During the last 2 years, tremendous progress has been made in global data-driven weather models trained on reanalysis data. In this study, the Pangu-Weather model is compared to several numerical weather prediction models with and without probabilistic post-processing for temperature and wind speed forecasting. The results confirm that global data-driven models are promising for operational weather forecasting and that post-processing can improve these forecasts considerably.

We have compared hourly precipitation totals measured by rain gauges installed and maintained by citizens against professional weather stations managed by the national weather services of Finland, Norway and Sweden. The manufacturer of the citizen rain gauges is Netatmo. Despite the heterogeneity of citizens' measurements, our results show that the two data sources are comparable with each other, though with some limitations. The results also show how to improve the accuracy of citizens' data.

An unprecedented amount of rainfall data is available nowadays, such as ensemble model output, weather radar estimates, and in situ observations from networks of both traditional and opportunistic sensors. Nevertheless, the exact amount of precipitation, to some extent, eludes our knowledge. The objective of our study is precipitation reconstruction through the combination of numerical model outputs with observations from multiple data sources.

seNorge_2018 is a collection of observational gridded datasets for daily total precipitation and daily mean, minimum, and maximum temperature for the Norwegian mainland covering the time period from 1957 to the present day. The fields have 1 km of grid spacing. The data are used for applications in climatology, hydrology, and meteorology. seNorge_2018 provides a "gridded truth", especially in data-dense regions. The uncertainty increases with decreasing data density.

The observational gridded climate datasets are among the primary sources of information for climate analysis and monitoring. The seNorge2 high-resolution dataset of daily total precipitation (1957–2017) constitutes a valuable meteorological input for snow and hydrological simulations which are routinely conducted over Norway for research and to support operational applications for civil protection purposes. The dataset and the seNorge2 software are publicly available for download.