ST4.2

EDI
Nowcasting, forecasting, operational monitoring and post-event analysis of the space weather and space climate in the Sun-Earth system

Space Weather (SW) and Space Climate (SC) are collective terms that describe the Sun-Earth system interactions on timescales varying between minutes and decades and include processes at the Sun, in the heliosphere, magnetosphere, ionosphere, thermosphere and at the lower atmosphere. Being able to predict (forecast and nowcast) the extreme events and develop the strategy for mitigation vital as the space assets and critical infrastructures, such as communication and navigation systems, power grids, and aviation, are all extremely sensitive to the external environment. Post-event analysis is crucially important for the development and maintenance of numerical models, which can predict extreme SW events to avoid failure of the critical infrastructures.

This session aims to address both the current state of the art of SW products and new ideas and developments that can enhance the understanding of SW and SC and their impact on critical infrastructure. We invite presentations on various SW and SC-related activities in the Sun-Earth system: forecast and nowcast products and services; satellite observations; model development, validation, and verification; data assimilation; development and production of geomagnetic and ionospheric indices. Talks on SW effects on applications (e.g. on airlines, pipelines and power grids, space flights, auroral tourism, etc.) in the Earth’s environment are also welcomed.

Convener: Guram Kervalishvili | Co-conveners: Yulia Bogdanova, Therese Moretto Jorgensen, Claudia Borries, Alan Thomson
Presentations
| Wed, 25 May, 15:55–18:30 (CEST)
 
Room L1

Presentations: Wed, 25 May | Room L1

Chairpersons: Guram Kervalishvili, Yulia Bogdanova
15:55–16:00
16:00–16:10
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EGU22-13384
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solicited
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Highlight
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On-site presentation
Yaqi Jin, Wojciech J. Miloch, Lucilla Alfonsi, Luca Spogli, Jaroslav Urbář, Claudio Cesaroni, Antonio Cicone, Alan G. Wood, James Rawlings, Golnaz Sahtahmassebi, Lasse B.N. Clausen, Per Høeg, Jaime Muñoz Redondo, Maria José Brazal Aragón, and Paweł Wojtkiewicz

The state of the Earth’s ionosphere is an important aspect of the Sun-Earth system. It reflects  dynamical coupling of the solar wind with the Earth’s magnetosphere. Its understanding has also an applied aspect in the context of the space weather effects. For example, ionospheric plasma irregularities impact the propagation of radio waves, and they can degrade radio communication or positioning with the Global Navigation Satellite Systems (GNSS). The European Space Agency’s Swarm+ 4DIonosphere initiative aims at advancing our understanding and characterisation of the processes in the ionosphere to better model and eventually predict the state of the ionosphere. Within this framework, through the project “Swarm Variability of Ionospheric Plasma” (Swarm-VIP), we analyse spatiotemporal characteristics of ionospheric plasma at different geomagnetic latitudes and uncover coupling between various scales in the ionosphere. Taking advantage of the orbital characteristics of the Swarm satellites and using complementary analysis techniques, such as wavelets or Fast Iterative Filtering, we ascertain the dominant scales at given geomagnetic conditions. The result of the study is a semi-empiric model of the ionosphere based on the generalised linear modeling approach. The model determines the probability of occurrence of different scales in ionospheric plasma with respect to geomagnetic conditions. It also gives insight into ionospheric structuring and related space weather effects. The Swarm-VIP model is provided globally, along the whole orbits of the Swarm satellites, and a special emphasis is put on the polar regions, Arctic and Antarctica, and the European sector, where the validation study is carried out with a network of the ground-based instruments.

How to cite: Jin, Y., Miloch, W. J., Alfonsi, L., Spogli, L., Urbář, J., Cesaroni, C., Cicone, A., Wood, A. G., Rawlings, J., Sahtahmassebi, G., Clausen, L. B. N., Høeg, P., Redondo, J. M., Brazal Aragón, M. J., and Wojtkiewicz, P.: Variability of ionospheric plasma studied and modelled based on data from the Swarm satellites, EGU General Assembly 2022, Vienna, Austria, 23–27 May 2022, EGU22-13384, https://doi.org/10.5194/egusphere-egu22-13384, 2022.

16:10–16:16
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EGU22-2155
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Virtual presentation
Erik Schmölter and Jens Berdermann

A modeling approach for long time predictions (more than 12 h) of ionospheric disturbances driven by solar storm events is presented. Intended for an operational framework, this model shall deliver fast and precise localized warnings for these disturbances in the future. For this reason, a data base of historical solar storm impacts covering two solar cycles is used to reconstruct future events and resulting ionospheric disturbances. Here, we will present the basic components of the model and show first validation results based on predicted and observed geomagnetic activity, global total electron content and selected solar storms. Two storm events (including the St. Patrick’s Day geomagnetic storm during the 17 March 2015) are analyzed in more detail to illustrate the model capabilities. We will also discuss possible future improvements of the individual model parts, as well as the planned extensions and applications.

