Team title: Ensemble Forecasts in Space Weather
Team ID: S3-03
Team Leads:
Jordan Guerra (NOAA, USA), jordan.guerraaguilera@villanova.edu
Keywords (impact):
Keywords (activity type): Modeling, Forecasting, 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:
- Forecasting of impulsive events (Flares, CME, SEPs),
- Definition of benchmarks, performance metrics, and uncertainty assessment for impulsive event forecasts,
- Automatization and improving of operational forecasts
Clusters with overlapping topics:
S3: Solar eruptions
H1: Heliospheric magnetic field and solar wind
H2: CME structure, evolution and propagation through heliosphere
G1: Geomagnetic environment
G2A: Atmosphere variabilityG2B: Ionosphere variability
Link to team external website:
https://www.lorentzcenter.nl/lc/web/2019/1195/info.php3?wsid=1195