Engineering topics

David Giles

Publications and source records attributed to David Giles.

3 records

NASA NTRSDate not supplied

Open-Source Science-led Development of the Atmosphere Observing System (AOS) Mission Science Data System (SDS)

The Earth System Observatory (ESO) Atmosphere Observing System (AOS) mission will provide space-based and suborbital observations of collocated cloud, dynamic, precipitation and aerosol processing leading to improved weather, air quality, and climate predictions. The AOS Science Data System (SDS) will be a system of systems developed within the Cloud to manage the research and operational processing of AOS mission orbital and suborbital sensors and curate these data for reprocessing (e.g., in near real-time or by collection) and transfer them to a NASA Distributed Active Archive Center (DAAC) for long-term storage. Further, AOS SDS will follow guidelines provided by NASA Earth Science Data Systems (ESDS) program including standard conventions for data file formats, naming, and metadata to improve data interoperability, interpretability, usability, discovery, provenance, and spatiotemporal representativeness. The AOS mission follows NASA’s lead in making a commitment to Open-Source Science (OSS) including the sharing of data, software, and knowledge in an open and timely manner. Each of the AOS SDS system components will be developed with open-source concepts including components of SDS itself as well as AOS mission algorithms. Further, the AOS SDS assumes the role to lead and facilitate OSS activities for the AOS mission. This presentation describes the framework of the AOS SDS and its integral part in facilitating OSS within the AOS mission.

David Giles↗

NASA NTRSDate not supplied

Evaluating Machine Learning Approaches to Plume Tracking

On July 15, 2022, the Hunga Tonga-Hunga Ha’apai (HTHH) submarine volcano erupted, propelling trace gasses and ash through the troposphere and up into the stratosphere. Previous studies manually tracked the aerosol and trace gas plumes over time across different positions in the southern hemisphere. Using imagery from NASA’s Earth Observing System, including MODIS aerosol products and OMI sulfur dioxide products, this research demonstrates how open-source machine learning (ML) models, like Meta’s Segment Anything Model (SAM), can perform automatic plume tracking following the Hunga Tonga eruption. This extensible methodology, and modular data processing and modeling pipeline, establishes a framework for systematically and rapidly studying natural disasters, including additional volcanic eruptions and large-scale wildfires. By combining advanced machine learning techniques, such as SAM’s zero-shot learning, with large volumes of NASA’s Earth Observation and remote sensing data, this work shows how AI and open science can accelerate research and generate actionable results, even for unprecedented events. The tools and technologies presented here can help translate earth science to action from NASA’s current and future Earth observing satellite missions, and assist researchers and stakeholders in understanding, mapping, and responding to natural disasters in a changing world.

machine learning↗

NASA NTRSDate not supplied

Tracking the Hunga Tonga-Hunga Ha’apai Eruption Stratospheric Aerosol and Trace Gas Plumes Using Machine Learning

The Hunga Tonga-Hunga Ha’apai (HTHH) submarine volcano had an explosive eruption phase on January 15, 2022, that thrusted ash, gases, and water vapor through the troposphere and into the stratosphere. The stratospheric volcanic plume included aerosol precursor gases such as SO2 and H2S as well as anomalously high water vapor concentrations due to the submarine oceanic origin. With these atmospheric constituents, the sulfuric gases and water vapor formed sulfate (SO4) particles via gas-to-particle reactions and these aerosols likely increased in size due to hygroscopic growth within anomalously humid regions. Strong easterlies and gravity waves propagated the volcanic impacts throughout the stratosphere. Orbital and suborbital passive sensor retrievals detected changes in the aerosol and trace gas characteristics within the atmospheric column for cloud-free regions over the southern hemisphere. While the CALIPSO lidar can detect aerosol layers in the stratosphere, passive sensors such as MODIS retrieved the total column aerosol abundance and characteristics. Previous studies used manual tracking methods to determine volcanic plume positions and compared them to ground observations. In this study, we examine the machine learning (ML) approaches including segmentation, object detection, and object tracking to identify and track aerosol and trace gas plumes using orbital and suborbital sensor data. This ML implementation strives to provide a more systematic approach to separate total column effects from those of the stratosphere. Similar ML tracking may be useful for stratospheric impact events observed historically by CALIPSO and in the future with EarthCare and the Atmosphere Observing System (AOS) lidar-capable missions.

Rhys Leahy↗