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 ΔT_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 ΔT_x variability. Copyright © 2010 Royal Meteorological Society

This work proposes a methodology for characterizing the time evolution of water quality time series taking into consideration the inherent problems that often appear in this type of data such as non-linear trends, series having missing data, outliers, irregular measurement patterns, seasonal behavior, and serial correlation. The suggested approach, based on regression models with a Gaussian autoregressive moving average (ARMA) error, provides a framework where those problems can be dealt with simultaneously. Also the model takes into account the effect of influential factors, such as river flows, water temperature, and rainfall. The proposed approach is general and can be applied to different types of water quality series. We applied the modeling framework to four monthly conductivity series recorded at the Ebro river basin (Spain). The results show that the model fits the data reasonably well, that time evolution of the conductivity series is non-homogeneous over the year and, in some cases, non-monotonic. In addition, the results compared favorably over those obtained using simple linear regression, pre-whitening, and trend-free-pre-whitening techniques.

In this work a microfluidic chip is proposed, consisting of three different channels for studying cell migration under chemical and mechanical gradients such as collagen concentrations of matrices and growth factors.