SEARCH · Search NASA
Results for “Global ocean state estimation”
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.
Quote a phrase for an exact phrase match. Source license links do not imply unrestricted reuse.
The Development of the New GEOS-MITgcm Atmosphere-Ocean Model for Coupled Data Assimilation System
During the last two plus decades, The Goddard Earth Observing System (GEOS) and Massachusetts Institute of Technology (MIT) modeling groups have developed, respectively, atmosphere-only and ocean-only global general circulation models. These two models (GEOS and MITgcm) have demonstrated their data assimilation capabilities with the recent releases of the Modern Era Reanalysis for Research Applications, Version 2 (MERRA-2) atmospheric reanalysis and the Estimating the Circulation and Climate of the Ocean, Version 4 (ECCO-v4) ocean (and sea ice) state estimate. Independently, the two modeling groups have also produced global atmosphere-only and ocean-only simulations with km-scale grid spacing which proved invaluable for process studies and for the development of satellite and in-situ sampling strategies.Recently, a new effort has been made to couple these two models and to leverage their data-assimilation and high resolution capabilities (i.e., eddy-permitting ocean, cloud-permitting atmosphere). The focus in the model development is put on sub-seasonal to decadal time scales. In this talk, I discuss the new coupled model and present some first coupled simulation results. This will include a high-resolution coupled GEOS-MIT simulation, whereby we have coupled a cubed-sphere-720 (~ 1/8 deg) configuration of the GEOS atmosphere to a lat-lon-cap-1080 (~ 1/12 deg) configuration of the MIT ocean. We compare near-surface diagnostics of this fully coupled ocean-atmosphere set-up to equivalent atmosphere-only and ocean-only simulations. In the comparisons we focus in particular on the differences in air-sea interactions between sea surface temperature (SST) and wind for the coupled and uncoupled simulations.
A New Atmosphere-Ocean Model for Studying Air-Sea Interactions and Coupled Data Assimilation
During the last two plus decades, The Goddard Earth Observing System (GEOS) and Massachusetts Institute of Technology (MIT) modeling groups have developed, respectively, atmosphere-only and ocean-only global general circulation models. These two models (GEOS and MITgcm) have demonstrated their data assimilation capabilities with the recent releases of the Modern Era Reanalysis for Research Applications, Version 2 (MERRA-2) atmospheric reanalysis and the Estimating the Circulation and Climate of the Ocean, Version 4 (ECCO-v4) ocean (and sea ice) state estimate. Independently, the two modeling groups have also produced global atmosphere-only and ocean-only simulations with km-scale grid spacing which proved invaluable for process studies and for the development of satellite and in-situ sampling strategies.Recently, a new effort has been made to couple these two models and to leverage their data-assimilation and high resolution capabilities (i.e., eddy-permitting ocean, cloud-permitting atmosphere). The focus in the model development is put on sub-seasonal to decadal time scales. In this talk, I discuss the new coupled model and present some first coupled simulation results. This will include a high-resolution coupled GEOS-MIT simulation, whereby we have coupled a cubed-sphere-720 (~ 1/8°) configuration of the GEOS atmosphere to a lat-lon-cap-1080 (~ 1/12°) configuration of the MIT ocean. We compare near-surface diagnostics of this fully coupled ocean-atmosphere set-up to equivalent atmosphere-only and ocean-only simulations. In the comparisons we focus in particular on the differences in air-sea interactions between sea surface temperature (SST) and wind for the coupled and uncoupled simulations.
