A novel retrieval of global dust optical depth and effective diameter based on MODIS thermal infrared observations

IF 11.4 1区 地球科学 Q1 ENVIRONMENTAL SCIENCES
Jianyu Zheng , Hongbin Yu , Yaping Zhou , Yingxi Shi , Zhibo Zhang , Claudia Di Biagio , Paola Formenti , Alexander Smirnov
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引用次数: 0

Abstract

Airborne mineral dust significantly influences Earth's climate through perturbing Earth's radiation budget, modulating cloud formation and microphysical properties, and fertilizing the biosphere. Recent field campaigns have revealed substantially more coarse-mode dust particles in the atmosphere than previously recognized, yet current satellite retrievals and climate models inadequately represent these particles. This study presents a novel retrieval algorithm for dust aerosol optical depth at 10 μm (AOD10μm) and effective diameter (Deff) using Moderate Resolution Imaging Spectroradiometer (MODIS) thermal infrared (TIR) observations over global land and ocean. Building upon the previous synergistic approach for MODIS and the Cloud-Aerosol Lidar with Orthogonal Polarization (CALIOP), we improve the retrieval from CALIOP-track-limited coverage to full-swath MODIS observations at 10-km resolution over both ocean and land surfaces. The retrieval improvements include: (1) application of climatological CALIOP dust vertical profiles (2007–2017) to constrain dust vertical distribution for off-CALIOP-track pixels; (2) the improved optimization method to effectively handle non-monotonic cost functions arising from temperature inversions within the Saharan Air Layer; and (3) extension to land surfaces through incorporation of MODIS-retrieved surface emissivity and ERA5 reanalysis data. Validation against coarse-mode AOD from global AERONET (N = 4703) and MAN (N = 1673) observations yields R = 0.82 and 0.85 for AOD10μm, with retrieval uncertainty characterized as ε = 15 % × AOD + 0.04. The retrieved Deff demonstrates excellent agreement with in-situ measurements collected from 24 field campaigns around the globe (R = 0.84, MBE = 0.23 μm, RMSE = 0.73 μm), capturing the particle size variation from near-source regions (Deff = 7–8 μm) to long-range transport (Deff = 3–5 μm). Case studies of dust events over the Namibian coast and trans-Atlantic corridor demonstrate the retrieval's capability to resolve episodic dust properties and size-dependent deposition during transport. This improved retrieval addresses the critical observational gap for coarse and super-coarse dust particles (D > 5 μm), providing essential constraints for dust life cycle studies and climate model evaluation.
基于MODIS热红外观测的全球尘埃光学深度和有效直径反演新方法
空气中的矿物粉尘通过扰乱地球的辐射收支、调节云的形成和微物理特性以及给生物圈施肥,显著地影响着地球的气候。最近的野外活动表明,大气中的粗态尘埃颗粒比以前认识到的要多得多,但目前的卫星检索和气候模型还不能充分代表这些颗粒。本文提出了一种利用全球陆地和海洋的中分辨率成像光谱辐射计(MODIS)热红外(TIR)观测数据反演10μm气溶胶光学深度(AOD10μm)和有效直径(Deff)的新算法。基于先前MODIS与正交偏振云气溶胶激光雷达(CALIOP)的协同方法,我们改进了从CALIOP轨迹有限覆盖到海洋和陆地表面10公里分辨率的全幅MODIS观测数据的检索。反演改进包括:(1)利用CALIOP气象沙尘垂直剖面(2007-2017)约束离CALIOP轨道像元的沙尘垂直分布;(2)改进的优化方法有效处理撒哈拉空气层内温度逆温引起的非单调代价函数;(3)结合modis反演的地表发射率和ERA5再分析数据扩展到地表。对全球AERONET (N = 4703)和MAN (N = 1673)观测数据进行粗模AOD验证,AOD10μm的检索不确定度为ε = 15% × AOD + 0.04, R = 0.82和0.85。所获取的Deff与全球24个现场测量结果(R = 0.84, MBE = 0.23 μm, RMSE = 0.73 μm)非常吻合,捕获了从近源区域(Deff = 7-8 μm)到远程迁移区域(Deff = 3-5 μm)的粒径变化。纳米比亚海岸和跨大西洋走廊的沙尘事件的案例研究表明,检索能够解决运输过程中偶发性沙尘特性和大小相关的沉积。这种改进的检索解决了粗粒和超粗粒沙尘(D > 5 μm)的关键观测缺口,为沙尘生命周期研究和气候模式评估提供了必要的约束。
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来源期刊
Remote Sensing of Environment
Remote Sensing of Environment 环境科学-成像科学与照相技术
CiteScore
25.10
自引率
8.90%
发文量
455
审稿时长
53 days
期刊介绍: Remote Sensing of Environment (RSE) serves the Earth observation community by disseminating results on the theory, science, applications, and technology that contribute to advancing the field of remote sensing. With a thoroughly interdisciplinary approach, RSE encompasses terrestrial, oceanic, and atmospheric sensing. The journal emphasizes biophysical and quantitative approaches to remote sensing at local to global scales, covering a diverse range of applications and techniques. RSE serves as a vital platform for the exchange of knowledge and advancements in the dynamic field of remote sensing.
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