APL2 – Space weather appcraft: from the drawing board to delivery
Talks
APL2 Mon 4/11 12:00-13:00, room C2D – Almedina
Author(s): Gemma Richardson, Ciaran Beggan, Jonathan Eastwood, Colin Forsyth, Mervyn Freeman, Juliane Huebert, Mike Heyns, Adrian LaMoury, Andy Smith, Oli Chambers
British Geological Survey; British Geological Survey; Imperial College London; University College London; British Antarctic Survey; British Geological Survey; Imperial College London; Imperial College London; Northumbria University; British Geological Survey
Abstract: As one of the SWIMMR (Space Weather Instrumentation Measurements Modelling and Risk) projects, SAGE (SWIMMR Activities in Ground Effects) was tasked with implementing models and forecasts of magnetic field variation in the UK and creating operational tools to predict the magnitude of Geomagnetically Induced Currents (GIC) in grounded infrastructure. The tools, based on scientific models, convert magnetic field measurements and forecasts to geoelectric field variation using models of subsurface conductivity. The geoelectric field is used to estimate GIC in the high voltage power network, pipe-to-soil potential in the high-pressure gas pipeline network and the potential effects of GIC via a rail index. Nowcasts are produced from real-time magnetic field observations while forecasts of the magnetic field come from either a machine learning (SPIDER) or physics-based MHD model (GorgonOps).
All the code was uploaded to a private GitHub repository for the UK Met Office which can be built and deployed automatically to Amazon Web Services system. Fourteen different products are provided, which are available in real-time to MOSWOC space weather forecaster The project was completed in March 2024 allowing a full test of the system during the 10th May G5 geomagnetic storm.
Author(s): George Vasalos, Athanasios Papaioannou, Kathryn Whitman, Philip Quinn, Anastasios Anastasiadis, Markus Leila Mays, Janet Barzilla, Chinwe Didigu, Christopher Light, Claudio Corti, Joycelyn Jones, Anna Chulaki, Hannah Hermann, Edward Semones
National Observatory of Athens/IAASARS; National Observatory of Athens/IAASARS; KBR; Leidos; National Observatory of Athens/IAASARS; NASA Goddard Space Flight Center; Leidos; NASA Goddard Space Flight Center; NASA Goddard Space Flight Center; NASA Goddard Space Flight Center; NASA Goddard Space Flight Center; NASA Goddard Space Flight Center; NASA Goddard Space Flight Center; NASA Johnson Space Center
Abstract: The prediction of space weather conditions is rapidly getting more demanding. This is because near-future planned crewed missions to the Moon and Mars are currently either deployed or under development. The constant breakthroughs in the field of computer science make it urgent for the relevant tools to catch up and use state-of-the-art techniques for modeling and prediction concepts.
The Advanced Solar Particle Events Casting System (ASPECS) tool includes various scientific models in its core that get input from multiple different sources. What started as a system with a sequential triggering structure is now a complex, modular system that can alter its components whenever that is needed.
This presentation will explore ASPECS’s journey from research to operational deployment, highlighting key methodologies and real-time data handling. By sharing our stories from our beginning to our current state and best practices, we aim to demonstrate the importance of user-centric approaches and seamless integration of research into operational frameworks for enhanced space weather forecasting accuracy and reliability.
This work was supported through the ADVISOR – OptimizAtion, DeliVery & Installation of the ASPECS tOol for Space WeatheR research within ISEP project, contract SMS0016862
Author(s): Mark Miesch, George Millward, Manasi Gopala, Charlotte Martinkus, Mike Marsh, Anders Englyst
CIRES/University of Colorado; CIRES/University of Colorado; NOAA Space Weather Prediction Center; CIRES/University of Colorado; UK Met Office; UK Met Office
Abstract: The first operational numerical model to leverage high-performance computing (HPC) resources at NOAA’s Space Weather Prediction Center (SWPC) was the WSA-Enlil model. WSA is the Wang-Sheeley-Arge model, which computes the ambient solar wind out to 21.5 Rsun (0.1 AU) based on photospheric magnetograms (operationally provided by the GONG network) and a semi-empirical expression for wind speed. This is then fed into Enlil, an MHD model that propagates the wind to the Earth. The WSA-Enlil model was made operational in 2012 and is still used today to forecast the arrival time of coronal mass ejections (CMEs). One of the motivations of this transition to operations was the availability of multi-perspective coronagraph observations from NASA’s STEREO mission, which was launched in 2006. However, to leverage these assets, there had to be a way to assimilate coronagraph observations into the WSA-Enlil model. This led to the development of SWPC’s CME Analysis Tool (CAT), written in IDL. The CAT allows SWPC forecasters to interactively fit multi-perspective coronagraph observations to an idealized cone model of a CME. Cone models for one or more CMEs can then be ingested into the Enlil model to follow and forecast their propagation. NOAA/SWPC and the space weather branch of the United Kingdom Met Office (UKMO) are now developing a modernized version of CAT called pyCAT. pyCAT features an interactive browser-based front end (javascript) with a python back end and an event and image database managed by MongoDB. After initial development and deployment at both the NOAA/SWPC and the UKMO forecast centers, we plan to make pyCAT available as an open-source application to promote Research to Operations and Operations to Research (R2O2R). In this talk I will describe the motivation, design, and expected use of pyCAT and encourage the community to participate as users and potentially contributors. The goal is a modern, containerized, user-friendly, extensible, operational application for forecasting CME events that can be progressively improved to leverage future advances in multi-perspective observing platforms and numerical models.
