Solar wind propagation delay predictions between L1 and Earth based on machine learning

2021 ◽  
Author(s):  
Carsten Baumann ◽  
Aoife E. McCloskey
Keyword(s):  
Machine Learning ◽  
Solar Wind ◽  
Wind Speed ◽  
Lead Time ◽  
Solar Wind Speed ◽  
Propagation Delay ◽  
Learning Model ◽  
Interplanetary Shocks ◽  
Feature Sets ◽  
Machine Learning Model

<p>GNSS positioning errors, spacecraft operations failures and power outages potentially originate from space weather in general and the solar wind interaction with the geomagnetic field in particular. Depending on the solar wind speed, information from L1 solar wind monitor spacecraft only give a lead time to take safety measures between 20 and 90 minutes.  This very short lead time requires end users to have the most reliable warnings when potential impacts will actually occur. In this study we present a machine learning algorithm that is suitable to predict the solar wind propagation delay between Lagrangian point L1 and the Earth.  This work introduces the proposed algorithm and investigates its operational applicability to a realtime scenario.</p><p>The propagation delay is measured from interplanetary shocks passing the Advanced Composition Explorer (ACE) first and their sudden commencements within the magnetosphere later, as recorded by ground-based magnetometers. Overall 380 interplanetary shocks with data ranging from 1998 to 2018 builds up the database that is used to train the machine learning model. We investigate two different feature sets. The training of one machine learning model DSCOVR real time solar wind (RTSW) like data which contains all three components of solar wind speed is used. For the other machine learning model ACE RTSW like data which only provide bulk solar wind speed will be used for training. Both feature sets also contain the position of the spacecrafts. The performance assessment of the machine learning model is examined on the basis of a 10-fold cross-validation. The major advantage of the machine learning approach is its simplicity when it comes to its application. After training, values for the different features have to be fed into the algorithms only and the evaluation of the propagation delay can be continuous.</p><p>Both machine learning models will be validated against a simple convective solar wind propagation delay model as it is also used in operational space weather centers. For this purpose time periods will be investigated where L1 spacecraft and Earth satellites just outside the magnetosphere probe the same features of the interplanetary magnetic field. This method allows a detailed validation of the solar wind propagation delay apart from the technique that relies on interplanetary shocks.</p>

scholarly journals Timing of the solar wind propagation delay between L1 and Earth based on machine learning

2021 ◽  
Author(s):  
Carsten Baumann ◽  
Aoife E. McCloskey
Keyword(s):  
Machine Learning ◽  
Solar Wind ◽  
Wind Speed ◽  
Space Weather ◽  
Solar Wind Speed ◽  
Propagation Delay ◽  
Learning Approach ◽  
Machine Learning Model ◽  
Machine Learning Approach ◽  
Gb Model

Erroneous GNSS positioning, failures in spacecraft operations and power outages due to geomagnetically induced currents  are severe threats originating from space weather. Having knowledge of potential impacts on modern society in advance is key for many end-user applications. This covers not only the timing of severe geomagnetic storms but also predictions of substorm onsets at polar latitudes. In this study we aim at contributing to the timing problem of space weather impacts and propose a new method to predict the solar wind propagation delay between Lagrangian point L1 and the Earth based on machine learning, specifically decision tree models. The propagation delay is measured from the identification of interplanetary discontinuities detected by the Advanced Composition Explorer (ACE) and their subsequent sudden commencements in the magnetosphere recorded by ground-based magnetometers. A database of the propagation delay has been constructed on this principle including 380 interplanetary shocks with data ranging from 1998 to 2018. The feature set of the machine learning approach consists of six features, namely the three components of each the solar wind speed and position of ACE around L1. The performance assessment of the machine learning model is examined on the basis of a 10-fold cross-validation. The machine learning results are compared to physics-based models, i.e., the flat propagation delay and the more sophisticated method based on the normal vector of solar wind discontinuities (vector delay). After hyperparameter optimization, the trained gradient boosting (GB) model is the best machine learning model among the tested ones. The GB model achieves an RMSE of 4.5 min with respect to the measured solar wind propagation delay and also outperforms the physical flat and vector delay models by 50 % and 15 % respectively. To increase the confidence in the predictions of the trained GB model, we perform a operational validation, provide drop-column feature importance and analyse the feature impact on the model output with Shapley values. The major advantage of the machine learning approach is its simplicity when it comes to its application. After training, values for the solar wind speed and spacecraft position from only one datapoint have to be fed into the algorithm for a good prediction.

scholarly journals Weather prediction using random forest machine learning model

2021 ◽  
Vol 22 (2) ◽  
pp. 1208
Author(s):  
R. Meenal ◽  
Prawin Angel Michael ◽  
D. Pamela ◽  
E. Rajasekaran
Keyword(s):  
Machine Learning ◽  
Wind Speed ◽  
Random Forest ◽  
Solar Radiation ◽  
Regression Models ◽  
Tamil Nadu ◽  
Weather Prediction ◽  
Learning Model ◽  
Statistical Regression ◽  
Machine Learning Model

