An evaluation of the accuracy of models for the determination of the weighted mean temperature of the atmosphere

The estimates of the zenith wet delay resulting from the analysis of data from space techniques, such as GPS and VLBI, have a strong potential in climate modeling and weather forecast applications. In order to be useful to meteorology, these estimates have to be converted to precipitable water vapor, a process that requires the knowledge of the weighted mean temperature of the atmosphere, which varies both in space and time. In recent years, several models have been proposed to predict this quantity. Using a database of mean temperature values obtained by ray-tracing radiosonde profiles of more than 100 stations covering the globe, and about 2.5 year’s worth of data, we have analyzed several of these models. Based on data from the European region, we have concluded that the models provide identical levels of precision, but different levels of accuracy. Our results indicate that regionally-optimized models do not provide superior performance compared to the global models.

The importance of online tourism demand

In recent decades, the combination of tourism and Information and Communication Technologies (ICT), has originated considerable changes in tourists’ behaviour. The analysis of tourism demand resulting from the Internet is of growing importance, given the increasing number of online reservations observed in recent years. However, in order to analyse the new trends caused by online bookings, the availability of data enabling the measurement and characterization of this phenomenon is essential. This has, however, been a considerable limitation, given that either no data on key variables is available or the available data is sometimes of questionable quality. For professionals and researchers in the area of tourism, the high volume of tourists who use the Internet to make hotel and travel reservations is worth of consideration, given that it may potentiate the discovery of new source markets, the identification of clients with different characteristics and may help explain the dynamics between suppliers or countries. The existence of predictive studies to support decision-making and planning, by professionals of the tourism sector, is of great importance. Panel data models are a useful and appropriate method for the analysis and modelling of tourism demand. These models consider both the time series and the cross-sectional dimensions of the data and allow for the inclusion of social variables. The results of estimation of tourism demand, through panel data models, show that the Internet and the sharp technological development have encouraged the increasing demand for tourism. The growing number of tourism companies online will naturally promote or potentiate an increase of tourism demand.