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All sourcesNASA NTRSDOE OSTI
NASA NTRS1994

An error covariance model for sea surface topography and velocity derived from TOPEX/POSEIDON altimetry

In order to facilitate the use of satellite-derived sea surface topography and velocity oceanographic models, methodology is presented for deriving the total error covariance and its geographic distribution from TOPEX/POSEIDON measurements. The model is formulated using a parametric model fit to the altimeter range observations. The topography and velocity modeled with spherical harmonic expansions whose coefficients are found through optimal adjustment to the altimeter range residuals using Bayesian statistics. All other parameters, including the orbit, geoid, surface models, and range corrections are provided as unadjusted parameters. The maximum likelihood estimates and errors are derived from the probability density function of the altimeter range residuals conditioned with a priori information. Estimates of model errors for the unadjusted parameters are obtained from the TOPEX/POSEIDON postlaunch verification results and the error covariances for the orbit and the geoid, except for the ocean tides. The error in the ocean tides is modeled, first, as the difference between two global tide models and, second, as the correction to the present tide model, the correction derived from the TOPEX/POSEIDON data. A formal error covariance propagation scheme is used to derive the total error. Our global total error estimate for the TOPEX/POSEIDON topography relative to the geoid for one 10-day period is found tio be 11 cm RMS. When the error in the geoid is removed, thereby providing an estimate of the time dependent error, the uncertainty in the topography is 3.5 cm root mean square (RMS). This level of accuracy is consistent with direct comparisons of TOPEX/POSEIDON altimeter heights with tide gauge measurements at 28 stations. In addition, the error correlation length scales are derived globally in both east-west and north-south directions, which should prove useful for data assimilation. The largest error correlation length scales are found in the tropics. Errors in the velocity field are smallest in midlatitude regions. For both variables the largest errors caused by uncertainty in the geoid. More accurate representations of the geoid await a dedicated geopotential satellite mission. Substantial improvements in the accuracy of ocean tide models are expected in the very near future from research with TOPEX/POSEIDON data.

Tsaoussi, Lucia S.↗
NASA NTRS2001

Design and Implementation of a Parallel Multivariate Ensemble Kalman Filter for the Poseidon Ocean General Circulation Model

A multivariate ensemble Kalman filter (MvEnKF) implemented on a massively parallel computer architecture has been implemented for the Poseidon ocean circulation model and tested with a Pacific Basin model configuration. There are about two million prognostic state-vector variables. Parallelism for the data assimilation step is achieved by regionalization of the background-error covariances that are calculated from the phase-space distribution of the ensemble. Each processing element (PE) collects elements of a matrix measurement functional from nearby PEs. To avoid the introduction of spurious long-range covariances associated with finite ensemble sizes, the background-error covariances are given compact support by means of a Hadamard (element by element) product with a three-dimensional canonical correlation function. The methodology and the MvEnKF configuration are discussed. It is shown that the regionalization of the background covariances; has a negligible impact on the quality of the analyses. The parallel algorithm is very efficient for large numbers of observations but does not scale well beyond 100 PEs at the current model resolution. On a platform with distributed memory, memory rather than speed is the limiting factor.

Keppenne, Christian L.↗
NASA NTRS2001

NASA's Role in the North American Carbon Program

NASA's participation in the North American Carbon Program will include a significant effort for quantifying terrestrial and adjacent ocean sources and sinks of carbon using a variety of land, ocean, and atmospheric measurements. This effort will require improved remote sensing algorithms, new measurement technologies, refined model parameterizations of carbon cycle processes, advanced satellite data assimilation methodologies, and a data management strategy. NASA's plan for pursuing these activities will be presented. Examples of NASA data management capabilities such as the SeaWiFS on-line field data system will be discussed.

McClain, Charles R.↗
NASA NTRS2003

Experimental and Petrological Constraints on Lunar Differentiation from the Apollo 15 Green Picritic Glasses