The complex numerical climate models pose a big challenge for scientists in weather predictions, especially for tropical system. This paper is focused on presenting the importance of weather prediction using machine learning (ML) technique. Recently many researchers recommended that the machine learning models can produce sensible weather predictions in spite of having no precise knowledge of atmospheric physics. In this work, global solar radiation (GSR) in MJ/m2/day and wind speed in m/s is predicted for Tamil Nadu, India using a random forest ML model. The random forest ML model is validated with measured wind and solar radiation data collected from IMD, Pune. The prediction results based on the random forest ML model are compared with statistical regression models and SVM ML model. Overall, random forest machine learning model has minimum error values of 0.750 MSE and R2 score of 0.97. Compared to regression models and SVM ML model, the prediction results of random forest ML model are more accurate. Thus, this study neglects the need for an expensive measuring instrument in all potential locations to acquire the solar radiation and wind speed data.

scholarly journals DESIGN OF MACHINE LEARNING MODEL FOR CHARACTER RECOGNITION

2021 ◽  
Vol 9 (2) ◽  
pp. 376-380
Author(s):  
Rituraj Jain, Et. al.
Keyword(s):  
Machine Learning ◽  
Logistic Regression ◽  
Regression Model ◽  
Character Recognition ◽  
Logistic Regression Model ◽  
Learning Model ◽  
Major Step ◽  
Feature Sets ◽  
Machine Learning Model ◽  
Selection Of

Hand written character recognition or text recognition is the ability of the system to identify character or text automatically. Documents can be in any form such as documents, photographs, touch-screens or other devices. Each character will have its own feature sets. The classification or identification of characters are done based on proper selection of feature sets. Feature selection is the major step for any classification process. The machine learning model is created based on feature sets. Logistic regression model is used in this model to identify different characters. 97.33% accuracy is achieved using logistic regression model compared to existing work.                       

scholarly journals New methods to improve the vertical extrapolation of near-surface offshore wind speeds

2021 ◽  
Author(s):  
Mike Optis ◽  
Nicola Bodini ◽  
Mithu Debnath ◽  
Paula Doubrawa
Keyword(s):  
Machine Learning ◽  
Wind Speed ◽  
Model Performance ◽  
Learning Model ◽  
Offshore Wind ◽  
Offshore Wind Turbine ◽  
Near Surface ◽  
Wind Speeds ◽  
Machine Learning Model ◽  
Surface Measurements

Abstract. Accurate characterization of the offshore wind resource has been hindered by a sparsity of wind speed observations that span offshore wind turbine rotor-swept heights. Although public availability of floating lidar data is increasing, most offshore wind speed observations continue to come from buoy-based and satellite-based near-surface measurements. The aim of this study is to develop and validate novel vertical extrapolation methods that can accurately estimate wind speed time series across rotor-swept heights using these near-surface measurements. We contrast the conventional logarithmic profile against three novel approaches: a logarithmic profile with a long-term stability correction, a single-column model, and a machine-learning model. These models are developed and validated using 1 year of observations from two floating lidars deployed in U.S. Atlantic offshore wind energy areas. We find that the machine-learning model significantly outperforms all other models across all stability regimes, seasons, and times of day. Machine-learning model performance is considerably improved by including the air-sea temperature difference, which provides some accounting for offshore atmospheric stability. Finally, we find no degradation in machine-learning model performance when tested 83 km from its training location, suggesting promising future applications in extrapolating 10-m wind speeds from spatially resolved satellite-based wind atlases.

scholarly journals Echo state network model for analyzing solar-wind effects on the AU and AL indices

2022 ◽  
Vol 40 (1) ◽  
pp. 11-22
Author(s):  
Shin'ya Nakano ◽  
Ryuho Kataoka
Keyword(s):  
Neural Network ◽  
Solar Wind ◽  
Wind Speed ◽  
Solar Wind Speed ◽  
Synthetic Data ◽  
Nonlinear Effects ◽  
Echo State Network ◽  
Solar Wind Density ◽  
Machine Learning Model ◽  
Solar Wind Parameters

Abstract. The properties of the auroral electrojets are examined on the basis of a trained machine-learning model. The relationships between solar-wind parameters and the AU and AL indices are modeled with an echo state network (ESN), a kind of recurrent neural network. We can consider this trained ESN model to represent nonlinear effects of the solar-wind inputs on the auroral electrojets. To identify the properties of auroral electrojets, we obtain various synthetic AU and AL data by using various artificial inputs with the trained ESN. The analyses of various synthetic data show that the AU and AL indices are mainly controlled by the solar-wind speed in addition to Bz of the interplanetary magnetic field (IMF) as suggested by the literature. The results also indicate that the solar-wind density effect is emphasized when solar-wind speed is high and when IMF Bz is near zero. This suggests some nonlinear effects of the solar-wind density.

Machine learning model of the plasmasphere to forecast satellite charging caused by solar storms.

