MET Nordic analysis of hourly precipitation over Scandinavia

MET Nordic is an hourly gridded dataset developed by the Norwegian Meteorological Institute (MET Norway), providing near-surface meteorological variables at 1 km resolution for Scandinavia, Finland, and the Baltic countries. Variables include temperature at two metres, precipitation, sea-level pressure, relative humidity, wind speed and direction, global radiation, long-wave downwelling radiation, and cloud area fraction.

The dataset integrates forecasts from the MetCoOp Ensemble Prediction System (MEPS) and various observational sources, including crowdsourced temperature and precipitation data from citizen-managed weather stations. These additional data sources improve the analysis and short-term forecasts. 

The MET Nordic dataset is produced in real time (MET Nordic RT), serving civil protection and public weather services (e.g. Yr.no). Additionally, when new methods are introduced, we rerun the dataset back to 2012 in order to create an updated archive of historical analyses and forecasts. MET Nordic rerun can be used in hydrological models, case studies, and also for training machine learning models. In January 2026 we officially released rerun version 4 (MET Nordic rerun v4). This poster describes the input data, methods, and results for version 4 of the MET Nordic analysis, with a focus on hourly precipitation. 

In MET Nordic v4, observational data from multiple rain gauge types are adjusted for wind undercatch as well as for systematic differences between crowdsourced and conventional observations. These adjustments aim to reduce systematic errors in hourly precipitation analysis – however they may also increase uncertainty in individual cases. The corrected observations are then quality-controlled using our in-house library, Titanlib, available at https://github.com/metno/titanlib.

The spatial analysis method has also been updated in MET Nordic v4. The new method, Ensemble-based Statistical Interpolation (EnSI), combines model output and observations in a multi-scale framework. A “started Box-Cox transformation” is applied when analyzing variables that deviate from Gaussian distributions. EnSI was evaluated using 231 heavy precipitation events, including a reconstruction of hourly precipitation and temperature during the 2023 “Hans” extreme weather event in Scandinavia. Results show that the multi-scale approach improves both accuracy and precision compared to a single-scale scheme.

MET Nordic is publicly available and documented on https://github.com/metno/NWPdocs/wiki/MET-Nordic-dataset. The EnSI spatial analysis method is implemented in the GridPP post-processing tool, available at https://github.com/metno/gridpp.