How to cite: Schmölter, E. and Berdermann, J.: An ionospheric disturbance forecast model based on real-time solar wind analysis with the best-fitting historical storm events, EGU General Assembly 2022, Vienna, Austria, 23–27 May 2022, EGU22-2155, https://doi.org/10.5194/egusphere-egu22-2155, 2022.

16:16–16:22
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EGU22-2233
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On-site presentation
Piero Diego, Mirko Piersanti, Dario Del Moro, Alexandra Parmentier, Matteo Martucci, Farnacesco Palma, Alessandro Sotgiu, Christina Plainaki, Giulia D'Angelo, Francesco Berrilli, Dario Recchiuti, Emanuele Papini, Gianluca Napoletano, Antonio Cicone, Roberto Iuppa, Roberta Sparvoli, Pietro Ubertini, Roberto Battiston, and Piergiorgio Picozza

On May 12,  2021 the interplanetary counterpart of the May 9,  2021 coronal mass ejection impacted the Earth’s magnetosphere, giving rise to a strong geomagnetic storm.  This work discusses the evolution of the various events linking the solar activity to the Earth’s ionosphere with special focus on the effects observed in the circumterrestrial environment. We investigate the propagation of the interplanetary coronal mass ejection and its interaction with the magnetosphere - ionosphere system in terms of both magnetospheric current systems and particle redistribution, by jointly analysing data from interplanetary, magnetospheric, and low Earth orbiting satellites. The principal magnetospheric current system activated during the different phases of the geomagnetic storm is correctly identified through the  direct  comparison  between  geosynchronous  orbit  observations  and  model  predictions. From the particle point of view, we have found that the primary impact of the storm development is a net and rapid loss of relativistic electrons from the entire outer radiation belt. Our analysis shows no evidence for any short-term recovery to pre-storm levels during the days following the main phase.  Storm effects also included a small Forbush decrease driven by the interplay between the interplanetary shock and subsequent magnetic cloud arrival.

How to cite: Diego, P., Piersanti, M., Del Moro, D., Parmentier, A., Martucci, M., Palma, F., Sotgiu, A., Plainaki, C., D'Angelo, G., Berrilli, F., Recchiuti, D., Papini, E., Napoletano, G., Cicone, A., Iuppa, R., Sparvoli, R., Ubertini, P., Battiston, R., and Picozza, P.: On the magnetosphere-ionosphere coupling during the May 2021 Geomagnetic storm., EGU General Assembly 2022, Vienna, Austria, 23–27 May 2022, EGU22-2233, https://doi.org/10.5194/egusphere-egu22-2233, 2022.

16:22–16:28
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EGU22-1655
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ECS
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On-site presentation
Simon Thomas, Pierre-Louis Blelly, Aurelie Marchaudon, Julian Eisenbeis, and Samuel Bird

The IRAP Plasmasphere Ionosphere Model (IPIM) describes the transport equations of
ionospheric plasma species along magnetic closed field lines. As input, the previous iteration
of IPIM used basic models to provide estimations of the solar wind conditions, convection,
and precipitation within the ionosphere. In this presentation, we discuss the development of
a new operational version of IPIM as part of the EUHFORIA project to monitor and forecast
space weather conditions and hazards. The developments of the model include using in-situ
solar wind observations from the OMNI data set, ionospheric radar data of plasma motions
from the Super Dual Auroral Radar Network (SuperDARN), and precipitation data from the
Ovation model, as inputs to the model. We present the first results from the latest IPIM
version, focussing on case study coronal mass ejections on 14th December 2006 and 14th
July 2012 which featured clear magnetic clouds and long-lasting southward magnetic field.
For these events, we explore simulations of plasma densities, temperature, and motions,
and compare with observations from EISCAT ionospheric radars and ionosonde launches.
With these results in mind, we will discuss the skill of using IPIM as a space weather
forecasting and analysis tool.

How to cite: Thomas, S., Blelly, P.-L., Marchaudon, A., Eisenbeis, J., and Bird, S.: Using IPIM to Simulate the Ionosphere’s Response to Extreme Space Weather, EGU General Assembly 2022, Vienna, Austria, 23–27 May 2022, EGU22-1655, https://doi.org/10.5194/egusphere-egu22-1655, 2022.

16:28–16:34
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EGU22-8782
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Virtual presentation
Massimo Materassi, Yenca Migoya-Orue, Tommaso Alberti, Sandro Radicella, and Giuseppe Consolini

Modeling the Earth’s ionosphere is a big challenge, due to the complexity of the system. Any ionospheric model misses the behavior of the real system for some fluctuating component, that appears almost impossible to predict, and is particularly threatening for the human technologies (e.g., GNSS navigation). While producing models extremely rich, including many physical agents acting on the Earth’s ionosphere, it is necessary to understand whether the residual, non-modeled component is predictable in principle as a “simple" dynamical system, or is conversely so chaotic to be practically stochastic, and should be treated probabilistically.

The question of how chaotic and how predictable the time series of vertical total electron content (vTEC) are, depending on the different locations and solar activity conditions, is dealt with by employing tools of dynamical system theory.