Phase equilibrium experiments on the most magnesian Apollo 15C green picritic glass composition indicate a multiple saturation point with olivine and orthopyroxene at 1520 C and 1.3 GPa (about 260 km depth in the moon). This composition has the highest Mg# of any lunar picritic glass and the shallowest multiple saturation point. Experiments on an Apollo 15A composition indicate a multiple saturation point with olivine and orthopyroxene at 1520 C and 2.2 GPa (about 440 km depth in the moon). The importance of the distinctive compositional trends of the Apollo 15 groups A, B, and C picritic glasses merits the reanalysis of NASA slide 15426,72 with modern electron microprobe techniques. We confirm the compositional trends reported by Delano (1979, 1986) in the major element oxides SiO2, TiO2, Al2O3, Cr2O3, FeO, MnO, MgO, and CaO, and we also obtained data for the trace elements P2O5, K2O, Na2O, NiO, S, Cu, Cl, Zn, and F. Petrogenetic modeling demonstrates that the Apollo 15 A-B-C glass trends could not have been formed by fractional crystallization or any continuous assimilation/fractional crystallization (AFC) process. The B and C glass compositional trends could not have been formed by batch or incremental melting of an olivine + orthopyroxene source or any other homogeneous source, though the A glasses may have been formed by congruent melting over a small pressure range at depth. The B compositional trend is well modeled by starting with an intermediate A composition and assimilating a shallower, melted cumulate, and the C compositional trend is well modeled by a second assimilation event. The assimilation process envisioned is one in which heat and mass transfer were separated in space and time. In an initial intrusive event, a picritic magma crystallized and provided heat to melt magma ocean cumulates. In a later replenishment event, the picritic magma incrementally mixed with the melted cumulate (creating the compositional trends in the green glass data set), ascended to the lunar surface, and erupted as a fire fountain. A barometer created from multiple saturation points provides a depth estimate of other glasses in the A-B-C trend and of the depths of assimilation. This barometer demonstrates that the Apollo 15 A-B-C trend originated over a depth range of approx.460 km to approx.260 km within the moon.

Elkins-Tanton, Linda T.↗
NASA NTRS2017

Global Assimilation of X Project Loon Stratospheric Balloon Observations

Project Loon has an overall goal of providing worldwide internet coverage using a network of long-duration super-pressure balloons. Beginning in 2013, Loon has launched over 1600 balloons from multiple tropical and middle latitude locations. These GPS tracked balloon trajectories provide lower stratospheric wind information over the oceans and remote land areas where traditional radiosonde soundings are sparse, thus providing unique coverage of lower stratospheric winds. To fully investigate these Loon winds we: 1) compare the Loon winds to winds produced by a global data assimilation system (DAS: NASA GEOS) and 2) assimilate the Loon winds into the same comprehensive DAS. Results show that in middle latitudes the Loon winds and DAS winds agree well and assimilating the Loon winds have only a small impact on short-term forecasting of the Loon winds, however, in the tropics the loon winds and DAS winds often disagree substantially (8 m/s or more in magnitude) and in these cases assimilating the loon winds significantly improves the forecast of the loon winds. By highlighting cases where the Loon and DAS winds differ, these results can lead to improved understanding of stratospheric winds, especially in the tropics.

Loo↗
NASA NTRS2019

Planetary Boundary Layer Height from AIRS and MERRA-2 Products at NASA GES DISC, and Insights from Data Intercomparison

The Atmospheric Infrared Sounder (AIRS) is the hyperspectral infrared sounder onboard NASA's Aqua satellite, launched in 2002. The NASA Goddard Earth Sciences Data and Information Services Center (GES DISC), in collaboration with NASA Sounder Team at JPL, provides processing, archiving, and distribution services for NASA sounders: the Aqua AIRS mission and the subsequent Suomi-National Polar-orbiting Partnership Cross-track Infrared Sounder (CrIS) mission. The Planetary Boundary Layer (PBL) Height is a new variable added in the AIRS Version 6 support product. It is derived based on gradients of the retrieved atmospheric thermodynamic profile, and gives the pressure at the top of PBL over the ocean. The GES DISC also provides services for the second Modern-Era Retrospective analysis for Research and Applications (MERRA-2) product generated by the Goddard Earth Observing System Model, Version 5 (GEOS-5) data assimilation system. The monthly PBL Height variable has been available in the Giovanni system, which is a Web-based application developed by the GES DISC providing a simple and intuitive way to visualize, analyze, and access vast amounts of Earth science remote sensing data. In this work, we will present the monthly PBL Height data from AIRS and MERRA-2 and the services to support data intercomparison, such as access, plotting, subsetting, re-gridding, and generation of a multi-year monthly mean. We will also show intercomparison results, and evaluate whether (over the ocean) AIRS can observe PBL features similar to the reanalysis product at monthly and longer-term scales.

AIRS↗
NASA NTRS2001

The NASA Seasonal-to-Interannual Prediction Project (NSIPP)

The goal of the project is to develop an assimilation and forecast system based on a coupled atmosphere-ocean-land-surface-sea-ice model capable of using a combination of satellite and in situ data sources to improve the prediction of ENSO and other major S-I signals and their global teleconnections. The objectives of this annual report are to: (1) demonstrate the utility of satellite data, especially surface height surface winds, air-sea fluxes and soil moisture, in a coupled model prediction system; and (2) aid in the design of the observing system for short-term climate prediction by conducting OSSE's and predictability studies.

