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Office of Oceanic and Atmospheric Research (OAR) Office of Oceanic and Atmospheric Research (OAR)

Office of Oceanic and Atmospheric Research (OAR)

This collection includes documents from NOAA's Office of Oceanic and Atmospheric Research. All included documents have been either written by OAR staff or funded through OAR programs and grants. Included are documents, reports, and other materials.

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  • A Subjective Bayesian Framework for Synthesizing Deep Uncertainties in Climate Risk Management
    A Subjective Bayesian Framework for Synthesizing Deep Uncertainties in Climate Risk Management
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  • Coupled Urban Change and Natural Hazard Consequence Model for Community Resilience Planning
    Coupled Urban Change and Natural Hazard Consequence Model for Community Resilience Planning
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  • observations
    observations
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  • Assessing Predictability of Marine Heatwaves With Random Forests
    Assessing Predictability of Marine Heatwaves With Random Forests
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  • The emergence of heat and humidity too severe for human tolerance
    The emergence of heat and humidity too severe for human tolerance
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  • A Machine Learning Approach for Probabilistic MultiModel Ensemble Predictions of Indian Summer Monsoon Rainfall
    A Machine Learning Approach for Probabilistic Multi-Model Ensemble Predictions of Indian Summer Monsoon Rainfall
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  • Mesoscale convective clustering enhances tropical precipitation
    Mesoscale convective clustering enhances tropical precipitation
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  • Atmospheric circulation types controlling rainfall in the Central American Isthmus
    Atmospheric circulation types controlling rainfall in the Central American Isthmus
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  • Ocean Observations Under Two Major Hurricanes Evolution of the Response Across the Storm Wakes
    Ocean Observations Under Two Major Hurricanes: Evolution of the Response Across the Storm Wakes
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  • Classification of GLM Flashes Using Random Forests
    Classification of GLM Flashes Using Random Forests
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  • Constructing a model including the cryptic sulfur cycle in Chesapeake Bay requires judicious choices for key processes and parameters
    Constructing a model including the cryptic sulfur cycle in Chesapeake Bay requires judicious choices for key processes and parameters
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  • Identification of Dominant WarmSeason Latent Heat Flux Patterns in the Lower Mississippi River Alluvial Valley
    Identification of Dominant Warm-Season Latent Heat Flux Patterns in the Lower Mississippi River Alluvial Valley
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  • Response of Hurricane Harveys rainfall to anthropogenic aerosols A sensitivity study based on spectral bin microphysics with simulated aerosols
    Response of Hurricane Harvey’s rainfall to anthropogenic aerosols: A sensitivity study based on spectral bin microphysics with simulated aerosols
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  • Learning to understand disentangling the outcomes of stakeholder participation in climate change governance
    Learning to understand: disentangling the outcomes of stakeholder participation in climate change governance
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  • What do climate impacts health and migration reveal about vulnerability and adaptation in the Marshall Islands
    What do climate impacts, health, and migration reveal about vulnerability and adaptation in the Marshall Islands?
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  • The representation of alkalinity and the carbonate pump from CMIP5 to CMIP6 Earth system models and implications for the carbon cycle
    The representation of alkalinity and the carbonate pump from CMIP5 to CMIP6 Earth system models and implications for the carbon cycle
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  • Tracing differences in iron supply to the MidAtlantic Ridge valley between hydrothermal vent sites implications for the addition of iron to the deep ocean
    Tracing differences in iron supply to the Mid-Atlantic Ridge valley between hydrothermal vent sites: implications for the addition of iron to the deep ocean
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  • Impact of urban aerosols on the cloud condensation activity using a clustering model
    Impact of urban aerosols on the cloud condensation activity using a clustering model
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  • Sources of surface O3 in the UK tagging O3 within WRFChem
    Sources of surface O3 in the UK: tagging O3 within WRF-Chem
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  • Machine Learning for Improving SurfaceLayerFlux Estimates
    Machine Learning for Improving Surface-Layer-Flux Estimates
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    Description:
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6581 - 6600 / 20711
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