{"id":20202811,"url":"https://github.com/froukje/cygnss-deployment","last_synced_at":"2026-04-07T23:31:43.424Z","repository":{"id":178146201,"uuid":"577290604","full_name":"froukje/cygnss-deployment","owner":"froukje","description":"Web Interface for Wind Speed Prediction with a Neural Net","archived":false,"fork":false,"pushed_at":"2022-12-13T16:01:45.000Z","size":1700,"stargazers_count":0,"open_issues_count":0,"forks_count":0,"subscribers_count":1,"default_branch":"main","last_synced_at":"2025-03-03T09:16:21.856Z","etag":null,"topics":["cygnss","deep-learning","docker-compose","mongodb","prefect","streamlit","windspeed"],"latest_commit_sha":null,"homepage":"","language":"Jupyter Notebook","has_issues":true,"has_wiki":null,"has_pages":null,"mirror_url":null,"source_name":null,"license":null,"status":null,"scm":"git","pull_requests_enabled":true,"icon_url":"https://github.com/froukje.png","metadata":{"files":{"readme":"README.md","changelog":null,"contributing":null,"funding":null,"license":null,"code_of_conduct":null,"threat_model":null,"audit":null,"citation":null,"codeowners":null,"security":null,"support":null,"governance":null,"roadmap":null,"authors":null,"dei":null,"publiccode":null,"codemeta":null}},"created_at":"2022-12-12T12:14:25.000Z","updated_at":"2023-01-25T12:51:51.000Z","dependencies_parsed_at":null,"dependency_job_id":"1c7aef4d-36d1-4bd3-9a22-f557cac5bcf3","html_url":"https://github.com/froukje/cygnss-deployment","commit_stats":null,"previous_names":["froukje/cygnss-deployment"],"tags_count":0,"template":false,"template_full_name":null,"purl":"pkg:github/froukje/cygnss-deployment","repository_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/froukje%2Fcygnss-deployment","tags_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/froukje%2Fcygnss-deployment/tags","releases_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/froukje%2Fcygnss-deployment/releases","manifests_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/froukje%2Fcygnss-deployment/manifests","owner_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/owners/froukje","download_url":"https://codeload.github.com/froukje/cygnss-deployment/tar.gz/refs/heads/main","sbom_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/froukje%2Fcygnss-deployment/sbom","scorecard":null,"host":{"name":"GitHub","url":"https://github.com","kind":"github","repositories_count":286080680,"owners_count":31533823,"icon_url":"https://github.com/github.png","version":null,"created_at":"2022-05-30T11:31:42.601Z","updated_at":"2026-04-07T16:28:08.000Z","status":"ssl_error","status_checked_at":"2026-04-07T16:28:06.951Z","response_time":105,"last_error":"SSL_connect returned=1 errno=0 peeraddr=140.82.121.5:443 state=error: unexpected eof while reading","robots_txt_status":"success","robots_txt_updated_at":"2025-07-24T06:49:26.215Z","robots_txt_url":"https://github.com/robots.txt","online":false,"can_crawl_api":true,"host_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub","repositories_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories","repository_names_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repository_names","owners_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/owners"}},"keywords":["cygnss","deep-learning","docker-compose","mongodb","prefect","streamlit","windspeed"],"created_at":"2024-11-14T04:58:19.569Z","updated_at":"2026-04-07T23:31:43.408Z","avatar_url":"https://github.com/froukje.png","language":"Jupyter Notebook","funding_links":[],"categories":[],"sub_categories":[],"readme":"# Web Interface for Wind Speed Prediction\n\n### About\n\nThe objective of this repository is to deploy a pre-trained *CyGNSSnet* to predict global ocean wind speed in near time. The results are shown on a web interface, which provides different illustrations of the predicted wind speed and its error compared to [ERA5 windspeed](https://www.ecmwf.int/en/forecasts/datasets/reanalysis-datasets/era5) data. \n\n*CyGNSSnet* is a neural net developed to predict wind speed from [CYGNSS](https://podaac.jpl.nasa.gov/dataset/CYGNSS_L2_V3.0)(**Cy**clone **G**lobal **N**avigation **S**atellite **S**ystem) data. The code for *CyGNSSnet* itself is not public. For more information or if you need to access it contact Caroline Arnold (arnold@dkrz.de) or the Helmholtz AI consultant team for Earth and Environment (consultant-helmholtz.ai@dkrz.de). For more information on *CyGNSSnet*, see [Asgarimehr et al, Remote Sensing of Environment (2022)](https://doi.org/10.1016/j.rse.2021.112801)   \n### Workflow\n\n![Workflow](/Workflow.png)\n\n\n\n### Quick start\n\nTo start the deployment run ```sh set_up_infrastructure.sh```.\n\nThis clones the git repository and starts the deployment using docker-compose.\nMake sure you have docker and docker-compose installed. \n\nIf you have already the cloned the git repository move to the directory ```docker_cygnss_deployment``` and run \n\n```\ndocker-compose up\n``` \n\nTo stop the container, run following command:\n```\ndocker-compose -f ./docker-compose.yml down --remove-orphans\n```\n\nNote: In order to run it you need access to the external submodule containing the CyGNSSnet. \n\nThe deployment is scheduled using prefect. It is executed every day and downloads the CyGNSS data for the current date minus 10 days. Then the predictions are calculated, stored in a mongodb database and displayed on a streamlit dashboard.\n\nTo access the streamlit dashboard: http://localhost:8501\n\nTo access the mongodb database: http://localhost:8081\n\nTo access the prefect ui: http://localhost:5000\n\n\n### Repository Structure\n\n```\nAPI.py: download CyGNSS data\nPreprocessing.py: download ERA5 data and preprocess data\ndashboard.py: streamlit dashboard\nplots.py: helper functions to create the plots for the streamlit dashboard\nprefect-deploy.py: Deployment scheduled for every day\nexternals/: folder with CyGNSSnet code\nnotebooks/: folder with some notebooks that were created during the development\ndocker_cygnss_deployment/: folder with docker files to start deployment\n```\n    \n## Data source\n\n- CYGNSS. CYGNSS Level 2 Science Data Record Version 3.1. Ver. 3.1. PO.DAAC, CA, USA. accessed 2022/2023 at 10.5067/CYGNS-L2X31\n- Copernicus Climate Change Service (C3S) (2017): ERA5: Fifth generation of ECMWF atmospheric reanalyses of the global climate . Copernicus Climate Change Service Climate Data Store (CDS), 2022/2023. https://cds.climate.copernicus.eu/cdsapp#!/home\n","project_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Ffroukje%2Fcygnss-deployment","html_url":"https://awesome.ecosyste.ms/projects/github.com%2Ffroukje%2Fcygnss-deployment","lists_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Ffroukje%2Fcygnss-deployment/lists"}