Solar Radiation, Modelling and Remote Sensing
Accurate solar radiation knowledge and its characterization on the Earth’s surface are of high interest in many aspects of environmental and engineering sciences. Modeling of solar irradiance from satellite imagery has become the most widely used method for retrieving solar irradiance information un...
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| Year of Publication: | 2019 |
| Language: | English |
| Physical Description: | 1 electronic resource (230 p.) |
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| 100 | 1 | |a Kaskaoutis, Dimitris |4 auth | |
| 245 | 1 | 0 | |a Solar Radiation, Modelling and Remote Sensing |
| 260 | |b MDPI - Multidisciplinary Digital Publishing Institute |c 2019 | ||
| 300 | |a 1 electronic resource (230 p.) | ||
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| 520 | |a Accurate solar radiation knowledge and its characterization on the Earth’s surface are of high interest in many aspects of environmental and engineering sciences. Modeling of solar irradiance from satellite imagery has become the most widely used method for retrieving solar irradiance information under total sky conditions, particularly in the solar energy community. Solar radiation modeling, forecasting, and characterization continue to be broad areas of study, research, and development in the scientific community. This Special Issue contains a small sample of the current activities in this field. Both the environmental and climatology community, as the solar energy world, share a great interest in improving modeling tools and capabilities for obtaining more reliable and accurate knowledge of solar irradiance components worldwide. The work presented in this Special Issue also remarks on the significant role that remote sensing technologies play in retrieving and forecasting solar radiation information. | ||
| 546 | |a English | ||
| 653 | |a PAR | ||
| 653 | |a motion vector field | ||
| 653 | |a radiative transfer | ||
| 653 | |a global horizontal irradiance | ||
| 653 | |a evapotranspiration | ||
| 653 | |a HRV | ||
| 653 | |a Kato bands | ||
| 653 | |a understory light condition | ||
| 653 | |a California Delta | ||
| 653 | |a validation | ||
| 653 | |a aerosol impact | ||
| 653 | |a remote sensing | ||
| 653 | |a solar radiation | ||
| 653 | |a nowcasting | ||
| 653 | |a India | ||
| 653 | |a cloud categories | ||
| 653 | |a Clouds and the Earth Radiant Energy System (CERES) | ||
| 653 | |a brightness temperature | ||
| 653 | |a Himawari-8/Advanced Meteorological Imager (Himawari-8/AHI) | ||
| 653 | |a water vapor | ||
| 653 | |a clear sky index | ||
| 653 | |a water resource management | ||
| 653 | |a broadband albedo at the top of the atmosphere (TOA albedo) | ||
| 653 | |a data fusion | ||
| 653 | |a solar energy | ||
| 653 | |a shortwave radiation | ||
| 653 | |a AMESIS | ||
| 653 | |a satellite-derived dataset | ||
| 653 | |a insolation | ||
| 653 | |a solar variability | ||
| 653 | |a subcanopy light regime | ||
| 653 | |a clustering analysis | ||
| 653 | |a solar energy systems | ||
| 653 | |a forest canopy | ||
| 653 | |a radiance | ||
| 653 | |a MSG | ||
| 653 | |a GOES satellites | ||
| 653 | |a radiation model | ||
| 653 | |a solar radiation trends | ||
| 653 | |a clear sky | ||
| 653 | |a downward shortwave radiation | ||
| 653 | |a reflected shortwave radiation at the top of the atmosphere (RSR) | ||
| 653 | |a SEVIRI | ||
| 653 | |a photosynthetically active radiation | ||
| 653 | |a surface solar radiation | ||
| 653 | |a solar irradiance | ||
| 653 | |a earth observation | ||
| 653 | |a high turbidity | ||
| 653 | |a Geostationary Korea Multi-Purse Satellite/Advanced Meteorological Imager (GK-2A/AMI) | ||
| 653 | |a Solis scheme | ||
| 653 | |a solar radiation forecasting | ||
| 653 | |a surface energy balance | ||
| 653 | |a light attenuation | ||
| 776 | |z 3-03921-004-1 | ||
| 700 | 1 | |a Polo, Jesús |4 auth | |
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