Articles | Volume 27, issue 4
https://doi.org/10.5194/npg-27-473-2020
© Author(s) 2020. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
Special issue:
https://doi.org/10.5194/npg-27-473-2020
© Author(s) 2020. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
Research article
| 
06 Oct 2020
Research article |
| 06 Oct 2020
| 06 Oct 2020
Statistical postprocessing of ensemble forecasts for severe weather at Deutscher Wetterdienst
Deutscher Wetterdienst, Offenbach, Germany
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18 citations as recorded by crossref.
- Probabilistic forecasting of photovoltaic power supply — A hybrid approach using D-vine copulas to model spatial dependencies A. Schinke-Nendza et al. 10.1016/j.apenergy.2021.117599
- Quantile mapping correction of analog ensemble forecast for solar irradiance M. Kakimoto et al. 10.1016/j.solener.2022.03.015
- A comparison of statistical and dynamical downscaling methods for short‐term weather forecasts in the US Northeast M. Alessi & A. DeGaetano 10.1002/met.1976
- Do Machine Learning Approaches Offer Skill Improvement for Short-Term Forecasting of Wind Gust Occurrence and Magnitude? J. Coburn & S. Pryor 10.1175/WAF-D-21-0118.1
- Time‐series‐based ensemble model output statistics for temperature forecasts postprocessing D. Jobst et al. 10.1002/qj.4844
- Adaptive Blending of Probabilistic Precipitation Forecasts with Emphasis on Calibration and Temporal Forecast Consistency M. Rempel et al. 10.1175/AIES-D-22-0020.1
- Analog Ensemble Forecasting System for Low-Visibility Conditions over the Main Airports of Morocco B. Alaoui et al. 10.3390/atmos13101704
- Lead‐time‐continuous statistical postprocessing of ensemble weather forecasts J. Wessel et al. 10.1002/qj.4701
- Machine learning-based prediction of Alpine foehn events using GNSS troposphere products: first results for Altdorf, Switzerland M. Aichinger-Rosenberger et al. 10.5194/amt-15-5821-2022
- Robust DC optimal power flow with modeling of solar power supply uncertainty via R-vine copulas K. Aigner et al. 10.1007/s11081-022-09761-0
- Preface: Advances in post-processing and blending of deterministic and ensemble forecasts S. Hemri et al. 10.5194/npg-27-519-2020
- Skewed and Mixture of Gaussian Distributions for Ensemble Postprocessing M. Taillardat 10.3390/atmos12080966
- Biomass Production Potential in a River under Climate Change Scenarios P. Orlińska-Woźniak et al. 10.1021/acs.est.1c03211
- Enhancing real-time flood forecasting and warning system by integrating ensemble techniques and hydrologic model simulations A. Patel et al. 10.2166/wcc.2024.052
- Calibrated ensemble forecasts of the height of new snow using quantile regression forests and ensemble model output statistics G. Evin et al. 10.5194/npg-28-467-2021
- Generating synthetic rainfall fields by R‐vine copulas applied to seamless probabilistic predictions P. Schaumann et al. 10.1002/qj.4751
- A Calibrated and Consistent Combination of Probabilistic Forecasts for the Exceedance of Several Precipitation Thresholds Using Neural Networks P. Schaumann et al. 10.1175/WAF-D-20-0188.1
- Dynamic downscaling ensemble forecast of an extreme rainstorm event in South China by COSMO EPS L. Ji et al. 10.3389/feart.2022.969742
Latest update: 13 Dec 2024
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.
Forecasts of ensemble systems are statistically aligned to synoptic observations at DWD in order...
