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Search indexed NASA NTRS and DOE OSTI research on propulsion, heat transfer, battery materials and energy systems. Follow report and document links to the original sources.

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All sourcesNASA NTRSDOE OSTI
NASA NTRS1994

An Approximate Kalman Filter for Ocean Data Assimilation; An Example with an Idealized Gulf Stream Model

A practical method of data assimilation for use with large, nonlinear, ocean general circulation models is explored. A Kalman filter based on approximations of the state error covariance matrix is presented, employing a reduction of the effective model dimension, the error's asymptotic steady-state limit, and a time-invariant linearization of the dynamic model for the error integration. The approximations lead to dramatic computational savings in applying estimation theory to large complex systems. We examine the utility of the approximate filter in assimilating different measurement types using a twin experiment of an idealized Gulf Stream. A nonlinear primitive equation model of an unstable east-west jet is studied with a state dimension exceeding 170,000 elements. Assimilation of various pseudo measurements is examined, including velocity, density, and volume transport at localized arrays, and realistic distributions of satellite altimetry and acoustic tomography observations. Results are compared in terms of their effects on the accuracies of the estimation. The approximate filter is shown to outperform a previous study that used an empirical nudging scheme. The examples demonstrate that useful approximate estimation errors can be computed in a practical manner for general circulation models.

['Kalman↗
NASA NTRS2010

The Impact of Ocean Data Assimilation on Seasonal-to-Interannual Forecasts: A Case Study of the 2006 El Nino Event

This study investigates the impact of four different ocean analyses on coupled forecasts of the 2006 El Nino event. Forecasts initialized in June 2006 using ocean analyses from an assimilation that uses flow-dependent background error covariances are compared with those using static error covariances that are not flow dependent. The flow-dependent error covariances reflect the error structures related to the background ENSO instability and are generated by the coupled breeding method. The ocean analyses used in this study result from the assimilation of temperature and salinity, with the salinity data available from Argo floats. Of the analyses, the one using information from the coupled bred vectors (BV) replicates the observed equatorial long wave propagation best and exhibits more warming features leading to the 2006 El Nino event. The forecasts initialized from the BV-based analysis agree best with the observations in terms of the growth of the warm anomaly through two warming phases. This better performance is related to the impact of the salinity analysis on the state evolution in the equatorial thermocline. The early warming is traced back to salinity differences in the upper ocean of the equatorial central Pacific, while the second warming, corresponding to the mature phase, is associated with the effect of the salinity assimilation on the depth of the thermocline in the western equatorial Pacific. The series of forecast experiments conducted here show that the structure of the salinity in the initial conditions is important to the forecasts of the extension of the warm pool and the evolution of the 2006 El Ni o event.

Yang, Shu-Chih↗
NASA NTRS1989

The influence of platform attitude dynamics on the Doppler frequency synthesis of SIR-B ocean data near Hurricane Josephine

The Space Shuttle Challenger was maneuvered into an SAR data-acquisition mode to capture a SIR-B scene of Hurricane Josephine, which presented itself as an unscheduled target of opportunity during NASA Mission 41-G during October 1984. Steeply sloped seas approaching 10 m in height were observed several hundred km in advance of the hurricane track. Mission attitude data indicate that the Shuttle pitch, roll, and yaw were unstable during the data take. The statistical clutterlock used by the ground SAR processor yields a Doppler frequency estimate that tracks the Shuttle yaw variation but is ambiguous with a single discontinuity equal to one multiple of the radar pulse repetition frequency. The SIR-B signal data have been compressed under several Doppler parameterizations to investigate the ambiguity of the clutterlock. Resultant ocean images are analyzed for their contrast and resolution properties.

Tilley, David G.↗
NASA NTRS1976

Design, fabrication and systems integration of a satellite tracked, free-drifting ocean data buoy

Engineering details are presented of a small free-drifting buoy configuration designed for use in the study of continental shelf water circulation patterns in the Chesapeake Bight of the Western North Atlantic Ocean. The buoy incoporated French instrumentation and was interrogated by the French EOLE satellite to provide position and four channels of temperature data. The buoy design included a variable depth drogue and a power supply sufficient for six weeks of continuous operations. Proof tests of the configuration indicated an adequate design and subsequent field experiments verified the proper functioning of the system.

Wallace, J. W.↗
NASA NTRS2014

Background Error Covariance Estimation Using Information from a Single Model Trajectory with Application to Ocean Data Assimilation

An attractive property of ensemble data assimilation methods is that they provide flow dependent background error covariance estimates which can be used to update fields of observed variables as well as fields of unobserved model variables. Two methods to estimate background error covariances are introduced which share the above property with ensemble data assimilation methods but do not involve the integration of multiple model trajectories. Instead, all the necessary covariance information is obtained from a single model integration. The Space Adaptive Forecast error Estimation (SAFE) algorithm estimates error covariances from the spatial distribution of model variables within a single state vector. The Flow Adaptive error Statistics from a Time series (FAST) method constructs an ensemble sampled from a moving window along a model trajectory.SAFE and FAST are applied to the assimilation of Argo temperature profiles into version 4.1 of the Modular Ocean Model (MOM4.1) coupled to the GEOS-5 atmospheric model and to the CICE sea ice model. The results are validated against unassimilated Argo salinity data. They show that SAFE and FAST are competitive with the ensemble optimal interpolation (EnOI) used by the Global Modeling and Assimilation Office (GMAO) to produce its ocean analysis. Because of their reduced cost, SAFE and FAST hold promise for high-resolution data assimilation applications.

