Disputation: Nina Schuhen
Doctoral candidate Nina Schuhen at the Department of Geosciences, Faculty of Mathematics and Natural Sciences, is defending the thesis Statistical post-processing of weather forecast ensembles: obtaining optimal deterministic and probabilistic predictions at multiple time scales for the degree of Philosophiae Doctor.
Nina Schuhen. Photo: Private
The PhD defence and trial lecture are fully digital and streamed using Zoom. The host of the session will moderate the technicalities while the chair of the defence will moderate the disputation.
Ex auditorio questions: the chair of the defence will invite the audience to ask ex auditorio questions either written or oral. This can be requested by clicking 'Participants -> Raise hand'.
The Ensemble Kalman filter, and its use in data assimilation in ensemble forecasting
Weather forecasts for hours or days ahead are calculated in numerical models. Once they are published or issued to the end-user the forecasts are commonly not updated. However, errors in the forecasts might become apparent after a few observations have been recorded. This doctoral work proposes a new technique 'Rapid Adjustment of Forecast Trajectories' which updates forecasts using statistical post-processing, and results in substantially improved predictions that also provide more value to the end-user.
Main research findings
Popular scientific article about Schuhen’s dissertation:
Statistical post-processing of weather forecast ensembles: obtaining optimal deterministic and probabilistic predictions at multiple time scales
Weather forecasts are produced by complex numerical models, issued to end users and then updated after a certain period of time, usually at least several hours. During this time, it might become obvious that the current forecasts are somehow flawed and of little use. Nonetheless, they are not changed until being replaced by a new batch from the most recent run of the model. This work proposes a new statistical post-processing method, Rapid Adjustment of Forecast Trajectories, that improves the quality of predictions even after they have been issued and thus increases their potential value to customers.
The inherent correlation between errors at different forecast times allows for adjustments being applied to future predictions based on very recent observations. Thus, both fast-developing and systematic forecast errors can be corrected in a flexible and swift manner. It complements other, conventional statistical post-processing and results in a significant gain in forecast quality. In fact, adjusted predictions from older runs of the numerical model can become more skilful than those from the newest run. This novel technique can be applied to any forecast time range, from a few hours to several days and weeks, while being very economical and versatile.
Photo and other information:
Press photo: Nina Schuhen, portrait; 500px. Photo: Private