Rienecker, Michele↗
NASA NTRS2019

Global Assimilation of Loon Stratospheric Balloon Observations and Their Trajectories Relative to Tropical Waves

Project Loon has an overall goal of providing worldwide internet coverage using a network of long-duration super-pressure balloons. Beginning in 2013, Loon has launched over 1600 balloons from multiple tropical and middle latitude locations. These GPS tracked balloon trajectories provide lower stratospheric wind information over the oceans and remote land areas where traditional radiosonde soundings are sparse, thus providing unique coverage of lower stratospheric winds. To fully investigate these Loon winds we: 1) compare the Loon winds to winds produced by a global data assimilation system (DAS: NASA GEOS) and 2) assimilate the Loon winds into the same comprehensive DAS. During May through December 2016 Loon balloons were often able to remain near the equator by selectively adjusting the Loon altitude. Our results based on global wind analyses show that the expected mean poleward motion from the Brewer-Dobson circulation can be circumvented by vertically adjusting the Loon altitudes with the phasing with the meridional wind of equatorial Rossby waves, allowing the Loon balloons to remain in the tropics.

Coy, L.↗
NASA NTRS2023

Assimilation of Blended Satellite Soil Moisture Data Products to Further Improve Noah-MP Model Skills

Microwave satellite remote sensing has enabled observations of soil moisture (SM) at the global scale, and multiple SM data products have been developed in the past decades. However, single-sensor-based measurements are insufficient for continuous spatiotemporal coverage. In the context of its climate program, the Climate Change Initiative, the European Space Agency (ESA) has developed robust, long term, global scale, multi instrument satellite derived time series of climate data record for key component of the climate system, including soil moisture (CCI), while the Soil Moisture Operational Product System (SMOPS) was specifically developed by National Oceanic and Atmospheric Administration (NOAA) to offer the real time blended SM datasets through merging all available individual products. Before combining, all individual SM data ingested into both SMOPS and CCI blended products are scaled to Global Land Data Assimilation System (GLDAS) 0-10 cm SM climatology. Benefiting from land surface model evolution and the availability of high-quality forcing data, GLDAS has become more comprehensive to track SM changes and dynamic trends. The development of GLDAS and the scaling procedure in CCI and SMOPS leave an open scientific and operational question: do the blended satellite SM data products have added value comparing to the GLDAS product? This study clearly reveals that both CCI and SMOPS can provide the reliable SM observations with independent information, although their climatology matches well with GLDAS. Relative to assimilation of GLDAS 0-10 cm SM data, Noah-MP model can be further improved by assimilating the blended satellite SM observations with respect to the quality-controlled in situ measurements. The strong consistency of results presented in this paper proves that the blended satellite SM data products are more useful than the GLDAS product in terms of improving Noah-MP model performance.

Jifu Yin↗
NASA NTRS2004

Patterns and Variability in Global Ocean Chlorophyll: Satellite Observations and Modeling

Recent analyses of SeaWiFS data have shown that global ocean chlorophyll has increased more than 4% since 1998. The North Pacific ocean basin has increased nearly 19%. These trend analyses follow earlier results showing decadal declines in global ocean chlorophyll and primary production. To understand the causes of these changes and trends we have applied the newly developed NASA Ocean Biogeochemical Assimilation Model (OBAM), which is driven in mechanistic fashion by surface winds, sea surface temperature, atmospheric iron deposition, sea ice, and surface irradiance. The model utilizes chlorophyll from SeaWiFS in a daily assimilation. The model has in place many of the climatic variables that can be expected to produce the changes observed in SeaWiFS data. This enables us to diagnose the model performance, the assimilation performance, and possible causes for the increase in chlorophyll. A full discussion of the changes and trends, possible causes, modeling approaches, and data assimilation will be the focus of the seminar.

Gregg, Watson↗
NASA NTRS1991

Assimilation of altimeter topography into oceanic models

The primary goals of the authors are to build an intuition for assimilation techniques and to investigate the impact of variable altimeter topography on simple or complex oceanic models. In particular, applying various techniques and sensitivity studies to model and data constraints plays a key role. We are starting to use quasi-geostrophic, semigeostrophic, and primitive-equation (PE) models and to test the schemes in regions of interest to the World Ocean Circulation Experiment (WOCE), as well as in the northeast Atlantic and the Mediterranean. The impact of scatterometer wind forcing on the results is also investigated. The use of Geosat, European Remote Sensing satellite (ERS-1), and TOPEX/POSEIDON altimetry data is crucial in fine tuning the models and schemes to the selected areas of interest.

