Assimilation of TOPEX/Poseidon Sea Level Data Into a Global Ocean Circulation Model
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TOPEX/Poseidon↗
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Advances in Estimation of the Earth Orientation Parameters From Observation
Historically, a two-step procedure for estimation of Universal Time and polar motion from observations was adopted: night averages from individual observatories formed a raw non-equidistant time series that was edited for outliers, smoothed, and formed the final data product. This approach was by inertia used for processing modern space geodesy observations. I will present an alternative one-step approach and its application for processing a dataset of VLBI observations since 1990.0 through present. The essence of this approach is to represent the Earth orientation parameters in a form of an expansion into basic functions and estimate of the expansion coefficients directly from observations. A further extension of this approach is to assimilate not only space geodesy data, but atmospheric and ocean angular momentum from assimilation numerical weather models and their forecasts. That makes a smooth bridge between Earth rotation estimation and prediction. I will talk about my experience of running data analysis in such a way on a semi-operational basis, present results, and discuss advantages and disadvantages of this approach.
Leonid Petrov↗
Marine research using data from TOPEX/Poseidon
The Proudman Oceanographic Laboratory (POL), the Institute of Oceanographic Sciences Deacon Laboratory (IOSDL), and the British National Space Centre (BNSC) plan to use radar altimeter data from the TOPEX/POSEIDON mission in five main areas of research. These are: (1) investigation of variations in Southern Ocean sea levels and circulation; (2) related numerical modeling and data assimilation; (3) global and regional ocean tides; (4) North Sea and northeast Atlantic studies; and (5) winds and waves. Each one of these topics is described briefly below while more information can be extracted from the United Kingdom response to the TOPEX/POSEIDON Announcement of Opportunity.
Woodworth, Philip L.↗
What is data assimilation really solving, and how is the calculation actually done?
Data assimilation is a procedure in which observations are combined with models. The observations correct model errors on the one hand, and the models extrapolate the data information in space, time, and among different properties on the other. The result of assimilation is generally a more complete and more accurate description of the state of the modeled system than those obtained by either observations or model simulations alone. However, data assimilation is not a panacea for correcting every model error or for compensating all deficiencies of observations.
smoother↗
NASA GMAO Integrated Earth System Analysis
An Integrated Earth System Analysis (IESA) is the only possible way to assimilate all the available observations of separate components of the earth system (atmosphere, ocean, cryosphere, land, constituents, etc) into a single consistent Coupled Data Assimilation System (CDAS). The NASA Global Modeling and Assimilation Office (GMAO) is developing an IESA for Reanalysis in an incremental fashion by coupling different components- one at a time, for e.g., the MERRA-2 reanalysis coupled aerosol and meteorological data assimilation systems. Following a similar approach the ocean (including sea-ice) is being coupled to the atmosphere, starting with the air-sea interface. This presentation briefly outlines GMAO's reanalysis road map and summarizes our recent work in that direction.
Akella, Santha↗
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↗
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↗
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↗
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↗
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↗
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↗
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 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↗
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↗
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↗
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↗
Consequences of different air-sea feedbacks on ocean using MITgcm and MERRA-2 forcing: Implications for coupled data assimilation systems
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Ehud Strobach↗
Integration of AI/ML with Data Assimilation for Earth System Prediction
Focal Area(s): Development of dynamically consistent AI/ML methods that can integrate with the data assimilation cycle to improve the efficiency and effectiveness of state estimation and prediction at short to medium range forecast horizons. Science Challenge: This white paper addresses a first step toward complete integration of AI/ML and DA. Here, we address model emulation and its application to simple coupled atmosphere-ocean dynamics for use in data assimilation applications.
54 ENVIRONMENTAL SCIENCES↗