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Yonghoon Choi

Publications and source records attributed to Yonghoon Choi.

34 records · Page 2
All sourcesNASA NTRSDOE OSTI
NASA NTRSDate not supplied

Biomass Burning and Fossil Fuel Apportionment via Greenhouse Gas Enhancement Ratios Over Southeast Asia as Measured During ASIA-AQ

As Southeast Asian economies continue to grow, so will their contribution to global greenhouse gas (GHG) emissions, driven primarily by increases in fossil fuel combustion. Left unchecked, these emissions will negatively impact air quality and climate, therefore it is imperative that emission sources are properly identified and accounted for in emission inventories. Enhancement ratios of GHGs have been used to characterize regional emissions as either dominated by fossil fuel combustion or biomass burning. In particular, airborne assessment of short-term continuous emission ratios has proven useful to quantify relative contributions of fossil fuel and biomass burning to GHG emissions. The 2024 Airborne and Satellite Investigation of Asian Air Quality (ASIA-AQ) campaign, flying over the Philippines, Korea, Thailand, and Taiwan, sampled a variety of emissions including significant biomass burning, local urban pollution, and transport events. This work will explore the impact of GHG emissions on the distinctive pollution of the sampled locations. As airborne GHG measurements over Southeast Asia are scarce if not nonexistent, this work provides a crucial link between established ground-based measurements and state-of-the-art satellite observations such as those from South Korea’s Geostationary Environment Monitoring Spectrometer (GEMS). Analysis of GHG enhancement ratios in East Asia will lead to more accurate emission inventories which can be used to implement more effective GHG control measures leading to improved air quality and minimizing the effect on climate.

ASIA-AQ↗
NASA NTRSDate not supplied

Three Years of Airborne Observations During NASA Activate: A Statistical Summary of Chemical, Optical, and Microphysical Aerosol Properties Over the Western North Atlantic Ocean

Airborne observations are critical to understanding Earth’s climate system and surface-level air quality by providing both spatial and vertical information that is not possible through ground measurement networks or satellite observations. Airborne platforms allow assessments of long-range transport, vertical redistribution, boundary-layer dynamics, and cloud-aerosol-interactions that are critical to model evaluation and satellite validation. Still, the high cost and effort of aircraft operations can limit measurement campaigns to short, focused time periods. Multiple deployments are often necessary for seasonal comparisons, and results can be limited if target phenomenon are infrequent. Subsequent analyses typically focus on case studies, especially if deployment conditions are climatologically anomalous. The NASA ACTIVATE (Aerosol Cloud meTeorology Interactions oVer the western ATlantic Experiment) mission was designed to overcome these limitations by operating semi-continuously over multiple months during multiple seasons. Flight plans were intentionally simple, repetitive, and systematic to provide a statistically robust dataset that could be utilized similarly to a ground-network. Here, we present an overview of three years (2020-2022) of aerosol measurements from the NASA HU-25 Falcon aircraft supporting the ACTIVATE mission. Data from 179 flights were considered, the majority of which were based at NASA Langley Research Center and flown locally over the Wester North Atlantic Ocean just east of Hampton, VA, USA. Airmass characteristics tended to transition from continental outflow near the coast to mostly marine over the open ocean. Seasonal, diurnal, spatial, and vertical trends in aerosol microphysical, optical, and chemical properties are discussed in the context of synoptic scale meteorology. Statistical benefits of the ACTIVATE sampling strategy are discussed with regard to future model evaluation.

Luke D Ziemba↗
NASA NTRSDate not supplied

Atmospheric Variability and Measurement Uncertainty: Pitfalls in Averaging in situ Data

Satellite measurements and atmospheric models, two essential components of the integrated global observing system, provide crucial tools for monitoring and predicting regional and global foci, spanning numerous Earth Science fields. Unfortunately, models can lack the spatial and temporal resolution needed to resolve finer scale structure. Satellite measurements also have similar tempo-spatial restriction issues, but additionally can only measure certain species and can have biases that must be evaluated. Ground measurement networks are critical components of this system, by providing both independent inputs of species needed by models (including those that satellites do not provide) and assisting with investigation of biases in satellite products. Similarly, aircraft measurements play a vital role in closing the gaps between satellite measurements, model products, and ground monitoring networks by providing high accuracy, high-resolution data on local to regional spatial scales. Therefore, both ground-based and airborne observations are widely used to assess model predictions and satellite observations. One of the great challenges in using both ground and aircraft data in this fashion is matching the data temporally and spatially to the model/satellite data. A meaningful comparison with model or satellite requires a solid assessment of the variability of the in-situ measurements, which include both the instrument uncertainty and the statistical uncertainty due to atmospheric variability. While instrument uncertainty is generally more straightforwardly characterized, it can be challenging to accurately capture this variability uncertainty as it often presents in a non-Gaussian manner (e.g. emission plumes, frontal passages). We will present results examining spatial and temporal variability over a selection of scales relevant to satellite measurements and models of several in situ measurement species spanning both airborne and ground measurements. The extent of non-Gaussian variability will be quantified, and we will discuss additional statistical parameters that help assess the fitness of gaussian variability assumption when temporally or spatially averaging.

satellite validation↗