ALBERT

Description: No abstract available

Description: No abstract available

Description: The highest priority science goals for Ice Giant missions are: 1) Interior structure of the Planet, and 2) Bulk composition that includes isotopes and noble gases. The interaction between the planetary interior and the atmosphere requires sustained global measurements. Noble gas and Isotope measurements require in situ measurement. Drag modulated aerocapture utilizing ADEPT offers more mass delivered to the Ice Giants than with propulsive orbit insertion. The Galileo Probe entered at a hot spot which created interpretation challenges. Juno is providing valuable orbital measurements, but without in situ measurements the story is incomplete. Planetary scientists interested in Ice Giant missions should perform mission design studies with these new Entry System technologies to assess the feasibility within the context of the international collaboration framework. A mission architecture that includes probe(s) along with an orbiting spacecraft can deploy the probes at the desired location while taking simultaneous measurements from orbit to provide invaluable data that can correlate both global and local measurements. Entry System Technologies currently being developed by NASA are poised to enable missions that position the Orbiter & Probes through drag modulated aerocapture (ADEPT), and HEEET enables the Probes to survive the extreme environments encountered for entry into the atmospheric interior.

Description: Decision-support tools for maintaining pairwise aircraft separation rely on conflict detection to alert the operator when the predicted trajectories of aircraft will result in a loss of separation. But aircraft frequently do not follow their predicted trajectories exactly. This can cause missed alerts and the failure of strategic separation procedures. We present a technique for modeling a bounded region of uncertainty around a four-dimensional predicted trajectory and an algorithm for detecting conflicts between trajectories modeled in this way that avoids missed alerts as long as the aircraft remain within the specified regions of uncertainty. In addition, we present an algorithm for detecting the intrusion of a trajectory modeled in this way into an area hazard modeled as a polygonal region. The size of the region of uncertainty can vary along the trajectory continually and independently in the along-path, cross-track, and vertical dimensions, providing an opportunity to reduce the likelihood of false alerts while protecting against typical prediction errors. The algorithm has been implemented in the Autonomous Operations Planner, a NASA Langley prototype decision support tool for airborne self-separation.

Description: The presentation discusses operational suitability metrics computed from the closed-loop simulations of EO/IR and DAA systems.

Description: This paper determined the feasibility of an adaptive hexapod simulator motion algorithm based on aircraft roll stability. An experiment was conducted that used a transport aircraft model in the Vertical Motion Simulator at NASA Ames Research Center. Eighteen general aviation pilots flew a heading-capture task and a stall task consecutively under four motion configurations: baseline hexapod, adaptive hexapod, optimized hexapod, and full motion. The adaptive motion was more similar to the baseline hexapod motion in the heading-capture task when the aircraft was more stable, and more similar to the optimized hexapod motion in the stall task when the aircraft was more unstable. Pilot motion ratings and task performance in the heading-capture task under the adaptive hexapod motion were more similar to baseline hexapod motion compared to optimized hexapod motion. However, motion ratings and task performance in the stall task under the adaptive motion were not significantly more similar to the optimized hexapod motion compared to baseline hexapod motion. Motion ratings and overall task performance under optimized hexapod motion as opposed to baseline hexapod motion were always more similar to the full motion condition. This paper showed that adaptive motion based on aircraft stability is feasible and can be implemented in a straightforward way. More research is required to test the adaptive motion algorithm in different tasks.

Description: NASAs Earth Observing System Data and Information System (EOSDIS) open source Cumulus software is designed as a common set of code and services that can be used to create a pipeline to deliver and manage earth science data in the cloud. Cumulus strives to create an ecosystem on the foundation of open source that unites those with shared problems and goals by encouraging users to contribute solutions back to the platform. Large parts of ingesting and managing data are common and much of what is created can be used by others. Our goal is to maximize collaboration and code reuse while allowing users to design a custom solution that meets their needs without having to take on extraneous functionality. In this talk we will describe how the Cumulus ecosystem works beyond just open source software. We will review the technology, the successes and challenges, and the evolution and future of Cumulus as an ecosystem.

Description: Social media data can provide useful real-time and historical information relating to the natural world, but managing this data poses challenges. Scientists at GES DISC are exploring the potential of Twitter data to augment precipitation data from the Global Precipitation Measurement (GPM) mission. However, the format of Twitter data is unconventional in the context of NASA data centers, resulting in frustration for scientists who need to work with the data. This study investigated procedures and standards needed to properly manage Twitter data to make them compatible with these data centers. After comparing databases, the study found that the MongoDB database was best suited for the storage of raw Twitter data due to its flexibility, ability to be accessed by multiple users, and querying functionality. The study used the Python package Zarr to transform processed Twitter data into a gridded format similar to that of satellite data. Each Tweet was mapped onto a time-space grid; each grid location contained information about Tweet attributes and precipitation. The study developed a pipeline for downloading, storing, and gridding Twitter data and transformed Twitter data into an understandable format for users of NASA satellite data.

Description: According to CDC, a sharp increase in reported Valley fever cases (Coccidioidomycosis) has been found between 2009 and 2012. Environmental conditions play an important role for Valley fever outbreaks. For example, dust storms can significantly increase the amount of the fungus Coccidioides in the air and associated strong winds can transport the fungus to other areas. In last years AGU session, we reported a preliminary study to investigate hydrometeorological conditions and their connection with dust storm activities in southwestern United States. We found wind is a major contributing factor for the seasonal variation of dust storm activities. Interannual variation of the regional hydrometeorological conditions are closely linked to the large-scale environment such as the Pacific Decadal Oscillation (PDO). Strong winds are linked with a number of weather events such cold front passages, thunderstorms that produce downbursts and strong winds, the Santa Ana winds, etc. In this presentation, we will report the results of our latest investigation on meteorological conditions associated with Valley fever outbreaks in southwestern United States, using NLDAS (North American Land Data Assimilation System), GPM (Global Precipitation Measurement), and MERRA-2 (Modern Era Retrospective-analysis for Research and Applications, Version 2) hourly datasets, from the NASA Goddard Earth Sciences (GES) Data and Information Services Center (DISC).

Description: Machine learning (ML) is being increasingly utilized in Earth science research. Benefits of ML include efficiency, reduction of human error, and ability to extract hidden patterns within data. However, the mutual lack of each others domain knowledge by ML and Earth science stands as a barrier to timely and effective implementation. Earth science, in particular, faces challenges in generating sample data, compared to those of traditional ML problems such as face recognition or stock predictions, where data is abundant and not lacking in ground truth, which is necessary for labeling. Earth science data are more varying in formats, such as HDF5 and image resolutions, and are not standardized across instruments, even within a given Earth science discipline. Previous studies have been done to outline the specific challenges that Earth science faces with ML, while others have focused on using existing publications to mine information efficiently. Other resources such as Scikit-Learn have developed decision trees for choosing appropriate machine learning algorithms, but application within Earth science subjects becomes much more complex. For the current study, we propose a methodology and tool that aids in implementation of ML in Earth science using natural language processing (NLP). Our work comprises three main parts: (1) analyzing existing publications related to ML and Earth science, using natural language processing: (2) extracting from the publications information on ML models subjects in Earth Science: and (3) visualizing the extracted relationships as a network graph. The resulting network graph should aid the Earth science communities in applying optimal ML algorithms and guiding data preparation through visualization of similar studies. The network graph and analysis of document similarity will be the basis of our next step, which is to develop a decision tree for selecting optimal machine learning methodologies for specified Earth science applications.