In particular, we calculate the correlation dimension D2 and the Kolmogorov entropy rate K2 for the vTEC time series at different latitudes in both northern and southern hemispheres and during both high and low solar activity periods.

The quantity D2 is a proxy of the degree of chaos and dynamical complexity: the larger D2 is, the higher the number of dynamical variables needed to describe the phenomenon is. Instead, K2 measures the speed of destruction of the mutual information between the signal and a delayed copy of it, so that (K2)-1 is a sort of maximum time horizon for predictability.

The analysis of the D2 and K2 for the vTEC time series will then allow to give a measure of chaos and predictability of the Earth ionosphere. Being such analysis performed for different locations and different solar activity conditions, these characteristics will indicate possible differences depending on location.

How to cite: Materassi, M., Migoya-Orue, Y., Alberti, T., Radicella, S., and Consolini, G.: Chaos and Predictability in vTEC time series, EGU General Assembly 2022, Vienna, Austria, 23–27 May 2022, EGU22-8782, https://doi.org/10.5194/egusphere-egu22-8782, 2022.

16:34–16:40
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EGU22-5220
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Virtual presentation
Anna Morozova, Teresa Barata, and Tatiana Barlyaeva

The total electron content (TEC) over the Iberian Peninsula was modeled using a PCA-NN models based on the decomposition of the observed TEC series using the principal component analysis (PCA) and reconstruction of the daily mean TEC and daily PCA modes’ amplitudes by different types of neural networks (NN) using several types of space weather parameters as predictors. Lags of 1 and 2 days between the TEC and space weather parameters are used.

Two main goals are set:

  • To find a NN configuration(s) that produces forecasts of reasonable quality with minimal amount of input data
  • To find a best set of space weather parameters that work as predictors for PCA-NN models

Here we present preliminary results related to the second goal: PCA-NN models with different sets of predictors are compared. Among predictors we consider proxies for the solar UV and XR fluxes, number of the solar flares of different types, parameters of the solar wind and of the interplanetary magnetic field, and geomagnetic indices.

How to cite: Morozova, A., Barata, T., and Barlyaeva, T.: Comparison of the performance of PCA-NN models for daily mean TEC over the Iberian Peninsula: the role of space weather parameters as predictors for TEC, EGU General Assembly 2022, Vienna, Austria, 23–27 May 2022, EGU22-5220, https://doi.org/10.5194/egusphere-egu22-5220, 2022.

Coffee break
Chairpersons: Guram Kervalishvili, Yulia Bogdanova
17:00–17:02
17:02–17:08
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EGU22-8476
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Presentation form not yet defined
Denny Oliveira, Eftyhia Zesta, Piyush Mehta, Richard Licata, Marcin Pilinski, W. Kent Tobiska, and Hisashi Hayakawa

Orbits of human assets such as satellites, crewed spacecraft and stations in low-Earth orbit (LEO) are very sensitive to the highly dynamic environment in which they fly. Atmospheric drag caused by the interaction between the orbiting object and the local thermospheric neutral mass density affects the satellite’s lifetime and orbital tracking, which becomes increasingly inaccurate or uncertain with storm intensity. Given the planned increase of government and private satellite presence in LEO, the need for accurate density predictions for collision avoidance and lifetime optimization, particularly during extreme events, has become an urgent matter and requires comprehensive international collaboration. Additionally, long-term solar activity models and historical data suggest that the solar activity will significantly increase in the following years and decades. In this presentation, we briefly summarize the main achievements in the research of thermospheric density response to magnetic storms occurring particularly after the launching of many satellites with state-of-the-art accelerometers for density determination (CHAMP, GRACE, GOCE, Swarm). We argue that specification models (e.g., HASDM) perform reasonably well during storm main and recovery phases of extreme storms, but forecasting models (e.g., JB2008) do not perform well throughout the storm cycle. We will discuss how forecasting models can be improved by looking into two directions: first, to the past, by adapting historical extreme storm datasets for density predictions, and second, to the future, by facilitating the assimilation of large-scale data sets that will be collected in future events. We invite the community to the discussion on the possible use of several hundreds of satellites with lower resolution density measurements along with data assimilation schemes or the use of ~100 high precision tracked satellites as a more effective approach for future density determinations.

How to cite: Oliveira, D., Zesta, E., Mehta, P., Licata, R., Pilinski, M., Tobiska, W. K., and Hayakawa, H.: Perspectives on the thermosphere response to extreme magnetic storms: Current status of neutral mass density modeling, EGU General Assembly 2022, Vienna, Austria, 23–27 May 2022, EGU22-8476, https://doi.org/10.5194/egusphere-egu22-8476, 2022.

17:08–17:14
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EGU22-2946
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Virtual presentation
Piyush Mehta, Richard Licata, Daniel Weimer, Douglas Drob, W Kent Tobiska, and Jean Yoshi

Modeling of the upper atmosphere, specifically the thermosphere mass density, remains the primary source of uncertainty in satellite drag and orbital operations in low Earth orbit (LEO). The variations in mass density are dominated by changes in solar irradiance on the timescales of the solar cycle, however, short-term space weather changes can significantly impact the state of the thermosphere, especially during geomagnetic storms. Because of our limited understanding of such variations and the resulting inaccurate modeling, quantifying the uncertainty in density specification and forecasting becomes critical for space operations including decision making for collision avoidance and safeguarding of our space assets.

