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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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NASA NTRS2013

On the Reprocessing and Reanalysis of Observations for Climate

The long observational record is critical to our understanding of the Earths climate, but most observing systems were not developed with a climate objective in mind. As a result, tremendous efforts have gone into assessing and reprocessing the data records to improve their usefulness in climate studies. The purpose of this paper is to both review recent progress in reprocessing and reanalyzing observations, and to summarize the challenges that must be overcome in order to improve our understanding of climate and variability. Reprocessing improves data quality through more scrutiny and improved retrieval techniques for individual observing systems, while reanalysis merges many disparate observations with models through data assimilation, yet both aim to provide an climatology of Earth processes. Many challenges remain, such as tracking the improvement of processing algorithms and limited spatial coverage. Reanalyses have fostered significant research, yet reliable global trends in many physical fields are not yet attainable, despite significant advances in data assimilation and numerical modeling. Oceanic reanalyses have made significant advances in recent years, but will only be discussed here in terms of progress toward integrated Earth system analyses. Climate data sets are generally adequate for process studies and large-scale climate variability. Communication of the strengths, limitations and uncertainties of reprocessed observations and reanalysis data, not only among the community of developers, but also with the extended research community, including the new generations of researchers and the decision makers is crucial for further advancement of the observational data records. It must be emphasized that careful investigation of the data and processing methods are required to use the observations appropriately.

Uncertainty↗
NASA NTRSDate not supplied

Preliminary Results From GMAO’s JEDI Based Ocean/Sea-ice Data Assimilation System

Preparations for GMAO’s transition to next generation coupled DA framework includes MOM6 for the ocean and CICE6 for sea ice, alongside assembling the data assimilation systems. The coupled DA system will primarily rely on the Joint Effort for Data Assimilation Integration (JEDI) and involves the use of the Sea-ice Ocean and Coupled Analysis (SOCA) package. The early stages of the work involved developing a coupled, cycling (ocean-only) DA workflow named “Swell”. Some other highlights from this transdisciplinary work: Collaborated with NOAA/EMC to ensure direct comparisons using the same observations and time (July -September 2021) during model development. Conducted in-kind work with the JCSDA to improve various JEDI components. Swell coupled DA framework will be used to assimilate observations from NASA’s PACE and SWOT missions

Dorukhan Ardag↗
NASA NTRS1991

Ocean circulation using altimetry

Our group has been very actively involved in promoting satellite altimetry as a unique tool for observing ocean circulation and its variability. TOPEX/POSEIDON is particularly interesting as it is optimized for this purpose. It will probably be the first instrument really capable of observing the seasonal and interannual variability of subtropical and polar gyres and the first to eventually document the corresponding variability of their heat flux transport. The studies of these phenomena require data of the best quality, unbiased extraction of the signal, mixing of these satellite data with in situ measurements, and assimilation of the whole set into a dynamic description of ocean circulation. Our group intends to develop responses to all these requirements. We will concentrate mostly on the circulation of the South Atlantic and Indian Oceans: This will be done in close connection with other groups involved in the study of circulation of the tropical Atlantic Ocean, in the altimetry measurements (in particular, those of the tidal issue), and in the techniques of data assimilation in ocean circulation models.

Minster, Jean-Francois↗
NASA NTRS1991

Study of mass and heat transport of the tropical Atlantic Ocean using models and altimeter data

The specific objectives of this proposal are: (1) to assess the quality of the TOPEX/POSEIDON surface altimeter data in regard to its use for a large, low-frequency monitoring of the surface topography of the tropical Atlantic Ocean; (2) to develop a method, on a demonstration basis, to derive from the tropical Atlantic the depth of the thermocline and the heat content changes from the surface altimeter data field; (3) to develop a method of assimilation of altimeter data into Oceanic General Circulation Models (OGCM's) for the purpose of preparing an operational, permanent, three-dimensional now casting of the tropical Atlantic Ocean (a TOGA objective); and (4) to derive from these models global circulation fields and a time series of mass and meridional heat transports across the tropical Atlantic region (a WOCE objective).

