC Sudhir Nadiga, IMSG.
CMEMS Mediterranean MFC. Update on specific development for CMEMS Med-MFC V2 Analysis and Forecast Product 1.Data Assimilation: Grid point EOFs ( * )
Application of HYCOM in Eddy- Resolving Global Ocean Prediction Community Effort: Community Effort: NRL, Florida State, U. of Miami, GISS, NOAA/NCEP, NOAA/AOML,
Impact of Blended MW-IR SST Analyses on NAVY Numerical Weather Prediction and Atmospheric Data Assimilation James Cummings, James Goerss, Nancy Baker Naval.
The Mediterranean Forecasting INGV-Bologna.
Validation of ORCA05 regional configuration of the Arctic North Atlantic Christophe HERBAUT and Marie-Noëlle HOUSSAIS Charles DELTEL LOCEAN, Université.
Ocean Data Assimilation for SI Prediction at NCEP David Behringer, NCEP/EMC Diane Stokes, NCEP/EMC Sudhir Nadiga, NCEP/EMC Wanqiu Wang, NCEP/EMC US GODAE.
Page 1© Crown copyright 2004 The Uses of Marine Surface Data in Climate Research David Parker, Hadley Centre, Met Office MARCDAT-2, Met Office, Exeter,
U.S. GODAE: Global Ocean Prediction with Community Effort: Community Effort: NRL, U. of Miami, FSU, NASA-GISS, NOAA/NCEP, NOAA/AOML, NOAA/PMEL, PSI, FNMOC,
HYCOM data assimilation Short term: ▪ Improve current OI based technique Assimilate satellite data (tracks) directly Improve vertical projection technique.
Marine Modeling and Analysis Branch Ocean Waves.
Validation of a set of input source terms based of altimeters data Jean-Michel Lef è vre, Lotfi Aouf, Fabrice Ardhuin and Pierre Queffeulou. WISE 2008,
Simulations of the snow covered sea ice surface and microwave effective temperature Rasmus T. Tonboe, Gorm Dybkjær, Jacob L. Høyer EU FP6 Damocles, EU.
RTOFS Monitoring and Evaluation Metrics Avichal Mehra MMAB/EMC/NCEP/NWS.
Real-time Ocean Reanalyses Intercomparison: Ocean/Climate Monitoring Using Ensemble Ocean Reanalysis Products Y. Xue1, M. Balmaseda2, Y. Fujii3, G. Vecchi4,
The impact of Argo data on ocean and climate forecasting
Bruce Cornuelle, Josh Willis, Dean Roemmich
A Comparison of Profiling Float and XBT Representations of Upper Layer Temperature Structure of the Northwestern Subtropical North Atlantic Robert L.
NSO8055 Okeanograafiline prognoos
WaveFlow KO Øyvind Breivik (MET Norway), Joanna Staneva (HZG), Jean Bidlot (ECMWF) and George Nurser (NOC)
Y. Xue1, C. Wen1, X. Yang2 , D. Behringer1, A. Kumar1,
Progress in Seasonal Forecasting at NCEP
Supervisor: Eric Chassignet
GODAE Final Symposium, 12 – 15 November 2008, Nice, France
Presentation transcript:

VERIFICATION STRATEGY FOR OCEANS, WAVES & SEA-ICE Robert Grumbine, Avichal Mehra and Arun Chawla NWS/NCEP/EMC

Ocean –Daily/Weekly monitoring of fields using GODAE Class Metrics: Class 1 (analysis of surface fields, e.g. SST, SSH, Sea Ice Cover) Class 2 (vertical sections, e.g. ARGO profiles, WOCE sections) Class 3 (derived products, e.g. Florida Current transports, GS North Wall location) Class 4 (forecast skill metrics, e.g. SST, SSH, profiles) –Daily metrics/stats made available at: Waves –Monitoring of Class 1 and Class 4 metrics –Metrics based on bulk spectral parameters (Significant wave height, Peak Period etc.) –Skill scores typically developed over month long (or more) –Use collocated buoys and altimeters –No consensus on metrics in frequency space yet VERIFICATION

First order variables defining Sea Ice –Sea Ice Concentrations Moderately well observed Poorly predicted In need of metrics –Sea Ice Drift Speeds Well observed (buoys, SSMI) Established metrics –Sea Ice thickness Poorly observed In need of metrics Work in progress but much to do SEA ICE VERIFICATION

Class 1: Sea Ice Cover in the polar regions OCEAN-ICE VERIFICATION

Class 2 & 4: Global RTOFS profiles vs ARGO data OCEAN VERIFICATION

Class 4: Global wave model skill scores for 72 hour forecast Monthly skill scoresSeasonal skill scores Skill scores based on comparisons at all available NDBC buoys, time in MM/YY New Physics introduced WAVES VERIFICATION

Class 1: Wave hindcast Significant wave height bias maps (in m) using collocated altimeter tracks WAVES VERIFICATION

RTOFS Verification and Metrics –Garraffo et al., Modeling of 137Cs as a tracer in a regional model for the Western Pacific, after the Fukushima Daiichi Nuclear power plant accident of March Weather and Forecasting. doi: – Ryan et al., GODAE Ocean View Class 4 forecast verification framework: Global ocean inter-comparison. J Oper Oceanogr. 7(3) – Divakaran et al., GODAE OceanView Inter-comparison for the Australian Region. J Oper Oceanogr. doi: / X –Hernandez et al., 2015.Recent progress in performance evaluations and near real-time assessment of operational ocean products. J. Oper. Oceanogr. (accepted) Waves –Chawla et al A multigrid wave forecasting model: A new paradigm in operational wave forecasting. Wea. & Forecasting, 28, 1057 – 1078 –Chawla et al Validation of a thirty year wave hindcast using the Climate Forecast System Reanalysis winds.Ocean Mod. RECENT PUBLICATIONS

Waves (contd) –Alves et al The Operational Implementation of a Great Lakes Wave Forecasting System at NOAA/NCEP. Wea. & Forecasting, 29.6: –Alves et al The NCEP-FNMOC Combined Wave Ensemble Product: Expanding Benefits of Interagency Probabilistic Forecasts to the Oceanic Environment. Bull. American Meteo. Soc., 94(12), –Van der Westhuysen, et al. 2013: Development and validation of the Nearshore Wave Prediction System. Proc. 93rd AMS Annual Meeting, Austin, TX. Sea Ice –Robert Grumbine: 2013: Keeping Ice'S Simplicity -- A Modeling Start, MMAB Tech Note 314, 32 pp – Robert Grumbine: 2013: Long Range Sea Ice Drift Model Verification, MMAB Tech Note 315, 22 pp.. RECENT PUBLICATIONS (CONTD)

System coupling metrics immature Not that there are no measures, but …. Examples : –Coupled ocean – waves Mixed layer depths Stokes drift estimates (3 rd moments of spectra, Ardhuin et al 2009) –Coupled ice – ocean Heat flux modulations –Coupled wave – ice Scattering & damping COUPLED SYSTEMS VERIFICATION