Aerosol observation using multi-wavelength Mie-Raman lidars of the Ad-Net and aerosol component analysis

Tomoaki Nishizawa,Rei Kudo,Nobuo Sugimoto,Yukari Hara,Itsushi Uno,Atsushi Shimizu

doi:10.1051/epjconf/201817609005

Tomoaki Nishizawa, Rei Kudo + Show 4 more

Open Access

https://doi.org/10.1051/epjconf/201817609005

Copy DOI

Abstract

We deployed multi-wavelength Mie-Raman lidars (MMRL) at three sites of the AD-Net and have conducted continuous measurements using them since 2013. To analyze the MMRL data and better understand the externally mixing state of main aerosol components (e.g., dust, sea-salt, and black carbon) in the atmosphere, we developed an integrated package of aerosol component retrieval algorithms, which have already been developed or are being developed, to estimate vertical profiles of the aerosol components. This package applies to the other ground-based lidar network data (e.g., EARLINET) and satellite-borne lidar data (e.g., CALIOP/CALIPSO and ATLID/EarthCARE) as well as the other lidar data of the AD-Net.

Highlights

To assess the effects of aerosols on the atmospheric environment and climate, it is essential to observe the optical and microphysical properties and chemical composition of aerosols and to understand their spatial distribution and temporal variation
We report the multi-wavelength Mie-Raman lidars (MMRL) system and analysis results of aerosol optical properties derived from the MMRL measurements (Section 2)
We developed an algorithm to retrieve extinction coefficients of the aerosol components using the MMRL data

Summary

INTRODUCTION

To assess the effects of aerosols on the atmospheric environment and climate, it is essential to observe the optical and microphysical properties and chemical composition of aerosols and to understand their spatial distribution and temporal variation. We apply a moving average in window ranges (half width) of 60m vertical resolution and 30min temporal resolution to the derived αa, βa, and δa to reduce the impact of signal noise (Fig. 3). For mineral dust cases (6day data), the mean Sa was 50±7sr at 355nm and 54±9sr at 532nm; The mean δa was 13±8 at 355nm and 16±6 at 532nm These values are close to those for the “Dusty-mixture” categorized in Illingworth et al [8] and imply mixture of mineral dust with air pollution particles. We will improve this aerosol characterization analysis by increasing data samples for noteworthy aerosol events

AEROSOL COMPONET RETRIEVAL USING AD-NET LIDARS

Integrated package of aerosol component retrieval algorithms