scholarly journals Probabilistic Forecasts of Storm Sudden Commencements From Interplanetary Shocks Using Machine Learning

Space Weather ◽  
2020 ◽  
Vol 18 (11) ◽  
Author(s):  
A. W. Smith ◽  
I. J. Rae ◽  
C. Forsyth ◽  
D. M. Oliveira ◽  
M. P. Freeman ◽  
...  
Keyword(s):  
Machine Learning ◽  
Interplanetary Shocks ◽  
Probabilistic Forecasts ◽  
Sudden Commencements

scholarly journals Measurement of geomagnetically induced current (GIC) around Tokyo, Japan

2021 ◽  
Vol 73 (1) ◽  
Author(s):  
Shinichi Watari ◽  
Satoko Nakamura ◽  
Yusuke Ebihara
Keyword(s):  
Solar Flare ◽  
Geomagnetic Storms ◽  
Power Grids ◽  
Geomagnetic Variation ◽  
Induced Current ◽  
Interplanetary Shocks ◽  
Geomagnetic Disturbances ◽  
Geomagnetically Induced Current ◽  
Middle Latitudes ◽  
Sudden Commencements

AbstractWe need a typical method of directly measuring geomagnetically induced current (GIC) to compare data for estimating a potential risk of power grids caused by GIC. Here, we overview GIC measurement systems that have appeared in published papers, note necessary requirements, report on our equipment, and show several examples of our measurements in substations around Tokyo, Japan. Although they are located at middle latitudes, GICs associated with various geomagnetic disturbances are observed, such as storm sudden commencements (SSCs) or sudden impulses (SIs) caused by interplanetary shocks, geomagnetic storms including a storm caused by abrupt southward turning of strong interplanetary magnetic field (IMF) associated with a magnetic cloud, bay disturbances caused by high-latitude aurora activities, and geomagnetic variation caused by a solar flare called the solar flare effect (SFE). All these results suggest that GIC at middle latitudes is sensitive to the magnetospheric current (the magnetopause current, the ring current, and the field-aligned current) and also the ionospheric current.

scholarly journals Signatures of storm sudden commencements in geomagnetic H, Y and Z fields at Indian observatories during 1958−1992

1999 ◽  
Vol 17 (11) ◽  
pp. 1426-1438 ◽  
Author(s):  
R. G. Rastogi
Keyword(s):  
Sri Lanka ◽  
Equatorial Electrojet ◽  
Interplanetary Shocks ◽  
Low Latitude ◽  
Disturbance Vector ◽  
Storm Sudden Commencement ◽  
Interplanetary Physics ◽  
Sudden Commencements ◽  
Electrojet Current ◽  
Storms And Substorms

Abstract. The work describes an intensive study of storm sudden commencement (SSC) impulses in horizontal (H), eastward (Y) and vertical (Z) fields at four Indian geomagnetic observatories between 1958–1992. The midday maximum of ΔH has been shown to exist even at the low-latitude station Alibag which is outside the equatorial electrojet belt, suggesting that SSC is associated with an eastward electric field at equatorial and low latitudes. The impulses in Y field are shown to be linearly and inversely related to ΔH at Annamalainagar and Alibag. The average SC disturbance vector is shown to be about 10–20°W of the geomagnetic meridian. The local time variation of the angle is more westerly during dusk hours in summer and around dawn in the winter months. This clearly suggests an effect of the orientation of shock front plane of the solar plasma with respect to the geomagnetic meridian. The ΔZ at SSC have a positive impulse as in ΔH. The ratio of ΔZ/ΔH are abnormally large exceeding 1.0 in most of the cases at Trivandrum. The latitudinal variation of ΔZ shows a tendency towards a minimum over the equator during the nighttime hours. These effects are explained as (1) resulting from the electromagnetic induction effects due to the equatorial electrojet current in the subsurface conducting layers between India and Sri Lanka, due to channelling of ocean currents through the Palk Strait and (2) due to the concentration of induced currents over extended latitude zones towards the conducting graben between India and Sri Lanka just south of Trivandrum.Key words. Interplanetary physics (interplanetary shocks) · Ionosphere (equatorial ionosphere) · Magnetospheric physics (storms and substorms)

scholarly journals Probabilistic forecasting of coastal wave height during typhoon warning period using machine learning methods

2019 ◽  
Vol 21 (2) ◽  
pp. 343-358 ◽  
Author(s):  
Shien-Tsung Chen
Keyword(s):  
Machine Learning ◽  
Wave Height ◽  
Fuzzy Inference ◽  
Forecasting Model ◽  
Probabilistic Forecasting ◽  
Inference Model ◽  
Machine Learning Methods ◽  
Probabilistic Forecasts ◽  
Forecasting Method ◽  
Height Data