The Development of the New GEOS-MITgcm Atmosphere-Ocean Model for Coupled Data Assimilation System
During the last two plus decades, The Goddard Earth Observing System (GEOS) and Massachusetts Institute of Technology (MIT) modeling groups have developed, respectively, atmosphere-only and ocean-only global general circulation models. These two models (GEOS and MITgcm) have demonstrated their data assimilation capabilities with the recent releases of the Modern Era Reanalysis for Research Applications, Version 2 (MERRA-2) atmospheric reanalysis and the Estimating the Circulation and Climate of the Ocean, Version 4 (ECCO-v4) ocean (and sea ice) state estimate. Independently, the two modeling groups have also produced global atmosphere-only and ocean-only simulations with km-scale grid spacing which proved invaluable for process studies and for the development of satellite and in-situ sampling strategies.Recently, a new effort has been made to couple these two models and to leverage their data-assimilation and high resolution capabilities (i.e., eddy-permitting ocean, cloud-permitting atmosphere). The focus in the model development is put on sub-seasonal to decadal time scales. In this talk, I discuss the new coupled model and present some first coupled simulation results. This will include a high-resolution coupled GEOS-MIT simulation, whereby we have coupled a cubed-sphere-720 (~ 1/8°) configuration of the GEOS atmosphere to a lat-lon-cap-1080 (~ 1/12°) configuration of the MIT ocean. We compare near-surface diagnostics of this fully coupled ocean-atmosphere set-up to equivalent atmosphere-only and ocean-only simulations. In the comparisons we focus in particular on the differences in air-sea interactions between sea surface temperature (SST) and wind for the coupled and uncoupled simulations.
Development of the GEOS-MIT gcm Atmosphere-Ocean Model for Coupled Data Assimilation
During the last two plus decades, The Goddard Earth Observing System (GEOS) and Massachusetts Institute of Technology (MIT) modeling groups have developed, respectively, atmosphere-only and ocean-only global general circulation models. These two models (GEOS and MIT-GCM (General Circulation Model)) have demonstrated their data assimilation capabilities with the recent releases of the Modern Era Reanalysis for Research Applications, Version 2 (MERRA-2) atmospheric reanalysis and the Estimating the Circulation and Climate of the Ocean, Version 4 (ECCO-v4) ocean (and sea ice) state estimate. Independently, the two modeling groups have also produced global atmosphere-only and ocean-only simulations with km-scale grid spacing which proved invaluable for process studies and for the development of satellite and in-situ sampling strategies. Recently, a new effort has been made to couple these two models and to leverage their data-assimilation and high resolution capabilities (i.e., eddy-permitting ocean, cloud-permitting atmosphere). The focus in the model development is put on sub-seasonal to decadal time scales. In this talk, I discuss the new coupled model and present some first coupled simulation results. This will include a high-resolution coupled GEOS-MIT simulation, whereby we have coupled a cubed-sphere-720 (approximately 1/8 degrees) configuration of the GEOS atmosphere to a latitude-longitude-cap-1080 (approximately 1/12 degrees) configuration of the MIT ocean. We compare near-surface diagnostics of this fully coupled ocean-atmosphere set-up to equivalent atmosphere-only and ocean-only simulations. In the comparisons we focus in particular on the differences in air-sea interactions between sea surface temperature (SST) and wind for the coupled and uncoupled simulations.
Synthesis and Assimilation Systems - Essential Adjuncts to the Global Ocean Observing System
Ocean assimilation systems synthesize diverse in situ and satellite data streams into four-dimensional state estimates by combining the various observations with the model. Assimilation is particularly important for the ocean where subsurface observations, even today, are sparse and intermittent compared with the scales needed to represent ocean variability and where satellites only sense the surface. Developments in assimilation and in the observing system have advanced our understanding and prediction of ocean variations at mesoscale and climate scales. Use of these systems for assessing the observing system helps identify the strengths of each observation type. Results indicate that the ocean remains under-sampled and that further improvements in the observing system are needed. Prospects for future advances lie in improved models and better estimates of error statistics for both models and observations. Future developments will be increasingly towards consistent analyses across components of the Earth system. However, even today ocean synthesis and assimilation systems are providing products that are useful for many applications and should be considered an integral part of the global ocean observing and information system.
ResORR: A Globally Scalable and Satellite Data-Driven Algorithm for River Flow Regulation Due to Reservoir Operations
We propose a globally scalable algorithm, ResORR (Reservoir Operations driven River Regulation), to predict regulated river flow and tested it over the heavily regulated basin of the Cumberland River in the US. ResORR was found able to model regulated river flow due to upstream reservoir operations of the Cumberland River. Over a mountainous basin dominated by high rainfall, ResORR was effective in capturing extreme flooding modified by upstream hydropower dam operations. On average, ResORR improved regulated river flow simulation by more than 50% across all performance metrics when compared to a hydrologic model without a regulation module. ResORR is a timely software algorithm for understanding human regulation of surface water as satellite-estimated reservoir state is expected to improve globally with the recently launched Surface Water and Ocean Topography (SWOT) mission.
