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The Federal Highway Administration (FHWA) and the American Association of State Highway Transportation Officials (AASHTO) have adopted its use and translated the variables into civil engineering applications, specifically pavement design. FHWA's Long Term Pavement Performance (LTPP) Climate Tool provides access to the MERRA-2 database and generates site-specific climate data in compatible formats for AASHTO Pavement ME Design (Pavement ME). AASHTO will soon use MERRA-2 climatic data for AASHTOWare applications. MERRA-2 data sets include accurate hourly solar radiation values based on measured cloud-covered fractions which can significantly improve the accuracy of predicted pavement temperatures. Studies reveal challenges in matching the predictions from the Enhanced Integrated Climatic Model (EICM) with field observations and pavement performance. Variations in measurement of climatic attributes have also been reported to Operating Weather Stations (OWS). MERRA-2 data provides opportunities for enhancements to the climatic parameters and climatic module calculations for pavement design using Pavement ME. It provides improved climatology, higher frequency outputs including hourly data updates, and additional locations beyond the United States. Available data categories include: temperature, precipitation, surface pressure, cloud cover, humidity, wind speed, and solar radiation. The objectives of this project are to (1) evaluate the impact of using NASA's Modern-Era Retrospective Analysis for Research and Applications version 2 (MERRA-2) through the FHWA LTPP Climate Tool to improve the climatic inputs and related models for Pavement ME; (2) enhance and simplify climate input parameters for Pavement ME that can be implemented by transportation agencies; and (3) develop climate-related models based on identified parameter enhancements

The Defense Advanced Research Projects Agency (DARPA) is soliciting innovative proposals in the area of global scale, multimodal geospatial data cloud platform and analytics development. Proposed research should investigate innovative approaches that enable revolutionary advances in science, devices, or systems. Specifically excluded is research that primarily results in evolutionary improvements to the existing state of practice. GCA is a 24-month, three-phase program. At present, DARPA seeks innovative proposals covering the tasks in Phase 1 (6 month base effort) and Phase 2 (12 month costed option) of the program. Proposals must address both Phase 1 and Phase 2. Phase 3 will be the subject of a separate procurement. For Phases 1 and 2, DARPA seeks proposals in two technical areas (TAs). TA-1 (Scalable Geospatial Data Platform) will provide access to geospatial data and an extensible computing platform on which TA-2 performers can efficiently access and process massive amounts of curated geospatial data. TA-2 (Analytical Applications and Competitions) will create software for use in one or more of the analytics competitions (predicting food shortages, locating fracking construction detection, illegal fishing detection, open call) using data and platforms provided by TA-1 proposers. In Phase 1, DARPA anticipates making up to three awards for TA-1 and up to 16 awards for TA-2. A proposer may respond to one or both technical areas but a separate proposal is required for each TA. A TA-2 proposer can propose to one or more competition areas, using costed options if proposing to more than one competition.

DARPA is soliciting innovative research proposals for the design, development and demonstration of a Video Synthetic Aperture Radar (ViSAR) system that enables US aircraft to engage maneuvering ground targets through clouds dust and other obscurants. Proposed research should investigate innovative approaches that enable revolutionary advances in science, devices, or systems. Specifically excluded is research that primarily results in evolutionary improvements to the existing state of practice.

This BAA focuses on developing new technologies to allow secure data sharing; trusted computing; smart routing; cyber defense; Multi-Level Security (MLS) trust at the tactical edge; and a comprehensive, multi-security domain, user-defined operational picture to effectively and efficiently improve the state-of-the-art for defense enterprise, cloud, and mobile/tactical computing/operations.

The goal of the National Robotics Initiative (NRI) is to support fundamental research that will accelerate the development and use of robots in the United States that work beside or cooperatively with people. The original NRI program focused on innovative robotics research that emphasized the realization of collaborative robots (co-robots) working in symbiotic relationships with human partners. The NRI-2.0 program significantly extends this theme to focus on issues of scalability: how teams of multiple robots and multiple humans can interact and collaborate effectively; how robots can be designed to facilitate achievement of a variety of tasks in a variety of environments, with minimal modification to the hardware and software; how robots can learn to perform more effectively and efficiently, using large pools of information from the cloud, other robots, and other people; and how the design of the robots’ hardware and software can facilitate large-scale, reliable operation