2021 ◽  
Author(s):  
Stefano Bianco ◽  
Irina Zhelavskaya ◽  
Yuri Shprits
Keyword(s):  
Machine Learning ◽  
Solar Wind ◽  
Plasma Density ◽  
Extreme Ultraviolet ◽  
Geomagnetic Storms ◽  
Learning Model ◽  
Horizon 2020 ◽  
Machine Learning Model ◽  
Energetic Radiation ◽  
Solar Storms

<p>Solar storms are hazardous events consisting of a high emission of particles and radiation from the sun that can have adverse effect both in space and on Earth. In particular, the satellites can be damaged by energetic particles through surface and deep dielectric charging. The Prediction of Adverse effects of Geomagnetic storms and Energetic Radiation (PAGER) is an EU Horizon 2020 project, which aims to provide a forecast of satellite charging through a pipeline of algorithms connecting the solar activity with the satellite charging. The plasmasphere modeling is an essential component of this pipeline, as plasma density is a crucial parameter for evaluating surface charging. Moreover, plasma density in the plasmasphere has very significant scientific applications, as it controls the growth of waves and how waves interact with particles. Successful plasmasphere machine learning models have been already developed, using as input several geomagnetic indices. However, in the context of the PAGER project one is constrained to use solar wind features and Kp index, whose forecasts are provided by other components of the pipeline. Here, we develop a machine learning model of the plasma density using solar wind features and the Kp geomagnetic index. We validate and test the model by measuring its performance in particular during geomagnetic storms on independent datasets withheld from the training set and by comparing the model predictions with global images of He+ distribution in the Earth’s plasmasphere from the IMAGE Extreme UltraViolet (EUV) instrument. Finally, we present the results of both local and global plasma density reconstruction. </p>

scholarly journals New methods to improve the vertical extrapolation of near-surface offshore wind speeds

2021 ◽  
Vol 6 (3) ◽  
pp. 935-948
Author(s):  
Mike Optis ◽  
Nicola Bodini ◽  
Mithu Debnath ◽  
Paula Doubrawa
Keyword(s):  
Machine Learning ◽  
Wind Speed ◽  
Model Performance ◽  
Learning Model ◽  
Offshore Wind ◽  
Offshore Wind Turbine ◽  
Near Surface ◽  
Wind Speeds ◽  
Machine Learning Model ◽  
Surface Measurements

Abstract. Accurate characterization of the offshore wind resource has been hindered by a sparsity of wind speed observations that span offshore wind turbine rotor-swept heights. Although public availability of floating lidar data is increasing, most offshore wind speed observations continue to come from buoy-based and satellite-based near-surface measurements. The aim of this study is to develop and validate novel vertical extrapolation methods that can accurately estimate wind speed time series across rotor-swept heights using these near-surface measurements. We contrast the conventional logarithmic profile against three novel approaches: a logarithmic profile with a long-term stability correction, a single-column model, and a machine-learning model. These models are developed and validated using 1 year of observations from two floating lidars deployed in US Atlantic offshore wind energy areas. We find that the machine-learning model significantly outperforms all other models across all stability regimes, seasons, and times of day. Machine-learning model performance is considerably improved by including the air–sea temperature difference, which provides some accounting for offshore atmospheric stability. Finally, we find no degradation in machine-learning model performance when tested 83 km from its training location, suggesting promising future applications in extrapolating 10 m wind speeds from spatially resolved satellite-based wind atlases.

Drivers of fog and low stratus - a satellite-based evaluation with machine learning

2020 ◽  
Author(s):  
Eva Pauli ◽  
Hendrik Andersen ◽  
Jörg Bendix ◽  
Jan Cermak ◽  
Sebastian Egli
Keyword(s):  
Machine Learning ◽  
Wind Speed ◽  
Land Cover ◽  
Surface Pressure ◽  
Land Surface ◽  
Learning Model ◽  
Meteorological Conditions ◽  
Spatial And Temporal Patterns ◽  
Continental Scale ◽  
Machine Learning Model

<p>In this study the distribution of fog and low stratus (FLS) relative to land cover and meteorological conditions in continental Europe is investigated using a state-of-the-art machine learning technique  and geostationary satellite data. While analyses of the spatial and temporal patterns of FLS exist, the relationships to land cover and meteorological conditions have not been studied explicitly, quantitatively and on a continental scale.<br>The machine learning model is built using daily means of a FLS dataset from Egli et al. (2017) as the predictand, and different land surface and meteorological parameters as predictors over continental Europe from 2006-2015. The application of a machine learning approach provides the ability to explicitly, synchronously and quantitatively link suspected determinants of FLS to its occurrence.<br>The model shows good performance with R² values ranging between 0.5 and 0.9, depending on model grid size, season and further settings such as the exclusion of low pressure values and subtraction of seasonality. It is thus able to adequately represent the dynamics that drive FLS development. Based on a systematic analysis of this model, the most important features for FLS prediction are mean surface pressure, wind speed, and FLS on the previous day. High mean surface pressure, high FLS cover on the previous day, low evapotranspiration, wind speed and land surface temperature lead to higher predicted FLS values.<br>Generally the results show that it is possible to predict FLS occurrence over continental Europe using meteorological as well as land surface parameters with good performance indicating the benefits of using machine learning in the analysis of non-linear, multivariate systems such as the land-atmosphere system. Further studies will integrate the machine learning model into a land surface based model grid and implement fog and low cloud properties as predictands.</p>

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