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

Sgoff, C., Acevedo, W., Paschalidi, Z., Ulbrich, S., Bauernschubert, E., Kratzsch, T. and Potthast, R. ORCID: https://orcid.org/0000-0001-6794-2500 (2022) Assimilation of crowd-sourced surface observations over Germany in a regional weather prediction system. Quarterly Journal of the Royal Meteorological Society, 148 (745). pp. 1752-1767. ISSN 1477-870X doi: 10.1002/qj.4276

Abstract/Summary

Near-surface temperature and humidity observations over Germany, comingon the one hand from the citizen weather station’s network Netatmo and onthe other hand from synoptic weather stations, were assimilated into the lim-ited 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 assimila-tion of the observations by taking into account the diurnal cycle of temperatureand humidity variables. Our results show that the assimilation of bias-correctedobservations from Netatmo stations reduces the forecast error considerably;meanwhile, the assimilation of Netatmo observations without bias correctionleads to a strong warm bias with a negative impact on forecast performance.In contrast, for the assimilation of synoptic observations the usage of ourbias-correction approach does not lead to any significant decrease in the fore-cast 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 Netatmodata, provided an effective bias-correction approach is applied.

Item Type	Article
URI	https://reading-clone.eprints-hosting.org/id/eprint/110429
Identification Number/DOI	10.1002/qj.4276
Refereed	Yes
Divisions	Science > School of Mathematical, Physical and Computational Sciences > Department of Mathematics and Statistics
Publisher	Royal Meteorological Society
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