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  • Atlantic Coast (South Africa) -- Environmental conditions -- Forecasting.  (1)
  • Data assimilation  (1)
  • 1
    Online Resource
    Online Resource
    San Diego :Elsevier Science & Technology,
    Keywords: Atlantic Coast (South Africa) -- Environmental conditions -- Forecasting. ; Electronic books.
    Type of Medium: Online Resource
    Pages: 1 online resource (438 pages)
    Edition: 1st ed.
    ISBN: 9780080476049
    Series Statement: Issn Series ; v.Volume 14
    Language: English
    Note: Cover -- Title Page -- Copyright Page -- Table of Contents -- Series Editor's introduction -- Ministers' page: Towards forecasting a changing ocean: An African Perspective -- Sponsorship page -- Foreword -- List of contributors -- PART I: BY WAY OF INTRODUCTION -- Chapter 1. A plan comes together -- UNIQUE ENVIRONMENT -- TEN YEARS OF CLOSE REGIONAL COLLABORATION -- OBSERVING AND PREDICTING IN THE BCLME WITHIN THE INTERNATIONAL CONTEXT -- FAST-TRACKING THE DEVELOPMENT OF A REGIONAL OBSERVING SYSTEM AND PREDICTIVE CAPABILITY -- ABOUT THIS BOOK -- ACKNOWLEDGEMENT -- REFERENCES -- Chapter 2. Forecasting within the context of Large Marine Ecosystem Programs -- LME DEFINITION: DELINEATION AND MAJOR STRESSORS -- LME INDICATOR MODULES -- APPLICATION OF INDICATOR MODULES TO LME MANAGEMENT SUPPORTED BY THE GLOBAL ENVIRONMENT FACILITY (GEF) -- SCIENCE-BASED ASSESSMENTS OF LME BIOMASS YIELDS -- RECOVERING FISHERIES BIOMASS -- LME MODELING AND DRIVING FORCES OF CHANGE -- REFERENCES -- Chapter 3. The Global Ocean Observing System for Africa (GOOS Africa): Monitoring and Predicting in Large Marine Ecosystems -- INTRODUCTION -- THE LARGE MARINE ECOSYSTEM (LME) CONCEPT AND STRATEGY -- THE RISE OF THE GLOBAL OCEAN OBSERVING SYSTEM IN AFRICA (GOOS-AFRICA) -- GOOS-AFRICA STRATEGIC PARTNERSHIPS -- THE AFRICAN LMES ARE CORE AND VITAL STRATEGIC PARTNERS FOR GOOS-AFRICA -- GOOS-AFRICA CONTRIBUTION TO INTEGRATED MONITORING AND PREDICTING OF LARGE MARINE ECOSYSTEMS -- CONCLUDING REMARKS: SUCCESS STORIES -- GOOS-AFRICA FORWARD LOOK -- ACKNOWLEDGEMENTS -- PART II: SETTING THE SCENE -- Chapter 4. Large scale physical variability of the Benguela Current Large Marine Ecosystem (BCLME) -- INTRODUCTION -- MAJOR PHYSICAL PROCESSES IN THE BCLME -- ATMOSPHERIC FORCING OF THE BCLME -- LARGE SCALE MODES OF VARIABILITY -- WATER MASSES AND VERTICAL STRUCTURE OF THE BCLME. , NUMERICAL OCEAN MODELLING IN THE BCLME -- SCHEMATIC CIRCULATION DEDUCED FROM A NUMERICAL MODEL -- NUMERICAL MODELLING OF THE PHYSICAL PROCESSES IN THE BCLME -- ACKNOWLEDGEMENTS -- REFERENCES -- Chapter 5. Low oxygen water (LOW) variability in the Benguela system: Key processes and forcing scales relevant to forecasting -- INTRODUCTION -- SYNTHESIS OF SYSTEM PROCESSES AND VARIABILITY -- REMOTE FORCING: EASTERN TROPICAL SOUTHEAST ATLANTIC (ETSA - BENGUELA LINKAGE) -- BENGUELA SHELF VARIABILITY -- PROCESSES REQUIRING DIAGNOSTIC ASSESSMENT -- PROCESSES WITH FORECASTING POTENTIAL -- WHAT ARE THE GAPS? -- SUMMARY -- ACKNOWLEDGEMENTS -- REFERENCES -- Chapter 7. The variability and potential for prediction of harmful algal blooms in the southern Benguela ecosystem -- ABSTRACT -- INTRODUCTION -- THE SPATIAL [GEOGRAPHIC] DISTRIBUTION OF HABS -- SEASONAL INCIDENCE OF HABS -- THE TIMING OF HABS: ACROSS-SHELF AND ALONGSHORE TRANSPORT -- CONCLUSION: THE POTENTIAL FOR PREDICTION -- Chapter 8. Resource and ecosystem variability, including regime shifts, in the Benguela Current system -- ABSTRACT -- INTRODUCTION -- RESOURCE VARIABILITY -- ECOSYSTEM VARIABILITY -- PREDICTING VARIABILITY -- MAKING PREDICTIONS -- A WAY FORWARD -- CONCLUSIONS -- ACKNOWLEDGEMENTS -- Chapter 6. Variability of plankton with reference to fish variability in the Benguela Current Large Marine Ecosystem - An overview -- ABSTRACT -- INTRODUCTION -- EVENT-SCALE VARIABILITY -- SEASONAL CHANGES -- INTERANNUAL AND DECADAL CHANGES -- CONCLUSIONS -- REFERENCES -- Chapter 9. Modelling, forecasting and scenarios in comparable upwelling ecosystems --California, Canary, Humboldt -- ABSTRACT -- INTRODUCTION -- PHYSICS -- ECOLOGY -- TELECONNECTIONS BETWEEN ECOSYSTEMS -- CONCLUSIONS AND FURTHER GENERAL THOUGHTS -- ACKNOWLEDGMENTS -- PART III: HOPES, DREAMS AND REALITY. , Chapter 10. Influences of large scale climate modes and Agulhas system variability on the BCLME region -- INTRODUCTION -- ATMOSPHERIC VARIABILITY OF THE BCLME REGION -- BENGUELA NIÑOS AND SST VARIABILITY IN THE TROPICAL EASTERN ATLANTIC OCEAN -- INFLUENCE OF VARIABILITY IN THE SOUTHERN AGULHAS SYSTEM ON THE BCLME REGION -- SUMMARY -- ACKNOWLEDGEMENTS -- Chapter 11. Developing a basis for detecting and predicting long-term ecosystem changes -- ABSTRACT -- INTRODUCTION -- ECOSYSTEM CHANGES TO BE MONITORED -- APPROPRIATE ECOSYSTEM INDICATORS AND MODELS -- DESIRED END PRODUCTS AND DATA REQUIREMENTS -- SCHEDULE FOR IMPLEMENTATION -- CONCLUSIONS -- ACKNOWLEDGEMENTS -- Chapter 12. The requirements for forecasting harmful algal blooms in the Benguela -- INTRODUCTION -- PHYSICAL-BIOLOGICAL COUPLINGS UNDERLYING HABS -- IDENTIFICATION OF THE PHYSICAL PROCESSES IMPORTANT TO BLOOM CONCENTRATION AND TRANSPORT -- REAL-TIME OBSERVATION OF HABS -- NUMERICAL MODELLING AND PREDICTION OF HAB DYNAMICS -- CONCLUSIONS -- Chapter 13. Low oxygen water (LOW) forcing scales amenable to forecasting in the Benguela ecosystem -- INTRODUCTION -- SCALES OF LOW VARIABILITY AMENABLE TO FORECASTING -- REMOTE EQUATORIAL FORCING: 2 MONTH FORECASTING SCALE -- SHELF SCALE FORCING: 7 DAY FORECASTING SCALE -- IMPORTANCE OF COUPLED MECHANISMS -- OBSERVATIONAL PROGRAMME -- SUMMARY -- ACKNOWLEDGEMENTS -- Chapter 14. Forecasting shelf processes of relevance to living marine resources in the BCLME -- ABSTRACT -- INTRODUCTION -- LOW OXYGEN WATER EVENTS -- MESOSCALE PROCESSES -- BOUNDARY PROCESSES -- OTHER SHELF PROCESSES -- DISCUSSION AND CONCLUSIONS -- ACKNOWLEDGEMENTS -- REFERENCES -- Chapter 15. Environmental data requirements of maritime operations in the Benguela coastal ocean -- INTRODUCTION -- OIL AND GAS INDUSTRY ENVIRONMENTAL INFORMATION NEEDS -- DIAMOND MINING -- SHIPPING -- PORTS. , FISHING -- SOVEREIGNTY AND RESOURCE PROTECTION -- MARITIME FORECASTING IN SUPPORT OF RISK MANAGEMENT -- SUMMARY AND CONCLUSION -- ACKNOWLEDGEMENTS -- PART IV: THE WAY AHEAD -- Chapter 16. Towards a future integrated forecast system -- SUMMARY -- INTRODUCTION -- CANDIDATE PREDICTIVE CAPABILITIES FOR THE BCLME -- SYSTEM REQUIREMENTS FOR THE CANDIDATE PREDICTIVE CAPABILITIES -- CONCLUSIONS AND RECOMMENDATIONS -- Chapter 17. Forecasting a large marine ecosystem -- SUMMARY -- INTRODUCTION -- MODELLING PRACTICE IN THE 21ST CENTURY -- SHORT-TERM LME FORECASTING -- MEDIUM-TERM LME FORECASTING -- LONG-TERM LME FORECASTING -- WHAT-IF? PREDICTION -- A VISION OF THE FUTURE -- Index -- Large Marine Ecosystems Series.
