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[1] This article focuses on the comparison of the total ozone column data from the Ozone Monitoring Instrument (OMI) flying aboard the NASA EOS-Aura satellite platform with ground-based measurement recorded by Brewer spectroradiometers located at five Spanish remote sensing ground stations between January 2005 and December 2007. The satellite data are derived from two algorithms: OMI Total Ozone Mapping Spectrometer (OMI-TOMS) and OMI Differential Optical Absorption Spectroscopy (OMI-DOAS). The largest relative differences between these OMI total ozone column estimates reach 5% with a significant seasonal dependence. The agreement between OMI ozone data and Brewer measurements is excellent. Total ozone columns from OMI-TOMS are on average a mere 2.0% lower than Brewer data. For OMI-DOAS data the bias is a mere 1.4%. However, the relative difference between OMI-TOMS and Brewer measurements shows a notably lower seasonal dependence and variability than the differences between OMI-DOAS and ground-based data. For both OMI ozone data products these relative differences show significant dependence on the satellite ground pixel solar zenith angle for cloud-free cases as well as for cloudy conditions. However, the OMI ozone data products are shown to reveal opposite behavior with respect to the two antagonistic sky conditions. No significant dependency of the ground-based to satellite-based differences with respect to the satellite cross-track position is seen for either OMI retrieval algorithm.

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ABSTRACT:Here we present an exploratory statistical analysis aimed at the minimization of the 'screen bias' from affected ancient air temperature time series over the Western Mediterranean. Our approach lies in the statistical analysis of about 6 years of daily paired temperature observations taken using the ancient Montsouri shelter and the modern Stevenson screen for daily maximum (T x ) and minimum (T n ) temperature data recorded at two experimental sites: the meteorological gardens of La Coruña and Murcia, Spain (locations under the influence of the Oceanic/Atlantic/Galician and Mediterranean arid and semi-arid climate types, respectively), where ongoing field trials have been carried out. Descriptive statistical analysis of the paired series shows pre-sheltered temperatures tended to induce a strong warm bias in T x data (of about 1°C at the annual scale but with a clear seasonal cycle with higher values in summer and lower in winter), while T n readings have a small (∼0.2°C, and sustained all year round) cold bias compared to the modern period. Statistical relationships between the screen bias and other related meteorological variables show the highest correlation coefficients between the 'screen bias' and T x , T n and the diurnal temperature range (DTR) recorded under the replicated ancient shelters at both locations and point to the reliability of these variables as potential predictors of the T x . We generate a parsimonious regression model based on the data from both experimental sites, which takes into account polynomial terms of lower order for the predictor variables (T x and DTR recorded under the ancient shelter) and harmonic terms, in order to represent the seasonal cycle of the screen bias. The goodness-of-fit of the model is satisfactory, as it explains up to 51.7% of the additional T x variability.

Abstract. We use an automatic weather station and surface mass balance dataset spanning four melt seasons collected on Hurd Peninsula Glaciers, South Shetland Islands, to investigate the point surface energy balance, to determine the absolute and relative contribution of the various energy fluxes acting on the glacier surface and to estimate the sensitivity of melt to ambient temperature changes. Long-wave incoming radiation is the main energy source for melt, while short-wave radiation is the most important flux controlling the variation of both seasonal and daily mean surface energy balance. Short-wave and long-wave radiation fluxes do, in general, balance each other, resulting in a high correspondence between daily mean net radiation flux and available melt energy flux. We calibrate a distributed melt model driven by air temperature and an expression for the incoming short-wave radiation. The model is calibrated with the data from one of the melt seasons and validated with the data of the three remaining seasons. The model results deviate at most 140 mm w.e. from the corresponding observations using the glaciological method. The model is very sensitive to changes in ambient temperature: a 0.5 • C increase results in 56 % higher melt rates.