Identification

Title

Forecasting of SYMH and ASYH indices for geomagnetic storms of solar cycle 24 including St. Patrick’s day, 2015 storm using NARX neural network

Abstract

Artificial Neural Network (ANN) has proven to be very successful in forecasting a variety of irregular magnetospheric/ionospheric processes like geomagnetic storms and substorms. SYMH and ASYH indices represent longitudinal symmetric and the asymmetric component of the ring current. Here, an attempt is made to develop a prediction model for these indices using ANN. The ring current state depends on its past conditions therefore, it is necessary to consider its history for prediction. To account for this effect Nonlinear Autoregressive Network with exogenous inputs (NARX) is implemented. This network considers input history of 30 min and output feedback of 120 min. Solar wind parameters mainly velocity, density, and interplanetary magnetic field are used as inputs. SYMH and ASYH indices during geomagnetic storms of 1998-2013, having minimum SYMH < similar to 85 nT are used as the target for training two independent networks. We present the prediction of SYMH and ASYH indices during nine geomagnetic storms of solar cycle 24 including the recent largest storm occurred on St. Patrick's day, 2015. The present prediction model reproduces the entire time profile of SYMH and ASYH indices along with small variations of similar to 10-30 min to the good extent within noise level, indicating a significant contribution of interplanetary sources and past state of the magnetosphere. Therefore, the developed networks can predict SYMH and ASYH indices about an hour before, provided, real-time upstream solar wind data are available. However, during the main phase of major storms, residuals (observed-modeled) are found to be large, suggesting the influence of internal factors such as magnetospheric processes.

Resource type

document

Resource locator

Unique resource identifier

code

http://n2t.net/ark:/85065/d7qj7mc6

codeSpace

Dataset language

eng

Spatial reference system

code identifying the spatial reference system

Classification of spatial data and services

Topic category

geoscientificInformation

Keywords

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keyword value

Text

originating controlled vocabulary

title

Resource Type

reference date

date type

publication

effective date

2016-01-01T00:00:00Z

Geographic location

West bounding longitude

East bounding longitude

North bounding latitude

South bounding latitude

Temporal reference

Temporal extent

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Dataset reference date

date type

publication

effective date

2019-05-07T00:00:00Z

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Conformity

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Use constraints

Limitations on public access

None

Responsible organisations

Responsible party

contact position

OpenSky Support

organisation name

UCAR/NCAR - Library

full postal address

PO Box 3000

Boulder

80307-3000

email address

opensky@ucar.edu

web address

http://opensky.ucar.edu/

name: homepage

responsible party role

pointOfContact

Metadata on metadata

Metadata point of contact