Abstract. We introduce a new method to detect and monitor sudden stratospheric warming (SSW) events using Global Navigation Satellite System (GNSS) radio occultation (RO) data at high northern latitudes and demonstrate it for the well-known January–February 2009 event. We first construct RO temperature, density, and bending angle anomaly profiles and estimate vertical-mean anomalies in selected altitude layers. These mean anomalies are then averaged into a daily updated 5∘ latitude × 20∘ longitude grid over 50–90∘ N. Based on the gridded mean anomalies, we employ the concept of threshold exceedance areas (TEAs), the geographic areas wherein the anomalies exceed predefined threshold values such as 40 K or 40 %. We estimate five basic TEAs for selected altitude layers and thresholds and use them to derive primary-, secondary-, and trailing-phase TEA metrics to detect SSWs and to monitor in particular their main-phase (primary- plus secondary-phase) evolution on a daily basis. As an initial setting, the main phase requires daily TEAs to exceed 3×106 km2, based on which main-phase duration, area, and overall event strength are recorded. Using the January–February 2009 SSW event for demonstration, and employing RO data plus cross-evaluation data from analysis fields of the European Centre for Medium-Range Weather Forecasts (ECMWF), we find the new approach has strong potential for detecting and monitoring SSW events. The primary-phase metric shows a strong SSW emerging on 20 January, reaching a maximum on 23 January and fading by 30 January. On 22–23 January, temperature anomalies over the middle stratosphere exceeding 40 K cover an area of more than 10×106 km2. The geographic tracking of the SSW showed that it was centered over east Greenland, covering Greenland entirely and extending from western Iceland to eastern Canada. The secondary- and trailing-phase metrics track the further SSW development, where the thermodynamic anomaly propagated downward and was fading with a transient upper stratospheric cooling, spanning until the end of February and beyond. Given the encouraging demonstration results, we expect the method to be very suitable for long-term monitoring of how SSW characteristics evolve under climate change and polar vortex variability, using both RO and reanalysis data.