Synergy of Satellite‐ and Ground‐Based Aerosol Optical Depth Measurements Using an Ensemble Kalman Filter Approach

Abstract

Satellite‐ and ground‐based remote sensing are two widely used techniques to measure aerosol properties. However, neither is perfect in that satellite retrievals suffer from various sources of uncertainties, and ground observations have limited spatial coverage. In this study, focusing on improving estimates of aerosol information on large scale, we develop a data synergy technique based on the ensemble Kalman filter (EnKF) to effectively combine these two types of measurements and yield a monthly mean aerosol optical depth (AOD) product with global coverage and improved accuracy. We first construct a 474‐member ensemble using 11 monthly mean AOD data sets to represent the variability of the AOD field. Then Moderate Resolution Imaging Spectroradiometer AOD retrievals are selected as the background field into which ground‐based measurements from 135 Aerosol Robotic Network sites are assimilated using the EnKF. Compared with satellite data, the bias and root‐mean‐square errors of the combined field are greatly reduced, and correlation coefficients are greatly improved. Moreover, cross validation shows that at locations where surface observations were not assimilated, the reduction in root‐mean‐square error and bias and the increase in correlation can still reach ~20%. Locations where the spatial representativeness of AOD is large or the site density is high are where the greatest changes are typically found. This study shows that the EnKF technique effectively extends the information obtained at surface sites to a larger area, paving the way for combining information from different types of measurements to yield better estimates of aerosol properties as well as their space‐time variability.

Analysis of the Influence of Precipitation and Wind on PM2.5 and PM10 in the Atmosphere

“…erefore, the research on management of PM2.5 and PM10 has become a hot topic in recent years. Besides, it is found to significantly affect the cloud formation and precipitation through aerosol-radiation interaction or aerosol-cloud interaction [10][11][12][13][14], even though the net effect remains highly debated [15][16][17]. e main factors affecting the concentration of atmospheric particulate matter include emission sources and meteorological factors [18][19][20][21][22][23][24].…”

Reducing Aerosol Forcing Uncertainty by Combining Models With Satellite and Within‐The‐Atmosphere Observations: A Three‐Way Street

“…This quantity is widely used as a constraint on climate models or for model evaluation (e.g., Buchard et al, 2017;Gelaro et al, 2017;Gliß et al, 2021;Kinne et al, 2006;Randles et al, 2017;Rubin et al, 2017;Schutgens et al, 2020), though non-negligible differences among AOD retrieval products must yet be addressed (J. Li et al, 2020;Sogacheva et al, 2020). Tables summarizing currently available satellite AOD products are given in Sogacheva et al (2020) and Kahn and Samset (2022).…”

The Unmanned Systems Research Laboratory (USRL): A New Facility for UAV-Based Atmospheric Observations

“…In-situ observations of aerosol and cloud interaction are essential for improving our fundamental understanding of their role in the climate system [59]. Properties of aerosolcloud interactions in the climate change Arctic hotspot are an important input to climate models, while the impact of clouds remains one of the largest uncertainties [60,61]. The 7th Pallas Cloud Experiment (PaCE) in Finland was organized in September 2017 and focused on cloud-aerosol interactions through in-situ and remote sensing methods.…”