The Naval Research Laboratory’s MSIS model is one of the most widely used models in operations, especially in the commercial industry. Several different versions of the models have been developed, the most recent being MSIS 2.0. A new methodology for calibration of the MSIS model with exospheric temperatures inverted using accelerometer-derived density estimates has recently been developed. In this work, we apply a similar but updated methodology to the MSIS 2.0 model and use machine learning, specifically a neural network, to develop a version of the MSIS 2.0 model calibrated to the accelerometer-derived density estimates  that also provides reliable uncertainty estimates.

How to cite: Mehta, P., Licata, R., Weimer, D., Drob, D., Tobiska, W. K., and Yoshi, J.: Accounting for Uncertainties in MSIS 2.0, EGU General Assembly 2022, Vienna, Austria, 23–27 May 2022, EGU22-2946, https://doi.org/10.5194/egusphere-egu22-2946, 2022.

17:14–17:24
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EGU22-4842
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ECS
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solicited
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Virtual presentation
Martin Reiss, Christian Möstl, Rachel Bailey, Hannah Rüdisser, Ute Amerstorfer, Tanja Amerstorfer, Andreas Weiss, Jürgen Hinterreiter, and Andreas Windisch

Predicting the Bz magnetic field embedded in interplanetary coronal mass ejections (ICMEs), also called the Bz problem, is a core challenge in space weather research and prediction. We tackle this problem with a new approach by taking upstream in situ measurements of the ICME sheath region and the first few hours of the magnetic obstacle to predict the downstream Bz component. To do so, we trained a machine learning algorithm on 348 ICMEs (extracted from the open source ICMECATv2.0 catalog) observed by the Wind, STEREO-A, and STEREO-B satellites to predict the minimum value of Bz. The predictive tool was built to mimic a real-time scenario, where the ICMEs sweep over the spacecraft, which allows us to continually provide updates and improved predictions of Bz as time passes and more of the CME structure is observed. The final model, which is based on random forests, can predict the minimum value of Bz with a reasonable level of agreement compared to observations. In this presentation, we will discuss the main challenges we face in using a data-driven machine learning application to solve the Bz problem, and outline the lessons learned and future strategies for predicting and potentially mitigating the effects of ICMEs arriving at Earth.

How to cite: Reiss, M., Möstl, C., Bailey, R., Rüdisser, H., Amerstorfer, U., Amerstorfer, T., Weiss, A., Hinterreiter, J., and Windisch, A.: Predicting the Bz magnetic field component from upstream in situ observations of coronal mass ejections using machine learning, EGU General Assembly 2022, Vienna, Austria, 23–27 May 2022, EGU22-4842, https://doi.org/10.5194/egusphere-egu22-4842, 2022.

17:24–17:30
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EGU22-2776
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ECS
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Highlight
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On-site presentation
Lukas Drescher, Sofia Kroisz, Manuela Temmer, Sandro Krauss, Barbara Suesser-Rechberger, Saniya Behzadpour, and Torsten Mayer-Guerr

The FFG funded project SWEETS (space weather effects on low Earth orbiting satellites) covers the analysis of a large sample of more than 300 ICMEs (interplanetary coronal mass ejections) from 2002 to 2017 and how they relate to the orbit decay of satellites. Based on the results by Krauss et al. (2018, 2020), we investigate the correlation between the interplanetary magnetic field of ICMEs and the variation of the neutral density in the thermosphere. So far, the satellite drops were calculated from either accelerometer measurements or kinematic orbits for the satellite GRACE at a height of approximately 490 km. Presently, we are working on constructing kinematic orbits for satellites in various heights so we will be able to cover altitudes between 300 to 800 km and a wider timeframe. The algorithm is also going to be improved with respect to multiple ICME events and the calculation of a so-called “effective Bz” component and its duration.

With the correlation and the real-time in-situ magnetic field data from satellites at L1 we were able to construct a nowcast. The nowcast algorithm is the basis of a new service called SODA (Satellite Orbit DecAy) which will be implemented in the ESA Space Safety Program (Ionospheric Weather Expert Service Center).

How to cite: Drescher, L., Kroisz, S., Temmer, M., Krauss, S., Suesser-Rechberger, B., Behzadpour, S., and Mayer-Guerr, T.: Nowcasting the Orbit Decay of Earth orbiting Satellites, EGU General Assembly 2022, Vienna, Austria, 23–27 May 2022, EGU22-2776, https://doi.org/10.5194/egusphere-egu22-2776, 2022.

