scholarly journals On the application of ensemble modeling techniques to improve ambient solar wind models

2013 ◽  
Vol 118 (2) ◽  
pp. 600-607 ◽  
Author(s):  
Pete Riley ◽  
Jon A. Linker ◽  
Zoran Mikić
Keyword(s):  
Solar Wind ◽  
Ensemble Modeling ◽  
Modeling Techniques ◽  
Ambient Solar Wind

Ensemble modeling of the ambient solar wind

2013 ◽  
Author(s):  
Pete Riley ◽  
Jon A. Linker ◽  
Zoran Mikič
Keyword(s):  
Solar Wind ◽  
Ensemble Modeling ◽  
Ambient Solar Wind

scholarly journals Assessing the Quality of Models of the Ambient Solar Wind

Space Weather ◽  
2018 ◽  
Vol 16 (11) ◽  
pp. 1644-1667 ◽  
Author(s):  
P. MacNeice ◽  
L. K. Jian ◽  
S. K. Antiochos ◽  
C. N. Arge ◽  
C. D. Bussy-Virat ◽  
...  
Keyword(s):  
Solar Wind ◽  
Ambient Solar Wind

scholarly journals Understanding biochemical design principles with ensembles of canonical non-linear models

2020 ◽  
Author(s):  
Lukas Bromig ◽  
Andreas Kremling ◽  
Alberto Marin-Sanguino
Keyword(s):  
Parameter Space ◽  
Design Principles ◽  
Product Inhibition ◽  
Performance Criteria ◽  
Biochemical Network ◽  
Model Parameters ◽  
Ensemble Modeling ◽  
Simple Rules ◽  
Modeling Techniques ◽  
Parameter Values

AbstractSystems biology applies concepts from engineering in order to understand biological networks. If such an understanding was complete, biologists would be able to design ad hoc biochemical components tailored for different purposes, which is the goal of synthetic biology. Needless to say that we are far away from creating biological subsystems as intricate and precise as those found in nature, but mathematical models and high throughput techniques have brought us a long way in this direction. One of the difficulties that still needs to be overcome is finding the right values for model parameters and dealing with uncertainty, which is proving to be an extremely difficult task. In this work, we take advantage of ensemble modeling techniques, where a large number of models with different parameter values are formulated and then tested according to some performance criteria. By finding features shared by successful models, the role of different components and the synergies between them can be better understood. We will address some of the difficulties often faced by ensemble modeling approaches, such as the need to sample a space whose size grows exponentially with the number of parameters, and establishing useful selection criteria. Some methods will be shown to reduce the predictions from many models into a set of understandable “design principles” that can guide us to improve or manufacture a biochemical network. Our proposed framework formulates models within standard formalisms in order to integrate information from different sources and minimize the dimension of the parameter space. Additionally, the mathematical properties of the formalism enable a partition of the parameter space into independent subspaces. Each of these subspaces can be paired with a set of criteria that depend exclusively on it, thus allowing a separate sampling/screening in spaces of lower dimension. By applying tests in a strict order where computationally cheaper tests are applied first to each subspace and applying computationally expensive tests to the remaining subset thereafter, the use of resources is optimized and a larger number of models can be examined. This can be compared to a complex database query where the order of the requests can make a huge difference in the processing time. The method will be illustrated by analyzing a classical model of a metabolic pathway with end-product inhibition. Even for such a simple model, the method provides novel insight.Author summaryA method is presented for the discovery of design principles, understood as recurrent solutions to evolutionary problems, in biochemical networks.The method takes advantage of ensemble modeling techniques, where a large number of models with different parameter values are formulated and then tested according to some performance criteria. By finding features shared by successful models, a set of simple rules can be identified that enables us to formulate new models that are known to perform well, a priori. By formulating the models within the framework of Biochemical Systems Theory (BST) we manage to overcome some of the obstacles often faced by ensemble modeling. Further analysis of the selected modeling with standard machine learning techniques enables the formulation of simple rules – design principles – for building good performing networks. We illustrate the method with a well-known case study: the unbranched pathway with end-product inhibition. The method manages to identify the known features of this well-studied pathway while providing additional guidelines on how the pathway kinetics can be tuned to achieve a desired functionality – e.g. demand vs supply control – as well as to identifying important tradeoffs between performance, robustness and and stability.

scholarly journals Using gradient boosting regression to improve ambient solar wind model predictions

Space Weather ◽  
2021 ◽  
Author(s):  
R. L. Bailey ◽  
M. A. Reiss ◽  
C. N. Arge ◽  
C. Möstl ◽  
C. J. Henney ◽  
...  
Keyword(s):  
Solar Wind ◽  
Gradient Boosting ◽  
Wind Model ◽  
Solar Wind Model ◽  
Model Predictions ◽  
Ambient Solar Wind

Using Gradient Boosting Regressors to forecast the ambient solar wind from coronal magnetic models

2021 ◽  
Author(s):  
Rachel Bailey ◽  
Martin A. Reiss ◽  
Christian Möstl ◽  
C. Nick Arge ◽  
Carl Henney ◽  
...  
Keyword(s):  
Solar Wind ◽  
Interplanetary Space ◽  
Gradient Boosting ◽  
Final Model ◽  
Machine Learning Approach ◽  
Solar Wind Flow ◽  
Solar Storms ◽  
Input Variables ◽  
Almost All ◽  
Ambient Solar Wind