Demey, Pierre↗
NASA NTRS2021

Land Surface Modeling Over the Dry Chaco: The Impact of Model Structures, and Soil, Vegetation and Land Cover Parameters

In this study, we tested the impact of a revised set of soil, vegetation and land cover parameters on the performance of three different state-of-the-art land surface models (LSMs) within the NASA Land Information System (LIS). The impact of this revision was tested over the South American Dry Chaco, an ecoregion characterized by deforestation and forest degradation since the 1980s. Most large-scale LSMs may lack the ability to correctly represent the ongoing deforestation processes in this region, because most LSMs use climatological vegetation indices and static land cover information. The default LIS parameters were revised with improved soil parameters, satellite-based interannually varying vegetation indices (leaf area index and green vegetation fraction) instead of climatological vegetation indices, and yearly land cover information instead of static land cover. A relative comparison in terms of water budget components and “efficiency space” for various baseline and revised experiments showed that large regional and long-term differences in the simulated water budget partitioning relate to different LSM structures, whereas smaller local differences resulted from updated soil, vegetation and land cover parameters. Furthermore, the different LSM structures redistributed water differently in response to these parameter updates. A time-series comparison of the simulations to independent satellite-based estimates of evapotranspiration and brightness temperature (Tb) showed that no LSM setup significantly outperformed another for the entire region and that not all LSM simulations improved with updated parameter values. However, the revised soil parameters generally reduced the bias between simulated surface soil moisture and pixel-scale in situ observations and the bias between simulated Tb and regional Soil Moisture Ocean Salinity (SMOS) observations. Our results suggest that the different hydrological responses of various LSMs to vegetation changes may need further attention to gain benefits from vegetation data assimilation.

Michiel Maertens↗
NASA NTRS2015

Evaluating the Utility of Satellite Soil Moisture Retrievals over Irrigated Areas and the Ability of Land Data Assimilation Methods to Correct for Unmodeled Processes

Earth's land surface is characterized by tremendous natural heterogeneity and human-engineered modifications, both of which are challenging to represent in land surface models. Satellite remote sensing is often the most practical and effective method to observe the land surface over large geographical areas. Agricultural irrigation is an important human-induced modification to natural land surface processes, as it is pervasive across the world and because of its significant influence on the regional and global water budgets. In this article, irrigation is used as an example of a human-engineered, often unmodeled land surface process, and the utility of satellite soil moisture retrievals over irrigated areas in the continental US is examined. Such retrievals are based on passive or active microwave observations from the Advanced Microwave Scanning Radiometer for the Earth Observing System (AMSR-E), the Advanced Microwave Scanning Radiometer 2 (AMSR2), the Soil Moisture Ocean Salinity (SMOS) mission, WindSat and the Advanced Scatterometer (ASCAT). The analysis suggests that the skill of these retrievals for representing irrigation effects is mixed, with ASCAT-based products somewhat more skillful than SMOS and AMSR2 products. The article then examines the suitability of typical bias correction strategies in current land data assimilation systems when unmodeled processes dominate the bias between the model and the observations. Using a suite of synthetic experiments that includes bias correction strategies such as quantile mapping and trained forward modeling, it is demonstrated that the bias correction practices lead to the exclusion of the signals from unmodeled processes, if these processes are the major source of the biases. It is further shown that new methods are needed to preserve the observational information about unmodeled processes during data assimilation.

