MDAL is a near real-time, satellite-based surface albedo product based on the geostationary Meteosat Second Generation mission. We propose an update to the processing algorithm that generates MDAL and evaluate the results of these changes through comparison with the pre-update, currently operational MDAL product as well as reference data using different satellite-based albedo products and in situ measurements. We find that the update provides a valuable improvement.

The quality control and validation activities performed by the Swarm data quality team reveal the good-quality LPs. The analysis demonstrated that the current baseline plasma data products are improved with respect to previous baseline. The LPs have captured the ionospheric plasma variability over more than half of a solar cycle, revealing the data quality dependence on the solar activity. The quality of the LP data will further improve promotion of their application to a broad range of studies.

Methods for reconstruction of winter long data gaps in snow depth time series are compared. The methods use snow depth data from neighboring stations or calculate snow depth from temperature and precipitation data. All methods except one are able to reproduce the average snow depth and maximum snow depth in a winter reasonably well. For reconstructing the number of snow days with snow depth ≥ 1 cm, results suggest using a snow model instead of relying on data from neighboring stations.

We have produced a standardised high-quality set of measurements to create definitive data for four Indonesian Geomagnetic Observatories for 2010–2018. We explain the steps taken to update the existing data collection and processing protocols and suggest improvements to further enhance the quality of the magnetic time series at each observatory. The new data will fill the gap in the western Pacific region and provide input into geomagnetic field modeling and secular variation studies.

Researchers are making increasing use of machine learning to improve accuracy, efficiency and consistency. During such a study of the aurora, it was noted that biases or distortions had crept into the data because of the conditions (or ergonomics) of the human trainers. As using machine-learning techniques in auroral research is relatively new, it is critical that such biases are brought to the attention of the academic and citizen science communities.