Applications of the GPCP Satellite-Gauge Merged Precipitation Data Set to the Goals of PACS and GCIP
Recently, under the direction of GEWEX/GCIP there has been an attempt to link North American monsoon rainfall to precipitation in the Great Plains for the purposes of predictability on the seasonal time scale. Rain gauge analyses have been used to show continental-scale relationships. However, the interannual variability of warm season precipitation over the United States is linked to the east Pacific climate system. The Global Precipitation Climatology Project (GPCP) satellite estimates of precipitation over oceans add a vital piece of information towards the goals of PACS and GCIP. This preliminary study will begin by focusing on the East Pacific sector. El-Nino-Southern Ossilation (ENSO) modulates the position and strength of the Inter-Tropical Convergence Zone (ITCZ), which affects the precipitation source for the Mexican monsoon. For example, the five driest summers in coastal Mexico occurred during the five strongest El Ninos (as determined by positive values of Nino 3.4) since 1979. Previous work has investigated lag-lead relationships between the North American monsoon and continental rainfall. GPCP has the potential to extend these relationships to a global scale. Finally data sets with a finer time and space scale, including Tropical Rainfall Measurement Mission (TRMM) products, will be used to examine wave dynamics. Kelvin waves, in particular, may be trigger mechanisms for the onset of the North American monsoon.
Adaptive Error Estimation in Linearized Ocean General Circulation Models
Data assimilation methods are routinely used in oceanography. The statistics of the model and measurement errors need to be specified a priori. This study addresses the problem of estimating model and measurement error statistics from observations. We start by testing innovation based methods of adaptive error estimation with low-dimensional models in the North Pacific (5-60 deg N, 132-252 deg E) to TOPEX/POSEIDON (TIP) sea level anomaly data, acoustic tomography data from the ATOC project, and the MIT General Circulation Model (GCM). A reduced state linear model that describes large scale internal (baroclinic) error dynamics is used. The methods are shown to be sensitive to the initial guess for the error statistics and the type of observations. A new off-line approach is developed, the covariance matching approach (CMA), where covariance matrices of model-data residuals are "matched" to their theoretical expectations using familiar least squares methods. This method uses observations directly instead of the innovations sequence and is shown to be related to the MT method and the method of Fu et al. (1993). Twin experiments using the same linearized MIT GCM suggest that altimetric data are ill-suited to the estimation of internal GCM errors, but that such estimates can in theory be obtained using acoustic data. The CMA is then applied to T/P sea level anomaly data and a linearization of a global GFDL GCM which uses two vertical modes. We show that the CMA method can be used with a global model and a global data set, and that the estimates of the error statistics are robust. We show that the fraction of the GCM-T/P residual variance explained by the model error is larger than that derived in Fukumori et al.(1999) with the method of Fu et al.(1993). Most of the model error is explained by the barotropic mode. However, we find that impact of the change in the error statistics on the data assimilation estimates is very small. This is explained by the large representation error, i.e. the dominance of the mesoscale eddies in the T/P signal, which are not part of the 21 by 1" GCM. Therefore, the impact of the observations on the assimilation is very small even after the adjustment of the error statistics. This work demonstrates that simult&neous estimation of the model and measurement error statistics for data assimilation with global ocean data sets and linearized GCMs is possible. However, the error covariance estimation problem is in general highly underdetermined, much more so than the state estimation problem. In other words there exist a very large number of statistical models that can be made consistent with the available data. Therefore, methods for obtaining quantitative error estimates, powerful though they may be, cannot replace physical insight. Used in the right context, as a tool for guiding the choice of a small number of model error parameters, covariance matching can be a useful addition to the repertory of tools available to oceanographers.