land data↗
NASA NTRS2002

Hawaii Ocean Mixing Experiment: Program Summary

It is becoming apparent that insufficient mixing occurs in the pelagic ocean to maintain the large scale thermohaline circulation. Observed mixing rates fall a factor of ten short of classical indices such as Munk's "Abyssal Recipe." The growing suspicion is that most of the mixing in the sea occurs near topography. Exciting recent observations by Polzin et al., among others, fuel this speculation. If topographic mixing is indeed important, it must be acknowledged that its geographic distribution, both laterally and vertically, is presently unknown. The vertical distribution of mixing plays a critical role in the Stommel Arons model of the ocean interior circulation. In recent numerical studies, Samelson demonstrates the extreme sensitivity of flow in the abyssal ocean to the spatial distribution of mixing. We propose to study the topographic mixing problem through an integrated program of modeling and observation. We focus on tidally forced mixing as the global energetics of this process have received (and are receiving) considerable study. Also, the well defined frequency of the forcing and the unique geometry of tidal scattering serve to focus the experiment design. The Hawaiian Ridge is selected as a study site. Strong interaction between the barotropic tide and the Ridge is known to take place. The goals of the Hawaiian Ocean Mixing Experiment (HOME) are to quantify the rate of tidal energy loss to mixing at the Ridge and to identify the mechanisms by which energy is lost and mixing generated. We are challenged to develop a sufficiently comprehensive picture that results can be generalized from Hawaii to the global ocean. To achieve these goals, investigators from five institutions have designed HOME, a program of historic data analysis, modeling and field observation. The Analysis and Modeling efforts support the design of the field experiments. As the program progresses, a global model of the barotropic (depth independent) tide, and two models of the baroclinic (depth varying) tide, all validated with near-Ridge data, will be applied, to reveal the mechanisms of tidal energy conversion along the Ridge, and allow spatial and temporal integration of the rate of conversion. Field experiments include a survey to identify "hot spots" of enhanced mixing and barotropic to baroclinic conversion, a Nearfield study identifying the dominant mechanisms responsible for topographic mixing, and a Farfield program which quantifies the barotropic energy flux convergence at the Ridge and the flux divergence associated with low mode baroclinic waves radiation. The difference is a measure of the tidal power available for mixing at the Ridge. Field work is planned from years 2000 through 2002, with analysis and modeling efforts extending through early 2006. If successful, HOME will yield an understanding of the dominant topographic mixing processes applicable throughout the global ocean. It will advance understanding of two central problems in ocean science, the maintenance of the abyssal stratification, and the dissipation of the tides. HOME data will be used to improve the parameterization of dissipation in models which presently assimilate TOPEX-POSEIDON observations. The improved understanding of the dynamics and spatial distribution of mixing processes will benefit future long-term programs such as CLIVAR.

Ray, Richard D.↗
NASA NTRS2019

GEOS-Carb III: Delivering Mature Carbon Flux and Concentration Datasets in Support of NASA's Carbon Monitoring System

The GEOS-Carb project seeks to make high quality, data-driven products that support NASA's Carbon Monitoring System Program. These include bottom-up land and ocean flux estimates along with fossil fuel emissions, all informed by satellite data and the MERRA-2 reanalysis. These flux data support atmospheric carbon products that include both simulations and data assimilation products. We show examples of this work and discuss applications to future mission planning.

Ott, L.↗
NASA NTRS2019

Improving Surface Soil Moisture Estimates in West Africa Through GRACE Data Assimilation

Recent annual trends of precipitation and terrestrial water storage (TWS) in West Africa have been increasing over the past decade. Despite a significant impact of soil moisture on the TWS in West Africa, there is little research on the recent spatial and temporal behaviors of surface soil moisture (SSM) along with the hydrological trends and variability in West Africa. In this study, we assimilate TWS estimates from the Gravity Recovery and Climate Experiment (GRACE) mission into the Catchment Land Surface Model (CLSM) and evaluate its impacts on SSM simulations for the years, 2002–2017. The evaluation is performed using reference datasets: the African Monsoon Multidisciplinary Analysis (AMMA) in situ soil moisture observations, three currently available microwave satellite SSM observations from the Advanced Scatterometer (ASCAT), the Soil Moisture Ocean Salinity (SMOS), and the Soil Moisture Active Passive (SMAP) satellites and also the triple collocation analysis (TCA). Overall, modeled SSM shows good agreement with the reference datasets in terms of the anomaly SSM correlations. However, both modeled and ASCAT SSM are limited in their representation of the drying rates, as observed by ground observations, SMOS and SMAP estimates. Further, GRACE data assimilation results in improved SSM simulations in the humid regions with large annual TWS variability. This study demonstrates the utility of land data assimilation to inform hydrological conditions in West Africa, where soil moisture monitoring is necessary for water resource and livestock management.

West Africa↗
NASA NTRS2015

The Rise of GNSS Reflectometry for Earth Remote Sensing

The Global Navigation Satellite System (GNSS) reflectometry, i.e. GNSS-R, is a novel remote-sensing technique first published in that uses GNSS signals reflected from the Earth's surface to infer its surface properties such as sea surface height (SSH), ocean winds, sea-ice coverage, vegetation, wetlands and soil moisture, to name a few. This communication discusses the scientific value of GNSS-R to (a) furthering our understanding of ocean mesoscale circulation toward scales finer than those that existing nadir altimeters can resolve, and (b) mapping vegetated wetlands, an emerging application that might open up new avenues to map and monitor the planet's wetlands for methane emission assessments. Such applications are expected to be demonstrated by the availability of data from GEROS-ISS, an ESA experiment currently in phase A, and CyGNSS [3], a NASA mission currently in development. In particular, the paper details the expected error characteristics and the role of filtering played in the assimilation of these data to reduce the altimetric error (when averaging many measurements).

wetland mapping↗