Cited by 27Atmospheric aerosols and cloudsAeolian processes and effectsAtmospheric chemistry and aerosols

Monitoring intense dust storms over the Indian region using satellite data – a case study

Gharai, Biswadip, Jose, Subin, Mahalakshmi, D.V.·2013·International Journal of Remote Sensing

Abstract

Dust storms are normally considered to be natural hazards. During such events, dust aerosol is loaded into the atmosphere, directly reducing visibility and effectively reflecting solar radiation back to space. In the present study, an intense dust storm was monitored during the first week of June 2010 using Moderate Resolution Imaging Spectroradiometer (MODIS) Terra and Aqua data over the Indian region. A dust cloud was detected using a combination of MODIS reflective and emissive channels and moving trace/spread monitored by its multi-temporal data. The MODIS Terra-derived aerosol optical depth at 550 nm (AOD550) and the aerosol index (AI) obtained from the Ozone Monitoring Instrument (OMI) were used in conjunction with National Centers for Environmental Prediction/National Center for Atmospheric Research (NCEP/NCAR) reanalysis wind fields for the monitoring of dust clouds. The study reveals that the movement of a high concentration of dust clouds coincided with the NCEP/NCAR reanalysis meridional and zonal wind fields (>8 m s−1) at pressure levels of 700 hPa. The Cloud–Aerosol Lidar Pathfinder Satellite Observations (CALIPSOs) that derive vertical feature mask images also suggested that the vertical extent of the dust aerosol layer was at a height of about 6 km over northern India on 2 June 2010. The roles of long-range transport of dust over the entire Gangetic plane are analysed using back trajectories from the National Oceanic and Atmospheric Administration (NOAA) Hybrid Single-Particle Lagrangian Integrated Trajectory (HYSPLIT) model. Back trajectory analysis suggests that dust clouds moving over long distances entered from the western side of India on 1 June 2010.

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Crossref · 10.1080/01431161.2013.813655

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  • Chen and Wang used a single layer of Convolutional Neural Network to learn features from SAR images automatically.

    “As a preliminary study, Chen and Wang in [155] used a single layer of Convolutional Neural Network (CNN) to learn features from SAR images automatically.”

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Authors
Gharai, B., Jose, S., & Mahalakshmi, D. V.
Affiliations
  • National Remote Sensing Centre (IN)
  • a Atmosphere and Ocean Sciences Group, Earth and Climate Science Area (ECSA), National Remote Sensing Center, Department of Space, Govt. of India , Balanagar , Hyderabad , 500 037 , India
Corresponding author
Gharai, Biswadip
Venue
International Journal of Remote Sensing
Published
2013-07-04
Volume
34
Issue
20
Pages
7038-7048
ISSN
  • 0143-1161 (ISSN-L)
  • 1366-5901
OpenAlex
W1979120231
Cited by
27
References
0
Citations by year
2026: 1 · 2025: 1 · 2024: 1 · 2023: 2 · 2022: 4 · 2021: 1 · 2020: 3 · 2019: 1 · 2018: 1 · 2017: 1 · 2016: 5 · 2015: 4 · 2014: 1 · 2013: 1
FWCI
2.79
Type
journal article
Language
en
Open access
closed
Locations
International Journal of Remote Sensing (publishedVersion)
Funders
  • National Aeronautics and Space Administration
  • National Oceanic and Atmospheric Administration
Topics
  • Physical Sciences › Environmental Science › Global and Planetary Change › Atmospheric aerosols and clouds
  • Physical Sciences › Earth and Planetary Sciences › Earth-Surface Processes › Aeolian processes and effects
  • Physical Sciences › Earth and Planetary Sciences › Atmospheric Science › Atmospheric chemistry and aerosols
Keywords
dust storms, dust aerosols, long-range dust transport, Indian region, MODIS Terra and Aqua, aerosol optical depth, aerosol index, OMI, backward trajectory analysis, HYSPLIT, Gangetic plains

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