Emissions of Nitrous Oxide and Methane in North America

Methane (CH_4) and nitrous oxide (N_2O) are the second- and third-most important long-lived greenhouse gas species after carbon dioxide (CO_2) in terms of radiative forcing. This thesis describes the magnitude, spatial distribution, and seasonality of N_2O and CH_4 sources over North America using atmospheric data. We also investigate the environmental drivers and/or anthropogenic source sectors that can explain these emissions patterns. Overall, this thesis provides information on the magnitude, distribution, and likely drivers of greenhouse gas emissions to aid existing or future climate change mitigation policies in the US and Canada. We estimate anthropogenic N_2O and CH_4 emissions that greatly exceed most existing inventory estimates. Our US budgets for N_2O and CH_4 are approximately 2.8 and 1.5 times higher, respectively, than inventory estimates from the US EPA. Much of the discrepancy in methane appears to stem from oil and natural gas industry and agricultural emissions. In contrast, we estimate natural CH_4 sources that are smaller than most existing process-based biogeochemical models. These estimated fluxes have a spatial distribution centered around the Hudson Bay Lowlands. Most existing models estimate fluxes that are far more spatially distributed across the Canadian shield. These estimates provide negative information on the spatial distribution of fluxes relative to a spatially-constant model. We find that a simple model using only three environmental variables can describe flux patterns (as seen by the atmospheric observations) as well as any process-based estimate.Earth and Planetary Science

Game Theory and Prescriptive Analytics for Naval Wargaming Battle Management Aids

NPS NRP Project PosterThe Navy is taking advantage of advances in computational technologies and data analytic methods to automate and enhance tactical decisions and support warfighters in highly complex combat environments. Novel automated techniques offer opportunities to support the tactical warfighter through enhanced situational awareness, automated reasoning and problem-solving, and faster decision timelines. This study will investigate how game theory and prescriptive analytics methods can be used to develop real-time wargaming capabilities to support warfighters in their ability to explore and evaluate the possible consequences of different tactical COAs to improve tactical missions. This study will develop a conceptual design of a real-time tactical wargaming capability. This study will explore data analytic methods including game theory, prescriptive analytics, and artificial intelligence (AI) to evaluate their potential to support real-time wargaming.N2/N6 - Information WarfareThis research is supported by funding from the Naval Postgraduate School, Naval Research Program (PE 0605853N/2098). https://nps.edu/nrpChief of Naval Operations (CNO)Approved for public release. Distribution is unlimited.

Game Theory and Prescriptive Analytics for Naval Wargaming Battle Management Aids

NPS NRP Executive SummaryThe Navy is taking advantage of advances in computational technologies and data analytic methods to automate and enhance tactical decisions and support warfighters in highly complex combat environments. Novel automated techniques offer opportunities to support the tactical warfighter through enhanced situational awareness, automated reasoning and problem-solving, and faster decision timelines. This study will investigate how game theory and prescriptive analytics methods can be used to develop real-time wargaming capabilities to support warfighters in their ability to explore and evaluate the possible consequences of different tactical COAs to improve tactical missions. This study will develop a conceptual design of a real-time tactical wargaming capability. This study will explore data analytic methods including game theory, prescriptive analytics, and artificial intelligence (AI) to evaluate their potential to support real-time wargaming.N2/N6 - Information WarfareThis research is supported by funding from the Naval Postgraduate School, Naval Research Program (PE 0605853N/2098). https://nps.edu/nrpChief of Naval Operations (CNO)Approved for public release. Distribution is unlimited.

Game Theory and Prescriptive Analytics for Naval Wargaming Battle Management Aids

NPS NRP Technical ReportThe Navy is taking advantage of advances in computational technologies and data analytic methods to automate and enhance tactical decisions and support warfighters in highly complex combat environments. Novel automated techniques offer opportunities to support the tactical warfighter through enhanced situational awareness, automated reasoning and problem-solving, and faster decision timelines. This study will investigate how game theory and prescriptive analytics methods can be used to develop real-time wargaming capabilities to support warfighters in their ability to explore and evaluate the possible consequences of different tactical COAs to improve tactical missions. This study will develop a conceptual design of a real-time tactical wargaming capability. This study will explore data analytic methods including game theory, prescriptive analytics, and artificial intelligence (AI) to evaluate their potential to support real-time wargaming.N2/N6 - Information WarfareThis research is supported by funding from the Naval Postgraduate School, Naval Research Program (PE 0605853N/2098). https://nps.edu/nrpChief of Naval Operations (CNO)Approved for public release. Distribution is unlimited.