Merle, Jacques↗
NASA NTRSDate not supplied

Insights Into the Hydrology of the Congo Peatlands Through Land Surface Modeling and Data Assimilation

The 16.8 million ha of peatlands in the Cuvette Centrale wetland complex in the Congo Basin is one of the largest peatland regions on Earth but still highly understudied. Understanding the hydrological functioning of these peatlands and the effects of external disturbances thereon remains a major challenge. Recent research suggested fundamental hydrological differences between the Congo peatlands and the well-studied Southeast Asian peatlands. The Congo peatlands have a doming gradient that is up to ten times smaller, and they are influenced by river hydrology to some extent. In this study, we explore the Congo peatland hydrology through land surface modeling and data assimilation. We build upon our recently developed tropical PEATCLSM module (Apers et al., 2022) that was parameterized based on data from Southeast Asian peatlands due to the lack of field data from other tropical peatland regions. In a first step, we derive Congo-specific peat hydraulic and discharge function parameters from a scalar parametrization of water level dynamics in the Congo peatlands, using observed water level data at two locations. These Congo-specific parameters differ considerably from the original literature-based parameters from Southeast Asian peatlands. In a second step, we apply our original and Congo-specific parameters in an assimilation scheme for L-band brightness temperature (Tb) data from the Soil Moisture and Ocean Salinity (SMOS) mission. The data assimilation results are used in two ways. First, the effect of these parameters on the simulated peatland hydrology and the observation-minus-forecast Tb residuals is evaluated. It is hypothesized that the new parameters reduce the previously reported modeling errors over the Congo peatlands and reduce the residuals in Tb as well. Second, we analyze the data assimilation diagnostics to learn about other model improvement possibilities. In preliminary results, we found long periods of temporally autocorrelated total water storage increments (difference of pre- and post-update) that coincided with anomalies in river stages measured upstream of the peatlands. Since PEATLCSM neglects possible river influence, this concurrence suggests that the typically used grid-based approach of land surface models should be combined with a river routing scheme over the Congo peatlands.

Sebastian Apers↗
NASA NTRS2001

Towards operational ocean state estimation

An assimilation effort is undertaken with the goal of understanding seasonal-to-interannual changes of the tropical Pacific Ocean from the late '80s to the present, encompassing the WOCE and TOPEX/POSEIDON (T/P) periods.

method↗
NASA NTRS1997

Geosat Data Assimilation with Application to the Eastern North Atlantic

An attempt is made to determine the three-dimensional ocean circulation from satellite altimeter measurements by assimilating Geosat sea surface height data into an eddy-resolving QuasiGeostrophic (QG) model of the eastern North Atlantic Ocean. Results are tested against independent information from hydrographic field observations and moored current meter data collected during the Geosat ERM. The comparison supports the concept of inferring aspects of the three-dimensional flow field from sea surface height observations by combining altimetric measurements with the dynamics of ocean circulation models. A Holland-type QG model with open boundaries is set up on a 2000 km X 2000 km domain of the eastern North Atlantic between 25 deg. and 45 deg. N, 32 deg. and 8 deg. W. By using a simple nudging technique, about two years of Geosat altimeter data are assimilated into the model every five days as space-time objective analyses on the model grid. The error information resulting from the analysis is used during the assimilation procedure to account for data uncertainties. Results show an intense eddy field, which in the surface layer interacts with a meandering Azores Front. Compared to Geosat, the model leads to smoothed fields that follow the observations. Model simulations are significantly correlated with hydrographic data from March 1988 and June 1989, both close to the surface and in the subsurface. Good agreement is also found between the model velocity fields and moored current meter data in the top two model layers. The agreement is visually weak in the bottom layer, although a coherence analysis reveals an agreement between the model simulation and current meter data over the full water column at periods exceeding 80 days.

