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  • AGU (American Geophysical Union)  (3)
  • Wiley  (2)
  • COPERNICUS GESELLSCHAFT MBH  (1)
  • Cambridge Univ. Press  (1)
  • Milton :Taylor & Francis Group,
  • 1
    Online Resource
    Online Resource
    Milton :Taylor & Francis Group,
    Keywords: Satellite geodesy-Technique. ; Electronic books.
    Description / Table of Contents: Satellite remote sensing, in particular by radar altimetry, is a crucial technique for observations of the ocean surface and of many aspects of land surfaces, and of paramount importance for climate and environmental studies. It provides a state-of-the-art overview of the satellite altimetry techniques and related missions.
    Type of Medium: Online Resource
    Pages: 1 online resource (645 pages)
    Edition: 1st ed.
    ISBN: 9781498743464
    Series Statement: Earth Observation of Global Changes Series
    DDC: 551.48
    Language: English
    Note: Cover -- Title Page -- Copyright Page -- Contents -- Preface -- Editors -- Contributors -- Chapter 1: Satellite Radar AltimetryPrinciple, Accuracy, and Precision -- 1.1 Introduction -- 1.1.1 Satellite Altimetry Measurement Principle -- 1.1.2 Satellite Radar Altimetry Historical Perspective -- 1.1.2.1 Satellite Altimetry Missions -- 1.1.2.2 Geographical Perspective and International Cooperation -- 1.1.2.3 Altimetry Products: History of Continuous Progress -- 1.1.3 Altimetry System Requirements -- 1.2 Radar Instrument -- 1.2.1 Radar Altimeter Instrument Principles -- 1.2.2 Observation Geometry -- 1.2.3 Radar Operation -- 1.2.4 Transmitted Waveform -- 1.2.5 Instrument Architecture -- 1.2.6 Instrument Example: Poseidon-3 of Jason-2 Mission -- 1.2.6.1 Poseidon-3 Architecture -- 1.2.6.2 Poseidon-3 Main Characteristics -- 1.2.7 Key Instrument Performance -- 1.2.8 Echo Formation -- 1.3 Echo characterization and processing -- 1.3.1 Speckle Noise -- 1.3.2 Analytical and Numerical Models -- 1.3.3 Estimation Strategies -- 1.3.4 New Altimeters -- 1.3.5 Non-Ocean Surfaces -- 1.4 Precise Orbit Determination -- 1.4.1 Orbit Determination Technique -- 1.4.1.1 Performance Requirements -- 1.4.1.2 Radial Error Properties -- 1.4.2 Orbit Determination Measurement Systems -- 1.4.3 Satellite Trajectory Modeling and Parameterization -- 1.4.4 Major Modeling Evolution since the Beginning of the 1990s -- 1.4.5 Long-Term Orbit Error and Stability Budget -- 1.4.6 Foreseen Modeling Improvement -- 1.5 Geophysical Corrections -- 1.5.1 Sea State Bias Correction -- 1.5.1.1 Origins of the Sea State Effects and Correction -- 1.5.1.2 Theoretical Solutions -- 1.5.1.3 Empirical Solutions. , 1.5.2 Atmospheric Propagation Effect Corrections -- 1.5.2.1 Ionospheric Correction -- 1.5.2.2 Dry Tropospheric Correction -- 1.5.2.3 The Wet Tropospheric Correction -- 1.6 Altimetry Product Auxiliary Information: Reference Surfaces, Tides, and High-Frequency Signal -- 1.6.1 Reference Surfaces -- 1.6.2 Tides, High-Frequency Signals -- 1.6.2.1 The Tide Correction -- 1.6.2.2 The High-Frequency Correction -- 1.6.2.3 S1 and S2 Atmospheric and Ocean Signals -- 1.7 Altimetry Time and Space Sampling: Orbit Selection and Virtual Constellation Approach -- 1.7.1 Sampling Properties of a Single Altimeter Orbit -- 1.7.2 Orbit Sub-Cycles and Sampling Properties -- 1.7.3 Altimeter Virtual Constellation and Phasing -- 1.8 Altimetry error budget -- 1.8.1 Error Budget for Mesoscale Oceanography -- 1.8.2 Error Budget for Mean Sea Level Trend Monitoring -- 1.8.3 Error Budget for Sub-Mesoscale -- References -- Chapter 2: Wide-Swath AltimetryA Review -- 2.1 Introduction -- 2.2 Ocean and Hydrology Sampling Requirements -- 2.3 Approaches to Wide-Swath Altimetry -- 2.3.1 From Nadir Altimetry to Wide-Swath Altimetry: Three-Dimensional Geolocation -- 2.3.2 