Spatio-temporal variations in aerosol optical and cloud parameters over Southern India retrieved from MODIS satellite data

Balakrishnaiah, G.; Kumar, Krishan; Reddy, B. Suresh Kumar; Gopal, K. Rama; Reddy, R.R.; Reddy, L. Siva Sankara; Swamulu, C.; Ahammed, Y. Nazeer; Narasimhulu, K.; Moorthy, K. Krishna; Babu, S. Suresh

doi:10.1016/j.atmosenv.2011.10.032

Cited by 56 publications

(45 citation statements)

References 40 publications

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“…The southeastern and southwestern Sindh had low AOD values, which are greater than 0.49 during the autumn season. Pre-monsoon period prevails in the month of June, monsoon in the months of July and August and postmonsoon in the month of September (Alam et al, 2010;Balakrishnaiah et al, 2012). Pre-monsoon shows high AOD in northern Sindh including sites, like Jacobabad, Sukkur, Ghotki, and Larkana which experience air masses from the Punjab that impacts on aerosol concentration.…”

Section: Seasonal Variations In Aerosol Optical Depth (Aod)mentioning

confidence: 99%

“…Karachi shows AOD values ranges 0.30 to 0.65 instead to show highest AOD values due to continuous sea breezes from Arabian Sea that keeps AOD in motion. Balakrishnaiah et al (2012). determined aerosols marked impact on six southern India sites, namely TVM, BLR, ATP, HYD, PUNE and VSK Fig.…”

Section: Spatial Distribution Of Annual Mean Aerosol Optical Depthmentioning

confidence: 99%

“…Their ability to absorb or scatter solar radiations have direct impact to increase or decrease the temperature of the atmosphere (Ichuku et al, 2004;IPCC, 2013). In addition, their indirect affect modifies the cloud lifetime, albedo, and the precipitation by changing the size and density of cloud droplets in the atmosphere (Lohmann., 2002;Alam et al, 2010;Balakrishnaiah et al, 2012). In addition, as smaller cloud droplets are less efficient to produce precipitation, an increased aerosol population will cause to a longer cloud life.…”

Section: Introductionmentioning

confidence: 99%

“…Aerosols and clouds play a very significant role in the climatic system (Balakrishnaiah et al, 2012;Alam et al, 2014). Aerosols demonstrate both spatial and temporal variations, which may lead to variations in the cloud optical properties (Alam et al, 2010).…”

Section: Introductionmentioning

confidence: 99%

“…Increasing anthropogenic aerosol concentrations lead to form cloud condensation nuclei (CCN) that ranges from smaller effective radii to longer-lived clouds, affecting the hydrological cycle (Ramanathan et al, 2001;Balakrishnaiah et al, 2012). A wide range of studies has shown aerosol and cloud interactions with large uncertainties in the magnitude of radiative forcing (e.g., Twomey et al, 1984;Coakley et al, 1987;Kaufman and Nakajima, 1993;Kaufman and Fraser, 1997a, b;Ackerman et al, 2000;Rosenfeld, 2000;Ramanathan et al, 2001;Rosenfeld et al, 2002;Schwartz et al, 2002;Kim et al, 2003;Andreae et al, 2004;Koren et al, 2004;Penner et al, 2004;Koren et al, 2005;Alam et al, 2010;Balakrishnaiah, 2012;Alam et al, 2014). Alam et al (2010) and Balakrishnaiah et al (2012) have examined the spatial and temporal variations in aerosol particles and the impact of these variations over different locations in Pakistan and India.…”

Section: Introductionmentioning

confidence: 99%

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Spatio-Temporal Distribution of Aerosol and Cloud Properties over Sindh Using MODIS Satellite Data and a HYSPLIT Model

Sharif

Alam

Afsar

2015

Aerosol Air Qual. Res.