Considerations for Cross Domain / Mission Resource Allocation and Replanning

NPS NRP Executive SummaryNaval platforms are inherently multi-mission - they execute a variety of missions simultaneously. Ships, submarines, and aircraft support multiple missions across domains, such as integrated air and missile defense, ballistic missile defense, anti-submarine warfare, strike operations, naval fires in support of ground operations, and intelligence, surveillance, and reconnaissance. Scheduling and position of these multi-mission platforms is problematic since one warfare area commander desires one position and schedule, while another may have a completely different approach. Commanders struggle to decide and adjudicate these conflicts, because there is plenty of uncertainty about the enemy and the environment. This project will explore emerging innovative data analytic technologies to optimize naval resource allocation and replanning across mission domains. NPS proposes a study that will evaluate the following three solution concepts for this application: (1) game theory, (2) machine learning, and (3) wargaming. The study will first identify a set of operational scenarios that involve distributed and diverse naval platforms and resources and a threat situation that requires multiple concurrent missions in multiple domains. The NPS team will use these scenarios to evaluate the three solution concepts and their applicability to supporting resource allocation and replanning. This project will provide valuable insights into innovative data analytic solution concepts to tackle the Navy's challenge of conducing multiple missions with cross-domain resources.N2/N6 - Information WarfareThis research is supported by funding from the Naval Postgraduate School, Naval Research Program (PE 0605853N/2098). https://nps.edu/nrpChief of Naval Operations (CNO)Approved for public release. Distribution is unlimited.

Considerations for Cross Domain / Mission Resource Allocation and Replanning

NPS NRP Project PosterNaval platforms are inherently multi-mission - they execute a variety of missions simultaneously. Ships, submarines, and aircraft support multiple missions across domains, such as integrated air and missile defense, ballistic missile defense, anti-submarine warfare, strike operations, naval fires in support of ground operations, and intelligence, surveillance, and reconnaissance. Scheduling and position of these multi-mission platforms is problematic since one warfare area commander desires one position and schedule, while another may have a completely different approach. Commanders struggle to decide and adjudicate these conflicts, because there is plenty of uncertainty about the enemy and the environment. This project will explore emerging innovative data analytic technologies to optimize naval resource allocation and replanning across mission domains. NPS proposes a study that will evaluate the following three solution concepts for this application: (1) game theory, (2) machine learning, and (3) wargaming. The study will first identify a set of operational scenarios that involve distributed and diverse naval platforms and resources and a threat situation that requires multiple concurrent missions in multiple domains. The NPS team will use these scenarios to evaluate the three solution concepts and their applicability to supporting resource allocation and replanning. This project will provide valuable insights into innovative data analytic solution concepts to tackle the Navy's challenge of conducing multiple missions with cross-domain resources.N2/N6 - Information WarfareThis research is supported by funding from the Naval Postgraduate School, Naval Research Program (PE 0605853N/2098). https://nps.edu/nrpChief of Naval Operations (CNO)Approved for public release. Distribution is unlimited.

Considerations for Cross Domain / Mission Resource Allocation and Replanning

NPS NRP Technical ReportNaval platforms are inherently multi-mission - they execute a variety of missions simultaneously. Ships, submarines, and aircraft support multiple missions across domains, such as integrated air and missile defense, ballistic missile defense, anti-submarine warfare, strike operations, naval fires in support of ground operations, and intelligence, surveillance, and reconnaissance. Scheduling and position of these multi-mission platforms is problematic since one warfare area commander desires one position and schedule, while another may have a completely different approach. Commanders struggle to decide and adjudicate these conflicts, because there is plenty of uncertainty about the enemy and the environment. This project will explore emerging innovative data analytic technologies to optimize naval resource allocation and replanning across mission domains. NPS proposes a study that will evaluate the following three solution concepts for this application: (1) game theory, (2) machine learning, and (3) wargaming. The study will first identify a set of operational scenarios that involve distributed and diverse naval platforms and resources and a threat situation that requires multiple concurrent missions in multiple domains. The NPS team will use these scenarios to evaluate the three solution concepts and their applicability to supporting resource allocation and replanning. This project will provide valuable insights into innovative data analytic solution concepts to tackle the Navy's challenge of conducing multiple missions with cross-domain resources.N2/N6 - Information WarfareThis research is supported by funding from the Naval Postgraduate School, Naval Research Program (PE 0605853N/2098). https://nps.edu/nrpChief of Naval Operations (CNO)Approved for public release. Distribution is unlimited.

Global emissions of refrigerants HCFC-22 and HFC-134a: Unforeseen seasonal contributions