Wide-Swath Altimetry Using Waveform Tracking -- 2.3.3 Wide-Swath Altimetry Using Radar Interferometry -- 2.4 The Interferometric Error Budget -- 2.4.1 Roll Errors -- 2.4.2 Phase Errors -- 2.4.3 Range Errors -- 2.4.4 Baseline Errors -- 2.4.5 Finite Azimuth Footprint Biases -- 2.4.6 Radial Velocity Errors -- 2.4.7 Calibration Methods -- 2.5 Wide-Swath Altimetry Phenomenology -- 2.5.1 Water Brightness -- 2.5.2 Wave Effects -- 2.5.2.1 The "Surfboard Effect" -- 2.5.2.2 Temporal Correlation Effects -- 2.5.2.3 Wave Bunching -- 2.5.2.4 The EM Bias. , 2.5.3 Layover and Vegetation Effects -- 2.6 Wide-Swath Altimetry Mission Design -- 2.7 Summary and Prospects -- References -- Acknowledgments -- Chapter 3: In Situ Observations Needed to Complement, Validate, and Interpret Satellite Altimetry -- 3.1 Introduction -- 3.2 Sea Surface Heights Obtained from Tide Gauge/GNSS Networks -- 3.2.1 Sea Level Measurements before the Altimeter Era -- 3.2.2 Tide Gauge and Altimeter Data Complementarity -- 3.2.3 Tide Gauges Used for Altimeter Calibration -- 3.2.4 Tide Gauge and Altimeter Data in Combination in Studies of Long-Term Sea Level Change -- 3.2.5 GNSS Equipment at Tide Gauges -- 3.2.6 New Developments in Tide Gauges and Data Availability -- 3.2.7 Tide Gauges and Altimetry in the Future -- 3.3 Upper-Ocean (0 to 2000 decibars) Steric Variability: The XBT and Argo Networks -- 3.3.1 The Relationship of SSH Variability with Subsurface T and S-Steric Height -- 3.3.2 A Brief History of Systematic Ocean Sampling by the XBT and Argo Networks -- 3.3.3 Ocean Heat Content and Steric Sea Level -- 3.3.4 The Global Pattern of SSH and Upper-Ocean Steric Height -- 3.3.5 Geostrophic Ocean Circulation -- 3.3.6 Horizontal Scales of Variability in the Ocean: The Challenge of Resolution -- 3.4 Deep-Ocean (greater than 2000 m) Steric Variability: Repeat Hydrography and Deep Argo -- 3.4.1 Ventilating the Deep Ocean: Deep Water Production and the Global MOC -- 3.4.2 Monitoring Deep Steric Variability through Repeat Hydrography -- 3.4.3 The Deep Ocean Contribution to Steric Sea Level -- 3.4.4 Future of Deep Observing: Deep Argo -- 3.6 Dynamic Topography and Surface Velocity -- 3.6.1 Eulerian Velocity Measurements -- 3.6.2 Lagrangian Velocity Measurements -- 3.6.3 Geostrophic Currents and Mean Dynamic Topography. , 3.6.4 Ageostrophic Motions -- 3.7 The Technology Revolution and the Future of Ocean Observations -- References -- Acknowledgments -- Chapter 4: Auxiliary Space-Based Systems for Interpreting Satellite Altimetry -- 4.1 Introduction -- 4.2 Measurements: Mean Geoid and Sea Surface -- 4.2.1 Parameterizing Gravity and the Geoid -- 4.2.2 GRACE and GOCE -- 4.2.3 Surface Gravity Data and Combination Geoids -- 4.2.4 Mean Sea Surface Models -- 4.3 Measurements: Time-Variable Gravity -- 4.4 Applications: Dynamic Ocean Topography -- 4.4.1 Importance of Consistency between Geoid and MSS -- 4.4.2 Improvements in MDT with GRACE and GOCE Geoids -- 4.4.3 Toward a Higher Spatial Resolution MDT -- 4.5 Applications: Global and Regional Ocean Mass Variations -- 4.6 Conclusions and Future Prospects -- References -- Chapter 5: A 25-Year Satellite Altimetry-Based Global Mean Sea Level Record -- 5.1 Introduction -- 5.2 The Altimeter Mean Sea Level Record -- 5.2.1 Computing Global and Regional Mean Sea Level Time Series -- 5.2.2 Altimeter Missions -- 5.2.3 Altimeter Corrections -- 5.2.4 Intermission Biases -- 5.2.5 Averaging Process -- 5.2.6 Validation of the GMSL Record with Tide Gauge Measurements -- 5.2.7 Mean Sea Level Variation and Uncertainties -- 5.2.7.1 Global Scale Uncertainty -- 5.2.7.2 Regional Scales -- 5.3 Interpreting the Altimeter GMSL Record -- 5.3.1 Steric Sea Level Contribution -- 5.3.2 The Cryosphere Contributions to GMSL -- 5.3.3 The Land Water Storage