Abstract This study applied machine learning methods to perform the probabilistic forecasting of coastal wave height during the typhoon warning period. The probabilistic forecasts comprise a deterministic forecast and the probability distribution of a forecast error. A support vector machine was used to develop a real-time forecasting model for generating deterministic wave height forecasts. The forecast errors of deterministic forecasting were then used as a database to generate probabilistic forecasts by using the modified fuzzy inference model. The innovation of the modified fuzzy inference model includes calculating the similarity of the data by performing fuzzy implication and resampling the potential data from the fuzzy database for probability distribution. The probabilistic forecasting method was applied to the east coast of Taiwan, where typhoons frequently cause large waves. Hourly wave height data from an offshore buoy and various typhoon characteristics were used as inputs of the probabilistic forecasting model. Validation results from real typhoon events verified that the proposed probabilistic forecasting model can generate the predicted confidence interval, which can properly enclose the observed wave height data, excluding some cases with extreme wave heights. Moreover, an objective measure was used to validate the proposed probabilistic forecasting method.

scholarly journals Artificial Learning Dispatch Planning with Probabilistic Forecasts: Using Uncertainties as an Asset

Energies ◽  
2020 ◽  
Vol 13 (3) ◽  
pp. 616
Author(s):  
Ana Carolina do Amaral Burghi ◽  
Tobias Hirsch ◽  
Robert Pitz-Paal
Keyword(s):  
Machine Learning ◽  
Learning Algorithm ◽  
Weather Forecast ◽  
Machine Learning Algorithm ◽  
Post Processing ◽  
Weather Forecasts ◽  
Additional Information ◽  
Model Following ◽  
Probabilistic Forecasts ◽  
Artificial Learning

Weather forecast uncertainty is a key element for energy market volatility. By intelligently considering uncertainties on the schedule development, renewable energy systems with storage could improve dispatching accuracy, and therefore, effectively participate in electricity wholesale markets. Deterministic forecasts have been traditionally used to support dispatch planning, representing reduced or no uncertainty information about the future weather. Aiming at better representing the uncertainties involved, probabilistic forecasts have been developed to increase forecasting accuracy. For the dispatch planning, this can highly influence the development of a more precise schedule. This work extends a dispatch planning method to the use of probabilistic weather forecasts. The underlying method used a schedule optimizer coupled to a post-processing machine learning algorithm. This machine learning algorithm was adapted to include probabilistic forecasts, considering their additional information on uncertainties. This post-processing applied a calibration of the planned schedule considering the knowledge about uncertainties obtained from similar past situations. Simulations performed with a concentrated solar power plant model following the proposed strategy demonstrated promising financial improvement and relevant potential in dealing with uncertainties. Results especially show that information included in probabilistic forecasts can increase financial revenues up to 15% (in comparison to a persistence solar driven approach) if processed in a suitable way.

scholarly journals Comparing Area Probability Forecasts of (Extreme) Local Precipitation Using Parametric and Machine Learning Statistical Postprocessing Methods

2018 ◽  
Vol 146 (11) ◽  
pp. 3651-3673 ◽  
Author(s):  
Kirien Whan ◽  
Maurice Schmeits
Keyword(s):  
Machine Learning ◽  
Deterministic Model ◽  
Weather Prediction ◽  
Location Parameter ◽  
Important Predictor ◽  
Lead Times ◽  
Hourly Precipitation ◽  
Severe Thunderstorms ◽  
Probabilistic Forecasts ◽  
Precipitation Thresholds

Abstract Probabilistic forecasts, which communicate forecast uncertainties, enable users to make better weather-based decisions. Using precipitation and numerous instability indices from the deterministic model HARMONIE–AROME (HA; a nonhydrostatic numerical weather prediction model) as potential predictors, we generate summer areal probabilistic maximum hourly precipitation forecasts across 11 regions of the Netherlands. We compare the skill of three statistical postprocessing methods: an extended logistic regression (ELR), a zero-adjusted gamma distribution (ZAGA), and a machine learning-based method, quantile regression forests (QRF). Forecast skill for low and moderate precipitation thresholds increases with the inclusion of extra predictors, in addition to HA precipitation. HA precipitation is the most important predictor at all lead times in ELR and QRF, while in ZAGA, the most important predictor for the location parameter shifts over lead times from HA precipitation to indices of atmospheric instability. All three methods improve upon a climatological forecast for low and moderate precipitation thresholds. ZAGA and QRF are generally the most skillful methods at moderate thresholds. QRF tends to be the most skillful method at higher thresholds, particularly during the afternoon period. Forecasts are reliable at low and moderate thresholds but tend to be overconfident at higher thresholds. QRF and ZAGA have more potential economic value than the deterministic forecast, with value remaining at high thresholds. A maximum local hourly precipitation threshold of 30 mm h−1 (a criterion in the Royal Netherlands Meteorological Institute’s code yellow warning for severe thunderstorms) is skillfully forecast by QRF in the afternoon period at short lead times.