Satellite Data Assimilation
Satellite altimetry provides the only routine observation of a dynamic variable of the global ocean. For instance, measurements from the TOPEX/POSEIDON altimeter have provided dramatic basin-wide images of the space-time evolution of sea level associated with the 1997-98 El Nino event. The nature of large-scale ocean circulation is being studied that underlies such sea level changes measured by satellite altimetry. To first approximation, properties of large-scale (order 1000 km and larger) sea level variability is latitudinally dependent. An analysis using a general circulation model shows that sea level changes are largely due to wind-driven baroclinic (depth dependent) circulation in the tropics (within 20 degrees of the equator), but are primarily due to the expansion and contraction of near surface water in temperate latitudes (between 20 deg. and 40 deg.) forced by seasonal heating and cooling. In contrast, wind-driven barotropic (depth independent) circulation dominates sea level variability in high latitudes (40 deg. and higher) characterized by periods that are as short as a few days. The presence of such inhomogeneity and the significance of high-frequency, large-scale sea level changes had not been fully recognized prior to this study, and are summarized in Fukumori et al. Numerical models provide theoretical relationships among properties that can be inverted using observations so as to estimate the entire state of the ocean, including properties that are otherwise difficult to measure remotely. The process is data assimilation. An approximate Kalman filter and smoother have been devised to assimilate three years of TOPEX/POSEIDON sea level data into a global ocean general circulation model. The figure below demonstrates the skill of the assimilation, and shows the altimeter assimilated estimate being in closer agreement than the simulation is with independent in situ measurements of subsur ace temperature and velocity, consistent with formal uncertainty estimates. Results from this study have been summarized and submitted for publication. The study demonstrates the feasibility of global ocean data assimilation and illustrates applications in monitoring and understanding of processes controlling the evolution of the ocean. Additional information is contained in the original.
Direct Radiative Effect of Aerosols Based on PARASOL and OMI Satellite Observations
Accurate portrayal of the aerosol characteristics is crucial to determine aerosol contribution to the Earth's radiation budget. We employ novel satellite retrievals to make a new measurement-based estimate of the shortwave direct radiative effect of aerosols (DREA), both over land and ocean. Global satellite measurements of aerosol optical depth, single-scattering albedo (SSA), and phase function from PARASOL (Polarization and Anisotropy of Reflectances for Atmospheric Sciences coupled with Observations from a Lidar) are used in synergy with OMI (Ozone Monitoring Instrument) SSA. Aerosol information is combined with land-surface bidirectional reflectance distribution function and cloud characteristics from MODIS (Moderate Resolution Imaging Spectroradiometer) satellite products. Eventual gaps in observations are filled with the state-of-the-art global aerosol model ECHAM5-HAM2. It is found that our estimate of DREA is largely insensitive to model choice. Radiative transfer calculations show that DREA at top-of-atmosphere is −4.6 +/- 1.5 W/sq m for cloud-free and −2.1 +/- 0.7 W/sq m for all-sky conditions, during year 2006. These fluxes are consistent with, albeit generally less negative over ocean than, former assessments. Unlike previous studies, our estimate is constrained by retrievals of global coverage SSA, which may justify different DREA values. Remarkable consistency is found in comparison with DREA based on CERES (Clouds and the Earth's Radiant Energy System) and MODIS observations.
Predictability of Ocean Heat Content From Electrical Conductance
Ocean heat content (OHC) is a key climate variable that needs to be monitored to know how Earth's energy imbalance is changing, yet observing OHC remains a challenge. The present study examines whether a depth integral of the ocean's electrical conductivity (“conductance”), which may be inferred from both in situ methods and satellite magnetometers over the global ocean, could help monitor OHC. The ocean's electrical conductivity locally depends on temperature, salinity, and pressure, but it is not as well known how the conductance depends on OHC and ocean salt content. By examining the output of an ocean state estimate shown to agree well with observations that have not been assimilated, this study evaluates the fundamental limitations of using perfectly known ocean conductance to predict OHC, rather than the challenges associated with accounting for observational error. It is found that the ocean's conductance and OHC fields are nonlinearly related but nevertheless highly correlated. A statistical framework tends to predict OHC more accurately than ocean salt content from ocean conductance in regions where conductivity is more sensitive to salinity than temperature. The annually (bidecadally) averaged OHC can be predicted from a combination of conductance and depth‐averaged conductivity ocean fields to within nearly 0.1% (1%) error globally and even more accurately in many poorly observed (e.g., ice‐covered) regions. Practical application of this statistical framework to monitor OHC requires examination of the effect of uncertainties in the observed bathymetry and ocean conductance, which vary with application.
