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Laura Judd

Publications and source records attributed to Laura Judd.

41 records · Page 3
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Hemispheric Airborne Measurements of Air Quality (HAMAQ)

Under NASA’s Earth Venture Suborbital program, Hemispheric Airborne Measurements of Air Quality (HAMAQ) will conduct a series of campaigns in 2028 under the Tropospheric Emissions: Monitoring of Pollution (TEMPO) geostationary satellite instrument. HAMAQ plans include two deployments, including the Mexico City megalopolis and another North American site yet to be selected. The effort will include two aircraft, NASA’s B777 for in situ sampling and G-III for remote sensing. These aircraft will be used to complete a system of integrated observations, combining satellite observations, ground-based monitoring and research observations with air quality modeling. HAMAQ field intensives will serve multiple objectives to include: improving the use of satellite observations in concert with traditional ground monitoring to inform air quality; assessing emissions to better understand their timing and source apportionment; advancing the development of satellite proxies for air quality; and assessing the factors controlling local air quality in each location sampled. Given the long lead time for this campaign, this poster welcomes discussion from the community on strategies and candidate sites for the second deployment. Given the broad applicability of the HAMAQ science objectives and observing strategy, possible partnerships to extend the pursuit of the larger vision of HAMAQ to also sample in Asia and Europe are of interest.

James H Crawford↗
NASA NTRSDate not supplied

PM 2.5 Concentrations over Major Metropolitan Regions Inferred from Airborne High Spectral Resolution Lidar Measurements Using Machine Learning Regression

We use measurements of near-surface aerosol backscatter, extinction, and depolarization acquired by four NASA Langley Research Center airborne High Spectral Resolution Lidars (HSRLs) to develop a machine learning regression methodology to infer PM2.5 concentrations at the surface and aloft. These airborne HSRL measurements were acquired over major metropolitan regions in the United States and Asia during more than 170 flights since 2010. Hourly surface PM2.5 measurements from the EPA air quality system and similar networks in other countries acquired within 10 km and 15 minutes of these near-surface HSRL measurements are used to train models that compute PM2.5 concentrations from the HSRL measurements. We examine several regression methods and find that exponential Gaussian Process algorithms consistently give the best performance in terms of the lowest root-mean-square (RMS) errors and the highest correlations. Model performance varies significantly depending on various combinations of HSRL aerosol measurements (e.g., aerosol backscatter, extinction, depolarization, backscatter color ratios, lidar ratios, aerosol optical thickness) and retrievals (e.g., mixed layer height, aerosol type) used in the regressions. Models that use near-surface measurements of aerosol backscatter and aerosol intensive properties such as depolarization, backscatter color ratio, and lidar ratio typically give the best performance with RMS errors around 4 mg/m3 and correlation coefficients above 0.9. HSRL measurements were often acquired when the aircraft flew systematic “raster-scan” patterns for several hours over these cities. These flight patterns enabled measurements of the spatial, temporal, and vertical variabilities in the distributions of aerosol backscatter and aerosol intensive properties and allowed us to derive the corresponding variabilities in PM2.5 concentrations. We present examples of such variabilities over urban areas in the United States as well as Asia. We describe also how the distribution of surface PM2.5 varies with aerosol type and use these retrievals to examine model simulations of surface PM2.5 in these metropolitan regions. We also discuss how this methodology may be applied to measurements from satellite lidars such as CALIOP on CALIPSO and ATLID on EarthCARE.

lidar↗
NASA NTRSDate not supplied

A NASA Airborne Lens Into Air Quality Field Studies of the Last Decade

Field studies add an enhanced perspective to our everyday observing system for air quality with the goals of better understanding the air we breathe and identifying solutions toward a healthier future. Over the last decade, over 10 air quality field studies were conducted around the US with other ranging internationally with support through large agency-led efforts down to the grass-roots collaborative style. This presentation will highlight how NASA airborne observations have fit as one piece of the integrated observing system for air quality during these field studies. Research topics to be discussed are centered around the idea of the transition to geostationary air quality satellite observations. Specific topics include how temporally and spatially resolved measurements help us learn about knowledge gaps in NOx emissions, satellite-proxies for surface air quality, as well as evaluating state-of-the-art chemical transport models and using the enhanced observations as tools for understand what models/satellites can and cannot resolve. Each research topic will aim to discuss how the field measurement strategies that made this work possible as well as specific challenges that still exist to take these results further. Lastly, this presentation will discuss strategies to be carried forward as well as new ones as a peek into future air quality airborne field work.

Laura Judd↗