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Public reporting burden for the collection of information is estimated to average 1 hour per response, including the time for reviewing instructions, searching existing data sources, gathering and maintaining the data needed, and completing and reviewing the collection of information. Send comments regarding this burden estimate or any other aspect of this collection of information, including suggestions for reducing this burden, to Washington Headquarters Services, Directorate for Information Operations and Reports, 1215 Jefferson Davis Highway, Suite 1204, Arlington VA 22202-4302. Respondents should be aware that notwithstanding any other provision of law, no person shall be subject to a penalty for failing to comply with a collection of information if it does not display a currently valid OMB control number.

Forecasting the precise time of the morning and evening stability transition is important to both civilian and military meteorologists. In the civilian community this information can be used to initialize prognostic convective models. Other applications include enhancing (1) infrared sensor simulations and (2) high-energy laser propagation projects. The observed stability transition duration over a mid-latitude desert has ranged from less than 1 min to more than 20 min. This article presents desert stability transition parameters, the Stability Transition Forecast Model (STFM) development, two scenarios for modeling the transition, and an empirically derived equation quantifying the monthly (seasonal) effects for forecasting the morning and evening stability transitions over the Tularosa Basin in New Mexico. Explanations for the general seasonal curve shape are proposed, and possible reasons for why the solar cycle component is the greatest contributor to the ideal STFM algorithm are discussed.

The majority of air and ground vehicle systems are reliant on specialized diesel fuel. This reliance increases the likelihood that operations may be operating in an energy constrained or contested environment given the state of international relations between global energy providers and consumers. Such a vulnerability has the potential to reduce operational effectiveness or efficiency if logistical supply chains were interrupted or impeded. The most effective and efficient methodology to reduce reliance on specialized diesel fuel is to hybridize our energy and power (E&P) systems, and support more diverse E&P solutions including renewable energy generation (photovoltaic (PV) arrays, wind generation, wave energy converters), nuclear, or decaying isotopes. In this paper/presentation, we present our advances in developing a set of predictive artificial intelligence and machine learning (AI/ML) algorithms that forecast E&P capabilities of a photovoltaic array indirectly and directly. These milestones are a product of two separate types of AI/ML approaches: (1) developing AI/ML based algorithms that predict ambient and panel temperature from various atmosphericbased sensor data which can then be used in combination with an irradiance profile and a MATLAB Simulink model to predict the E&P capabilities of the PV array (indirect method), and (2) developing AI/ML which predicts the resulting E&P capabilities of the PV array, using various atmospheric-based sensor data (direct method).