Improvements in the Uncertainty Model in the Goddard Institute for Space Studies Surface Temperature (GISTEMP) Analysis
We outline a new and improved uncertainty analysis for the Goddard Institute of Space Studies (GISS) Surface Temperature product version 4 (GISTEMP v4). Historical spatial variations in surface temperature anomalies are derived from historical weather station data and ocean data from ships, buoys and other sensors. Uncertainties arise from measurement uncertainty, changes in spatial coverage of the station record, and systematic biases due to technology shifts and land cover changes. Previously published uncertainty estimates for GISTEMP included only the effect of incomplete station coverage. Here, we update this term using currently available spatial distributions of source data, state‐of‐the‐art reanalyses and incorporate independently derived estimates for ocean data processing, station homogenization and other structural biases. The resulting 95% uncertainties are near 0.05° C in the global annual mean for the last 50 years, and increase going back further in time reaching 0.15° C in 1880. In addition, we quantify the benefits and inherent uncertainty due to the GISTEMP interpolation and averaging method. We use the total uncertainties to estimate the probability for each record year in the GISTEMP to actually be the true record year (to that date), and conclude with 86% likelihood that 2016 was indeed the hottest year of the instrumental period (so far).
Tropical Rainfall Measuring Mission
Tropical rainfall affects the lives and economics of a majority of the Earth's population. Tropical rain systems, such as hurricanes, typhoons, and monsoons, are crucial to sustaining the livelihoods of those living in the tropics. Excess rainfall can cause floods and great property and crop damage, whereas too little rainfall can cause drought and crop failure. The latent heat release during the process of precipitation is a major source of energy that drives the atmospheric circulation. This latent heat can intensify weather systems, affecting weather thousands of kilometers away, thus making tropical rainfall an important indicator of atmospheric circulation and short-term climate change. Tropical forests and the underlying soils are major sources of many of the atmosphere's trace constituents. Together, the forests and the atmosphere act as a water-energy regulating system. Most of the rainfall is returned to the atmosphere through evaporation and transpiration, and the atmospheric trace constituents take part in the recycling process. Hence, the hydrological cycle provides a direct link between tropical rainfall and the global cycles of carbon, nitrogen, and sulfur, all important trace materials for the Earth's system. Because rainfall is such an important component in the interactions between the ocean, atmosphere, land, and the biosphere, accurate measurements of rainfall are crucial to understanding the workings of the Earth-atmosphere system. The large spatial and temporal variability of rainfall systems, however, poses a major challenge to estimating global rainfall. So far, there has been a lack of rain gauge networks, especially over the oceans, which points to satellite measurement as the only means by which global observation of rainfall can be made. The Tropical Rainfall Measuring Mission (TRMM), jointly sponsored by the National Aeronautics and Space Administration (NASA) of the United States and the National Space Development Agency (NASDA) of Japan, provides visible, infrared, and microwave observations of tropical and subtropical rain systems.The satellite observations are complemented by ground radar and rain gauge measurements to validate satellite rain estimation techniques. Goddard Space Flight Center's involvement includes the observatory, four instruments, integration and testing of the observatory, data processing and distribution, and satellite operations. TRMM has a design lifetime of three years. Data generated from TRMM and archived at the GDAAC are useful not only for hydrologists, atmospheric scientists, and climatologists, but also for the health community studying infectious diseases, the ocean research community, and the agricultural community.
