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The scope of this paper is the development and recent field deployments of the High-Altitude Imaging Wind and Rain Airborne Profiler (HIWRAP), which was funded under the NASA Instrument Incubator Program (IIP) [1]. HIWRAP is a dual-frequency (Ka- and Ku-band), dual-beam (300 and 400 incidence angles), conical scanning, Doppler radar system designed for operation on the NASA high-altitude (65,000 ft) Global Hawk Unmanned Aerial System (UAS). It utilizes solid state transmitters along with a novel pulse compression scheme that results in a system with compact size, light weight, less power consumption, and low cost compared to radars currently in use for precipitation and Doppler wind measurements. By combining measurements at Ku- and Ka-band, HIWRAP is able to image winds through measuring volume backscattering from clouds and precipitation. In addition, HIWRAP is also capable of measuring surface winds in an approach similar to SeaWinds on QuikScat. To this end, HIWRAP hardware and software development has been completed. It was installed on the NASA WB57 for instrument test flights in March, 2010 and then deployed on the NASA Global Hawk for supporting the Genesis and Rapid Intensification Processes (GRIP) field campaign in August-September, 2010. This paper describes the scientific motivations of the development of HIWRAP as well as system hardware, aircraft integration and flight missions. Preliminary data from GRIP science flights is also presented.
The Lunar Data Node (LDN), under the auspices of the Geosciences Node of the Planetary Data System (PDS), is restoring Apollo data archived at the National Space Science Data Center. The Apollo data were arch ived on older media (7 -track tapes. microfilm, microfiche) and in ob solete digital formats, which limits use of the data. The LDN is maki ng these data accessible by restoring them to standard formats and archiving them through PDS. The restoration involves reading the older m edia, collecting supporting data (metadata), deciphering and understa nding the data, and organizing into a data set. The data undergo a pe er review before archive at PDS. We will give an update on last year' s work. We have scanned notebooks from Otto Berg, P.1. for the Lunar Ejecta and Meteorites Experiment. These notebooks contain information on the data and calibration coefficients which we hope to be able to use to restore the raw data into a usable archive. We have scanned Ap ollo 14 and 15 Dust Detector data from microfilm and are in the proce ss of archiving thc scans with PDS. We are also restoring raw dust de tector data from magnetic tape supplied by Yosio Nakamura (UT Austin) . Seiichi Nagihara (Texas Tech Univ.) and others in cooperation with NSSDC are recovering ARCSAV tapes (tapes containing raw data streams from all the ALSEP instruments). We will be preparing these data for archive with PDS. We are also in the process of recovering and archivi ng data not previously archived, from the Apollo 16 Gamma Ray Spectro meter and the Apollo 17 Infrared Spectrometer.
Satellite remote sensing technology has contributed to the transformation of multiple earth science domains, putting space observations at the forefront of innovation in earth science. With new satellite missions being launched every year, new types of earth science data are being incorporated into science models and decision-making systems in a broad array of organizations. Policy guidance can influence the degree to which user needs influence mission design and when, and ensure that satellite missions serve both the scientific and user communities without becoming unfocused and overly expensive. By considering the needs of the user community early on in the mission-design process, agencies can ensure that satellites meet the needs of multiple constituencies. This paper describes the mission development process in NASA and ESA and compares and contrasts the successes and challenges faced by these agencies as they try to balance science and applications within their missions.
NISAR would be the inaugural collaboration between National Aeronautics and Space Administration (NASA) and Indian Space Research Organization (ISRO) on an Earth Science mission, which would feature an L-Band SAR instrument and an S-Band SAR instrument. As partners, NASA and ISRO would each contribute different engineering elements to help achieve the proposed scientific objectives of the mission. ISRO-Vikram Sarabhai Space Centre would provide the GSLV-Mark II launch vehicle, which would deliver the spacecraft into the desired orbit. ISRO-Satellite Centre would provide the spacecraft based on its I3K structural bus, a commonly used platform for ISRO's communication satellite missions, which would provide the resources necessary to operate the science payload. NASA would augment the spacecraft capabilities with engineering payload systems to help store, and transmit the large volume of science data.
Venus, while having similar size, mass, and location in the solar system to Earth, varies from Earth in many ways. The differences include its climate, atmosphere, and surface conditions. Surface conditions present formidable engineering challenges due to the high temperature and pressure. To date, landed missions have not been able to last more than about 2 hours on the surface [1]. This has resulted in significant knowledge gaps about the surface conditions of this important body in the solar system. The science community has effectively no in-situ temporal data on Venus surface conditions (temperature, pressure, winds and chemistry). These data are critical for the development of a thorough understanding of Venus' weather and the processes by which chemical species interact with each other, and are transported throughout the atmospheric column. This will help understand aspects of the atmosphere/planet interactions such as momentum exchange. To date, no capability has been available to enable a long lived surface probe to make these kinds of measurements. However, recently developed Silicon Carbide based electronics, sensors, and other technologies have matured to a state where a simple, but powerful long-life scientific probe would be feasible for Venus. It is now possible to directly qualify the durability and functionality of these components in a simulated Venus surface environment and demonstrate the ability to return valuable scientific data.
