Mobile temperature and humidity measurements have been performed along a 14-km transect through the city of Utrecht, in the Netherlands (311 000 inhabitants), during the period March 2006–January 2009. The measurements took place on a bicycle during commuter traffic and resulted in 106 nighttime profiles (before sunrise) and 77 daytime (afternoon) profiles. It is shown how the intensity of the urban heat island depends on wind direction, cloudiness, and wind speed. Statistical models are constructed that relate the mean and maximum nighttime urban heat island intensity profiles to area-averaged sky-view factors and land use combined at both the micro- and local scales. Sky-view factors are estimated from a 0.5 m × 0.5 m surface elevation database, and land use is obtained from a 25 m × 25 m land-use database. The models are calibrated using the mobile measurements and provide estimates of the spatial distribution of the mean and maximum nighttime urban heat island intensity in Utrecht. Both models explain more than 75% of the variance. A separate nonlinear model is introduced that relates the temperature differences between the warmest part and coolest part of the transects to wind speed and cloudiness.

A better quantification of the urban heat islands (UHIs) in the Netherlands is urgently needed given the heat stress–related problems in the recent past combined with the expected temperature rise for the coming decades. Professional temperature observations in Dutch urban areas are scarce, however. Therefore, this research explores the use of observations from weather stations that were installed and maintained by weather amateurs. From a set of over 200 stations, suitable and representative data have been selected from 20 stations, using a set of objective selection criteria that are based on metadata. One year of data (January–December 2010) was considered. From these data, estimates have been obtained of the magnitude of the UHI in Dutch low-rise residential areas. A positive relation (linear model with r2 ≈ 0.7) was derived between the summer-averaged UHI and the (neighborhood scale) population density around the observational sites. It was found that the UHI in summer is strongest in nighttime conditions and that it increases with decreasing wind speed, decreasing cloud cover, and increasing sea level air pressure. The summer-averaged UHI was ~0.9°C. During nighttime in a relatively warm 1-month subperiod of the summer, the average UHI was ~1.4°C. During spring and autumn, the UHI was lower than in summer; during winter, no significant UHI was observed. The agreement in results among the different stations and the accordance of the magnitude and variation of the observed UHI with those described in the literature show that automatic observations from weather amateurs can be of sufficient quality for atmospheric research, provided that detailed metadata are available.

A sensitivity analysis on the influence of soil moisture on the squall line is performed through five numerical experiments. In four experiments, soil moisture is increased or decreased with respect to a control experiment. This is done in two manners: by affecting soil moisture most strongly in the wetter places in the modelled domain and by affecting soil moisture most strongly in the drier places. Minor deviations occur in the path of the squall line after modifying soil moisture most strongly in the wetter places. Systematic deviations occur in its path after increasing soil moisture most strongly in the drier places. A mechanism is proposed that connects the applied soil moisture modifications to larger-scale flow patterns that determine the path of the squall line. In all five experiments, the precipitation intensity of the squall line strongly declines when the system moves towards western areas with lower soil moisture values. It is concluded that a positive effect of local soil moisture on precipitation intensity in passing squall lines is likely on the considered length-scale of 100 km. Until now, this mechanism has only been shown for much smaller spatial scales.