Contributions to GMSL -- 5.3.3.1 Interannual Variations -- 5.3.3.2 Long-Term Variations -- 5.4 Closing the Sea Level Budget and Uncertainties -- 5.4.1 Glacial Isostatic Adjustment -- 5.4.2 Ocean Mass/Barystatic Sea Level from GRACE. , 5.4.3 Closure and Missing Components -- 5.5 How Altimetry Informs Us About the Future -- References -- Chapter 6: Monitoring and Interpreting Mid-Latitude Oceans by Satellite Altimetry -- 6.1 Introduction: Role of Mid-Latitude Oceans -- 6.2 Western Boundary Currents -- 6.3 Meridional Circulation and Interbasin Exchanges -- 6.4 Climate Change -- 6.5 Summary and Future Research -- References -- Acknowledgments -- Chapter 7: Monitoring and Interpreting the Tropical Oceans by Satellite Altimetry -- 7.1 Introduction -- 7.2 Tropical Atlantic Ocean -- 7.2.1 Intraseasonal and Eddy Activities -- 7.2.1.1 Eddy Structures -- 7.2.1.2 Tropical Instability Waves -- 7.2.2 The Seasonal Cycle -- 7.2.3 Equatorial Waves -- 7.2.4 Interannual Variability -- 7.3 Tropical Indo-Pacific Ocean -- 7.3.1 Tropical Pacific -- 7.3.1.1 Intraseasonal Variability -- 7.3.1.2 Seasonal Variability -- 7.3.1.3 Interannual and Decadal Variability -- 7.3.2 Tropical Indian Ocean -- 7.3.2.1 Intraseasonal Variability -- 7.3.2.2 Seasonal Cycle -- 7.3.2.3 Interannual Variability -- 7.3.2.4 Decadal and Multidecadal Changes -- 7.3.3 Indo-Pacific Linkage and Indonesian Throughflow -- 7.4 Summary -- References -- Acknowledgments -- Chapter 8: The High Latitude Seas and Arctic Ocean -- 8.1 Introduction -- 8.1.1 Satellite Altimetry in the High Latitude and Arctic Ocean -- 8.2 Mapping the Sea Ice Thickness in the Arctic Ocean -- 8.3 Sea Level Change -- 8.3.1 The Seasonal Cycle -- 8.3.2 Secular and Long-Term Sea Level Changes -- 8.3.3 Arctic Sea Level Budget -- 8.3.4 The Polar Gap and Accuracy Estimates -- 8.4 Mean Dynamic Topography -- 8.5 Ocean Circulation and Volume Transport -- 8.5.1 Surface Circulation -- 8.5.2 Volume Transport. , 8.6 Summary and Outlook.
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  • 2
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    AGU (American Geophysical Union) | Wiley
    In:  Journal of Geophysical Research-Oceans, 120 (1). pp. 94-112.
    Publication Date: 2015-09-28
    Description: Spaceborne sea surface salinity (SSS) measurements provided by the European Space Agency's (ESA) “Soil Moisture and Ocean Salinity” (SMOS) and the National Aeronautical Space Agency's (NASA) “Aquarius/SAC-D” missions, covering the period from May 2012 to April 2013, are compared against in situ salinity measurements obtained in the northern North Atlantic between 20°N and 80°N. In cold water, SMOS SSS fields show a temperature-dependent negative SSS bias of up to −2 g/kg for temperatures 〈5°C. Removing this bias significantly reduces the differences to independent ship-based thermosalinograph data but potentially corrects simultaneously also other effects not related to temperature, such as land contamination or radio frequency interference (RFI). The resulting time-mean bias, averaged over the study area, amounts to 0.1 g/kg. A respective correction applied previously by the Jet Propulsion Laboratory to the Aquarius data is shown here to have successfully removed an SST-related bias in our study area. For both missions, resulting spatial structures of SSS variability agree very well with those available from an eddy-resolving numerical simulation and from Argo data and, additionally they also show substantial salinity changes on monthly and seasonal time scales. Some fraction of the root-mean-square difference between in situ, and SMOS and Aquarius data (approximately 0.9 g/kg) can be attributed to short time scale ocean processes, notably at the Greenland shelf, and could represent associated sampling errors there.