Stammer, Detlef↗
NASA NTRS2012

NASA Tech Briefs, April 2012

Topics include: Computational Ghost Imaging for Remote Sensing; Digital Architecture for a Trace Gas Sensor Platform; Dispersed Fringe Sensing Analysis - DFSA; Indium Tin Oxide Resistor-Based Nitric Oxide Microsensors; Gas Composition Sensing Using Carbon Nanotube Arrays; Sensor for Boundary Shear Stress in Fluid Flow; Model-Based Method for Sensor Validation; Qualification of Engineering Camera for Long-Duration Deep Space Missions; Remotely Powered Reconfigurable Receiver for Extreme Environment Sensing Platforms; Bump Bonding Using Metal-Coated Carbon Nanotubes; In Situ Mosaic Brightness Correction; Simplex GPS and InSAR Inversion Software; Virtual Machine Language 2.1; Multi-Scale Three-Dimensional Variational Data Assimilation System for Coastal Ocean Prediction; Pandora Operation and Analysis Software; Fabrication of a Cryogenic Bias Filter for Ultrasensitive Focal Plane; Processing of Nanosensors Using a Sacrificial Template Approach; High-Temperature Shape Memory Polymers; Modular Flooring System; Non-Toxic, Low-Freezing, Drop-In Replacement Heat Transfer Fluids; Materials That Enhance Efficiency and Radiation Resistance of Solar Cells; Low-Cost, Rugged High-Vacuum System; Static Gas-Charging Plug; Floating Oil-Spill Containment Device; Stemless Ball Valve; Improving Balance Function Using Low Levels of Electrical Stimulation of the Balance Organs; Oxygen-Methane Thruster; Lunar Navigation Determination System - LaNDS; Launch Method for Kites in Low-Wind or No-Wind Conditions; Supercritical CO2 Cleaning System for Planetary Protection and Contamination Control Applications; Design and Performance of a Wideband Radio Telescope; Finite Element Models for Electron Beam Freeform Fabrication Process Autonomous Information Unit for Fine-Grain Data Access Control and Information Protection in a Net-Centric System; Vehicle Detection for RCTA/ANS (Autonomous Navigation System); Image Mapping and Visual Attention on the Sensory Ego-Sphere; HyDE Framework for Stochastic and Hybrid Model-Based Diagnosis; and IMAGESEER - IMAGEs for Education and Research.

Source record↗
NASA NTRS2020

Computationally Efficient Modeling and Data Assimilation of Near-Surface Variability

Near-surface (< 20m) ocean exhibits high variability due to coupled interactions, for e.g., with the atmosphere, sea ice, land, etc. Here we focus on atmospheric heat and momentum (wind) forcing, which are known to cause diurnal variability within the mixed layer. Only recently with a combination of sufficiently high vertical/horizontal resolution (75L, 1/4deg) and sub-daily atmospheric forcing fields, ocean models are starting to resolve this diurnal variability. However, the computation expense of such a high vertical resolution is burdensome in the context of coupled modeling and data assimilation. An alternative approach is to parameterize this diurnal variability with a prognostic model, that is embedded into the ocean model.In the first part of this presentation, we will demonstrate results with the above two approaches, by comparing them to profiles of near-surface temperature and salinity. In the context of data assimilation and reanalysis, this modeling capability opens the door to re-examine and perhaps improve specification of background (or, ensemble) error characteristics. The second half of this talk will focus on illustrating diurnally varying errors within an ensemble DA, and possible approaches to improve localization (horizontal/vertical) to extract maximum possible observational information content from in-situ and satellite observations of sea surface temperature.

Akella, Santha↗
NASA NTRS2016

Benefit of Modeling the Observation Error in a Data Assimilation Framework Using Vegetation Information Obtained From Passive Based Microwave Data

A primary operational goal of the United States Department of Agriculture (USDA) is to improve foreign market access for U.S. agricultural products. A large fraction of this crop condition assessment is based on satellite imagery and ground data analysis. The baseline soil moisture estimates that are currently used for this analysis are based on output from the modified Palmer two-layer soil moisture model, updated to assimilate near-real time observations derived from the Soil Moisture Ocean Salinity (SMOS) satellite. The current data assimilation system is based on a 1-D Ensemble Kalman Filter approach, where the observation error is modeled as a function of vegetation density. This allows for offsetting errors in the soil moisture retrievals. The observation error is currently adjusted using Normalized Difference Vegetation Index (NDVI) climatology. In this paper we explore the possibility of utilizing microwave-based vegetation optical depth instead.

Vegetation↗
NASA NTRS2019

Global Ocean Primary Production Trends in the Modern Ocean Color Satellite Record (1998-2015)

Ocean primary production (PP), representing the uptake of inorganic carbon through photosynthesis, supports marine life and affects carbon exchange with the atmosphere. It is difficult to ascertain its magnitude, variability, and trends due to our inability to measure it directly at large scales. Yet it is paramount for understanding changes in marine health, fisheries, and the global carbon cycle. Using assimilation of ocean color satellite data into an ocean biogeochemical model, we estimate that global net ocean PP has experienced a small but significant decline −0.8 PgC/y (−2.1%) per decade (P < 0.05) in the 18-year satellite record from 1998 to 2015. This decline is associated with shallowing surface mixed layer depth (−2.4% per decade) and decreasing nitrate concentrations (−3.2% per decade). Relative contributions to PP by various types of ocean phytoplankton have changed, with decreases in production by intermediate-sized phytoplankton represented by chlorophytes (−14.3% per decade). This is partially compensated by increases from the unique, more nutrient-efficient, coccolithophores (8.4% per decade). Geographically, the North and Equatorial Indian Oceans are responsible for much of the decline in PP, falling 0.16 and 0.69 PgC/y per decade, respectively. Reduced production by large, fast-growing diatoms along with chlorophytes characterizes the decline here. In contrast, increases in PP are found in the North and North Central Pacific. The increases here are led by chlorophytes in the North Pacific and the small cyanobacteria in the North Central Pacific. These results suggest that the multi-decadal satellite observational record, coupled with an underlying representation of marine biodiversity in a model, can monitor the uptake of carbon by phytoplankton and that changes, although small, are occurring in the global oceans.

