Articles | Volume 27, issue 4
https://doi.org/10.5194/npg-27-473-2020
https://doi.org/10.5194/npg-27-473-2020
Research article
 | 
06 Oct 2020
Research article |  | 06 Oct 2020

Statistical postprocessing of ensemble forecasts for severe weather at Deutscher Wetterdienst

Reinhold Hess

Related subject area

Subject: Predictability, probabilistic forecasts, data assimilation, inverse problems | Topic: Climate, atmosphere, ocean, hydrology, cryosphere, biosphere | Techniques: Theory
Extending ensemble Kalman filter algorithms to assimilate observations with an unknown time offset
Elia Gorokhovsky and Jeffrey L. Anderson
Nonlin. Processes Geophys., 30, 37–47, https://doi.org/10.5194/npg-30-37-2023,https://doi.org/10.5194/npg-30-37-2023, 2023
Short summary
Applying prior correlations for ensemble-based spatial localization
Chu-Chun Chang and Eugenia Kalnay
Nonlin. Processes Geophys., 29, 317–327, https://doi.org/10.5194/npg-29-317-2022,https://doi.org/10.5194/npg-29-317-2022, 2022
Short summary
A stochastic covariance shrinkage approach to particle rejuvenation in the ensemble transform particle filter
Andrey A. Popov, Amit N. Subrahmanya, and Adrian Sandu
Nonlin. Processes Geophys., 29, 241–253, https://doi.org/10.5194/npg-29-241-2022,https://doi.org/10.5194/npg-29-241-2022, 2022
Short summary
Ensemble Riemannian data assimilation: towards large-scale dynamical systems
Sagar K. Tamang, Ardeshir Ebtehaj, Peter Jan van Leeuwen, Gilad Lerman, and Efi Foufoula-Georgiou
Nonlin. Processes Geophys., 29, 77–92, https://doi.org/10.5194/npg-29-77-2022,https://doi.org/10.5194/npg-29-77-2022, 2022
Short summary
Inferring the instability of a dynamical system from the skill of data assimilation exercises
Yumeng Chen, Alberto Carrassi, and Valerio Lucarini
Nonlin. Processes Geophys., 28, 633–649, https://doi.org/10.5194/npg-28-633-2021,https://doi.org/10.5194/npg-28-633-2021, 2021
Short summary

Cited articles

Baldauf, M., Seifert, A., Förstner, J., Majewski, D., Raschendorfer, M., and Reinhardt, T.: Operational convective-scale numerical weather prediction with the COSMO model: description and sensitivities, Mon. Weather Rev., 139, 3887–3905, https://doi.org/10.1175/MWR-D-10-05013.1, 2011. a
Ben Bouallègue, Z., Pinson, P., and Friederichs, P.: Quantile forecast discrimination ability and value, Q. J. R. Meteorol. Soc., 141, 3415–3424, https://doi.org/10.1002/qj.2624, 2015. a
Bougeault, P., Toth, Z., Bishop, C., et al.: The THORPEX interactive grand global ensemble, Bull. Amer. Meteor. Soc., 91, 1059–1072, https://doi.org/10.1175/2010BAMS2853.1, 2010. a
Bröcker, J. and Smith, L. A.: Increasing the Reliability of Reliability Diagrams, Weather Forecast., 22, 651–661, https://doi.org/10.1175/WAF993.1 2006. a
Buizza, R.: Ensemble forecasting and the need for calibration, in: Statistical Postprocessing of Ensemble Forecasts, edited by Vannitsem, S., Wilks, D. S., and Messner, J. W., chap. 2, pp. 15–48, Elsevier, Amsterdam, 2018. a, b
Download
Short summary
Forecasts of ensemble systems are statistically aligned to synoptic observations at DWD in order to provide support for warning decision management. Motivation and design consequences for extreme and rare meteorological events are presented. Especially for probabilities of severe wind gusts global logistic parameterisations are developed that generate robust statistical forecasts for extreme events, while local characteristics are preserved.