<i>An Introduction to Atmospheric Radiation</i>

Liou, Kuo‐Nan; Bohren, Craig F.

doi:10.1063/1.2914664

Cited by 935 publications

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“…Solar insolation outside the earth's atmosphere at the mean distance between the earth and the sun [8] is termed as solar constant (I sc ) given by [7]:…”

Section: Theorymentioning

confidence: 99%

Solar Energy Potential in Nepal and Global Context

Adhikari¹,

Gurung

Bhattarai

2014

J. Inst. Engineering

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Solar radiation is the best option and cost effective energy resources of this world from 21 st century onwards. In this study monthly, seasonal and annual variation of global solar insolation at Biratnagar, Lukla, Kathmandu, Pokhara, of the year 2007 to 2012, Jumla of the year 2011 to 2012 and Simikot 2011/2012 were carried out. The study has shown the variation of global solar insolation with month, season, year, atmospheric condition, and altitude of the site concerned. The maximum value of monthly, seasonal, and yearly global solar insolation was reported at Jumla whereas the minimum monthly, seasonal, and yearly global solar insolation were observed at Kathmandu. From the observation the abundant solar irradiation in Nepal shows encouraging atmosphere for solar farming venture in near future relating to energy management for Nepal.

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“…Solar insolation outside the earth's atmosphere at the mean distance between the earth and the sun [8] is termed as solar constant (I sc ) given by [7]:…”

Section: Theorymentioning

confidence: 99%

Solar Energy Potential in Nepal and Global Context

Adhikari¹,

Gurung

Bhattarai

2014

J. Inst. Engineering

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“…Specifically, the bands centered around 0.86-µm (hereafter referred to as the "0.86-µm band") are used in both algorithms for retrieval over ocean. The bidirectional cloud reflection function (R) observed by satellites in the 0.86-µm band is defined as follows (Liou, 2002):…”

Section: Modis and Polder Ice Optical Thickness Retrieval Algorithmsmentioning

confidence: 99%

Influence of ice particle model on satellite ice cloud retrieval: lessons learned from MODIS and POLDER cloud product comparison

et al. 2009

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Abstract. The influence is investigated of the assumed ice particle microphysical and optical model on inferring ice cloud optical thickness (τ ) from satellite measurements of the Earth's reflected shortwave radiance. Ice cloud τ are inferred, and subsequently compared, using products from MODIS (MODerate resolution Imaging Spectroradiometer) and POLDER (POLarization and Directionality of the Earth's Reflectances). POLDER τ values are found to be substantially smaller than those from collocated MODIS data. It is shown that this difference is caused primarily by the use of different ice particle bulk scattering models in the two retrievals, and more specifically, the scattering phase function. Furthermore, the influence of the ice particle model on the derivation of ice cloud radiative forcing (CRF) from satellite retrievals is studied. Three sets of shortwave CRF are calculated using different combinations of the retrieval and associated ice particle models. It is shown that the uncertainty associated with an ice particle model may lead to two types of errors in estimating CRF from satellite retrievals. One stems from the retrieval itself and the other is due to the optical properties, such as the asymmetry factor, used for CRF calculations. Although a comparison of the CRFs reveals that these two types of errors tend to cancel each other, significant differences are still found between the three CRFs, which indicates that the ice particle model affects notCorrespondence to: Z. Zhang (zzbatmos@umbc.edu) only optical thickness retrievals but also CRF calculations. In addition to CRF, the effect of the ice particle model on the derivation of seasonal variation of τ from satellite measurements is discussed. It is shown that optical thickness retrievals based on the same MODIS observations, but derived using different assumptions of the ice particle model, can be substantially different. These differences can be divided into two parts. The first-order difference is mainly caused by the differences in the asymmetry factor. The second-order difference is related to seasonal changes in the sampled scattering angles and therefore dependent on the sun-satellite viewing geometry. Because of this second-order difference, the use of different ice particle models may lead to a different understanding of the seasonal variation of τ .