17:30–17:36
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EGU22-5210
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ECS
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Virtual presentation
Harriet Turner, Mathew Owens, and Matthew Lang

Accurate space weather forecasting requires knowledge of the solar wind conditions in near-Earth space. Data assimilation (DA) combines model output and observations to find an optimum estimation of reality and has led to large advances in terrestrial weather forecasting. It is now being applied to space weather forecasting. Here, we use solar wind DA to reconstruct the conditions from 30 solar radii to Earth's orbital radius and over all longitudes and produce solar wind speed forecasts. In this study, we assimilate observations from the Solar Terrestrial Relations Observatory (STEREO) and the Advanced Composition Explorer (ACE). Analysis of two periods of time, one in solar minimum and one in solar maximum, reveals that assimilating observations from multiple spacecraft is preferable over observations from a single spacecraft. The age of the observations also has an impact on forecast error, whereby the mean absolute error (MAE) increases by up to 23% when the forecast lead time exceeds the time associated with the longitudinal separation between the observing spacecraft and the forecast location. It was also found that removing CMEs from the DA input observations acts to reduce the forecast MAE by up to 10% through removal of false streams in the forecast time series. This work adds further evidence to the usefulness of the L5 space weather monitoring mission, but also shows that a mission to L4 would aid in future solar wind DA forecasting capabilities. 

How to cite: Turner, H., Owens, M., and Lang, M.: Effects of CME removal and observation age on solar wind data assimilation, EGU General Assembly 2022, Vienna, Austria, 23–27 May 2022, EGU22-5210, https://doi.org/10.5194/egusphere-egu22-5210, 2022.

17:36–17:42
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EGU22-13037
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On-site presentation
Christopher Pankratz, Greg Lucas, Jenny Knuth, Dusan Odstrcil, James Craft, and Thomas Berger

One of the critical models in space weather forecasting is the Enlil solar wind prediction model that can inform space weather forecasters the direction and speed of coronal mass ejections CMEs. The Enlil code calculates the propagation of the solar wind throughout the 3D heliosphere, but current visualization capabilities in the forecasting offices are restricted to 2D planes intersecting Earth. This limits forecasters to only be able to view CME properties that are traveling directly in the plane of the Earth.

 

Here, we present an update on a new visualization capability being developed to take advantage of the full Enlil 3D data volume and interactively visualize the CME expansion out of the plane of the Earth.  We have been collaborating closely with researchers and forecasters at the Met Office in the UK and the Space Weather Prediction Center (SWPC) in the USA to develop a tool to enable full view of the heliosphere in a manner that can be tailored to these different types of users. To accomplish this, we are deploying the Enlil solar wind model into a scalable Cloud-based model staging platform computing environment, which will allow the full 3D Enlil output to reside in-situ with the visualization engine.  We will discuss our progress in deploying and running the Enlil model in the Cloud-based testbed environment, the process of interacting directly with space weather forecasters to design a new interactive 3D visualization tool that meets their needs, and will demonstrate use of the actual visualization tool, which is deployed and running in the Amazon Web Services (AWS) Cloud environment.

How to cite: Pankratz, C., Lucas, G., Knuth, J., Odstrcil, D., Craft, J., and Berger, T.: Progress On a New Interactive 3-Dimensional Data Viewer for the Enlil Solar Wind Model, EGU General Assembly 2022, Vienna, Austria, 23–27 May 2022, EGU22-13037, https://doi.org/10.5194/egusphere-egu22-13037, 2022.

17:42–17:48
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EGU22-11658
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ECS
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On-site presentation
Angelica M. Castillo Tibocha, Jana de Wiljes, Yuri Y. Shprits, and Nikita A. Aseev

Reconstruction and prediction of the state of the near-Earth space environment is important for anomaly analysis, development of empirical models, and understanding of physical processes. Accurate reanalysis or predictions that account for uncertainties in the associated model and the observations, can be obtained by means of data assimilation. The ensemble Kalman filter (EnKF) is one of the most promising filtering tools for nonlinear and high dimensional systems in the context of terrestrial weather prediction. In this study, we adapt traditional ensemble-based filtering methods to perform data assimilation in the radiation belts. By performing a fraternal twin experiment, we assess the convergence of the EnKF to the standard Kalman filter (KF). Furthermore, with the split-operator technique, we develop two new three-dimensional EnKF approaches for electron phase space density that account for radial and local processes, and allow for reconstruction of the full 3D radiation belt space. The capabilities and properties of the proposed filter approximations are verified using Van Allen Probe and GOES data. Additionally, we validate the two 3D split-operator Ensemble Kalman filters against the 3D split-operator KF. We show how the use of the split-operator technique allows us to include more physical processes in our simulations and is a computationally efficient data assimilation tool that delivers an accurate approximation of the optimal KF solution, and is suitable for real-time forecasting.

How to cite: Castillo Tibocha, A. M., de Wiljes, J., Shprits, Y. Y., and Aseev, N. A.: Reconstructing the Dynamics of the Outer Electron Radiation Belt by Means of the Standard and Ensemble Kalman Filter With the VERB-3D Code, EGU General Assembly 2022, Vienna, Austria, 23–27 May 2022, EGU22-11658, https://doi.org/10.5194/egusphere-egu22-11658, 2022.

