Records
Links
Author
Belyaev, K.P. ; Tanajura, C.A.S. ; O'Brien, J.J.
Title
A data assimilation method used with an ocean circulation model and its application to the tropical Atlantic
Type
$loc['typeJournal Article']
Year
2001
Publication
Applied Mathematical Modelling
Abbreviated Journal
Applied Mathematical Modelling
Volume
25
Issue
8
Pages
655-670
Keywords
Data assimilation Fokker–Planck equation NOAA/GFDL MOM_2 ocean circulation model PIRATA project
Abstract
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Summary Language
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Series Editor
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Series Issue
Edition
ISSN
0307904X
ISBN
Medium
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Conference
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Approved
$loc['no']
Call Number
COAPS @ mfield @
Serial
819
Permanent link to this record
Author
Cintra, R. ; Campos Velho, H. ; Cocke, S.
Title
Multilayer Perceptron on data assimilation system applied to FSU global model
Type
$loc['typeConference Article']
Year
2016
Publication
Abbreviated Journal
Volume
Issue
Pages
Keywords
data assimilation ; artificial neural networks ; numerical weather prediction ; inverse problem
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3rd International Symposium on Uncertainty Quantification and Stochastic Modeling Maresias, Brazil: 15/2/2016 to 19/2/2016
Funding
Approved
$loc['no']
Call Number
COAPS @ mfield @
Serial
88
Permanent link to this record
Author
Cocke, S. ; Boisserie, M. ; Shin, D.-W.
Title
A coupled soil moisture initialization scheme for the FSU/COAPS climate model
Type
$loc['typeJournal Article']
Year
2013
Publication
Inverse Problems in Science and Engineering
Abbreviated Journal
Inverse Problems in Science and Engineering
Volume
21
Issue
3
Pages
420-437
Keywords
soil moisture initialization ; data assimilation ; precipitation assimilation ; nudging ; reanalysis
Abstract
Address
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Thesis
Publisher
Place of Publication
Editor
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Summary Language
Original Title
Series Editor
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Abbreviated Series Title
Series Volume
Series Issue
Edition
ISSN
1741-5977
ISBN
Medium
Area
Expedition
Conference
Funding
Approved
$loc['no']
Call Number
COAPS @ mfield @
Serial
199
Permanent link to this record
Author
Jacob, J. C. ; Armstrong, E. M. ; Bourassa, M. A. ; Cram, T. ; Elya, J. L. ; Greguska, F. R., III ; Huang, T. ; Ji, Z. ; Jiang, Y. ; Li, Y. ; McGibbney, L. J. ; Quach, N. ; Smith, S. R. ; Tsontos, V. M. ; Wilson, B. D. ; Worley, S. J. ; Yang, C. P.
Title
OceanWorks: Enabling Interactive Oceanographic Analysis in the Cloud with Multivariate Data
Type
$loc['typeAbstract']
Year
2018
Publication
American Geophysical Union
Abbreviated Journal
AGU
Volume
Fall Meeting
Issue
Pages
Keywords
910 Data assimilation, integration and fusion, INFORMATICSDE: 1916 Data and information discovery, INFORMATICSDE: 1926 Geospatial, INFORMATICSDE: 1942 Machine learning, INFORMATICS
Abstract
NASA's Advanced Information System Technology (AIST) Program sponsors the OceanWorks project to establish an integrated data analytics center at the Physical Oceanography Distributed Active Archive Center (PO.DAAC). OceanWorks provides a series of interoperable capabilities that are essential for cloud-scale oceanographic research. These include big data analytics, data search with subsecond response, intelligent ranking of search results, subsetting based on data quality metrics, and rapid spatiotemporal matchup of satellite measurements with distributed in situ data. The software behind OceanWorks is being developed as an open source project in the Apache Incubator Science Data Analytics Platform (SDAP – http://sdap.apache.org). In this presentation we describe how OceanWorks enables efficient, scalable, interactive and interdisciplinary oceanographic analysis with multivariate data. Interactivity is enabled by a number of SDAP features. First, SDAP provides Representational State Transfer (REST) interfaces to a number of built-in cloud analytics to compute time series, time-averaged maps, correlation maps, climatological maps, Hovmöller maps, and more. To access these, users simply navigate to a properly constructed parameterized URL in their web browser or issue web services calls in a variety of programming languages or in a Jupyter notebook. Alternatively, Python clients can make function calls via the NEXUS Command Line Interface (CLI). Authenticated users can even inject their own custom code via REST calls or the CLI. To enable interdisciplinary science, OceanWorks provides access to a rich collection of multivariate satellite and in situ measurements of the oceans (e.g., sea surface temperature, height and salinity, chlorophyll and circulation) and other Earth science data (e.g., aerosol optical depth and wind speed), coupled with on-demand processing capabilities close to the data. We partition the data across space or time into tiles and store them into cloud-aware databases that are collocated with the computations. We will provide examples of scientific studies directly enabled by OceanWorks' multivariate data and cloud analytics.
