An approach for verifying biogenic greenhouse gas emissions inventories with atmospheric CO 2 concentration data
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Stephen M Ogle↗
Engineering topics
Kenneth Davis
Publications and source records attributed to Kenneth Davis.
TPSAS-NF1676L-32105-DND
NASA Langley Research Center is developing the High-Altitude Lidar Observatory (HALO) system to address the observational needs of NASA’s weather, climate, carbon cycle, and atmospheric composition focus areas. HALO is a multi-function airborne lidar being developed to measure atmospheric H2O and CH4 mixing ratios and aerosol/cloud/ocean optical properties using the Differential Absorption Lidar (DIAL) and High Spectral Resolution Lidar (HSRL) techniques, respectively. To respond to a wide range of airborne process studies, HALO can be rapidly reconfigured to provide either CH4 DIAL+HSRL, H2O DIAL+HSRL, or CH4 DIAL+H2O DIAL measurements using three different laser transmitters. During spring 2018 NASA Langley demonstrated the world’s first combined airborne CH4 DIAL and HSRL measurements from the Langley King Air aircraft during four test flights. The HALO methane configuration also participated in the Long Island Sound Tropospheric Ozone Study (LISTOS) air quality field campaign on the NASA B200 aircraft. The flown instrument configuration employed the DIAL technique at 1645 nm for column and multi-layer range resolved measurements of CH4 concentrations, and the HSRL technique at 532 nm to make independent, unambiguous retrievals of aerosol extinction and backscatter. It also employed the standard backscatter technique at 1064 nm and is polarization-sensitive at the 1064/532 nm wavelengths. The addition of the HSRL channels provides context to the airborne CH4 DIAL measurements such as source attribution, transport, and vertical mixing through mixed layer height retrievals, as well as providing the critical capability to validate aerosol induced biases from passive space-borne measurements of column CH4. In this presentation we focus on the instrument capabilities, initial CH4 measurements over oil and gas production sites in coordination with the ACT-America campaign, CH4 signatures over the Long Island Sound domain during the LISTOS campaign, and prospects for future airborne campaigns with cohosted payloads consisting of active and passive sensors.
Rory Barton-Grimley↗
Short Term Effects of 2019 Early Summer Floods on Atmospheric CO2 in the Midwestern and Southern Regions of the United States as Seen from the NASA GEOS Model
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CO2↗
Evaluation of GHGSat Methane Emission Estimations of South Side Landfill in Indianapolis, Indiana
As global methane (CH4) continues to grow, accurate quantification of CH4 emission sources has become a high priority for climate management. Urban CH4 emissions in the US could potentially account close to 20% of the national CH4 anthropogenic emissions. While isolated insitu urban CH4 emissions quantification projects have been successful, they require sophisticated planning and prolonged analyses. Such studies are imperative, but they cannot easily operate in every city. An ability to quantify the magnitude of CH4 point sources in a variety of cities with a satellite could support wide scale urban CH4 emissions quantification. Whole-earth scanning satellite instruments such as the TROPOMI and GOSAT are too coarse and noisy to provide meaningful measurements over cities with mid-strength emissions and most point-source emissions in general. It may be possible to address this deficiency utilizing a product with the high spatial resolution of 30-50 m from a private company GHGSat Inc. These sensors are specifically designed to identify and quantify CH4 emissions from diverse types of point sources of varying intensities. In this work we analyze a year (05/2023-05/2024) of GHGSat scans over South Side Landfill in downtown Indianapolis, Indiana and assess the instruments’ accuracy and precision with respect to prior estimations. The results indicate that the data are helpful for estimating landfill’s emissions (~750 kg/hr ± 200 kg/hr) over months-to-a-year time frame but may not be appropriate for identifying daily or even seasonal variations with any trustable degree of certainty, at least not in an urban environment.
Nikolay Balashov↗