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- Author or Editor: Josep-Abel González x
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Abstract
A deeper knowledge of the effects and interactions of clouds in the climatic system requires developing both satellite and ground-based methods to assess their optical properties. A simple method based on a parameterized inversion of a radiative transfer model is proposed to estimate the optical depth of thick liquid water clouds from the atmospheric transmittance at 415 nm, solar zenith angle, surface albedo, effective droplet radius, and aerosol load. When concurrent measurements of atmospheric transmittance and liquid water path are available, the effective radius of the droplet size distribution can also be retrieved. The method is compared with a reference algorithm from Min and Harrison, which uses similar data, except aerosol load. When applied to measurements performed at the Southern Great Plains site of the Atmospheric Radiation Measurement Program, the mean bias deviation between the proposed method and the reference method is only −0.08 in units of optical depth, whereas the standard deviation is only 0.46. For the effective droplet radius estimations, the mean bias deviation is −0.13 μm, and the standard deviation is 0.14 μm. Maximum relative deviations are lower than 5% and 8% for cloud optical depth and effective radius, respectively. The effects on these retrievals of the assumed aerosol optical depth and surface albedo are also analyzed.
Abstract
A deeper knowledge of the effects and interactions of clouds in the climatic system requires developing both satellite and ground-based methods to assess their optical properties. A simple method based on a parameterized inversion of a radiative transfer model is proposed to estimate the optical depth of thick liquid water clouds from the atmospheric transmittance at 415 nm, solar zenith angle, surface albedo, effective droplet radius, and aerosol load. When concurrent measurements of atmospheric transmittance and liquid water path are available, the effective radius of the droplet size distribution can also be retrieved. The method is compared with a reference algorithm from Min and Harrison, which uses similar data, except aerosol load. When applied to measurements performed at the Southern Great Plains site of the Atmospheric Radiation Measurement Program, the mean bias deviation between the proposed method and the reference method is only −0.08 in units of optical depth, whereas the standard deviation is only 0.46. For the effective droplet radius estimations, the mean bias deviation is −0.13 μm, and the standard deviation is 0.14 μm. Maximum relative deviations are lower than 5% and 8% for cloud optical depth and effective radius, respectively. The effects on these retrievals of the assumed aerosol optical depth and surface albedo are also analyzed.
Abstract
Identification of clouds from satellite images is now a routine task. Observation of clouds from the ground, however, is still needed to acquire a complete description of cloud conditions. Among the standard meteorological variables, solar radiation is the most affected by cloud cover. In this note, a method for using global and diffuse solar radiation data to classify sky conditions into several classes is suggested. A classical maximum-likelihood method is applied for clustering data. The method is applied to a series of four years of solar radiation data and human cloud observations at a site in Catalonia, Spain. With these data, the accuracy of the solar radiation method as compared with human observations is 45% when nine classes of sky conditions are to be distinguished, and it grows significantly to almost 60% when samples are classified in only five different classes. Most errors are explained by limitations in the database; therefore, further work is under way with a more suitable database.
Abstract
Identification of clouds from satellite images is now a routine task. Observation of clouds from the ground, however, is still needed to acquire a complete description of cloud conditions. Among the standard meteorological variables, solar radiation is the most affected by cloud cover. In this note, a method for using global and diffuse solar radiation data to classify sky conditions into several classes is suggested. A classical maximum-likelihood method is applied for clustering data. The method is applied to a series of four years of solar radiation data and human cloud observations at a site in Catalonia, Spain. With these data, the accuracy of the solar radiation method as compared with human observations is 45% when nine classes of sky conditions are to be distinguished, and it grows significantly to almost 60% when samples are classified in only five different classes. Most errors are explained by limitations in the database; therefore, further work is under way with a more suitable database.
Abstract
To perform a climatic analysis of the annual UV index (UVI) variations in Catalonia, Spain (northeast of the Iberian Peninsula), a new simple parameterization scheme is presented based on a multilayer radiative transfer model. The parameterization performs fast UVI calculations for a wide range of cloudless and snow-free situations and can be applied anywhere. The following parameters are considered: solar zenith angle, total ozone column, altitude, aerosol optical depth, and single-scattering albedo. A sensitivity analysis is presented to justify this choice with special attention to aerosol information. Comparisons with the base model show good agreement, most of all for the most common cases, giving an absolute error within ±0.2 in the UVI for a wide range of cases considered. Two tests are done to show the performance of the parameterization against UVI measurements. One uses data from a high-quality spectroradiometer from Lauder, New Zealand [45.04°S, 169.684°E, 370 m above mean sea level (MSL)], where there is a low presence of aerosols. The other uses data from a Robertson–Berger-type meter from Girona, Spain (41.97°N, 2.82°E, 100 m MSL), where there is more aerosol load and where it has been possible to study the effect of aerosol information on the model versus measurement comparison. The parameterization is applied to a climatic analysis of the annual UVI variation in Catalonia, showing the contributions of solar zenith angle, ozone, and aerosols. High-resolution seasonal maps of typical UV index values in Catalonia are presented.
Abstract
To perform a climatic analysis of the annual UV index (UVI) variations in Catalonia, Spain (northeast of the Iberian Peninsula), a new simple parameterization scheme is presented based on a multilayer radiative transfer model. The parameterization performs fast UVI calculations for a wide range of cloudless and snow-free situations and can be applied anywhere. The following parameters are considered: solar zenith angle, total ozone column, altitude, aerosol optical depth, and single-scattering albedo. A sensitivity analysis is presented to justify this choice with special attention to aerosol information. Comparisons with the base model show good agreement, most of all for the most common cases, giving an absolute error within ±0.2 in the UVI for a wide range of cases considered. Two tests are done to show the performance of the parameterization against UVI measurements. One uses data from a high-quality spectroradiometer from Lauder, New Zealand [45.04°S, 169.684°E, 370 m above mean sea level (MSL)], where there is a low presence of aerosols. The other uses data from a Robertson–Berger-type meter from Girona, Spain (41.97°N, 2.82°E, 100 m MSL), where there is more aerosol load and where it has been possible to study the effect of aerosol information on the model versus measurement comparison. The parameterization is applied to a climatic analysis of the annual UVI variation in Catalonia, showing the contributions of solar zenith angle, ozone, and aerosols. High-resolution seasonal maps of typical UV index values in Catalonia are presented.