Watson W Gregg↗
NASA NTRS1986

Sequential estimation and satellite data assimilation in meteorology and oceanography

The role of dynamics in estimating the state of the atmosphere and ocean from incomplete and noisy data is discussed and the classical applications of four-dimensional data assimilation to large-scale atmospheric dynamics are presented. It is concluded that sequential updating of a forecast model with continuously incoming conventional and remote-sensing data is the most natural way of extracting the maximum amount of information from the imperfectly known dynamics, on the one hand, and the inaccurate and incomplete observations, on the other.

Ghil, M.↗
NASA NTRS1985

Future possibilities in objective analysis and data assimilation for atmospheric dynamics

The role that dynamics plays in estimating the state of the atmosphere from incomplete and noisy data is reviewed. Objective analysis represents an attempt at relying mostly on the data and minimizing the role of dynamics in the estimation. Data assimilation tries to balance properly the roles of dynamical and observational information. Sequential estimation is presented as the proper framework for understanding this balance, and the Kalman filter as the ideal, optimal procedure for data assimilation. The optimal filter computes forecast error covariances of a given atmospheric model exactly, and hence data assimilation should be closely connected with predictability studies. This connection is described, and consequences drawn for currently active areas of the atmospheric and related sciences, namely, mesoscale meteorology, long range forecasting, and upper ocean dynamics. Possibilities offered by judicious data assimilation in understanding barotropic adjustment, a phenomenon that appears to play a crucial role in atmospheric behavior on the scale of weeks to months, and hence in long range forecasting are addressed.

Ghil, M.↗
NASA NTRS2004

Regional Assimilation of NASA Atmospheric Infrared Sounder (AIRS) Data

The NASA Short-term Prediction Research and Transition (SPORT) Center seeks to accelerate the infusion of NASA Earth Science Enterprise (ESE) observations, data assimilation and modeling research into NW S forecast operations and decision-making. The Atmospheric Infrared Sounder (AIRS), is expected to advance climate research and weather prediction into the 21 st century. It is one of six instruments onboard Aqua, a satellite that is part of NASA s Earth Observing System. AIRS, along with two partner microwave sounding instruments, represents the most advanced atmospheric sounding system ever deployed in space. The system is capable of measuring the atmospheric temperature in the troposphere with radiosonde accuracies of 1 K over 1 km-thick layers under both clear and cloudy conditions, while the accuracy of the derived moisture profiles will exceed that obtained by radiosondes. It is imperative that the scientific community is prepared to take full advantage of next-generation satellite data that will become available within the next decade. The purpose of this paper is to describe a procedure designed to optimally assimilate AIRS data at high spatial resolution over both land and ocean. The assimilation system used in this study is the Local Analysis and Prediction System (LAPS) developed at the Forecast System Laboratory used extensively around the globe. Results will focus on quality control issues associated with AIRS, optimal assimilation strategies, and the impact of the AIRS data on subsequent numerical forecasts at 12 km produced by the next generation Weather Research and Forecast (WRF) model.

Chou, Shih-Hung↗
NASA NTRS2001

Establishing an IERS Sub-Center for Ocean Angular Momentum

With the objective of establishing the Special Bureau for the Oceans (SBO), a new archival center for ocean angular momentum (OAM) products, we have computed and analyzed a number of OAM products from several ocean models, with and without data assimilation. All three components of OAM (axial term related to length of day variations and equatorial terms related to polar motion) have been examined in detail, in comparison to the respective Earth rotation parameters. An 11+ year time series of OAM given at 5-day intervals has been made publicly available. Other OAM products spanning longer periods and with higher temporal resolution, as well as products calculated from ocean model/data assimilation systems, have been prepared and should become part of the SBO archives in the near future.

Ponte, Rui M.↗