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“…The basic equation for the transfer of radiation in a planeparallel atmosphere can be written in the form (Liou, 2002):…”

Section: Retrieval Methodsmentioning

confidence: 99%

“…Liou (2002) suggested setting = 0.999999 in all of the above equations when there is no absorbance, and a simple analytical solution has been derived by Xue and Cracknell (1995). For conservative scattering (ω = 1), simpler solutions can be derived with one of the eigenvalues.…”

Section: Retrieval Methodsmentioning

confidence: 99%

“…However, here we need two eigenvalues in order to derive a relationship between reflectance and AOD. In practice, however, we may set ω = 0.999999 and obtain the results for conservative scattering (Liou, 2002). For our model, we assume that the atmospheric optical depth (τ ) consists of two parts: the molecular Rayleigh scattering (τ molecular ), and the aerosol optical depth (τ aerosol ).…”

Section: Retrieval Methodsmentioning

confidence: 99%

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Retrieval of aerosol optical depth over land based on a time series technique using MSG/SEVIRI data

et al. 2012

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Abstract.A novel approach for the joint retrieval of aerosol optical depth (AOD) and aerosol type, using Meteosat Second Generation -Spinning Enhanced Visible and Infrared Imagers (MSG/SEVIRI) observations in two solar channels, is presented. The retrieval is based on a Time Series (TS) technique, which makes use of the two visible bands at 0.6 µm and 0.8 µm in three orderly scan times (15 min interval between two scans) to retrieve the AOD over land. Using the radiative transfer equation for plane-parallel atmosphere, two coupled differential equations for the upward and downward fluxes are derived. The boundary conditions for the upward and downward fluxes at the top and at the bottom of the atmosphere are used in these equations to provide an analytic solution for the AOD. To derive these fluxes, the aerosol single scattering albedo (SSA) and asymmetry factor are required to provide a solution. These are provided from a set of six pre-defined aerosol types with the SSA and asymmetry factor. We assume one aerosol type for a grid of 1 • ×1 • and the surface reflectance changes little between two subsequent observations. A k-ratio approach is used in the inversion to find the best solution of atmospheric properties and surface reflectance. The k-ratio approach assumes that the surface reflectance is little influenced by aerosol scattering at 1.6 µm and therefore the ratio of surface reflectances in the solar band for two subsequent observations can be wellapproximated by the ratio of the reflectances at 1.6 µm. A further assumption is that the surface reflectance varies only slightly over a period of 30 min. The algorithm makes use of numerical minimisation routines to obtain the optimal solution of atmospheric properties and surface reflectance by selection of the most suitable aerosol type from pre-defined sets.A detailed analysis of the retrieval results shows that it is suitable for AOD retrieval over land from SEVIRI data. Six AErosol RObotic NETwork (AERONET) sites with different surface types are used for detailed analysis and 42 other AERONET sites are used for validation. From 445 collocations representing stable and homogeneous aerosol type, we find that >75 % of the MSG-retrieved AOD at 0.6 and 0.8 µm values compare favourably with AERONET observed AOD values, within an error envelope of ± 0.05 ± 0.15τ and a high correlation coefficient (R>0.86). The AOD datasets derived Published by Copernicus Publications on behalf of the European Geosciences Union. L. Mei et al.: Retrieval of aerosol optical depth over landusing the TS method with SEVIRI data is also compared with collocated AOD products derived from NASA TERRA and AQUA MODIS (The Moderate-resolution Imaging Spectroradiometer) data using the Dark Dense Vegetation (DDV) method and the Deep Blue algorithms. Using the TS method, the AOD could be retrieved for more pixels than with the NASA Deep Blue algorithm. This method is potentially also useful for surface reflectance retrieval using SEVIRI observations. The current paper focuses on AOD retrieval ...

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An Introduction to Atmospheric Radiation

Cited by 935 publications

References 0 publications

Solar Energy Potential in Nepal and Global Context

Solar Energy Potential in Nepal and Global Context

Influence of ice particle model on satellite ice cloud retrieval: lessons learned from MODIS and POLDER cloud product comparison

Retrieval of aerosol optical depth over land based on a time series technique using MSG/SEVIRI data

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