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  • 2
    Publication Date: 2022-05-26
    Description: Author Posting. © American Geophysical Union, 2009. This article is posted here by permission of American Geophysical Union for personal use, not for redistribution. The definitive version was published in Journal of Geophysical Research 114 (2009): C05011, doi:10.1029/2007JC004548.
    Description: Twin experiments were made to compare the reduced rank Kalman filter (RRKF), ensemble Kalman filter (EnKF), and ensemble square-root Kalman filter (EnSKF) for coastal ocean problems in three idealized regimes: a flat bottom circular shelf driven by tidal forcing at the open boundary; an linear slope continental shelf with river discharge; and a rectangular estuary with tidal flushing intertidal zones and freshwater discharge. The hydrodynamics model used in this study is the unstructured grid Finite-Volume Coastal Ocean Model (FVCOM). Comparison results show that the success of the data assimilation method depends on sampling location, assimilation methods (univariate or multivariate covariance approaches), and the nature of the dynamical system. In general, for these applications, EnKF and EnSKF work better than RRKF, especially for time-dependent cases with large perturbations. In EnKF and EnSKF, multivariate covariance approaches should be used in assimilation to avoid the appearance of unrealistic numerical oscillations. Because the coastal ocean features multiscale dynamics in time and space, a case-by-case approach should be used to determine the most effective and most reliable data assimilation method for different dynamical systems.
    Description: P. Malanotte-Rizzoli and J. Wei were supported by the Office of Naval Research (ONR grant N00014-06-1- 0290); C. Chen and Q. Xu were supported by the U.S. GLOBEC/Georges Bank program (through NSF grants OCE-0234545, OCE-0227679, OCE- 0606928, OCE-0712903, OCE-0726851, and OCE-0814505 and NOAA grant NA-16OP2323), the NSF Arctic research grants ARC0712903, ARC0732084, and ARC0804029, and URI Sea Grant R/P-061; P. Xue was supported through the MIT Sea Grant 2006-RC-103; Z. Lai, J. Qi, and G. Cowles were supported through the Massachusetts Marine Fisheries Institute (NOAA grants NA04NMF4720332 and NA05NMF4721131); and R. Beardsley was supported through U.S. GLOBEC/Georges Bank NSF grant OCE-02227679, MIT Sea Grant NA06OAR1700019, and the WHOI Smith Chair in Coastal Oceanography.
    Keywords: Kalman filters ; Data assimilation ; Ocean modeling
    Repository Name: Woods Hole Open Access Server
    Type: Article
    Format: application/x-tex
    Format: application/pdf
    Format: text/plain
    Format: image/tiff
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