A long-term study of aerosol modulation of atmospheric and surface solar
                        heating over Pune, India

Kumar, Sumit; Devara, P. C. S.

doi:10.3402/tellusb.v64i0.18420

Cited by 26 publications

(7 citation statements)

References 43 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…For a case of a dust episode (e.g., AOD 440nm =0.8) the uncertainties of SSA 440nm increase to ±0.8 for GHI and for AE they are ±2.8 and ±11.4 respectively, pointing out that they become significant only for DNI and AE variability, while for all other cases is less than 4%. These results are comparable with similar sensitivity analysis of radiative transfer studies [66,73] considering overall these differences as a scale of error introduced by this approach.…”

Section: Radiative Transfer Modelling Techniquesupporting

confidence: 86%

Earth-Observation-Based Estimation and Forecasting of Particulate Matter Impact on Solar Energy in Egypt

et al. 2018

View full text Add to dashboard Cite

This study estimates the impact of dust aerosols on surface solar radiation and solar energy in Egypt based on Earth Observation (EO) related techniques. For this purpose, we exploited the synergy of monthly mean and daily post processed satellite remote sensing observations from the MODerate resolution Imaging Spectroradiometer (MODIS), radiative transfer model (RTM) simulations utilizing machine learning, in conjunction with 1-day forecasts from the Copernicus Atmosphere Monitoring Service (CAMS). As cloudy conditions in this region are rare, aerosols in particular dust, are the most common sources of solar irradiance attenuation, causing performance issues in the photovoltaic (PV) and concentrated solar power (CSP) plant installations. The proposed EO-based methodology is based on the solar energy nowcasting system (SENSE) that quantifies the impact of aerosol and dust on solar energy potential by using the aerosol optical depth (AOD) in terms of climatological values and day-to-day monitoring and forecasting variability from MODIS and CAMS, respectively. The forecast accuracy was evaluated at various locations in Egypt with substantial PV and CSP capacity installed and found to be within 5–12% of that obtained from the satellite observations, highlighting the ability to use such modelling approaches for solar energy management and planning (M&P). Particulate matter resulted in attenuation by up to 64–107 kWh/m2 for global horizontal irradiance (GHI) and 192–329 kWh/m2 for direct normal irradiance (DNI) annually. This energy reduction is climatologically distributed between 0.7% and 12.9% in GHI and 2.9% to 41% in DNI with the maximum values observed in spring following the frequent dust activity of Khamaseen. Under extreme dust conditions the AOD is able to exceed 3.5 resulting in daily energy losses of more than 4 kWh/m2 for a 10 MW system. Such reductions are able to cause financial losses that exceed the daily revenue values. This work aims to show EO capabilities and techniques to be incorporated and utilized in solar energy studies and applications in sun-privileged locations with permanent aerosol sources such as Egypt.

show abstract

Section: Radiative Transfer Modelling Techniquesupporting

confidence: 86%

Earth-Observation-Based Estimation and Forecasting of Particulate Matter Impact on Solar Energy in Egypt

et al. 2018

View full text Add to dashboard Cite

show abstract

“…However, two main monsoon seasons exist, northeast and southwest monsoons. Considering previous analyses of aerosol and air quality (Awang et al, 2000;Krishna Moorthy et al, 2007;Suresh Babu et al, 2007;Kumar and Devara, 2012;Chew et al, 2013;Xian et al, 2013), the monsoon periods in this study were classified as follows: (i) northeast monsoon (December-March), (ii) transition period of northeast to southwest monsoon or pre-monsoon (April-May), (iii) southwest monsoon (JuneSeptember), and (iv) transition period of southwest to northeast monsoon or post-monsoon (October-November).…”

