Teaching numerical modelling in the environmental sciences not only needs good software and course material but also an understanding of how to program the models in the computer. Conventional environmental modelling procedures require computer science and programming skills, which may detract from the important understanding of the environmental processes involved. An alternative strategy is to build a generic toolkit or modelling language that operates with concepts and operations that are familiar to the environmental scientist. PCRaster is such a spatio-temporal environmental modelling language developed at Utrecht University, the Netherlands. It is used for teaching modelling in classrooms and over the Web (distance learning) at three levels: (1) explaining environmental processes and models, where models with a fixed structure of model equations are evaluated by changing model parameters, (2) teaching model construction, where students learn to program spatial and temporal models with the language, and (3) teaching all phases of scientific modelling related to field research. So far, we have received positive responses to these courses, largely because the software provides a set of easily learned functions matching the conceptual thought processes of a geoscientist that can be used at all levels of teaching.
Citizen‐science thermometer measurements have the potential to provide information about surface air temperature fields on scales smaller than is typically quantified by the official monitoring network. As such, national meteorological services are becoming increasingly interested in these measurements as a possible source of data for use in weather monitoring or forecasting. However, in order for the information to be used, biases in the data need to be assessed. The most important source of bias is the potential overheating of the thermometer due to inadequate shielding or exposure. Previous research has indicated that information about the nature of the instrument and its exposure is important for correcting this bias. However, in the majority of cases this information is unavailable for amateur stations. In this paper a statistical correction for short wave radiation bias is developed for the air temperature data recorded at 159 Weather Observations Website (WOW) stations across the Netherlands during the period 2015–2016. Generalized additive mixed modelling (GAMM) is used to quantify and correct for short wave radiation bias in the hourly measurements using a background temperature field generated from the official 34 automatic weather stations along with satellite‐derived short wave radiation estimates. It is demonstrated that the corrected WOW data add local detail to the hourly temperature field, which may provide a useful source of data to supplement official measurements.
scite is a Brooklyn-based organization that helps researchers better discover and understand research articles through Smart Citations–citations that display the context of the citation and describe whether the article provides supporting or contrasting evidence. scite is used by students and researchers from around the world and is funded in part by the National Science Foundation and the National Institute on Drug Abuse of the National Institutes of Health.