Abstract

Abstract. Sub-grid variability (SGV) in atmospheric trace gases within satellite pixels is a key issue in satellite design and interpretation and validation of retrieval products. However, characterizing this variability is challenging due to the lack of independent high-resolution measurements. Here we use tropospheric NO2 vertical column (VC) measurements from the Geostationary Trace gas and Aerosol Sensor Optimization (GeoTASO) airborne instrument with a spatial resolution of about 250 m×250 m to quantify the normalized SGV (i.e., the standard deviation of the sub-grid GeoTASO values within the sampled satellite pixel divided by the mean of the sub-grid GeoTASO values within the same satellite pixel) for different hypothetical satellite pixel sizes over urban regions. We use the GeoTASO measurements over the Seoul Metropolitan Area (SMA) and Busan region of South Korea during the 2016 KORUS-AQ field campaign and over the Los Angeles Basin, USA, during the 2017 Student Airborne Research Program (SARP) field campaign. We find that the normalized SGV of NO2 VC increases with increasing satellite pixel sizes (from ∼10 % for 0.5 km×0.5 km pixel size to ∼35 % for 25 km×25 km pixel size), and this relationship holds for the three study regions, which are also within the domains of upcoming geostationary satellite air quality missions. We also quantify the temporal variability in the retrieved NO2 VC within the same hypothetical satellite pixels (represented by the difference of retrieved values at two or more different times in a day). For a given satellite pixel size, the temporal variability within the same satellite pixels increases with the sampling time difference over the SMA. For a given small (e.g., ≤4 h) sampling time difference within the same satellite pixels, the temporal variability in the retrieved NO2 VC increases with the increasing spatial resolution over the SMA, Busan region, and the Los Angeles Basin. The results of this study have implications for future satellite design and retrieval interpretation and validation when comparing pixel data with local observations. In addition, the analyses presented in this study are equally applicable in model evaluation when comparing model grid values to local observations. Results from the Weather Research and Forecasting model coupled with Chemistry (WRF-Chem) model indicate that the normalized satellite SGV of tropospheric NO2 VC calculated in this study could serve as an upper bound to the satellite SGV of other species (e.g., CO and SO2) that share common source(s) with NO2 but have relatively longer lifetime.

Highlights

  • Characterizing sub-grid variability (SGV) of atmospheric chemical constituent fields is important in both satellite re-Published by Copernicus Publications on behalf of the European Geosciences Union.W

  • We focus on the GeoTASO measurements made during the Korea–United States Air Quality (KORUS-AQ) field experiment in 2016 (Crawford et al, 2021)

  • We focus on GeoTASO retrievals of tropospheric NO2 vertical column (VC)

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Summary

Introduction

Characterizing sub-grid variability (SGV) of atmospheric chemical constituent fields is important in both satellite re-. Quantification of satellite SGV has historically been limited by insufficient spatial coverage of in situ measurements and is a key issue in designing, understanding, validating, and correctly interpreting satellite observations This is especially important in the satellite instrument development process during which the required measurement precision and retrieval resolution need to be defined in order to meet the mission science goals. Broccardo et al (2018) used aircraft measurements of NO2 from an imaging differential optical absorption spectrometer (iDOAS) instrument to study intra-pixel variability in satellite tropospheric NO2 column over South Africa, whilst Judd et al (2019) evaluated the impact of spatial resolution on tropospheric NO2 column comparisons with in situ observations using the NO2 measurements of the Geostationary Trace gas and Aerosol Sensor Optimization (GeoTASO).

Data and methods
GeoTASO instrument
The 2016 KORUS-AQ field campaign
The 2017 SARP field campaign
Satellite pixel random sampling for spatial variability
Satellite pixel random sampling for temporal variability
Spatial structure function
WRF-Chem simulation
Results
Discussions and implications
Conclusions
Full Text
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