HCFC-22 (CHClF[subscript 2]) and HFC-134a (CH[subscript 2]FCF[subscript 3]) are two major gases currently used worldwide in domestic and commercial refrigeration and air conditioning. HCFC-22 contributes to stratospheric ozone depletion, and both species are potent greenhouse gases. In this work, we study in situ observations of HCFC-22 and HFC-134a taken from research aircraft over the Pacific Ocean in a 3-y span [HIaper-Pole-to-Pole Observations (HIPPO) 2009–2011] and combine these data with long-term ground observations from global surface sites [National Oceanic and Atmospheric Administration (NOAA) and Advanced Global Atmospheric Gases Experiment (AGAGE) networks]. We find the global annual emissions of HCFC-22 and HFC-134a have increased substantially over the past two decades. Emissions of HFC-134a are consistently higher compared with the United Nations Framework Convention on Climate Change (UNFCCC) inventory since 2000, by 60% more in recent years (2009–2012). Apart from these decadal emission constraints, we also quantify recent seasonal emission patterns showing that summertime emissions of HCFC-22 and HFC-134a are two to three times higher than wintertime emissions. This unforeseen large seasonal variation indicates that unaccounted mechanisms controlling refrigerant gas emissions are missing in the existing inventory estimates. Possible mechanisms enhancing refrigerant losses in summer are (i) higher vapor pressure in the sealed compartment of the system at summer high temperatures and (ii) more frequent use and service of refrigerators and air conditioners in summer months. Our results suggest that engineering (e.g., better temperature/vibration-resistant system sealing and new system design of more compact/efficient components) and regulatory (e.g., reinforcing system service regulations) steps to improve containment of these gases from working devices could effectively reduce their release to the atmosphere

Human-Machine Weapons Engagement Decisions: Systems Safety in Complex Decision Environments

NPS NRP Executive SummaryThis project will study system safety requirements regarding the deployment of artificial intelligence (AI) for tactical decision and mission planning aids supporting complex human-machine decisions. Advances in computational technologies and AI methods present new opportunities for developing automated tactical decision aids to support warfighters making weapons engagement decisions. Tactical decisions become increasing complex and can overwhelm human decision-making as threats increase in number, speed, diversity and lethality. It is critical to ensure that the deployment of such AI-enabled decision aids considers system safety. The study will explore the cognitive strengths of humans and machines to identify effective teaming arrangements in a variety of tactical environments of increasing complexity. This study will investigate system safety risks to develop concepts, requirements, and methods that ensure that future automated tactical decision and mission planning aids are safely deployed. Research questions include: What are the safety risks related to the deployment of AI systems that support future automated tactical decision and mission planning aids? Can AI-enabled automated decision aids support system safety analysis in a way that characterizes the cognitive strengths of humans and machines? What concepts, requirements, and methods can ensure that future automated military decision aids are safely deployed? NPS proposes a model-based systems engineering (MBSE) method to develop safety requirements for integrating automated decision aids into weapons engagement and mission planning decisions. The MBSE method will capture and analyze requirements, functional models of weapons engagements, and conceptual architectures for human-machine teaming arrangements. The study will include a multi-disciplinary team of NPS researchers and will offer educational and research opportunities for NPS students. Research deliverables will include a project report and presentation that contain MBSE artifacts, conceptual models, and safety requirements.Naval Air Warfare Center Weapons Division (NAWCWD)ASN(RDA) - Research, Development, and AcquisitionThis research is supported by funding from the Naval Postgraduate School, Naval Research Program (PE 0605853N/2098). https://nps.edu/nrpChief of Naval Operations (CNO)Approved for public release. Distribution is unlimited.

Human-Machine Weapons Engagement Decisions: Systems Safety in Complex Decision Environments

NPS NRP Project PosterThis project will study system safety requirements regarding the deployment of artificial intelligence (AI) for tactical decision and mission planning aids supporting complex human-machine decisions. Advances in computational technologies and AI methods present new opportunities for developing automated tactical decision aids to support warfighters making weapons engagement decisions. Tactical decisions become increasing complex and can overwhelm human decision-making as threats increase in number, speed, diversity and lethality. It is critical to ensure that the deployment of such AI-enabled decision aids considers system safety. The study will explore the cognitive strengths of humans and machines to identify effective teaming arrangements in a variety of tactical environments of increasing complexity. This study will investigate system safety risks to develop concepts, requirements, and methods that ensure that future automated tactical decision and mission planning aids are safely deployed. Research questions include: What are the safety risks related to the deployment of AI systems that support future automated tactical decision and mission planning aids? Can AI-enabled automated decision aids support system safety analysis in a way that characterizes the cognitive strengths of humans and machines? What concepts, requirements, and methods can ensure that future automated military decision aids are safely deployed? NPS proposes a model-based systems engineering (MBSE) method to develop safety requirements for integrating automated decision aids into weapons engagement and mission planning decisions. The MBSE method will capture and analyze requirements, functional models of weapons engagements, and conceptual architectures for human-machine teaming arrangements. The study will include a multi-disciplinary team of NPS researchers and will offer educational and research opportunities for NPS students. Research deliverables will include a project report and presentation that contain MBSE artifacts, conceptual models, and safety requirements.Naval Air Warfare Center Weapons Division (NAWCWD)ASN(RDA) - Research, Development, and AcquisitionThis research is supported by funding from the Naval Postgraduate School, Naval Research Program (PE 0605853N/2098). https://nps.edu/nrpChief of Naval Operations (CNO)Approved for public release. Distribution is unlimited.