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Series Editor
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ISBN
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Approved
$loc['no']
Call Number
COAPS @ user @
Serial
1005
Permanent link to this record
Author
Jardak, M. ; Navon, I.M. ; Zupanski, M.
Title
Comparison of sequential data assimilation methods for the Kuramoto-Sivashinsky equation
Type
$loc['typeJournal Article']
Year
2009
Publication
International Journal for Numerical Methods in Fluids
Abbreviated Journal
Int. J. Numer. Meth. Fluids
Volume
Issue
Pages
Keywords
sequential data assimilation ; ensemble Kalman filter ; particle filter ; Kuramoto–Sivashinsky equation
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Publisher
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Series Editor
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Series Issue
Edition
ISSN
0271-2091
ISBN
Medium
Area
Expedition
Conference
Funding
Approved
$loc['no']
Call Number
COAPS @ mfield @
Serial
375
Permanent link to this record
Author
Perrie, W. ; Zhang, W. ; Bourassa, M. ; Shen, H. ; Vachon, P.W.
Title
Impact of Satellite Winds on Marine Wind Simulations
Type
$loc['typeJournal Article']
Year
2008
Publication
Weather and Forecasting
Abbreviated Journal
Wea. Forecasting
Volume
23
Issue
2
Pages
290-303
Keywords
Satellite observations ; Data assimilation ; Hurricanes ; Waves, oceanic ; Ocean modeling ; Numerical analysis
Abstract
Address
Corporate Author
Thesis
Publisher
Place of Publication
Editor
Language
Summary Language
Original Title
Series Editor
Series Title
Abbreviated Series Title
Series Volume
Series Issue
Edition
ISSN
0882-8156
ISBN
Medium
Area
Expedition
Conference
Funding
NASA, OVWST
Approved
$loc['no']
Call Number
COAPS @ mfield @
Serial
680
Permanent link to this record
Author
Smedstad, O.M. ; Hurlburt, H.E. ; Metzger, E.J. ; Rhodes, R.C. ; Shriver, J.F. ; Wallcraft, A.J. ; Kara, A.B.
Title
An operational Eddy resolving 1/16° global ocean nowcast/forecast system
Type
$loc['typeJournal Article']
Year
2003
Publication
Journal of Marine Systems
Abbreviated Journal
Journal of Marine Systems
Volume
40-41
Issue
Pages
341-361
Keywords
global ocean prediction ; prediction of mesoscale variability ; data assimilation ; ocean forecast verification
Abstract
Address
Corporate Author
Thesis
Publisher
Place of Publication
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Summary Language
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Series Editor
Series Title
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Series Volume
Series Issue
Edition
ISSN
0924-7963
ISBN
Medium
Area
Expedition
Conference
Funding
Approved
$loc['no']
Call Number
COAPS @ mfield @
Serial
481
Permanent link to this record
Author
Srinivasan, A. ; Chassignet, E.P. ; Bertino, L. ; Brankart, J.M. ; Brasseur, P. ; Chin, T.M. ; Counillon, F. ; Cummings, J.A. ; Mariano, A.J. ; Smedstad, O.M. ; Thacker, W.C.