17:48–17:54
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EGU22-13001
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Presentation form not yet defined
Ingmar Sandberg, Georgios Provatas, Constantinos Papadimitriou, Sigiava Aminalragia-Giamini, Keith Ryden, and Hugh Evans

The Environmental Monitoring Units (EMU), on-board two satellites of the EU Galileo constellation, monitor the radiation environment along the GNSS orbit providing measurements of the energetic electron fluxes in the outer Van Allen Belt. With new calibration studies that take into account more realistic shielding provided by the spacecraft and the characteristics of the encountered environment along the satellite orbit, we have derived a new version of the GSAT/EMU Level 1 dataset that provides high quality validated fluxes of trapped energetic electrons within the 0.2-4.5 MeV energy range.  

In this work, we present an overview of the EMU measured electron fluxes over the last five years including recently completed validation studies with Arase [ERG] and RBSP energetic electron measurements. The new dataset, available to users from European member states registered at https://gssc.esa.int, will be used in the assimilation processes and/or the validation of the ONERA Salammbô electron radiation belt models - under the EU Safespace activity and ESA S2P RBFAN activity - leading to improved forecasts of the state of the outer belt. In addition, the quality of the time-coverage of the dataset permits their use in the development and/or evaluation of quantitative radiation environment specification models.

 

This work has received funding from the European Union’s Horizon 2020 research and innovation programme "SafeSpace" under grant agreement No 870437, from the European Space Agency activity "Cross Calibration EMU Dataset with RBSP" under ESA Contract 4000135823/21/NL/GLC/mkn and the “SSA P3-SWE-X Space Environment Nowcast and Forecast Development” activity under ESA Contract 4000131381/20/D/CT.

How to cite: Sandberg, I., Provatas, G., Papadimitriou, C., Aminalragia-Giamini, S., Ryden, K., and Evans, H.: Monitoring Van Allen Radiation Belts using EU Galileo satellites: Observations and Data Products of energetic particle fluxes, EGU General Assembly 2022, Vienna, Austria, 23–27 May 2022, EGU22-13001, https://doi.org/10.5194/egusphere-egu22-13001, 2022.

17:54–18:00
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EGU22-7606
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ECS
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Virtual presentation
Martin Sabathier, Olivier Pannekoucke, and Vincent Maget

Space weather is of interest to the satellite industry as the quantity of radiation can rapidly change in the Earth’s radiation belts during solar events (called geomagnetic storms). Radiation belts dynamics are complex and modelled at ONERA by an advection-diffusion equation with added sources and losses terms: the Salammbô model. For several years, data assimilation has been used to reduce the uncertainties inherent to imprecise physics and numerical assumptions used in the Salammbô model alone. An Ensemble Kalman Filter has thus been developed and the overall process has been optimized from the physics-based point of view.

To improve the benefits of data assimilation and thus the accuracy of the prevision, we are considering the implementation of a Parametric Kalman Filter (PKF) [1,2,3]. We think it is a pertinent choice to reduce computational costs and use the information on the uncertainties dynamics brought by the evolution equation. The PKF also allows direct access to the variance and correlation length-scale within the domain, helping with uncertainty estimation. The prevision step of the PKF uses the dynamics of a system to yield the dynamics for a set of parameters (usually the variance and a local anisotropy tensor). These parameters are then used to approximate the covariance matrix coefficients used for the analysis and uncertainty estimation.

As mentioned above, the data assimilation technique currently used at ONERA is a slightly adapted Ensemble Kalman Filter (EnKF) which has mostly been used as a black box to merge the model prevision and the observations. In order to better understand uncertainties dynamics in the case of radiation belts, we run diagnostics on the ensemble to compute the PKF parameters and study their dynamics as propagated by the EnKF. This study shows encouraging results regarding the compatibility of the Salammbô model with the PKF.

Following the work of O.Pannekoucke in [1] and using SymPKF library [4], we find the dynamics of the parameters for a 1D heterogeneous diffusion equation resembling the equation governing the radiation belts. This test case allows for quick and easy study of particularities not covered in [1,2] such as boundary conditions handling with the PKF.

During my presentation I will introduce the PKF and the ensemble diagnostics with examples related to the Salammbô model. I will then compare the way we handle boundary conditions for the EnKF and the PKF.

 

[1] Pannekoucke, O., Bocquet, M., and Ménard, R.: Parametric covariance dynamics for the nonlinear diffusive Burgers equation, Nonlin. Processes Geophys., 25, 481–495, https://doi.org/10.5194/npg-25-481-2018, 2018.

[2] Olivier Pannekoucke, Sophie Ricci, Sebastien Barthelemy, Richard Ménard & Olivier Thual (2016) Parametric Kalman filter for chemical transport models, Tellus A: Dynamic Meteorology and Oceanography, 68:1, 31547, DOI: 10.3402/tellusa.v68.31547

[3] Olivier Pannekoucke (2021) An anisotropic formulation of the parametric Kalman filter assimilation, Tellus A: Dynamic Meteorology and Oceanography, 73:1, 1-27, DOI: 10.1080/16000870.2021.1926660

[4] Olivier Pannekoucke, Philippe Arbogast: SymPKF: a symbolic and computational toolbox for the design of parametric Kalman filter dynamics, arXiv(physics):2103.09226, 2021.

