دانلود مقاله ISI انگلیسی شماره 53137
ترجمه فارسی عنوان مقاله

تأثیر آئروسلهای اتمی بر ارزیابی انرژی خورشیدی برزیل: آزمایش با پایه های دید افقی مختلف در مدل انتقال شعاعی

عنوان انگلیسی
Atmospheric aerosol influence on the Brazilian solar energy assessment: Experiments with different horizontal visibility bases in radiative transfer model
کد مقاله سال انتشار تعداد صفحات مقاله انگلیسی
53137 2016 16 صفحه PDF
منبع

Publisher : Elsevier - Science Direct (الزویر - ساینس دایرکت)

Journal : Renewable Energy, Volume 90, May 2016, Pages 120–135

ترجمه کلمات کلیدی
سوختن زیست توده، اسپری های جوی، تابش خورشیدی، ارزیابی انرژی خورشیدی
کلمات کلیدی انگلیسی
Biomass burning; Atmospheric aerosols; Solar irradiation; Solar energy assessment

چکیده انگلیسی

The radiative transfer model BRASIL-SR is used by Brazilian Institute for Space Research for the assessment of the solar irradiation in Brazil. The model parameterizes the influence of aerosols in the solar radiation transmittance using climate averages of horizontal visibility, which does not represent the actual atmospheric condition in Brazil, especially during dry season. In clear sky conditions, aerosols are a major source of bias in solar radiation models. Their concentration have large spatial and temporal variability particularly in the Brazilian Midwestern region from April until October, due to forest fires, and in Southeastern region due to pollution from megacities. In this study, meteorological data from METAR comprising the years of 2006, 2007 and 2008 were analyzed to evaluate the seasonal variability of the horizontal visibility in Brazil to better represent the influence of aerosols on the model estimations of surface solar irradiation. New horizontal visibility values was generated to each month simulated, to provide input data to the BRASIL-SR model and site specific ground data were used to validate the model estimates. The global, direct beam and diffuse solar irradiation estimates obtained by making use of the new horizontal visibility data presented an overall lower BIAS and RMSE deviations.