Area-covering postprocessing of ensemble precipitation forecasts using topographical and seasonal conditions

Probabilistic weather forecasts from ensemble systems require statistical postprocessing to yield calibrated and sharp predictive distributions. This paper presents an area-covering postprocessing method for ensemble precipitation predictions. We rely on the ensemble model output statistics (EMOS) a...

Ausführliche Beschreibung

Gespeichert in:

Bibliographische Detailangaben
Veröffentlicht in:	Stochastic environmental research and risk assessment 2021-02, Vol.35 (2), p.215-230
Hauptverfasser:	Friedli, Lea, Ginsbourger, David, Bhend, Jonas
Format:	Artikel
Sprache:	eng
Schlagworte:	Aquatic Pollution Calibration Case studies Chemistry and Earth Sciences Computational Intelligence Computer Science Earth and Environmental Science Earth Sciences Environment Lead time Math. Appl. in Environmental Science Mathematical models Original Paper Performance degradation Physics Precipitation Predictions Probability Theory and Stochastic Processes Regression coefficients Statistical analysis Statistics Statistics for Engineering Training Waste Water Technology Water Management Water Pollution Control Weather forecasting
Online-Zugang:	Volltext
Tags:	Tag hinzufügen Keine Tags, Fügen Sie den ersten Tag hinzu!

Schreiben Sie den ersten Kommentar!