How to cite: Sabathier, M., Pannekoucke, O., and Maget, V.: First step of data assimilation technique Parametric Kalman Filter adaptation to Space Weather, EGU General Assembly 2022, Vienna, Austria, 23–27 May 2022, EGU22-7606, https://doi.org/10.5194/egusphere-egu22-7606, 2022.

18:00–18:06
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EGU22-1732
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ECS
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On-site presentation
Haruka Matsumoto, Henrik Svensmark, and Martin Enghoff

This study examines the relationship between cosmic rays and clouds during Forbush decreases (FDs) to understand the cause-effect relationships between cloud microphysics, cloud condensation nuclei (CCN), and ionisation in the atmosphere. The results of a Monte Carlo analysis of cloud parameters during FDs from newly calibrated satellite data, namely, the Pathfinder Atmospheres Extended (PATMOS-x) from 1978 to 2018, show the connections between some cloud parameters and FDs. For context, FD is the event where, the amount of cosmic rays arriving in the atmosphere decreases and recovers over several days. Other studies have shown that FDs impacted the cloud fraction, aerosol optical depth, CCN, water content, and cloud effective radius (reff ) in the atmosphere. Using the Monte Carlo analysis, nine atmospheric parameters from the dataset were evaluated for a significant response level to FDs. Each FD event added (after the first event) reduces the noise, but only the strongest events add a significant signal (exceptionally when the 2nd and 5th rank FD data are added, the signal/noise ration dropped due to change of satellite version). We found that cloud fraction shows statistically significant signals following FDs at an achieved significance level of 0.33%. Cloud emissivity also showed highly significant signals from the analysis, however these cannot be determined as physical cause by FDs since the response starts a week before the FDs. In contrast, the cloud optical depth, integrated total cloud water over the entire column, and reff did not show any significant signals in frameworks of the applied methods. The top-of-atmosphere brightness temperature at nominal wavelengths of 3.75, 11.0, and 12.0 µm and surface brightness temperature were analysed anew and showed significant signals. The estimated brightness temperature changes from a radiative transfer model (Fu-Liou model) show consistent results with the observed changes in cloud parameters during FD events. Among analysed several atmospheric/cloud/aerosol parameters, cloud fraction and the top-of-atmosphere brightness temperature at nominal wavelengths of 3.75, 11.0, 12.0 µm remain the only parameters depicting a statistically significant and correct-phase response to FDs.

How to cite: Matsumoto, H., Svensmark, H., and Enghoff, M.: Effects of Forbush Decreases on Clouds as determined from PATMOS-x, EGU General Assembly 2022, Vienna, Austria, 23–27 May 2022, EGU22-1732, https://doi.org/10.5194/egusphere-egu22-1732, 2022.

18:06–18:12
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EGU22-5411
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Presentation form not yet defined
Ermanno Pietropaolo, Raffaello Foldes, Alfredo Del Corpo, Massimo Vellante, and Raffaele Marino

Ground-based magnetometer stations offer a valuable and easy-to-access tool for sounding the Earth’s magnetic field disturbances in the inner magnetosphere with a multi-viewpoint system. Using Ultra-Low Frequency (ULF) measurements recorded from meridional aligned stations, it is possible to infer the Field Line Resonance (FLR) frequencies, using a well-established technique, namely the gradient method (Baransky et al. 1985 and Waters et al. 1991). Based on this technique, several authors developed (semi-)automated tools for estimating FLR from ground-based magnetometer measurements. Recently it has been observed (Foldes et al., 2021) that the Machine Learning (ML) approach represents a valuable tool to estimate FLRs from Fourier cross-phase spectra. However, it is commonly known that detecting FLRs using cross-phase spectra may often be unfeasible due to data gaps, noisy signals and/or quiescent ULF wave periods. To handle these situations, we implement an ML classification algorithm to detect periods in which resonance frequencies are clearly observable and thus can be easily estimated. Our algorithm can distinguish between periods with observed frequency from the others; moreover, it can determine if the considered field line is crossing the plasma boundary layer (PBL) at a given time. The results of our method are validated for a particular pair of stations (at L=2.9), along the Equatorial quasi-Meridional Magnetometer Array (EMMA), which provides an extensive data set with several different geomagnetic conditions. This kind of approach in the analysis of ground-based magnetic field measurements, combined in a two-stage ML pipeline with a regression algorithm (as in Foldes et al., 2021), may provide a prominent tool for monitoring the plasmasphere dynamics using a completely automated system.

How to cite: Pietropaolo, E., Foldes, R., Del Corpo, A., Vellante, M., and Marino, R.: Automated tool for estimating field line resonance frequencies using ground-based magnetometer measurements, EGU General Assembly 2022, Vienna, Austria, 23–27 May 2022, EGU22-5411, https://doi.org/10.5194/egusphere-egu22-5411, 2022.

