Session CD5 - The Ensemble Method in Space Weather Forecasting: bridging the gap between expectation and reality

Siegfried Gonzi (UK Met Office), Vic Pizzo (SWPC Boulder, USA), Eric Adamson (SWPC Boulder, USA), Emiliya Yordanova, onsite (Swedish Institute of Space Physics), Rachel Bailey, onsite

Ensemble techniques in terrestrial weather forecasting have come a long way and it is fair to say that modern weather forecasts are unthinkable without the aid of ensembles. At the risk of not reinventing the wheel the space weather community should learn from the terrestrial weather community. But it is less clear how much of that already existing knowledge can easily be applied to space weather forecasting. The community lacks a clear strategy of how to manage this problem. We are faced with a possible practical limitation of ensemble techniques in space weather forecasting due to a lack of observations in the heliosphere. The idea underpinning ensemble techniques is to draw uncertainties from possible prior states that show some semblance to a real state which is often derived from observations. Ensembles can help with gaining insights into how model and observation uncertainties unfold and manifest themselves in the forecasts. This will benefit model developers and forecasters alike. This is the first ever ESWW session that deals with ensemble methods and user needs. We invite contributions from colleagues working in all fields relating to space weather forecasting, modelling and observations. This includes Sun to Earth, radiation belt, magnetosphere modelling and forecasting techniques. We also invite colleagues to submit presentations that demonstrate that ensemble methods would only add little value to their work.

Poster Viewing
Thursday October 27, 08:30 - 13:30, Poster Area

Wednesday October 26, 14:15 - 15:15, Fire Hall

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Talks : Time schedule

Wednesday October 26, 14:15 - 15:15, Fire Hall
14:15OSPREI: A Coupled Ensemble Approach to Modeling CME-Driven Space Weather With Automatically Generated, User-Friendly OutputsKay, C et al.Oral
14:30Title: Deep Learning models in confronting ADAPT and satellite observations. Zhou, Y et al.Oral
14:45Reduced-physics solar wind models for large ensemble forecastingOwens, M et al.Oral
15:00Solar Predict: a tool to forecast the solar activity cycle: a Cycle 25 updateBrun, A et al.Oral


1To Ensemble or Not EnsembleCamporeale, E et al.Poster
2Daily ensemble forecasting from the Sun to 1 AU - The PAGER EU project.Arber, T et al.Poster
3Over 20-year global magnetohydrodynamic simulation of Earth's magnetosphereHonkonen, I et al.Poster
4How ensemble modelling can be easily employed to a simple Drag-Based Model: Drag-Based Ensemble Model (DBEM)Čalogović, J et al.Poster