Team title: Ensemble Forecasts in Space Weather
Team ID: S3-03
Team Leads:
Jordan Guerra (Villanova University, USA), jordan.guerraaguilera@villanova.edu
Participants:
Keywords (impact):
Keywords (activity type): Data Utilization, Assessment
Introduction:
Ensembles (which use a set of predictions to improve on a single-model output) have been very successful in improving operational weather forecasting and are also used in many other fields such as data science and economics. Ensemble techniques are even used in state-of-the-art machine learning competitions to improve performance. Their use in space weather forecasting could not only improve forecast accuracy but also provide simple model uncertainties that are crucial for improving end-user understanding of the products available.
Objectives:
The Testing, Understanding, and Leveraging Ensemble Predictions for Space weather (TULEPS) team was formed during a Lorentz Center Workshop in September 2019, with the main goal to make concrete steps towards improving space weather forecasting capabilities by implementing ensemble techniques that have been successful in other forecasting fields, especially terrestrial weather. Some specific topics of focus include
- Usefulness of ensembles
- Types of ensembles
- Combinations of forecasts
- Estimating uncertainty
- Forecast evaluation
Action topics:
