Publications

Ciren, Pubu; Kondragunta, Shobha (2014). Dust aerosol index (DAI) algorithm for MODIS. JOURNAL OF GEOPHYSICAL RESEARCH-ATMOSPHERES, 119(8), 4770-4792.

Abstract
A dust aerosol index (DAI) algorithm based on measurements in deep blue (412nm), blue (440nm), and shortwave IR (2130nm) wavelengths using Moderate Resolution Imaging Spectroradiometer (MODIS) observations has been developed. Contrary to some dust detection algorithms that use measurements at thermal IR bands, this algorithm takes advantage of the spectral dependence of Rayleigh scattering, surface reflectance, and dust absorption to detect airborne dust. The DAI images generated by this algorithm agree qualitatively with the location and extent of dust observed in MODIS true color images. Quantitatively, the dust index generated for hundreds of dust outbreaks observed between 2006 and 2013 were compared to Cloud Aerosol Lidar with Orthogonal Polarization (CALIOP) Vertical Feature Mask (VFM) product and the detections are found to be accurate at 70% over land and 82% over ocean. The Probability of Correct Detection (POCD) is 80% over land and 76% over ocean. The dust detections with DAI-based dust identification algorithm were also compared to 5 years of Aerosol Robotic Network (AERONET) observations for 13 stations with a wide range of geographical coverage. The average detection accuracy is similar to 70%, whereas the POCD is similar to 67%. The performance of DAI-based dust detection against AERONET is slightly weaker than that against CALIOP VFM because of the limited number of matchups for some stations. For stations close to source region or coastal and island stations, the accuracy and POCD can be as high as similar to 85% and similar to 89%, respectively.Key Points The developed algorithm is simple, fast, and sensitive to the presence of dust Can capture both the outbreak and transport of dust event very well Detections are found to be accurate at 70% over land and 82% over ocean

DOI:
10.1002/2013JD020855

ISSN:
2169-897X; 2169-8996