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All sourcesNASA NTRSDOE OSTI
NASA NTRSDate not supplied

NASA GEOS Composition Forecast System, GEOS-CF

GEOS is an Earth System Model designed to advance the use of satellite data products. Since it is a modular system, it can be run as a numerical weather prediction model and coupled to chemistry modules.

K. Emma Knowland↗
NASA NTRSDate not supplied

Improved Air Quality Forecasts using NASA’s Pandora and GEOS CF at International and Domestic Sites for SNWG Stakeholders

Air pollution prediction is complex due to the dynamic nature of atmospheric processes and the variability in data resolutions and availability. This project addresses these challenges by integrating and analyzing high-resolution ground-based measurements with global air quality models. Through this integration, we have gained a deeper understanding of how these processes interact under various conditions, which we then use to model and enhance the accuracy of forecasts.

Noussair Lazrak↗
NASA NTRS2013

Joint M3 and Diviner Analysis of the Mineralogy, Glass Composition, and Country Rock Content of Pyroclastic Deposits in Oppenheimer Crater

Here we present our analysis of the near- and mid-infrared spectral properties of pyroclastic deposits within the floor fractured Oppenheimer Crater that are hypothesized to be Vulcanian in origin. These are the first results of our global study of lunar pyroclastic deposits aimed at constraining the range of eruption processes on the Moon. In the near-infrared, we have employed a new method of spectral analysis developed in Horgan et al. (2013) of the 1 m iron absorption band in Chandrayaan-1 Moon Mineralogy Mapper (M3) spectra. By analyzing both the position and shape of the 1 m band we can detect and map the distribution of minerals, glasses, and mixtures of these phases in pyroclastic deposits. We are also using mid-infrared spectra from the Lunar Reconnaissance Orbiter Diviner Lunar Radiometer Experiment to develop ~200 m/pixel Christiansen Feature (CF) maps, which correlate with silica abundance. One of the benefits of using CF maps for analysis of pyroclastic deposits is that they can be used to detect silicic country rock that may have been emplaced by Vulcanian-style eruptions, and are sensitive to iron abundance in glasses, neither of which is possible in the near-infrared. M3 analysis reveals that the primary spectral endmembers are low-calcium pyroxene and iron-bearing glass, with only minor high-calcium pyroxene, and no detectable olivine. The large deposit in the south shows higher and more extensive glass concentrations than the surrounding deposits. We interpret the M3 spectra of the pyroclastic deposits as indicating a mixture of low-calcium pyroxene country rock and juvenile glass, and no significant olivine. Analysis of Diviner CF maps of the Oppenheimer crater floor indicates an average CF value of 8.16, consistent with a mixture of primarily plagioclase and some pyroxene. The average CF values of the pyroclastic deposits range from 8.31 in the SW to 8.24 in the SE. Since CF values within the deposits are as high as 8.49, the lower average CF values of the deposits suggest that each deposit is a mixture of crater floor material and highly mafic juvenile material consistent with either olivine or Fe-bearing pyroclastic glass. Synthesizing our M3 and Diviner results indicates that the crater floor consists of plagioclase with some pyroxene, and the pyroclastic deposits are a mix of this substrate and a glass-rich juvenile material. While we cannot determine the iron content of the glass from M3 spectra alone, the high Diviner CF values suggest that the glass is relatively iron-rich. Indeed, FeO abundances inferred from CF values using the method of Allen et al. (2012) imply that the large southern deposit exhibits a significant enhancement in iron content. This supports our hypothesis that the glass in this deposit is relatively iron-rich.

Bennett, Kristen A.↗
NASA NTRSDate not supplied

Improving Access to the GEOS Composition Forecast Model with API Development and Ingestion into Google Earth Engine

The GEOS Composition Forecast (GEOS-CF) model produces forecast and historical estimates of atmospheric composition and meteorology fields, which provide useful insight into air quality issues and events. In a year for which Canadian wildfires created adverse air quality conditions in the eastern United States, access to model fields such as PM2.5 are in high demand. The GEOS-CF team at the NASA Global Modeling and Assimilation Office (GMAO) first developed in-house solutions to improve data access via the CF API, and recently partnered with Google to ingest a collated set of model diagnostics into the Google Earth Engine (GEE) data repository. GEOS-CF model output is also being ingested into AWS storage. Creating these various open access points to GEOS-CF model diagnostics provides the public with an opportunity to easily interact with air quality information. Users are able to use a temporally consistent global grid of air quality fields in machine learning applications, mapping tools, and data informatics. Hosting GEOS-CF forecasts and the historical timeseries of these chemistry and meteorology fields in GEE allows users to create dynamic JavaScript-based air quality applications in the GEE code editor. GEOS-CF users can also access the model output via the GEE Python application programming interface (API), making it easy to perform various analyses with Python. This presentation will show two examples of accessing the GEOS-CF model through GEE. The first is an example application made in the GEE code editor which allows users to view time series plots and downscaled maps of surface level NO2. The second example exhibits using the GEE Python API to create a machine learning model to temporally gap-fill between air quality observations. These examples are an introduction to the many possible benefits of having open access to the GEOS-CF model through multiple platforms.

Callum Wayman↗
NASA NTRSDate not supplied

Improving Access to the GEOS Composition Forecast Model with API Development and Ingestion into Google Earth Engine

The GEOS Composition Forecast (GEOS-CF) model produces forecast and historical estimates of atmospheric composition and meteorology fields, which provide useful insight into air quality issues and events. In a year for which Canadian wildfires created adverse air quality conditions in the eastern United States, access to model fields such as PM2.5 are in high demand. The GEOS-CF team at the NASA Global Modeling and Assimilation Office (GMAO) first developed in-house solutions to improve data access via the CF API, and recently partnered with Google to ingest a collated set of model diagnostics into the Google Earth Engine (GEE) data repository. GEOS-CF model output is also being ingested into AWS storage. Creating these various open access points to GEOS-CF model diagnostics provides the public with an opportunity to easily interact with air quality information. Users are able to use a temporally consistent global grid of air quality fields in machine learning applications, mapping tools, and data informatics. Hosting GEOS-CF forecasts and the historical timeseries of these chemistry and meteorology fields in GEE allows users to create dynamic JavaScript-based air quality applications in the GEE code editor. GEOS-CF users can also access the model output via the GEE Python application programming interface (API), making it easy to perform various analyses with Python. This presentation will show two examples of accessing the GEOS-CF model through GEE. The first is an example application made in the GEE code editor which allows users to view time series plots and downscaled maps of surface level NO 2 . The second example exhibits using the GEE Python API to create a machine learning model to temporally gap-fill between air quality observations. These examples are an introduction to the many possible benefits of having open access to the GEOS-CF model through multiple platforms.

Callum Wayman↗