Contribution
Localized variational blending for nowcasting purposes.
Atencia, Aitor; Kann, Alexander; Wang, Yong; Meier, Florian
Meteorologische Zeitschrift Vol. 29 No. 3 (2020), p. 247 - 261
50 références bibliographiques
publié: Oct 16, 2020
publication en ligne: Apr 21, 2020
manuscrit accepté: Mar 4, 2020
revision du manuscrit reçu: Feb 6, 2020
révision du manuscrit demandée: Jan 15, 2014
manuscrit reçu: Oct 4, 2019
Open Access (article peut être télechargé gratuitement)
Abstract
Mesoscale models' improvement of recent years (like spin-up reduction, assimilation techniques, and assimilation of new observation) and increased computational resources justify a rapid update cycle (1 hour). Despite all these improvements, precipitation forecasts provided by these models are not able to beat the observation-based Lagrangian extrapolation nowcasting for the first forecast steps. In this paper, these two forecasting sources are merged by a new blending technique, more complex than the regular global weights. It takes advantage of a variational technique commonly used in building the analysis in data assimilation cycles. This methodology allows to keep the spatial correlation of the errors and to merge the forecast with locally different weights. The results show an improvement over the original sources of forecast in terms of deterministic, dichotomic and spatial scores.
Mots-clefs
Variational • Blending • Precipitation • Seamless