摘要：To prepare for implementation of a new aerosol retrieval specifically designed for dust aerosol over ocean in the operational Dark‐Target (DT) algorithms for the Moderate‐resolution Imaging Spectrometer (MODIS) and Visible Infrared Imaging Radiometer Suite (VIIRS) satellite sensors, we focus on the challenge of detecting dust. We first survey the literature on existing dust detection algorithms and then develop an innovative algorithm that combines near‐UV (deep blue), visible, and thermal infrared (TIR) wavelength spectral tests. The new detection algorithm is applied to Terra and Aqua MODIS granules and compared with other dust detection possibilities from existing MODIS products. Quantitative evaluation of the new dust detection algorithm is conducted using both a collocated AERONET‐MODIS data set and collocated Cloud‐Aerosol Lidar and Infrared Pathfinder Satellite Observation (CALIPSO)‐MODIS data set. From comparison with both AERONET and CALIOP measurements, we estimate the new dust detection algorithm detects about 30% of weakly dusty pixels and more than 80% of heavily dusty pixels, with false detections in the range of 1–2%. The very low false detection rate is particularly noteworthy in comparison with existing literature. Compared with the dust flag currently available as part of the MODIS cloud mask product (MOD35/MYD35), and dust classification based on commonly used thresholds with aerosol optical depth (AOD) and Angstrom exponent (AE), the new dust detection algorithm finds more dusty pixels and fewer false detections. Plain Language Abstract The Dark‐Target (DT) aerosol retrieval is applied to measurements from the Moderate‐resolution Imaging Spectrometer (MODIS) on the Terra and Aqua satellites to retrieve spectral aerosol optical depth (AOD) over land and ocean. The algorithm generally provides high‐quality retrievals within specified error bar. However, the DT‐Ocean algorithm tends to provide biased retrievals of AOD, Angstrom exponent (AE), and fine mode fraction (FMF) for scenes containing dust aerosol of African or Asian origin. These biases are scattering angle dependent, which suggests errors in the assumed optical properties and phase function from the spherical dust models used. Therefore, we aim to improve the DT retrieval of dust over ocean with a two‐step strategy. Here in Part 1, we describe Step 1 in which we develop an innovative dust detection algorithm that combines deep‐blue, visible, shortwave infrared, and thermal infrared wavelength spectral tests that are based on a survey of existing dust detection algorithms. Step 2 is described in Part 2, where we develop new nonspherical dust models and apply it to identified heavy dust pixels. Combing dust detection and nonspherical dust model has led to significant improvements in retrieved AOD, AE, and FMF in dust regions.