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In this study, aerosols spatial, seasonal and temporal variations over Sindh, Pakistan were analyzed which can lead to variations in the microphysics of clouds as well. All cloud optical properties were analyzed using Moderate Resolution Imaging Spectroradiometer (MODIS) data for 12 years from 2001 to 2013. We also monitored origin and movements of air masses that bring aerosol particles and may be considered as the natural source of aerosol particles in the region. For this purpose, the Hybrid Single Particle Lagrangian Integrated Trajectory (HYSPLIT) model was used to make trajectories of these air masses from their sources. Aerosol optical depth (AOD) high values were observed in summer during the monsoon period (June-August). The highest AOD values in July were recorded ranges from 0.41 to 1.46. In addition, low AOD values were found in winter season (December-February) particularly in December, ranges from 0.16 to 0.69. We then analyzed the relationship between AOD and Ångström exponent that is a good indicator of the size of an aerosol particle. We further described the relationships of AOD and four cloud parameters, namely water vapor (WV), cloud fraction (CF), cloud top temperature (CTT) and cloud top pressure (CTP) by producing regional correlation maps of their data values. The analyses showed negative correlation between AOD and Ångström exponent especially in central and western Sindh. The correlation between AOD and WV was throughout positive with high correlation values > 0.74 in whole Sindh except eastern most arid strip of the Thar Desert in the region. The correlation between AOD and CF was positive in southern Sindh and goes to negative in northern Sindh. AOD showed a positive correlation with CTP and CTT in northern Sindh and a negative correlation in southern Sindh. All these correlations were observed to be dependent on the meteorological conditions for all of the ten sites investigated.

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Section: Seasonal Variations In Aerosol Optical Depth (Aod)mentioning

confidence: 99%

Section: Spatial Distribution Of Annual Mean Aerosol Optical Depthmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Spatio-Temporal Distribution of Aerosol and Cloud Properties over Sindh Using MODIS Satellite Data and a HYSPLIT Model

Sharif

Alam

Afsar

2015

Aerosol Air Qual. Res.

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Ephemeral prediction of aerosol optical depth associated with precipitation: A data‐driven approach

Muruganandam,

Narayanan,

Kabilan

2023

Geological Journal

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The study investigates the influence of aerosol optical depth (AOD) in coastal areas where the aerosols have an extensive range owing to wind movements together with precipitation, for a period from June 2016 to Dec 2020. Therefore, the consequences of climate change at the microscale are determined in this research using AOD for the southern coastal area, while the studies are more prevalent only in the Indo‐Gangetic Plain. The entire investigation used data from NASA's VIIRS Deep Blue Aerosol satellite. The study examined whether precipitation played a role in AOD prevalence analysis. Four models with variables, namely AOD550, AOD ocean, precipitation, temperature and sea surface temperature, were implemented in this study. Machine learning algorithms were adopted for the prediction analysis, namely artificial neural network (ANN) and support vector machine (SVM) including Polykernel, Radial Basis kernel Function and Pearson VII Universal Kernel (PUK). The results show that precipitation (−0.03) had a negative correlation with AOD550 land but a positive relationship with AOD ocean (0.554). The main conclusions were that ANN does not agree well with the AOD prediction, although R2 and RMSE performed better than average, ANN proved to overperform during the testing phase of all the ratio splits of 60–40%, 70–30%, 80–20%, 90–10%, 98–2% and 99–1%. However, SVM offers more reliability with fewer data variability than ANN thus providing a better result for 98–2% (31 days) and 99–1% (16 days) prediction. The PUK function outperformed the overall SVM function for all 4 models with an RMSE of ±0.005 during training and ±0.0035 during testing, whose R2 value was also around ±0.90 during training and 1 during testing.

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Long‐term trends in aerosol optical properties and their relationship with cloud properties over southern India and Sri Lanka

Chathurangika

Haque

Nyasulu

et al. 2022

Intl Journal of Climatology

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Spatio-temporal variations in aerosol optical and cloud parameters over Southern India retrieved from MODIS satellite data

Cited by 56 publications

References 40 publications

Spatio-Temporal Distribution of Aerosol and Cloud Properties over Sindh Using MODIS Satellite Data and a HYSPLIT Model

Spatio-Temporal Distribution of Aerosol and Cloud Properties over Sindh Using MODIS Satellite Data and a HYSPLIT Model

Ephemeral prediction of aerosol optical depth associated with precipitation: A data‐driven approach

Long‐term trends in aerosol optical properties and their relationship with cloud properties over southern India and Sri Lanka

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