Effect of the relative optical air mass and the clearness index on solar erythemal UV irradiance

This paper analyses the effects of the clearness index (Kt) and the relative optical air mass (mr) on erythemal UV irradiance (UVER). The UVER measurements were made in Valencia (Spain) from 6:00 am to 6:00 pm between June 2003 and December 2012 and (140,000 data points). Firstly, two models were used to calculate values for the erythemal ultraviolet irradiance clearness index (KtUVER) as a function of the global irradiance clearness index (Kt). Secondly, a potential regression model to measure the KtUVER as a function of the relative optical air mass was studied. The coefficients of this regression were evaluated for clear and cloudy days, as well as for days with high and low ozone levels. Thirdly, an analysis was made of the relationship between the two effects in the experimental database, with it being found that the highest degree of agreement, or the joint highest frequencies, are located in the optical mass range mr∈[1.0, 1.2] and the clearness index range of Kt∈[0.8, 1.0]. This is useful for establishing the ranges of parameters where models are more efficient. Simple equations have been tested that can provide additional information for the engineering projects concerning thermal installations. Fourthly, a high dispersion of radiation data was observed for intermediate values of the clearness for UV and UVER.

An empirical model of erythemal ultraviolet radiation in the city of Valencia, Spain

This paper presents an improved empirical model that predicts ultraviolet erythemal radiation (UVER) and considers all aspects of atmospheric conditions in Valencia, Spain. The analyzed model is a potential function whose dependent variable is UVER radiation and independent variables are the clearness index and slant ozone column. A potential regression function with all the information contributed a small coefficient of determination and one chose to use a regression potential-exponential mathematical form which improved the coefficient of similar determination. A study was carried out on the influence of season on the regression parameters. This was found to be considerable due to the clearness index. The convergence between the values calculated by the model and the experimental values was analyzed using the mean bias error (MBE) and mean absolute bias error (MABE) statistical parameters. The clearness index and ozone column intervals were analyzed and found to give an improved prediction of the UVER clearness index using regression analysis. Also, a sensitivity analysis was performed on the regression coefficients and parameters. It is important to study the effects of UVER radiation predicted by the model on human health or on agriculture crop growth and yield.

Accurate modeling of spectral fine-structure in Earth radiance spectra measured with the Global Ozone Monitoring Experiment

We present what we believe to be a novel approach to simulating the spectral fine structure (<1 nm) in measurements of spectrometers such as the Global Ozone Monitoring Experiment (GOME). GOME measures the Earth's radiance spectra and daily solar irradiance spectra from which a reflectivity spectrum is commonly extracted. The high-frequency structures contained in such a spectrum are, apart from atmospheric absorption, caused by Raman scattering and by a shift between the solar irradiance and the Earth's radiance spectrum. Normally, an a priori high-resolution solar spectrum is used to simulate these structures. We present an alternative method in which all the required information on the solar spectrum is retrieved from the GOME measurements. We investigate two approaches for the spectral range of 390-400 nm. First, a solar spectrum is reconstructed on a fine spectral grid from the GOME solar measurement. This approach leads to undersampling errors of up to 0.5% in the modeling of the Earth's radiance spectra. Second, a combination of the solar measurement and one of the Earth's radiance measurement is used to retrieve a solar spectrum. This approach effectively removes the undersampling error and results in residuals close to the GOME measurement noise of 0.1%.