Author(s): Tanja Amerstorfer, Jackie A. Davies, David Barnes, Satabdwa Majumdar, Eva Weiler, Maike Bauer, Justin LeLouedec
Austrian Space Weather Office, GeoSphere Austria, Graz, Austria; RAL Space, Rutherford Appleton Laboratory, Didcot, UK; RAL Space, Rutherford Appleton Laboratory, Didcot, UK; Austrian Space Weather Office, GeoSphere Austria, Graz, Austria; Austrian Space Weather Office, GeoSphere Austria, Graz, Austria; Institute of Physics, University of Graz, Graz, Austria; Austrian Space Weather Office, GeoSphere Austria, Graz, Austria; Institute of Physics, University of Graz, Graz, Austria; Austrian Space Weather Office, GeoSphere Austria, Graz, Austria
Abstract: As ESA’s space weather monitoring mission to the Lagrange L5 point, Vigil, approaches, the integration of heliospheric imager data into real-time CME evolution modelling becomes increasingly important. To test and validate the model output, we can leverage the extensive repository of data from NASA’s STEREO mission, operational since 2007. STEREO’s heliospheric imagers (HI), on which Vigil’s wide-angle cameras will be based, offer a unique perspective by observing the Sun-Earth line from aside-on vantage point. However, despite HI’s capability to track coronal mass ejections (CMEs) from their origin to their impact on Earth, these data are currently underutilised in operational space weather predictions.
This presentation explores methodologies for effectively incorporating HI data into real-time applications in preparation for future Vigil exploitation. We apply our CME prediction model, ELEvoHI, to multiple STEREO HI time-elongation tracks with increasing maximum elongation, simulating the real-time acquisition of new data for enhanced predictions. As a benchmark model we present results from the ELEvo model without the usage of HI data. Additionally, within ELEvoHI we compare two approaches for determining CME direction: one incorporating additional coronagraph data and another relying solely on STEREO HI data.
Our goal is to demonstrate a possible CME prediction pipeline to use future Vigil wide-angle data in operational space weather forecasts.
Posters
Posters I Display Tue 5/11 – Wed 6/11, room C1A – Aeminium
Authors in attendance: Tue 5/11 10:15–11:30, 15:15-16:15; Wed 6/11 10:15–11:30
Author(s): Michaela Brchnelova, Willem-Pieter van der Laan
KU Leuven; Joint Meteorologische Groep, Nederlandse Ministerie van Defensie.
Abstract: As a result of our growing dependence on space-weather sensitive technologies combined with the current worsening of the global geopolitical situation, we have seen an increase in defence spending on the topics of space weather forecasting, nowcasting and the analysis of space weather effects on technology and infrastructure. Indeed, space weather may disrupt defence capabilities, cause false alarms and even threaten the safety of deployed personnel. To mitigate these risks, users such as Defence require timely and reliable space weather information.
Europe has a strong space weather research community, the good collaboration of which has repeatedly been proven by initiatives such as the Virtual Space Weather Modelling Centre or the T-FORS project. Often, however, the link between this research community and the community of the eventual end-users is limited, both in terms of the flow of the user inputs into the decision-making processes that drive the design of space weather tools and the user feedback regarding how well these tools perform in practice. We perceive that this return flow from the users is partially hindered by the sensitivity of the information and the lack of appropriate channels through which this information could be shared, as well as by the lack of awareness regarding how important such feedback is.
