Assimilation of crowd-sourced surface observations over Germany in a regional weather prediction system

Abstract

Near-surface temperature and humidity observations over Germany, coming on the one hand from the citizen weather station's network Netatmo and on the other hand from synoptic weather stations, were assimilated into the limited are mode of the Icosahedral Nonhydrostatic Model with 2-km resolution (ICON-D2). For that we use the Kilometre-Scale Ensemble Data Assimilation (KENDA) system and a bias-correction approach that improves the assimilation of the observations by taking into account the diurnal cycle of temperature and humidity variables. Our results show that the assimilation of bias-corrected observations from Netatmo stations reduces the forecast error considerably; meanwhile, the assimilation of Netatmo observations without bias correction leads to a strong warm bias with a negative impact on forecast performance. In contrast, for the assimilation of synoptic observations the usage of our bias-correction approach does not lead to any significant decrease in the forecast error, yet reduces the bias for the diurnal cycle of synoptic stations. Overall, it can be concluded that the forecast quality can gain from assimilating Netatmo data, provided an effective bias-correction approach is applied.