Error Covariance↗
NASA NTRS2014

Background Error Covariance Estimation using Information from a Single Model Trajectory with Application to Ocean Data Assimilation into the GEOS-5 Coupled Model

An attractive property of ensemble data assimilation methods is that they provide flow dependent background error covariance estimates which can be used to update fields of observed variables as well as fields of unobserved model variables. Two methods to estimate background error covariances are introduced which share the above property with ensemble data assimilation methods but do not involve the integration of multiple model trajectories. Instead, all the necessary covariance information is obtained from a single model integration. The Space Adaptive Forecast error Estimation (SAFE) algorithm estimates error covariances from the spatial distribution of model variables within a single state vector. The Flow Adaptive error Statistics from a Time series (FAST) method constructs an ensemble sampled from a moving window along a model trajectory. SAFE and FAST are applied to the assimilation of Argo temperature profiles into version 4.1 of the Modular Ocean Model (MOM4.1) coupled to the GEOS-5 atmospheric model and to the CICE sea ice model. The results are validated against unassimilated Argo salinity data. They show that SAFE and FAST are competitive with the ensemble optimal interpolation (EnOI) used by the Global Modeling and Assimilation Office (GMAO) to produce its ocean analysis. Because of their reduced cost, SAFE and FAST hold promise for high-resolution data assimilation applications.

Data Assimilation↗
NASA NTRS1991

Ocean data assimilation using optimal interpolation with a quasi-geostrophic model

A quasi-geostrophic (QG) stream function is analyzed by optimal interpolation (OI) over a 59-day period in a 150-km-square domain off northern California. Hydrographic observations acquired over five surveys were assimilated into a QG open boundary ocean model. Assimilation experiments were conducted separately for individual surveys to investigate the sensitivity of the OI analyses to parameters defining the decorrelation scale of an assumed error covariance function. The analyses were intercompared through dynamical hindcasts between surveys. The best hindcast was obtained using the smooth analyses produced with assumed error decorrelation scales identical to those of the observed stream function. The rms difference between the hindcast stream function and the final analysis was only 23 percent of the observation standard deviation. The two sets of OI analyses were temporally smoother than the fields from statistical objective analysis and in good agreement with the only independent data available for comparison.

Rienecker, Michele M.↗
NASA NTRS2002

Southern Ocean Seasonal Net Production from Satellite, Atmosphere, and Ocean Data Sets

A new climatology of monthly air-sea O2 flux was developed using the net air-sea heat flux as a template for spatial and temporal interpolation of sparse hydrographic data. The climatology improves upon the previous climatology of Najjar and Keeling in the Southern Hemisphere, where the heat-based approach helps to overcome limitations due to sparse data coverage. The climatology is used to make comparisons with productivity derived from CZCS images. The climatology is also used in support of an investigation of the plausible impact of recent global warming an oceanic O2 inventories.

Keeling, Ralph F.↗
NASA NTRS2016

Estimating Terra MODIS Polarization Effect Using Ocean Data

Terra MODIS has been known since pre-launch to have polarization sensitivity, particularly in shortest-wavelength bands 8 and 9. On-orbit reflectance trending of pseudo-invariant sites show a variation in reflectance as a function of band and scan mirror angle of incidence consistent with time-dependent polarization effects from the rotating double-sided scan mirror. The MODIS Characterization Support Team [MCST] estimates the Mueller matrix trending from this variation as observed from a single desert site, but this effect is not included in Collection 6 [C6] calibration. Here we extend the MCSTs current polarization sensitivity monitoring to two ocean sites distributed over latitude to helpestimate the uncertainties in the derived Mueller matrix. The Mueller matrix elements derived for polarization-sensitive Band 8 for a given site are found to be fairly insensitive to surface brdf modeling. The site-to-site variation is a measure of the uncertainty in the Mueller estimation.Results for band 8 show that the polarization correction reduces mirror-side striping by up to 50% and reduces the instrument polarization effect on reflectance time series of an ocean target.

Wald, Andrew E.↗
DOE OSTI2024

MODAQ 2.0 (Modular Ocean Data AcQuisition System v2.0) [SWR-24-138]

MODAQ 2.0 is the continuation of the Modular Ocean/Offshore Data AcQuisition system which offers new benefits of being based on generally available hardware and open-source development tools. The MODAQ 2.0 project is the amalgamation of ROS packages, tools, and guidance for developing data acquisition and control applications for marine energy devices using a ROS2 based architecture. The MODAQ 2.0 Reference Design includes several packages that are targeted for the marine energy sector and allow most developers with a basic background in programming to spin up a high-quality DAQ and control system. It is comprised of multiple software repositories which can be found at the MODAQ2 GitHub Organization: https://github.com/NREL-MODAQ2

Nichols, Casey↗
NASA NTRS1984

The NASA/JPL pilot ocean data system catalog

Data from various ground and satellite sensors collected and processed by various institutions cannot be used easily without a well planned method of determining their existence and location. The Global On-Line Data (GOLD) Catalog will provide a requestor with the ability to determine the existence of specific data holdings by specifying any combination of platform, measurement, location, or time. By this means, one can obtain details of data held at PODS and also at cooperating remote archives.

Johnson, J. A.↗
NASA NTRS1991

The NASA Ocean Data System at the Jet Propulsion Laboratory

Global change studies require satellite oceanographic observations archived in an active data center. This paper describes the activities of an experimental active oceanographic data archive center specializing in distribution of high-level oceanographic data products produced by research oceanographers from satellite altimeter, scatterometer, and microwave and infrared radiometer measurements. Methods to encourage data sharing practices were developed. Services initiated to help oceanographers conduct research more effectively included rapid delivery of data, reprocessing data sets, subsetting of large data sets, and distribution of multiple data sets on a single medium.

Halpern, David↗