Sentences with phrase «ocean data assimilation»

Now, the theoretical advantage of ocean data assimilation is that it «fills in» unsampled regions using the model dynamics and thermodynamics.

The NEMOVAR Ocean Data Assimilation System as Implemented in the ECMWF Ocean Analysis for System 4.

Nishida, T. 2011 Validation of the Global Ocean Data Assimilation System (GODAS) data in the NOAA National Centre for Environmental System (NCEP) by theory, Comparative Studies, Applications and Sea Truth.

The NEMOVAR Ocean Data Assimilation As Implemented in the ECMWF Ocean Analysis for System 4 Technical Memorandum 668 (ECMWF, 2011).

[48] YAN Chang - Xiang, ZHU Jiang The Impact of «Bad» Argo Profiles on Ocean Data Assimilation Atmospheric and oceanic science letters, 2010, VOL.

CAS = Commission for Atmospheric Sciences CMDP = Climate Metrics and Diagnostic Panel CMIP = Coupled Model Intercomparison Project DAOS = Working Group on Data Assimilation and Observing Systems GASS = Global Atmospheric System Studies panel GEWEX = Global Energy and Water Cycle Experiment GLASS = Global Land - Atmosphere System Studies panel GOV = Global Ocean Data Assimilation Experiment (GODAE) Ocean View JWGFVR = Joint Working Group on Forecast Verification Research MJO - TF = Madden - Julian Oscillation Task Force PDEF = Working Group on Predictability, Dynamics and Ensemble Forecasting PPP = Polar Prediction Project QPF = Quantitative precipitation forecast S2S = Subseasonal to Seasonal Prediction Project SPARC = Stratospheric Processes and their Role in Climate TC = Tropical cyclone WCRP = World Climate Research Programme WCRP Grand Science Challenges • Climate Extremes • Clouds, Circulation and Climate Sensitivity • Melting Ice and Global Consequences • Regional Sea - Ice Change and Coastal Impacts • Water Availability WCRP JSC = Joint Scientific Committee WGCM = Working Group on Coupled Modelling WGSIP = Working Group on Subseasonal to Interdecadal Prediction WWRP = World Weather Research Programme YOPP = Year of Polar Prediction

Schneider, E. K., B. Huang, Z. Zhu, D. G. DeWitt, J. L. Kinter, B. Kirtman, and J. Shukla, 1999: Ocean data assimilation, initialization and predictions of ENSO with a coupled GCM.

The Group for High Resolution SST (GHRSST) is a follow on activity form the Global Ocean Data Assimilation Experiment (GODAE) high - resolution sea surface temperature pilot project (GHRSST - PP) provides a new generation of global high - resolution (< 10 km) SST data products to the operational oceanographic, meteorological, climate and general scientific community, in real time and delayed mode.

The historical observations from Hadley Centre & Simple Ocean Data Assimilation (SODA) datasets are at the bottom.

Not exact matches

Numerical Modeling, Predictability and Data Assimilation in Weather, Ocean and Climate — A Symposium Honoring the Legacy of Anna Trevisan The Symposium will be held

Related ocean observing key expertise: Expertise in ocean observations (physical and biogeochemical, in - situ and satellite); data assimilation and climate prediction

These are the Simple Ocean Assimilation Data (SODA) scaled with the surface air temperature trends from the National Center for Enviromental Prediction (NCEP) / National Center for Atmospheric Research (NCAR).

Influence of physical forcing on planktonic ecosystems and elemental cycling; mesoscale ocean dynamics; primary production; coastal circulation; zooplankton population dynamics; harmful algal blooms; numerical modeling and data assimilation.

Marine planktonic ecosystem dynamics, biogeochemical cycling and ocean - atmosphere - land carbon system, ocean acidification, climate change and ocean circulation, satellite ocean color, air - sea gas exchange, numerical modeling, data analysis, and data assimilation

Coastal circulation dynamics, numerical modeling and data assimilation, biophysical interaction, air - sea interaction, coastal ocean observing system

Coastal ocean forecasting; variational methods for data assimilation, re-analysis and ocean observing system design; Mesoscale satellite remote sensing; physical - biological interactions on the continental shelf; oceanography of continental shelves.

An even more up to date way to get transports in the ocean is to use ocean models constraint by observations (so called data assimilation or ocean state estimation in several forms).

Metzger et al. (NRL Stennis Space Center), 5.0 (3.4 - 6.0), Modeling The Global Ocean Forecast System (GOFS) 3.1 was run in forecast mode without data assimilation, initialized with July 1, 2015 ice / ocean analyses, for ten simulations using National Centers for Environmental Prediction (NCEP) Climate Forecast System Reanalysis (CFSR) atmospheric forcing fields from 2005 - Ocean Forecast System (GOFS) 3.1 was run in forecast mode without data assimilation, initialized with July 1, 2015 ice / ocean analyses, for ten simulations using National Centers for Environmental Prediction (NCEP) Climate Forecast System Reanalysis (CFSR) atmospheric forcing fields from 2005 - ocean analyses, for ten simulations using National Centers for Environmental Prediction (NCEP) Climate Forecast System Reanalysis (CFSR) atmospheric forcing fields from 2005 - 2014.

Starting with the April Pan-Arctic Ice Ocean Modeling and Assimilation System (PIOMAS) volume distribution and the April National Snow and Ice Data Center (NSIDC) average ice extent the estimated extent loss for each 10 cm thickness of ice loss is calculated.

