Storm EVent ImageRy (SEVIR)

Stream data with DDA:

from dagshub.streaming import DagsHubFilesystem

fs = DagsHubFilesystem(".", repo_url="https://dagshub.com/DagsHub-Datasets/sevir-dataset")

fs.listdir("s3://sevir")

Description:

Collection of spatially and temporally aligned GOES-16 ABI satellite imagery, NEXRAD radar mosaics, and GOES-16 GLM lightning detections.

Contact:

Collection of spatially and temporally aligned GOES-16 ABI satellite imagery, NEXRAD radar mosaics, and GOES-16 GLM lightning detections.

Update Frequency:

New events will be added to SEVIR yearly

Managed By:

Mark S. Veillette

Collabs:

• ASDI:
  • Tags: weather

Resources:

1. resource:
   • Description: Dataset of storm imagery
   • ARN: arn:aws:s3:::sevir
   • Region: us-west-2
   • Type: S3 Bucket

Tags:

satellite imagery, weather, meteorological, aws-pds

Tutorials:

1. tutorial:

   • Title: Introduction to SEVIR
   • URL: https://nbviewer.jupyter.org/github/MIT-AI-Accelerator/eie-sevir/blob/master/examples/SEVIR_Tutorial.ipynb
   • AuthorName: Mark Veillette
   • AuthorURL: https://github.com/markveillette

2. tutorial:

   • Title: Using Generators for SEVIR data
   • URL: https://nbviewer.jupyter.org/github/MIT-AI-Accelerator/eie-sevir/blob/master/examples/Generators.ipynb
   • AuthorName: Mark Veillette
   • AuthorURL: https://github.com/markveillette

Tools & Applications:

1. tools & applications:
   • Title: sevir -- python utilities for working with SEVIR dataset
   • URL: https://github.com/MIT-AI-Accelerator/eie-sevir
   • AuthorName: Mark Veillette
   • AuthorURL: https://github.com/markveillette

Publication:

1. publication:
   • Title: Towards a More Realistic and Detailed Deep-Learning-Based Radar Echo Extrapolation Method
   • URL: https://doi.org/10.3390/rs14010024
   • AuthorName: Yuan Hu, Lei Chen, Zhibin Wang, Xiang Pan and Hao Li