<p>In this study we present a method for forecasting the ambient solar wind at L1 from coronal magnetic models. Ambient solar wind flows in interplanetary space determine how solar storms evolve through the heliosphere before reaching Earth, and accurately modelling and forecasting the ambient solar wind flow is therefore imperative to space weather awareness. We describe a novel machine learning approach in which solutions from models of the solar corona based on 12 different ADAPT magnetic maps are used to output the solar wind conditions some days later at the Earth. A feature analysis is carried out to determine which input variables are most important. The results of the forecasting model are compared to observations and existing models for one whole solar cycle in a comprehensive validation analysis. We find that the new model outperforms existing models and 27-day persistence in almost all metrics. The final model discussed here represents an extremely fast, well-validated and open-source approach to the forecasting of ambient solar wind at Earth, and is specifically well-suited for ensemble modelling or for application with other coronal models.</p>

CME arrival time predictions with a deformable front

2021 ◽  
Author(s):  
Jürgen Hinterreiter ◽  
Tanja Amerstorfer ◽  
Martin A. Reiss ◽  
Andreas J. Weiss ◽  
Christian Möstl ◽  
...  
Keyword(s):  
Solar Wind ◽  
Large Scale ◽  
Arrival Time ◽  
Interplanetary Space ◽  
Solar Wind Speed ◽  
Model Combination ◽  
First Results ◽  
Good Agreement ◽  
Ambient Solar Wind

<p>We present the first results of our newly developed CME arrival prediction model, which allows the CME front to deform and adapt to the changing solar wind conditions. Our model is based on ELEvoHI and makes use of the WSA/HUX (Wang-Sheeley-Arge/Heliospheric Upwind eXtrapolation) model combination, which computes large-scale ambient solar wind conditions in the interplanetary space. With an estimate of the solar wind speed and density, we are able to account for the drag exerted on different parts of the CME front. Initially, our model relies on heliospheric imager observations to confine an elliptical CME front and to obtain an initial speed and drag parameter for the CME. After a certain distance, each point of the CME front is propagating based on the conditions in the heliosphere. In this case study, we compare our results to previous arrival time predictions using ELEvoHI with a rigid CME front. We find that the actual arrival time at Earth and the arrival time predicted by the new model are in very good agreement.</p>

A comprehensive catalogue of solar wind properties and events in the inner heliosphere

2020 ◽  
Author(s):  
Rachel Bailey ◽  
Martin Reiss ◽  
Christian Möstl ◽  
Ute Amerstorfer ◽  
Cyril Simon Wedlund ◽  
...  
Keyword(s):  
Solar Wind ◽  
Solar System ◽  
Coronal Mass Ejections ◽  
Model Combination ◽  
Comprehensive Overview ◽  
Wind Flows ◽  
Interaction Regions ◽  
Wind Behaviour ◽  
Ambient Solar Wind ◽  
Inner Heliosphere

<p>The evolving ambient solar wind is one of the key links between the Sun and planetary bodies in our solar system. Here we present a comprehensive catalogue of solar wind properties, stream interaction regions, and coronal mass ejections at different locations in the inner heliosphere. Our database incorporates observational data products and also solar wind modelling results. The solar wind modelling is based on two different approaches for modelling the conditions in the ambient solar wind. While the WSA/THUX model combination solves the viscous form of the underlying Burgers equation to compute the two-dimensional solar wind conditions in our solar system, the second approach is a computationally fast machine learning method for predicting the ambient solar wind flows at Earth. Statistics of the ambient solar wind model results for more than 15 years in combination with a catalogue of coronal mass ejections observed at the Earth, Mars and STEREO satellites along with stream interaction regions provide a comprehensive overview of the past and present solar wind behaviour for shaping planetary space weather.</p>

The Hilbert-Huang Transform (HHT) as a tool for characterizing dynamical features of Mercury’s and Venus’ magnetospheres

2020 ◽  
Author(s):  
Tommaso Alberti ◽  
Anna Milillo ◽  
Monica Laurenza ◽  
Stefano Massetti ◽  
Stavro Ivanovski ◽  
...  
Keyword(s):  
Magnetic Field ◽  
Solar Wind ◽  
Large Scale ◽  
Global Features ◽  
Hilbert Huang Transform ◽  
Ulf Wave ◽  
Local And Global Features ◽  
Spatio Temporal ◽  
Planetary Magnetic Field ◽  
Ambient Solar Wind

<p class="western" align="justify"><span>The interaction between the interplanetary medium and planetary environments gives rise to different phenomena according to the spatio-temporal scales. Here we apply for the first time a novel data analysis method, i.e., the Hilbert-Huang Transform, to discriminate both local and global properties of Venus’ and Mercury’s environments as seen during two MESSENGER flybys. Hence, we may infer that the near-Venus environment is similar in terms of local and global features to the ambient solar wind, possibly related to the induced nature of Venus’ magnetosphere. Conversely, the near-Mercury environment presents some different local features with respect to the ambient solar wind, due to both interaction processes and intrinsic structures of the Hermean environment. Our findings support the ion kinetic nature of the Hermean plasma structures, with the foreshock and the magnetosheath regions being characterized by inhomogeneous ion-kinetic intermittent fluctuations, together with MHD and large-scale fluctuations, the latter being representative of the main structure of the magnetosphere. We also show that the HHT analysis allow to capture and reproduce some interesting features of the Hermean environment as flux transfer events, Kelvin-Helmholtz vortex, and ULF wave activity, thus providing a suitable method for characterizing physical processes of different nature. Our approach demonstrate to be very promising for the characterization of the structure and dynamics of planetary magnetic field at different scales, for the identification of different planetary regions, and for the detection of the “effective” planetary magnetic field that can be used for modelling purposes.</span></p>

Sign in / Sign up

Export Citation Format

Share Document