The goal of the National Robotics Initiative (NRI) is to support fundamental research that will accelerate the development and use of robots in the United States that work beside or cooperatively with people. The original NRI program focused on innovative robotics research that emphasized the realization of collaborative robots (co-robots) working in symbiotic relationships with human partners. The NRI-2.0 program significantly extends this theme to focus on issues of scalability: how teams of multiple robots and multiple humans can interact and collaborate effectively; how robots can be designed to facilitate achievement of a variety of tasks in a variety of environments, with minimal modification to the hardware and software; how robots can learn to perform more effectively and efficiently, using large pools of information from the cloud, other robots, and other people; and how the design of the robots' hardware and software can facilitate large-scale, reliable operation. In addition, the program supports innovative approaches to establish and infuse robotics into educational curricula, advance the robotics workforce through education pathways, and explore the social, behavioral, and economic implications of our future with ubiquitous collaborative robots. Collaboration between academic, industry, non-profit, and other organizations is encouraged to establish better linkages between fundamental science and engineering and technology development, deployment and use. Well-justified international collaborations that add significant value to the proposed research and education activities will also be considered.

The Exploiting Parallelism and Scalability (XPS) program aims to support groundbreaking research leading to a new era of parallel computing. Achieving the needed breakthroughs will require a collaborative effort among researchers representing all areas -- from services and applications down to the micro-architecture - and will be built on new concepts, theories, and foundational principles. New approaches to achieving scalable performance and usability need new abstract models and algorithms, new programming models and languages, and new hardware architectures, compilers, operating systems and run-time systems, and must exploit domain and application-specific knowledge. Research is also needed on energy efficiency, communication efficiency, and on enabling the division of effort between edge devices and clouds.

The proliferation of mobile and Internet-of-Things (IoT) devices, and their pervasiveness across nearly every sphere of our society, continues to raise questions about the architectures that organize tomorrow’s compute infrastructure. At the heart of this trend is the data that will be generated as myriad devices and application services operate simultaneously to digitize a complex domain like a smart building or smart industrial facility. A key shift is from edge devices consuming data produced in the cloud to edge devices being a voluminous producer of data. This shift reopens a broad variety of system-level research questions concerning data placement, movement, processing and sharing. Importantly, the shift also opens the door to compelling new applications with significant industrial and societal impact in domains such as healthcare, manufacturing, transportation, public safety, energy, buildings, and telecommunications. Edge computing is broadly defined as a networked systems architectural approach in which compute and storage resources are placed at the network edge, in proximity to the mobile and IoT devices.

The proliferation of mobile and Internet-of-Things (IoT) devices, and their pervasiveness across nearly every sphere of our society, continues to raise questions about the architectures that organize tomorrow's compute infrastructure. At the heart of this trend is the data that will be generated as myriad devices and application services operate simultaneously to digitize a complex domain like a smart building or smart industrial facility. A key shift is from edge devices consuming data produced in the cloud to edge devices being a voluminous producer of data. This shift reopens a broad variety of system-level research questions concerning data placement, movement, processing and sharing. Importantly, the shift also opens the door to compelling new applications with significant industrial and societal impact in domains such as healthcare, manufacturing, transportation, public safety, energy, buildings, and telecommunications.

The functionality of the NeuroImaging Tools and Resources Collaboratory (NITRC) has enabled three distinct components to flourish: Resources Registry (NITRC-R): a collaboratory enabling the distribution, enhancement, and adoption of neuroimaging tools and resources. Image Repository (NITRC-IR): a curated repository of free neuroimaging datasets meeting global standards. Computational Environment (NITRC-CE): a freely downloadable or pay-as-you-go virtual computing cloud-based platform that is pre-configured with popular neuroimaging tools. NITRC-R has become the major web-based collaborative environment enabling the distribution, enhancement, and adoption of neuroinformatics resources. It currently hosts more than 1,000 tools and resources in areas such as magnetic resonance imaging (MRI), computed tomography (CT), optical imaging, positron emission tomography/single-photon emission computed tomography (PET/SPECT), electroencephalography/magnetoencephalography/electrocorticography (EEG/MEG/ECoG), computational neuroscience, and imaging genomics. Since NITRC's inception, there have been more than 10 million total downloads of tools from NITRC-R.

Computing systems have undergone a fundamental transformation from the single-core processor-devices of the turn of the century to today's ubiquitous and networked devices with multicore/many-core processors along with warehouse-scale computing via the cloud. At the same time, semiconductor technology is facing fundamental physical limits and single-processor performance has plateaued. This means that the ability to achieve performance improvements through improved processor technologies alone has ended. In recognition of this obstacle, the recent National Strategic Computing Initiative (NSCI) encourages collaborative efforts to develop, "over the next 15 years, a viable path forward for future high-performance computing (HPC) systems even after the limits of current semiconductor technology are reached (the 'post-Moore's Law era')."

The Office of Naval Research is interested in receiving white papers and full proposals for both Applied Research and Advanced Technology Development that will accelerate exploitation of all relevant data for mission planning and execution in the conduct of Expeditionary Warfare (EXW) missions.