Space Weather JHelioviewer ☉

Machine Learning tools for Space Weather and Plasma Physics

Space Weather Monitor Application (SWMA) is an open-source application made for visualizing real-time space weather related data.

A Python wrapper for the Australian Bureau of Meteorology's Space Weather API.

Visual Studio Code language extension for SWMF configuration files

pysat support for space weather indices and data sets

Example of using machine learning for forecasting Vertical Total Electron Content (VTEC) in the ionosphere

Data Driven Thermospheric Density Modeling with Machine Learning

magneticearth.org: A place to learn about geomagnetism

Detect and characterize coronal dimming in the Solar Dynamics Observatory Extreme Ultraviolet Variability Experiment data

CME SHARP Active region visualisation tool

A model visualizer for MSIS

Framework for benchmarking spatiotemporal models for global ionosphere forecasting

🛰️ Production-ready ML system for geomagnetic storm prediction | 98% AUC, 70% recall | Threshold-optimized ensemble with real-time inference | 29-year dataset (1996-2025) | NOAA SWPC operational standards | Complete MLOps pipeline

Personal Active Dosimeter (PAD) training model with optional Dosimeter Display Unit (DDU). The system safely simulates radiation exposure without real hazards.

GIC Forecasting & Analysis — A data-driven framework for modeling Geomagnetically Induced Currents using solar wind data, SuperMAG observations, and advanced ML techniques. Integrates domain physics, feature engineering, and imputation strategies to tackle real-world space weather challenges.

This code accompanies the paper "Ensemble Forecasts of Solar Wind Connectivity to 1 Rs using ADAPT-WSA", to be published in the AGU Journal Space Weather in 2023

Explore the detection and prediction of Halo Coronal Mass Ejections (CMEs) using Aditya-L1's Solar Wind Ion Spectrometer (SWIS) data. This project processes Level-2 data with Python to develop an early warning system for space weather, validated against the CACTUS database.

This is very powerful to calculate ROTI in interactive approach

Example of using machine learning for forecasting Vertical Total Electron Content (VTEC) in the ionosphere