Title
A comparison of sequential assimilation schemes for ocean prediction with the HYbrid Coordinate Ocean Model (HYCOM): Twin experiments with static forecast error covariances
Type
$loc['typeJournal Article']
Year
2011
Publication
Ocean Modelling
Abbreviated Journal
Ocean Modelling
Volume
37
Issue
3-4
Pages
85-111
Keywords
Data assimilation ; Ocean modeling ; Ocean prediction ; Twin experiments ; Sequential assimilation ; MVOI ; EnOI ; SEEK ; ROIF ; EnROIF
Abstract
Address
Corporate Author
Thesis
Publisher
Place of Publication
Editor
Language
Summary Language
Original Title
Series Editor
Series Title
Abbreviated Series Title
Series Volume
Series Issue
Edition
ISSN
1463-5003
ISBN
Medium
Area
Expedition
Conference
Funding
Approved
$loc['no']
Call Number
COAPS @ mfield @
Serial
320
Permanent link to this record
Author
Yu, P
Title
Development of New Techniques for Assimilating Satellite Altimetry Data into Ocean Models
Type
$loc['typeManuscript']
Year
2006
Publication
Abbreviated Journal
Volume
Issue
Pages
Keywords
Data Assimilation, Reduced Space, First Baroclinic Mode, Ocean Models, Vertical Normal Mode Decomposition, Variational
Abstract
State of the art fully three-dimensional ocean models are very computationally expensive and their adjoints are even more resource intensive. However, many features of interest are approximated by the first baroclinic mode over much of the ocean, especially in the lower and mid latitude regions. Based on this dynamical feature, a new type of data assimilation scheme to assimilate sea surface height (SSH) data, a reduced-space adjoint technique, is developed and implemented with a three-dimensional model using vertical normal mode decomposition. The technique is tested with the Navy Coastal Ocean Model (NCOM) configured to simulate the Gulf of Mexico. The assimilation procedure works by minimizing the cost function, which generalizes the misfit between the observations and their counterpart model variables. The “forward” model is integrated for the period during which the data are assimilated. Vertical normal mode decomposition retrieves the first baroclinic mode, and the data misfit between the model outputs and observations is calculated. Adjoint equations based on a one-active-layer reduced gravity model, which approximates the first baroclinic mode, are integrated backward in time to get the gradient of the cost function with respect to the control variables (velocity and SSH of the first baroclinic mode). The gradient is input to an optimization algorithm (the limited memory Broyden-Fletcher-Goldfarb-Shanno (BFGS) method is used for the cases presented here) to determine the new first baroclinic mode velocity and SSH fields, which are used to update the forward model variables at the initial time. Two main issues in the area of ocean data assimilation are addressed: 1. How can information provided only at the sea surface be transferred dynamically into deep layers? 2. How can information provided only locally, in limited oceanic regions, be horizontally transferred to ocean areas far away from the data-dense regions, but dynamically connected to it? The first problem is solved by the use of vertical normal mode decomposition, through which the vertical dependence of model variables is obtained. Analyses show that the first baroclinic mode SSH represents the full SSH field very closely in the model test domain, with a correlation of 93% in one of the experiments. One common way to solve the second issue is to lengthen the assimilation window in order to allow the dynamic model to propagate information to the data-sparse regions. However, this dramatically increases the computational cost, since many oceanic features move very slowly. An alternative solution to this is developed using a mapping method based on complex empirical orthogonal functions (EOF), which utilizes data from a much longer period than the assimilation cycle and deals with the information in space and time simultaneously. This method is applied to map satellite altimeter data from the ground track observation locations and times onto a regular spatial and temporal grid. Three different experiments are designed for testing the assimilation technique: two experiments assimilate SSH data produced from a model run to evaluate the method, and in the last experiment the technique is applied to TOPEX/Poseidon and Jason-1 altimeter data. The assimilation procedure converges in all experiments and reduces the error in the model fields. Since the adjoint, or “backward”, model is two-dimensional, the method is much more computationally efficient than if it were to use a fully three-dimensional backward model.
Address
Department of Oceanography
Corporate Author
Thesis
$loc['Ph.D. thesis']
Publisher
Florida State University
Place of Publication
Tallahassee, FL
Editor
Language
Summary Language
Original Title
Series Editor
Series Title
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Edition
ISSN
ISBN
Medium
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Conference
Funding
NSF, ONR, NASA
Approved
$loc['no']
Call Number
COAPS @ mfield @
Serial
589
Permanent link to this record
Author
Yu, P. ; Morey, S.L. ; O'Brien, J.J.
Title
A reduced-dynamics variational approach for the assimilation of altimeter data into eddy-resolving ocean models
Type
$loc['typeJournal Article']
Year
2009
Publication
Ocean Modelling
Abbreviated Journal
Ocean Modelling
Volume
27
Issue
3-4
Pages
215-229
Keywords
Ocean modeling ; Data assimilation ; Variational adjoint methods
Abstract
Address
Corporate Author
Thesis
Publisher
Place of Publication
Editor
Language
Summary Language
Original Title
Series Editor
Series Title
Abbreviated Series Title
Series Volume
Series Issue
Edition
ISSN
1463-5003
ISBN
Medium
Area
Expedition
Conference
Funding
Approved
$loc['no']
Call Number
COAPS @ mfield @
Serial
400
Permanent link to this record