18:12–18:18
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EGU22-8377
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ECS
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Virtual presentation
Michaela Mooney, Mike Marsh, Colin Forsyth, Michael Sharpe, Teresa Hughes, Suzy Bingham, David Jackson, Jonathan Rae, and Gareth Chisham

The aurora is a readily visible phenomenon of interest to many members of the public. However, the aurora and associated phenomena can also significantly impact communications, ground-based infrastructure and high-altitude radiation exposure. Forecasting the location of the auroral oval is therefore a key component of space weather forecast operations. A version of the OVATION-Prime 2013 auroral precipitation model was implemented for operational use at the UK Met Office Space Weather Operations Centre (MOSWOC), delivering a 30-minute forecast of the auroral oval location and the probability of observing the aurora.

Using weather forecast evaluation techniques, we evaluate the ability of the operational version of the OVATION-Prime 2013 model to predict the location of the auroral oval and the probability of aurora occurring. We compare the forecasts with auroral boundaries determined from data from the IMAGE satellite between 2000 and 2002. Our analysis shows that the operational model performs well at predicting the location of the auroral oval, with a relative operating characteristic (ROC) score of 0.82. We analyse the model performance in detail during different levels of geomagnetic activity levels and in different spatial locations.

How to cite: Mooney, M., Marsh, M., Forsyth, C., Sharpe, M., Hughes, T., Bingham, S., Jackson, D., Rae, J., and Chisham, G.: Evaluating Auroral Forecasts Against Satellite Observations, EGU General Assembly 2022, Vienna, Austria, 23–27 May 2022, EGU22-8377, https://doi.org/10.5194/egusphere-egu22-8377, 2022.

18:18–18:24
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EGU22-84
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ECS
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On-site presentation
Stefano Maffei, Philip Livermore, Jonathan Mound, Joseph Eggington, Jonathan Eastwood, Sabrina Sanchez, and Mervyn Freeman

The auroral zones indicate the locations on the Earth’s surface where, on average, it is most likely to spot aurorae as a consequence of increased solar activity. The shape of the auroral zones and, similarly, the geographical locations most vulnerable to extreme space weather events are modulated by the geomagnetic field of internal origin. As the latter evolves in time, the formers will be subject to variations on the same timescales.

From available geomagnetic field forecasts (which provide an estimate of the future evolution of the geomagnetic field of internal origin) we derive AACGM latitudes and estimate the future evolution of the auroral zones. The novel aspect of this technique is that we make use of all available Gauss coefficients to produce the forecasts, while the majority of present techniques estimate the location of the auroral zones based on the dipolar coefficients only. Our results show that, while the shift of the geomagnetic dipole axis has a first order contribution, higher order Gauss coefficients contribute significantly to the location and shape of the auroral zones.

The same technique is then extended to estimate the future location of the geographical location that would be, on average, most exposed to extreme space weather event. We find that the space-weather related risk will not change significantly for the UK over the next 50 years. For the Canadian provinces of Quebec and Ontario, however, we predict a significant increase in the risk associated to extreme solar activity.

How to cite: Maffei, S., Livermore, P., Mound, J., Eggington, J., Eastwood, J., Sanchez, S., and Freeman, M.: The future evolution of the auroral zones, EGU General Assembly 2022, Vienna, Austria, 23–27 May 2022, EGU22-84, https://doi.org/10.5194/egusphere-egu22-84, 2022.

18:24–18:30
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EGU22-1995
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Highlight
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On-site presentation
Guram Kervalishvili, Jürgen Matzka, and Jan Rauberg

Global geomagnetic indices are widely used not only to characterize the geomagnetic disturbance level but also for the parameterization of physical and empirical models of the near-Earth space environment and in data (re)analysis. One of the most utilized index families is the three-hourly Kp and the ap, Ap, Cp, C9 indices derived and disseminated by the GFZ German Research Centre for Geosciences.

The new global geomagnetic open-ended, high-cadence, Kp-like Hpo index family (consisting of the half-hourly Hp30, ap30 and hourly Hp60, ap60) was developed within the Space Weather Atmosphere Models and Indices (SWAMI) project of the H2020 EU research activity. These open-ended Hpo indices are based on the data of the same 13 geomagnetic observatory and similar algorithms as the three-hourly Kp index. The open-ended indices are designed such that 15 Hp60 and 32 Hp60 values exceeding 9 (maximum amplitude for the Kp index) have been assigned since 1995.

Near real-time indices and archive indices back to 1995 are available for download under the CC BY 4.0 license and include the linear versions of the Hp30 and Hp60 indices, the ap30 and ap60 indices. Near real-time plots of the Hp30 and Hp60 indices for the current day and the previous six days are also provided. Here, the operational capabilities and examples of these indices will be presented.

How to cite: Kervalishvili, G., Matzka, J., and Rauberg, J.: The open-ended, high-cadence, Kp-like and fully operational geomagnetic Hpo indices for the ESA Space Weather G-ESC service network, EGU General Assembly 2022, Vienna, Austria, 23–27 May 2022, EGU22-1995, https://doi.org/10.5194/egusphere-egu22-1995, 2022.