MAIAC-based climatology of atmospheric iron-oxide dust species from DSCOVR EPIC observations

This study investigates the vertical distribution and seasonal climatology of absorbing iron-oxide minerals, specifically hematite and goethite, in atmospheric dust using the updated MAIAC EPIC version 3 algorithm. Leveraging data from July 2015 to December 2023, the key innovation is the improved Level 2 product which now incorporates Aerosol Layer Height (ALH), enabling the first-ever long-term characterization of their vertical and seasonal distribution globally. Our analysis reveals distinct seasonal and spatial patterns across major dust-emitting regions. Critically, we find that the spectral absorption properties, such as the imaginary refractive index (k) and Single Scattering Albedo (SSA), are strongly proportional to the mass fraction of iron oxides, highlighting the potential of SSA at UV wavelengths as a valuable tool for global monitoring. Furthermore, we confirm that hematite exhibits significant seasonal vertical variability; its concentration is high near the surface in winter but extends up to 2–4 km into the free troposphere in summer, a finding consistent with independent CALIOP observations. The resulting comprehensive climatology provides novel observational constraints for Earth System Models (ESMs), particularly for accurately modeling dust-radiative forcing. The developed methodology is readily applicable to other UV-NIR satellite platforms, such as PACE, GEMS, and TEMPO, demonstrating its broad utility for future atmospheric composition monitoring efforts.

Authors

Institutions

Publication Details

Journal
Frontiers in Remote Sensing
Published
2025-10-30
DOI
https://doi.org/10.3389/frsen.2025.1676851
Primary Topic
Atmospheric aerosols and clouds
Type
article
Field-Weighted Citation Impact
0.00

Funders

Controls
|||
ALL TIME
JAN
FEB
MAR
APR
MAY
JUN
JUL
AUG
SEP
OCT
article

MAIAC-based climatology of atmospheric iron-oxide dust species from DSCOVR EPIC observations

Sergey Korkin, Alexei Lyapustin, E. J. Hyer, Myungje Choi et al.
Frontiers in Remote Sensing
Atmospheric aerosols and clouds
article

MAIAC-based climatology of atmospheric iron-oxide dust species from DSCOVR EPIC observations

Sergey Korkin, Alexei Lyapustin, E. J. Hyer, Myungje Choi, Sujung Go, Yujie Wang
article en

Abstract

This study investigates the vertical distribution and seasonal climatology of absorbing iron-oxide minerals, specifically hematite and goethite, in atmospheric dust using the updated MAIAC EPIC version 3 algorithm. Leveraging data from July 2015 to December 2023, the key innovation is the improved Level 2 product which now incorporates Aerosol Layer Height (ALH), enabling the first-ever long-term characterization of their vertical and seasonal distribution globally. Our analysis reveals distinct seasonal and spatial patterns across major dust-emitting regions. Critically, we find that the spectral absorption properties, such as the imaginary refractive index (k) and Single Scattering Albedo (SSA), are strongly proportional to the mass fraction of iron oxides, highlighting the potential of SSA at UV wavelengths as a valuable tool for global monitoring. Furthermore, we confirm that hematite exhibits significant seasonal vertical variability; its concentration is high near the surface in winter but extends up to 2–4 km into the free troposphere in summer, a finding consistent with independent CALIOP observations. The resulting comprehensive climatology provides novel observational constraints for Earth System Models (ESMs), particularly for accurately modeling dust-radiative forcing. The developed methodology is readily applicable to other UV-NIR satellite platforms, such as PACE, GEMS, and TEMPO, demonstrating its broad utility for future atmospheric composition monitoring efforts.

Frontiers in Remote SensingVol. 6
Goddard Space Flight Center (US), Naval Postgraduate School (US), University of Maryland, Baltimore County (US)
National Aeronautics and Space Administration, Office of Naval Research
Openalex Percentile: Top 71%
Atmospheric aerosols and clouds
AI Navigator

Ask Laika to Summarize, Analyze, and Connect papers live on the map.

Summarize Papers & Methodologies

Extract key findings, datasets, and comparative methods across publications.

Benchmark Rankings & Visual Analytics

Rank top research institutions, authors, funders, topics, and journals by Field-Weighted Citation Impact (FWCI) and paper volume with instant charts.

Connect Distant Disciplines

Bridge topological clusters on the map to find hidden collaborative intersections.