In this talk, we first summarize the variety of impacts that space weather may have on defence and how these can be mitigated with accurate space weather forecasts, using real-world examples from the Dutch Defence. Then, we briefly introduce how the Dutch Defence applies space weather forecasting in practice and where, from our perspective, the current forecasting and operational gaps lie. We outline what we further need from the side of the research community (R2O) and also what challenges and options there exist in the return link (O2R), the latter of which could help the researchers validate their existing models and further improve their usability for the end-users. Finally, we present some of our own current efforts to bridge the gap between users and researchers.
Author(s): Masha Kuznetsova
NASA/GSFC Community Coordinated Modeling Center
Abstract: Improving space weather forecasting capabilities is a global challenge that requires collaboration and coordination. The presentation will overview on-going collaborative efforts to demonstrate potential of latest research findings, new models and new data to improve currently available space weather predictive capabilities. We will highlight possible pathways through R2O2R pipelines as well as examples of key elements of R2O2R pipelines including community-wide ensemble forecasts, frequently referred to as Scoreboards, with front end displays tailored for specific user groups. The presentation will discuss opportunities for open incorporation of new findings into existing operational systems as well as collaborative development of next generation operational capabilities.
Author(s): Leila Mays, Masha Kuznetsova, Joycelyn Jones, Eddie Semones, Janet Barzilla, Kathryn Whitman, Teresa Nieves-Chincilla, Michelangelo Romano, CCMC Team, SRAG Team, M2M SWAO Team
NASA GSFC; NASA GSFC; NASA GSFC; NASA JSC; NASA JSC; NASA JSC; NASA GSFC; NASA GSFC; NASA GSFC CCMC; NASA JSC SRAG; NASA GSFC M2M
Abstract: The Integrated Solar Energetic Proton Alert/Warning System (ISEP) project is a partnership between Johnson Space Center’s Space Radiation Analysis Group (SRAG) and the Community Coordinated Modeling Center (CCMC), and Moon to Mars Space Weather Analysis Office (M2M SWAO), whose goals are to identify, transition, and evaluate new models (R2O); develop SEP Scoreboard software tailored for SRAG; and implement these capabilities within CCMC as a non-operational prototype. The ISEP project is an effective NASA in-house R2O2R pipeline for space radiation environment predictive capabilities in support of human missions beyond LEO. As part of ISEP, over 10 SEP models have been implemented from the research community and are now available on the SEP Scoreboard display in real-time. One element of the partnership is the transitioning of ISEP models/software from CCMC to M2M, who provide expert space environment analysis for SRAG console operators. Central to this project was the development of the SEP Scoreboard web application. The SEP scoreboard automatically displays and ingests forecasts of SEP onset, duration, peak flux, probability, all-clear, and overall profile. ISEP is actively evaluating the SEP Scoreboard models and iterating with model developers on improvements. SRAG has developed a validation framework called SPHINX (Solar Particles in the Heliosphere validation INfrastructure for SpWx) as a generalized, automated tool that can validate any kind of forecasted quantity from any type of SEP prediction model. ISEP has also inspired a successful community SEP validation effort called SEPVAL.
Author(s): Justin Le Louëdec, Maike Bauer, Tanja Amerstorfer, Jackie A. Davies
Austrian Space Weather Office, Geosphere Austria; Austrian Space Weather Office, Geosphere Austria; Austrian Space Weather Office, Geosphere Austria; RAL Space, Rutherford Appleton Laboratory, Didcot, UK
Abstract: Observing and forecasting Coronal Mass Ejections (CME) in real-time is crucial due to the potentially strong geomagnetic storms generated and their impact on satellites and electrical devices. With its near-real-time availability, STEREO-HI beacon data is the perfect candidate for efficient
forecasting of CMEs. However, previous work concluded that prediction based on beacon data could not achieve the same accuracy as with high-resolution science data due to data gaps and lower quality. We present our novel pipeline entitled “beacon2science”, bridging
the gap between beacon and science data to improve CME arrival time forecasting. Through this pipeline, we first enhance the quality and spatial resolution of beacon data (improved noise-to-signal ratio and increased CME front visibility). We then increase the cadence of enhanced beacon images through learned interpolation to match science data’s 40-minute cadence. We maximise information coherence between consecutive frames with adapted model architecture and loss functions through the different steps.
To evaluate our method, we compare the enhanced images to science data and study the improvement in forecasting selected events. We use the Ellipse Evolution model based on Heliospheric Imager observations (ELEvoHI) to predict CME event arrival times for beacon, enhanced images, and science data. We show improved forecasting with enhanced images compared to beacon data, allowing efficient forecasting of CME arrival time with near-real-time STEREO-HI data.