Mapping diurnal aerosol properties in East Asia from Deep Space Climate Observatory

Liu, Xinhuiyu; Zeng, Zhao‐Cheng

doi:10.1109/igarss.2019.8900298

Cited by 1 publication

(1 citation statement)

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“…At best they might accumulate ensemble characteristics of these plumes over many fire seasons. Even so, diurnal biases might skew the ensemble statistics to provide incomplete or biased understanding of near‐source smoke (Lennartson et al., 2018; Liu & Zeng, 2019). Since 2015, a new generation of geosynchronous satellite sensors have enhanced spatial and spectral capabilities to allow aerosol retrieval using similar algorithms as used for MODIS.…”

Section: Introductionmentioning

confidence: 99%

Investigating the Spatial and Temporal Limitations for Remote Sensing of Wildfire Smoke Using Satellite and Airborne Imagers During FIREX‐AQ

Shi,

Levy,

Remer

et al. 2024

JGR Atmospheres

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Starting from point sources, wildfire smoke is important in the global aerosol system. The ability to characterize smoke near‐source is key to modeling smoke dispersion and predicting air quality. With hemispheric views and 10‐min refresh, imagers in Geostationary (GEO) orbit have advantages monitoring smoke over once‐per‐day sensors in low‐earth orbit (LEO). However, both can be inadequate in capturing the characteristics of smoke plumes close to their sources due to too‐coarse spatial resolution (both detector and product resolution), too‐sparse temporal resolution (from LEO sensors), and too‐conservative masking. In addition to satellite observations, the Fire Influence on Regional to Global Environments and Air Quality experiment offered sub‐orbital enhanced‐MODIS Airborne Simulator (eMAS) imagery at 50 m pixel resolution—including multiple eMAS flight tracks over individual fires in short time periods. It provided opportunity to explore smoke plume characterization at various spatial and temporal scales and quantify the limitations of space sensors for describing smoke magnitude near source as well as its temporal evolution. Here we applied modified aerosol algorithm to different imagers, relaxing its masking to estimate smoke's aerosol optical depth (AOD) as close as possible to its source. We found that GEO sensors with nominal 1 km spatial resolution can match the much finer resolution eMAS retrieved mean plume AOD, as long as the retrieval spatial resolution is finer than the width of the plumes. However, the plume's maximum AOD may be drastically underestimated by satellite products.

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Section: Introductionmentioning

confidence: 99%