Venus, while having similar size, mass, and location in the solar system to Earth, varies from Earth in many ways and holds many scientific mysteries despite many missions that have focused on it in the past. Primary differences include Venus' climate, atmosphere, and perhaps most notably the extreme surface conditions. The layers of sulfuric acid clouds and high pressure CO2 laden atmosphere make remote sensing at Venus much less effective than at other solar system bodies. In addition, surface conditions present formidable engineering challenges due to the high temperature, pressure, and reactive chemistry. To date, landed missions have not been able to last more than about 2 hours on the surface [1]. This has resulted in significant knowledge gaps about the surface conditions of this important body in the solar system. The science community has effectively no in-situ temporal data at the Venus surface. These data are critical for the development of a thorough understanding of Venus' weather and the processes by which chemical species interact with each other and are transported throughout the atmospheric column. The LLISSE platform, and its variants, are a foundation for future mission concepts based on a core set of long-lived technologies providing significant new science as well as demonstrating new technical capabilities. After completion, LLISSE has the potential to be a complimentary element to missions going to Venus and would provide unique and important science to missions whether they be orbiters or short duration landers.
Venus, while having similar size, mass, and location in the solar system to Earth, varies from Earth in many ways and holds many scientific mysteries despite many missions that have focused on it in the past. Primary differences include Venus' climate, atmosphere, and perhaps most notably the extreme surface conditions. The layers of sulfuric acid clouds and high pressure CO2 laden atmosphere make remote sensing at Venus much less effective than at other solar system bodies. In addition, surface conditions present formidable engineering challenges due to the high temperature, pressure, and reactive chemistry. To date, landed missions have not been able to last more than about 2 hours on the surface [1]. This has resulted in significant knowledge gaps about the surface conditions of this important body in the solar system. The science community has effectively no in-situ temporal data at the Venus surface. These data are critical for the development of a thorough understanding of Venus' weather and the processes by which chemical species interact with each other and are transported throughout the atmospheric column. The LLISSE platform, and its variants, are a foundation for future mission concepts based on a core set of long-lived technologies providing significant new science as well as demonstrating new technical capabilities. After completion, LLISSE has the potential to be a complimentary element to missions going to Venus and would provide unique and important science to missions whether they be orbiters or short duration landers.
The paper presents the first results from the Front- End Board (FEB) with the biggest Cyclone{sup R} V E FPGA 5CEFA9F31I7N, supporting 8 channels sampled up to 250 MSps at 14-bit resolution. Considered sampling for the SD is 120 MSps, however, the FEB has been developed with external anti-aliasing filters to keep a maximal flexibility. Six channels are targeted to the SD, two the rest for other experiments like: Auger Engineering Radio Array and additional muon counters. More channels and higher sampling generate larger size of registered events. We used the standard radio channel for a radio transmission from the detectors to the Central Data Acquisition Station (CDAS) to avoid at present a significant modification of a software in both sides: the detector and the CDAS (planned in a future for a final design). Seven FEBs have been deployed in the test detectors on a dedicated Engineering Array in a hexagon. Several variants of the FPGA code were tested for 120, 160, 200 and even 240 MSps DAQ. Tests confirmed a stability and reliability of the FEB design in real pampas conditions with more than 40 deg. C daily temperature variation and a strong sun exposition with a limited power budget only from a single solar panel. (authors)
The KM3NeT collaboration aims at the construction of a multi-km3 high-energy neutrino telescope in the Mediterranean Sea consisting of thousands of glass spheres (nodes), each of them containing 31 photomultiplier (PMT) of small photocathode area. The readout and data acquisition system of KM3NeT has to collect, treat and send to shore, in an economic way, the enormous amount of data produced by the photomultipliers. For this purpose, 31 high-resolution time-interval measuring channels are implemented on the Field-Programmable Gate Arrays (FPGA) based on Time to Digital Converter (TDC). TDC are very common devices in particles physics experiments. Architectures with low resources occupancy are desirable allowing the implementation of other instrumentation, communication and synchronization systems on the same device. The required resolution to measure both, time of flight and timestamp must be 1 ns. A 4-Oversampling technique with two high frequency clocks is used to achieve this resolution. The proposed TDC firmware is developed using very few resources in Xilinx Kintex-7.