GFDL NOAA (Msadek et al.), 4.82 (4.33 - 5.23), Modeling Our prediction for the September - averaged Arctic sea ice extent is 4.82 million square kilometers, with an uncertainty range going between 4.33 and 5.23 million km2 Our estimate is based on the GFDL CM2.1 ensemble forecast system in which both the ocean and atmosphere are initialized on August 1 using a coupled data assimilation system.

The method is a sea ice - ocean model ensemble run (without and with assimilation of sea - ice / ocean observations); the coupled ice - ocean model NAOSIM has been forced with atmospheric surface data from January 1948 to 7 July 2015.

The ice volume data used here is the Pan-Arctic Ice Ocean Modeling and Assimilation System (PIOMAS) calculated by the Polar Science Center at the Applied Physics Laboratory of the University of Washington.

Numerical Modeling, Predictability and Data Assimilation in Weather, Ocean and Climate — A Symposium Honoring the Legacy of Anna Trevisan The Symposium will be held

The data assimilation special interest group is organising a meeting to bring together experts to discuss the latest progress in ocean data assimil

There is an open question as to whether to concentrate on atmospheric data assimilation or to include ocean and land surface as well.

NRL - ocn - ice, 5.2 (4.3 - 6.0), Modeling (ice - ocean) The Global Ocean Forecast System (GOFS) 3.1 was run in forecast mode without data assimilation, initialized with June 1, 2016 ice / ocean analyses, for ten simulations using National Centers for Environmental Prediction (NCEP) Climate Forecast System Reanalysis (CFSR) atmospheric forcing fields from 2005 - ocean) The Global Ocean Forecast System (GOFS) 3.1 was run in forecast mode without data assimilation, initialized with June 1, 2016 ice / ocean analyses, for ten simulations using National Centers for Environmental Prediction (NCEP) Climate Forecast System Reanalysis (CFSR) atmospheric forcing fields from 2005 - Ocean Forecast System (GOFS) 3.1 was run in forecast mode without data assimilation, initialized with June 1, 2016 ice / ocean analyses, for ten simulations using National Centers for Environmental Prediction (NCEP) Climate Forecast System Reanalysis (CFSR) atmospheric forcing fields from 2005 - ocean analyses, for ten simulations using National Centers for Environmental Prediction (NCEP) Climate Forecast System Reanalysis (CFSR) atmospheric forcing fields from 2005 - 2014.

Our prediction is based on the GFDL - FLOR ensemble forecast system, which is a fully - coupled atmosphere - land - ocean - sea ice model initialized using a coupled data assimilation system.

Our estimate is based on the GFDL CM2.1 ensemble forecast system in which both the ocean and atmosphere are initialized on June 1 using a coupled data assimilation system.

About the data: Sea Ice Volume is calculated using the Pan-Arctic Ice Ocean Modeling and Assimilation System (PIOMAS, Zhang and Rothrock, 2003) developed at APL / PSC.

His current research includes global ocean modeling and data assimilation efforts as part of Estimating the Circulation & Climate of the Ocean (ECCO) consortium, as well as using ensemble methods for regional ocean analysis and predicocean modeling and data assimilation efforts as part of Estimating the Circulation & Climate of the Ocean (ECCO) consortium, as well as using ensemble methods for regional ocean analysis and predicOcean (ECCO) consortium, as well as using ensemble methods for regional ocean analysis and predicocean analysis and prediction.

Numerical Modeling, Predictability and Data Assimilation in Weather, Ocean and Climate — A Symposium Honoring the Legacy of Anna Trevisan The Symposium will be held on October 17 - 20, 2017, at the Institute of Atmospheric and Climate Science (ISAC) of the Italian Research Council (CNR), in Bologna (Italy).

Their results were derived through a set of different experiments testing the sensitivity to various assumptions and choices made for data inclusion and the ocean model assimilation set - up.

The analysis involved a brand new ocean analysis (ORAS4; Balmaseda et al., 2013) based on an optimal use of observations, data assimilation, and an ocean model forced with state - of - the - art description of the atmosphere (reanalyses).

«High clouds over the western tropical Pacific Ocean seem to systematically decrease when sea surface temperatures are higher,» says Arthur Y. Hou of Goddard's Data Assimilation Office.

ABSTRACT Sampling uncertainties in the voluntary observing ship (VOS)- based global ocean — atmosphere flux fields were estimated using the NCEP — NCAR reanalysis and ECMWF 40 - yr Re-Analysis (ERA - 40) as well as seasonal forecasts without data assimilation.

However, users should be aware that the input data, and thus the results, are not of equal quality at all locations (e.g., over remote ocean regions) due to the nonuniform distribution of observations used in the assimilation process.

The global Human — Earth System framework we propose, and represent schematically in Fig. 6, combines not only data collection, analysis techniques, and Dynamic Modeling, but also Data Assimilation, to bidirectionally couple an ESM containing subsystems for Global Atmosphere, Land (including both Land — Vegetation and Land - Use models) and Ocean and Ice, to a Human System Model with subsystems for Population Demographics, Water, Energy, Agriculture, Industry, Construction, and Transportatdata collection, analysis techniques, and Dynamic Modeling, but also Data Assimilation, to bidirectionally couple an ESM containing subsystems for Global Atmosphere, Land (including both Land — Vegetation and Land - Use models) and Ocean and Ice, to a Human System Model with subsystems for Population Demographics, Water, Energy, Agriculture, Industry, Construction, and TransportatData Assimilation, to bidirectionally couple an ESM containing subsystems for Global Atmosphere, Land (including both Land — Vegetation and Land - Use models) and Ocean and Ice, to a Human System Model with subsystems for Population Demographics, Water, Energy, Agriculture, Industry, Construction, and Transportation.