Characterization of the land surface freeze/thaw state with SMAP-Reflectometry (SMAP-R)
The use of the Soil Moisture Active Passive (SMAP) radar in received mode has allowed for a new dataset of Global Navigation Satellite System – Reflectometry (GNSS-R) measurements. GNSS-R techniques have been used for more than two decades and have proven successful perform altimetry studies and to retrieve ocean wind speed and soil moisture, monitor wetland dynamics, estimate snow depth, detect sea ice and determine sea ice types. In this paper, the SMAP-Reflectometry measurements are used to classify freeze/thaw (F/T) state over Alaska, studying the differences in the SNR signal at both V and H polarizations.
The Global Observing System in the Assimilation Context
Weather and climate analyses and predictions all rely on the global observing system. However, the observing system, whether atmosphere, ocean, or land surface, yields a diverse set of incomplete observations of the different components of Earth s environment. Data assimilation systems are essential to synthesize the wide diversity of in situ and remotely sensed observations into four-dimensional state estimates by combining the various observations with model-based estimates. Assimilation, or associated tools and products, are also useful in providing guidance for the evolution of the observing system of the future. This paper provides a brief overview of the global observing system and information gleaned through assimilation tools, and presents some evaluations of observing system gaps and issues.
Measurement Techniques for the High-Contrast and in-Field Stray-Light Performance of Oci
Scheduled for launch in January 2024, the PACE mission represents NASA’s next investment in ocean biology, clouds, and aerosol data records [1]. A key feature of PACE is the inclusion of an advanced satellite radiometer known as the Ocean Color Instrument (OCI), a global mapping radiometer that combines multispectral and hyperspectral remote sensing. A critical requirement for OCI is the high-contrast or spatial cross-talk specification (also referred to as in-field stray-light response). The requirement states that for global top-of-atmosphere radiances based on measured MODIS radiances, the global average residual contamination shall be less than 0.15% for any multispectral band. Accurate resolution of high contrast in TOA radiance images is important to estimate stray light contamination due to clouds, for studying small scale features like ocean fronts and for working in coastal and estuarine areas where the scales are 1km. This occurs in all wavelengths in the spatial direction. Knowledge of high contrast resolution makes up part of the artifact budget. Accurate measurement of the high-contrast performance of OCI requires laboratory Ground Support Equipment (GSE) that projects a scene of sufficient quality that the unwanted stray light of the GSE itself is not confused with the stray light response of the telescope. This paper concerns the development, analyses and test of the GSE to ensure the quality of the projected image is sufficient to verify the OCI requirements. Optical models were developed for both the instrument as well as the GSE and laboratory environment. Simulation of various non-ideal parameters were critical to accurately predict performance. Measurements using COTS cameras and lenses were also made of the projected GSE image to reasonably verify the optical model predictions. Measured and modelled results from OCI are discussed.
Measurement Techniques for the High-Contrast and in-Field Stray-Light Performance of OCI
Scheduled for launch in January 2024, the PACE mission represents NASA’s next investment in ocean biology, clouds, and aerosol data records [1]. A key feature of PACE is the inclusion of an advanced satellite radiometer known as the Ocean Color Instrument (OCI), a global mapping radiometer that combines multispectral and hyperspectral remote sensing. A critical requirement for OCI is the high-contrast or spatial cross-talk specification (also referred to as in-field stray-light response). The requirement states that for global top-of-atmosphere radiances based on measured MODIS radiances, the global average residual contamination shall be less than 0.15% for any multispectral band. Accurate resolution of high contrast in TOA radiance images is important to estimate stray light contamination due to clouds, for studying small scale features like ocean fronts and for working in coastal and estuarine areas where the scales are 1km. This occurs in all wavelengths in the spatial direction. Knowledge of high contrast resolution makes up part of the artifact budget. Accurate measurement of the high-contrast performance of OCI requires laboratory Ground Support Equipment (GSE) that projects a scene of sufficient quality that the unwanted stray light of the GSE itself is not confused with the stray light response of the telescope. This paper concerns the development, analyses and test of the GSE to ensure the quality of the projected image is sufficient to verify the OCI requirements. Optical models were developed for both the instrument as well as the GSE and laboratory environment. Simulation of various non-ideal parameters were critical to accurately predict performance. Measurements using COTS cameras and lenses were also made of the projected GSE image to reasonably verify the optical model predictions. Measured and modelled results from OCI are discussed.