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Cloud Computing for Science Data Processing in Support of Emergency Response

The new package can be quickly deployed on a cloud computing platform only for as long as processing of the time series data is required. Stennis Space Center, Mississippi In a crisis, up-to-date information is one of the most important commodities for decision-makers. Remote sensing data have been instrumental in regional scale damage detection and recovery progress monitoring after significant disasters. However, using remotely sensed data to support an emergency response requires not only the availability of hardware, software, and manpower to process and analyze the data, but also the time to stage the datasets that are required for analyses. Additionally, the volume of remote sensing data that needs to be processed to detect temporal changes accurately in a terrestrial or oceanic ecosystem can easily exceed several terabytes, even for a small region. This is because emergency response requires the use of well-calibrated remotely sensed data products, like those that are generated by the MODIS (Moderate Resolution Imaging Spectroradiometer) Adaptive Processing System (MODAPS). These data sets are stored and distributed by the Level 1 and Atmosphere Archive and Distribution System (LAADS), both located at Goddard Space Flight Center (GSFC), and are necessary to create the custom data products that are needed and used for emergency management situations. Generally, the MODIS datasets are downloaded from GSFC, stored at the user’s facility, and then processed locally. This approach is standardly used by researchers worldwide.

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Simulating Images of a Rising or Setting Sun

NASA’s Jet Propulsion Laboratory, Pasadena, California This software simulates Sun images observed on the ground and from space with an easy-to-use analytical approach and with high accuracy. It is a simple, analytical approach with the accuracy of a rigorous, multilayer model. The existing analytical approach cannot predict when the image of the Sun is blocked by the surface of the Earth, but the current approach can.

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Partition Level Application Test for Orion (PLATO)

Lyndon B. Johnson Space Center, Houston, Texas For Orion Exploration Flight Test One (EFT-1), the unit-under-test for flight software verification has been chosen as the entire integrated flight software load. At the time of this reporting, the unit test tool, while powerful, operates on very small units, usually classes. This leaves a sizable gap between unit testing and verification. Orion flight software is divided into ARINC 653 partitions, and partition level testing is in this large gap.

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Computer Modeling for Generation of Synthetic Radio Reflection-Transmission Tomography (RRTT) Data

Goddard Space Flight Center, Greenbelt, Maryland Numerical algorithms capable of generating synthetic radio tomography data (reflection transmission data) for asteroids, comets, and other near-Earth orbits (NEOs) were developed. Future missions to main asteroid belt objects, NEOs, and other small bodies of the solar system will aim to investigate the surface and subsurface compositions and internal structure of these objects with help of an onboard, low-frequency radio sounder. The resulting numerical model thus developed will be useful in performing trade studies required for designing an optimum radio sounder for the future missions. The forward numerical model will also be important for estimating structural properties of the small objects from the data collected by these future missions.

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Designing Planning Information for Automation into PRL

Lyndon B. Johnson Space Center, Houston, Texas Automation and autonomy are key elements in realizing the vision for space exploration. The NASA Exploration Technology Development Program (ETDP) has been developing several core autonomy capabilities, one of which is called a procedure representation language (PRL). PRL can be automatically translated into code that can be executed by NASA-developed autonomous executives.

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The OCO-2 Level 2 Retrieval Algorithm

Algorithm derives estimates of the column averaged atmospheric CO2. NASA’s Jet Propulsion Laboratory, Pasadena, California The Orbiting Carbon Observatory (OCO) strives for trace gas observations with unprecedented accuracy and precision. This requires a retrieval algorithm with many major improvements over existing retrieval software in the representation of the transfer of solar radiation through the atmosphere and instrument, such as full multiple-scattering calculation for each iteration step and correction for effects of polarization. In addition, the software allows retrieval of space-based and ground-based observations, so that potential algorithmic biases can be minimized for validation experiments. Furthermore, due to the flexible architecture of the software, spectra of existing similar instruments can be analyzed, which facilitates early testing using real space-based observations.

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Ionospheric Slant TEC Analysis Using GNSS-Based Estimation (IonoSTAGE)

At the time of this reporting, IonoSTAGE has been operated successfully under both UNIX and Macintosh operating systems. NASA’s Jet Propulsion Laboratory, Pasadena, California As signals emitted by global navigation satellite systems (GNSS) propagate toward users on or near the surface of the Earth, they experience delay due to the presence of charged particles in the ionosphere. Currently, ionospheric delay is the largest source of GNSS positioning error. To guarantee the safety of airline navigation based upon GNSS signals, satellite-based augmentation systems have been developed to ensure the accuracy, integrity, availability, and continuity of user position estimates derived from GNSS measurements.

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