    Type: Article , PeerReviewed
    Format: text
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  • 3
    Publication Date: 2022-01-31
    Description: Five initialization and ensemble generation methods are investigated with respect to their impact on the prediction skill of the German decadal prediction system "Mittelfristige Klimaprognose" (MiKlip). Among the tested methods, three tackle aspects of model‐consistent initialization using the ensemble Kalman filter (EnKF), the filtered anomaly initialization (FAI) and the initialization method by partially coupled spin‐up (MODINI). The remaining two methods alter the ensemble generation: the ensemble dispersion filter (EDF) corrects each ensemble member with the ensemble mean during model integration. And the bred vectors (BV) perturb the climate state using the fastest growing modes. The new methods are compared against the latest MiKlip system in the low‐resolution configuration (Preop‐LR), which uses lagging the climate state by a few days for ensemble generation and nudging toward ocean and atmosphere reanalyses for initialization. Results show that the tested methods provide an added value for the prediction skill as compared to Preop‐LR in that they improve prediction skill over the eastern and central Pacific and different regions in the North Atlantic Ocean. In this respect, the EnKF and FAI show the most distinct improvements over Preop‐LR for surface temperatures and upper ocean heat content, followed by the BV, the EDF and MODINI. However, no single method exists that is superior to the others with respect to all metrics considered. In particular, all methods affect the Atlantic Meridional Overturning Circulation in different ways, both with respect to the basin‐wide long‐term mean and variability, and with respect to the temporal evolution at the 26° N latitude.
    Type: Article , PeerReviewed
    Format: text
    Format: text
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  • 4
    Publication Date: 2019-09-23
    Description: Sources of near-surface oceanic variability in the central North Atlantic are identified from a combined analysis of climatology, surface drifter, and Geosat altimeter data as well as eddy-resolving math formula and math formula Community Modeling Effort North Atlantic model results. Both observational and numerical methods give a consistent picture of the concentration of mesoscale variability along the mean zonal flow bands. Three areas of high eddy energy can be found in all observational data sets: the North Equatorial Current, the North Atlantic Current, and the Azores Current. With increasing horizontal resolution the numerical models give a more realistic representation of the variability in the first two regimes, while no improvement is found with respect to the Azores Current Frontal Zone. Examination of the upper ocean hydrographic structure indicates baroclinic instability to be the main mechanism of eddy generation and suggests that the model deficiencies in the Azores Current area are related to deficiencies in the mean hydrographic fields. A linear instability analysis of the numerical model output reveals that instability based on the velocity shear between the mixed layer and the interior is also important for the generation of the mid-ocean variability, indicating a potential role of the mixed layer representation for the model. The math formula model successfully simulates the northward decrease of eddy length scales observed in the altimeter data, which follow a linear relationship with the first baroclinic Rossby radius. An analysis of the eddy-mean flow interaction terms and the energy budget indicates a release of mean potential energy by downgradient fluxes of heat in the main frontal zones. At the same time the North Atlantic Current is found to be supported by convergent eddy fluxes of zonal momentum.
    Type: Article , PeerReviewed
    Format: text
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  • 5
  • 6
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    COPERNICUS GESELLSCHAFT MBH
    In:  EPIC3Cryosphere, COPERNICUS GESELLSCHAFT MBH, 11, pp. 2265-2281, ISSN: 1994-0416
    Publication Date: 2017-11-06
    Description: Satellite sea ice concentrations (SICs), together with several ocean parameters, are assimilated into a regional Arctic coupled ocean–sea ice model covering the period of 2000–2008 using the adjoint method. There is substantial improvement in the representation of the SIC spatial distribution, in particular with respect to the position of the ice edge and to the concentrations in the central parts of the Arctic Ocean during summer months. Seasonal cycles of total Arctic sea ice area show an overall improvement. During summer months, values of sea ice extent (SIE) integrated over the model domain become underestimated compared to observations, but absolute differences of mean SIE to the data are reduced in nearly all months and years. Along with the SICs, the sea ice thickness fields also become closer to observations, providing added value by the assimilation. Very sparse ocean data in the Arctic, corresponding to a very small contribution to the cost function, prevent sizable improvements of assimilated ocean variables, with the exception of the sea surface temperature.
    Repository Name: EPIC Alfred Wegener Institut
    Type: Article , isiRev
    Format: application/pdf
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