Section: Methodology and Statistical Modelmentioning

confidence: 99%

Monsoonal variations in aerosol optical properties and estimation of aerosol optical depth using ground-based meteorological and air quality data in Peninsular Malaysia

et al. 2015

View full text Add to dashboard Cite

Abstract. Obtaining continuous aerosol-optical-depth (AOD) measurements is a difficult task due to the cloudcover problem. With the main motivation of overcoming this problem, an AOD-predicting model is proposed. In this study, the optical properties of aerosols in Penang, Malaysia were analyzed for four monsoonal seasons (northeast monsoon, pre-monsoon, southwest monsoon, and post-monsoon) based on data from the AErosol RObotic NETwork (AERONET) from February 2012 to November 2013. The aerosol distribution patterns in Penang for each monsoonal period were quantitatively identified according to the scattering plots of the Ångström exponent against the AOD. A new empirical algorithm was proposed to predict the AOD data. Ground-based measurements (i.e., visibility and air pollutant index) were used in the model as predictor data to retrieve the missing AOD data from AERONET due to frequent cloud formation in the equatorial region. The model coefficients were determined through multiple regression analysis using selected data set from in situ data. The calibrated model coefficients have a coefficient of determination, R 2 , of 0.72. The predicted AOD of the model was generated based on these calibrated coefficients and compared against the measured data through standard statistical tests, yielding a R 2 of 0.68 as validation accuracy. The error in weighted mean absolute percentage error (wMAPE) was less than 0.40 % compared with the real data. The results revealed that the proposed model efficiently predicted the AOD data. Performance of our model was compared against selected LIDAR data to yield good correspondence. The predicted AOD can enhance measured shortand long-term AOD and provide supplementary information for climatological studies and monitoring aerosol variation.

show abstract

“…However, two main monsoon seasons exist, namely, northeast and southwest monsoons. Considering previous analyses on aerosol or air quality (Awang et al, 2000;Krishna Moorthy et al, 2007;Kumar and Devara, 2012;Xian et al, 2013), the monsoon period classified as follows: (i) northeast monsoon (December-March), (ii) transition period of northeast to southwest monsoon or pre-monsoon (April-May), (iii) southwest monsoon (June-September), and (iv) transition period of southwest to northeast monsoon or post-monsoon (October-November).…”

Section: Methodology and Statistical Modelmentioning

confidence: 99%

Variations in optical properties of aerosols on monsoon seasonal change and estimation of aerosol optical depth using ground-based meteorological and air quality data

Tan¹,

Lim²,

Abdullah³

et al. 2014

Preprint

View full text Add to dashboard Cite

Abstract. In this study, the optical properties of aerosols in Penang, Malaysia were analyzed for four monsoonal seasons (northeast monsoon, pre-monsoon, southwest monsoon, and post-monsoon) based on data from the AErosol RObotic NETwork (AERONET) from February 2012 to November 2013. The aerosol distribution patterns in Penang for each monsoonal period were quantitatively identified according to the scattering plots of the aerosol optical depth (AOD) against the Angstrom exponent. A modified algorithm based on the prototype model of Tan et al. (2014a) was proposed to predict the AOD data. Ground-based measurements (i.e., visibility and air pollutant index) were used in the model as predictor data to retrieve the missing AOD data from AERONET because of frequent cloud formation in the equatorial region. The model coefficients were determined through multiple regression analysis using selected data set from in situ data. The predicted AOD of the model was generated based on the coefficients and compared against the measured data through standard statistical tests. The predicted AOD in the proposed model yielded a coefficient of determination R2 of 0.68. The corresponding percent mean relative error was less than 0.33% compared with the real data. The results revealed that the proposed model efficiently predicted the AOD data. Validation tests were performed on the model against selected LIDAR data and yielded good correspondence. The predicted AOD can beneficially monitor short- and long-term AOD and provide supplementary information in atmospheric corrections.

show abstract

A long-term study of aerosol modulation of atmospheric and surface solar heating over Pune, India

Cited by 26 publications

References 43 publications

Earth-Observation-Based Estimation and Forecasting of Particulate Matter Impact on Solar Energy in Egypt

Earth-Observation-Based Estimation and Forecasting of Particulate Matter Impact on Solar Energy in Egypt

Monsoonal variations in aerosol optical properties and estimation of aerosol optical depth using ground-based meteorological and air quality data in Peninsular Malaysia

Variations in optical properties of aerosols on monsoon seasonal change and estimation of aerosol optical depth using ground-based meteorological and air quality data

Contact Info

Product

Resources

About