MET Nordic analysis of hourly precipitation over Scandinavia

MET Nordic is a 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 first version of MET Nordic was released in 2018 to support applications such as civil protection and public weather services (e.g. Yr.no). 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 contribute to improved analysis and short-term forecasts.

This presentation describes the input data, methods, and results for version 4 of the MET Nordic analysis, with a focus on hourly precipitation. Observational data from multiple rain gauge types are quality controlled and adjusted for wind undercatch and systematic differences between crowdsourced and conventional observations.

Crowdsourced hourly precipitation was compared to conventional observations, showing general agreement but underestimation during intense events. To address this, we apply a quantile-quantile mapping approach to adjust crowdsourced data toward the conventional reference. Additionally, a method originally developed for precipitation correction of wind-induced undercatch using station-data only was adapted for use with model-based meteorological fields and station observations. These adjustments aim to reduce systematic errors in hourly precipitation analysis, with the understanding that improvements in average performance may come with increased uncertainty in individual cases.

The spatial analysis method has also been updated in version 4. 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 at https://thredds.met.no, with documentation at 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.