Airborne remote sensing data collected using the Brookhaven National Laboratory's (BNL) heavy-lift unoccupied aerial system (UAS) octocopter platform - the Osprey - operated by the Terrestrial Ecosystem Science and Technology (TEST) group (https://www.bnl.gov/testgroup). This package includes data from 17 flights flown over the NGEE-Arctic Council Mile Maker 64 (MM64) and Teller MM27 sites in July, 2019. The Osprey is a multi-sensor UAS platform that simultaneously measures very high spatial resolution optical red/green/blue (RGB) and thermal infrared (TIR) surface "skin" temperature imagery, as well as surface reflectance at 1 nm intervals in the visible to near-infrared spectral range from ~350-1000 nm measured at regular intervals along each flight path. This package provide the Level 0 (raw, unprocessed) data collected by the Osprey platform. Ancillary aircraft data, flight mission parameters, and general flight conditions provided by the onboard flight and data collection computers are also included. Data and metadata are provided as text (*.txt, *.json), tabular (*.dat, *.csv, *.waypoint), and image (*.jpg) formats. This metadata document contains flight campaign, instrument and file metadata, along with a description of the L0 data, and file naming scheme. The Next-Generation Ecosystem Experiments: Arctic (NGEE Arctic), was a research effort to reduce uncertainty in Earth System Models by developing a predictive understanding of carbon-rich Arctic ecosystems and feedbacks to climate. NGEE Arctic was supported by the Department of Energy's Office of Biological and Environmental Research. The NGEE Arctic project had two field research sites: 1) located within the Arctic polygonal tundra coastal region on the Barrow Environmental Observatory (BEO) and the North Slope near Utqiagvik (Barrow), Alaska and 2) multiple areas on the discontinuous permafrost region of the Seward Peninsula north of Nome, Alaska. Through observations, experiments, and synthesis with existing datasets, NGEE Arctic provided an enhanced knowledge base for multi-scale modeling and contributed to improved process representation at global pan-Arctic scales within the Department of Energy's Earth system Model (the Energy Exascale Earth System Model, or E3SM), and specifically within the E3SM Land Model component (ELM).
Monitoring global agricultural crop conditions during the growing season and estimating potential seasonal production are critically important for market development of US. agricultural products and for global food security. The Goddard Space Flight Center Earth Sciences Data and Information Services Center Distributed Active Archive Center (GES DISC DAAC) is developing an Agricultural Information System (AIS), evolved from an existing TRMM Online Visualization and Analysis System (TOVAS), which will operationally provide satellite remote sensing data products (e.g., rainfall) and services. The data products will include crop condition and yield prediction maps, generated from a crop growth model with satellite data inputs, in collaboration with the USDA Agricultural Research Service. The AIS will enable the remote, interoperable access to distributed data, by using the GrADS-DODS Server (GDS) and by being compliant with Open GIS Consortium standards. Users will be able to download individual files, perform interactive online analysis, as well as receive operational data flows. AIS outputs will be integrated into existing operational decision support systems for global crop monitoring, such as those of the USDA Foreign Agricultural Service and the U.N. World Food Program.
The Ground Data Systems Resource Allocation Process at the Jet Propulsion Laboratory provides medium- and long-range planning for the use of Deep Space Network and Mission Control and Computing Center resources in support of NASA's deep space missions and Earth-based science. Resources consist of radio antenna complexes and associated data processing and control computer networks. A semi-automated system was developed that allows operations personnel to interactively generate, edit, and revise allocation plans spanning periods of up to ten years (as opposed to only two or three weeks under the manual system) based on the relative merit of mission events. It also enhances scientific data return. A software system known as the Resource Allocation and Planning Helper (RALPH) merges the conventional methods of operations research, rule-based knowledge engineering, and advanced data base structures. RALPH employs a generic, highly modular architecture capable of solving a wide variety of scheduling and resource sequencing problems. The rule-based RALPH system has saved significant labor in resource allocation. Its successful use affirms the importance of establishing and applying event priorities based on scientific merit, and the benefit of continuity in planning provided by knowledge-based engineering. The RALPH system exhibits a strong potential for minimizing development cycles of resource and payload planning systems throughout NASA and the private sector.
We know that climate change is poised to reshape our world, but we lack clear enough predictions about precisely how. The preponderance of these changes is associated with human activity, specifically the emission of CO 2 and other greenhouse gases. Problematically, projections of climate change continue to be marred by unacceptably large uncertainties which hamper informed decision-making and cost society a chance to adapt proactively and effectively. These uncertainties stem from deficiencies in predictions of future greenhouse gas emissions, but also from inaccuracies in the representation of the physical models used to predict the climate response to such emissions. The uncertainties in projections associated with the inaccurate representation of climate physics, chemistry and biology are similar to those that plagued the first global climate models developed fifty years ago, despite more than a factor 10 8 increase in computer performance. Our transformational question is then, how can the accuracy of climate projections be dramatically improved by applying recent advances in the computational and data sciences to train the models with the wealth of data being constantly collected about the ongoing changes in the climate system?
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Unprecedented volumes of data will be generated by research programs that investigate the Earth as a system and the origin of the universe, which will in turn require analysis and interpretation that will lead to meaningful scientific insight. Providing a widely distributed research community with the ability to access, manipulate, analyze, and visualize these complex, multidimensional data sets depends on a wide range of computer science and technology topics. Data storage and compression, data base management, computational methods and algorithms, artificial intelligence, telecommunications, and high-resolution display are just a few of the topics addressed. A unifying theme throughout the papers with regards to advanced data handling and visualization is the need for interactivity, speed, user-friendliness, and extensibility.