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 A framework for probabilistic weather forecast post-processing across models and lead times using machine learning

2021 ◽  
Vol 379 (2194) ◽  
pp. 20200099 ◽  
Author(s):  
Charlie Kirkwood ◽  
Theo Economou ◽  
Henry Odbert ◽  
Nicolas Pugeault
Keyword(s):  
Machine Learning ◽  
Decision Support ◽  
Probability Distributions ◽  
Weather Prediction ◽  
Weather Forecast ◽  
Predictive Distribution ◽  
Lead Times ◽  
Climate Modelling ◽  
Post Processing ◽  
Probabilistic Forecasts

Forecasting the weather is an increasingly data-intensive exercise. Numerical weather prediction (NWP) models are becoming more complex, with higher resolutions, and there are increasing numbers of different models in operation. While the forecasting skill of NWP models continues to improve, the number and complexity of these models poses a new challenge for the operational meteorologist: how should the information from all available models, each with their own unique biases and limitations, be combined in order to provide stakeholders with well-calibrated probabilistic forecasts to use in decision making? In this paper, we use a road surface temperature example to demonstrate a three-stage framework that uses machine learning to bridge the gap between sets of separate forecasts from NWP models and the ‘ideal’ forecast for decision support: probabilities of future weather outcomes. First, we use quantile regression forests to learn the error profile of each numerical model, and use these to apply empirically derived probability distributions to forecasts. Second, we combine these probabilistic forecasts using quantile averaging. Third, we interpolate between the aggregate quantiles in order to generate a full predictive distribution, which we demonstrate has properties suitable for decision support. Our results suggest that this approach provides an effective and operationally viable framework for the cohesive post-processing of weather forecasts across multiple models and lead times to produce a well-calibrated probabilistic output. This article is part of the theme issue ‘Machine learning for weather and climate modelling’.

scholarly journals Applying machine learning methods to avalanche forecasting

2008 ◽  
Vol 49 ◽  
pp. 107-113 ◽  
Author(s):  
A. Pozdnoukhov ◽  
R.S. Purves ◽  
M. Kanevski
Keyword(s):  
Machine Learning ◽  
High Dimensional Data ◽  
High Dimensional ◽  
Support Vector ◽  
Avalanche Forecasting ◽  
Machine Learning Methods ◽  
Multiple Data ◽  
Complex Process ◽  
Probabilistic Forecasts ◽  
Vector Machines

AbstractAvalanche forecasting is a complex process involving the assimilation of multiple data sources to make predictions over varying spatial and temporal resolutions. Numerically assisted forecasting often uses nearest-neighbour methods (NN), which are known to have limitations when dealing with high-dimensional data. We apply support vector machines (SVMs) to a dataset from Lochaber, Scotland, UK, to assess their applicability in avalanche forecasting. SVMs belong to a family of theoretically based techniques from machine learning and are designed to deal with high-dimensional data. Initial experiments showed that SVMs gave results that were comparable with NN for categorical and probabilistic forecasts. Experiments utilizing the ability of SVMs to deal with high dimensionality in producing a spatial forecast show promise, but require further work.

Machine Learning Enhancement of Storm-Scale Ensemble Probabilistic Quantitative Precipitation Forecasts

2014 ◽  
Vol 29 (4) ◽  
pp. 1024-1043 ◽  
Author(s):  
David John Gagne ◽  
Amy McGovern ◽  
Ming Xue
Keyword(s):  
Machine Learning ◽  
Prediction Models ◽  
Weather Prediction ◽  
Precipitation Forecast ◽  
Spatial And Temporal Scales ◽  
Small Areas ◽  
Probabilistic Forecasts ◽  
Quantitative Precipitation Forecasts ◽  
Wide Variability ◽  
Added Uncertainty

Abstract Probabilistic quantitative precipitation forecasts challenge meteorologists due to the wide variability of precipitation amounts over small areas and their dependence on conditions at multiple spatial and temporal scales. Ensembles of convection-allowing numerical weather prediction models offer a way to produce improved precipitation forecasts and estimates of the forecast uncertainty. These models allow for the prediction of individual convective storms on the model grid, but they often displace the storms in space, time, and intensity, which results in added uncertainty. Machine learning methods can produce calibrated probabilistic forecasts from the raw ensemble data that correct for systemic biases in the ensemble precipitation forecast and incorporate additional uncertainty information from aggregations of the ensemble members and additional model variables. This study utilizes the 2010 Center for Analysis and Prediction of Storms Storm-Scale Ensemble Forecast system and the National Severe Storms Laboratory National Mosaic & Multi-Sensor Quantitative Precipitation Estimate as input data for training logistic regressions and random forests to produce a calibrated probabilistic quantitative precipitation forecast. The reliability and discrimination of the forecasts are compared through verification statistics and a case study.

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