No abstract

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.

High-resolution, regularly gridded air-temperature maps are frequently used in climatology, hydrology, and ecology. Within the Netherlands, 34 official automatic weather stations (AWSs) are operated by the National Met Service according to World Meteorological Organization (WMO) standards. Although the measurements are of high quality, the spatial density of the AWSs is not sufficient to reconstruct the temperature on a 1-km-resolution grid. Therefore, a new methodology for daily temperature reconstruction from 1990 to 2017 is proposed, using linear regression and multiple adaptive regression splines. The daily 34 AWS measurements are interpolated using eight different predictors: diurnal temperature range, population density, elevation, albedo, solar irradiance, roughness, precipitation, and vegetation index. Results are cross-validated for the AWS locations and compared with independent citizen weather observations. The RMSE of the reference method ordinary kriging amounts to 2.6 °C whereas using the new methods the RMSE drops below 1.0 °C. Especially for cities, a substantial improvement of the predictions is found. Independent predictions are on average 0.3 °C less biased than ordinary kriging at 40 high-quality citizen measurement sites. With this new method, we have improved the representation of local temperature variations within the Netherlands. The temperature maps presented here can have applications in urban heat island studies, local trend analysis, and model evaluation.