The 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 data.
CyGNSSnet is a neural net developed to predict wind speed from CYGNSS(Cyclone Global Navigation Satellite System) data. The code for CyGNSSnet itself is not public. For more information or if you need to access it contact Caroline Arnold ([email protected]) or the Helmholtz AI consultant team for Earth and Environment ([email protected]). For more information on CyGNSSnet, see Asgarimehr et al, Remote Sensing of Environment (2022)
To start the deployment run sh set_up_infrastructure.sh.
This clones the git repository and starts the deployment using docker-compose. Make sure you have docker and docker-compose installed.
If you have already the cloned the git repository move to the directory docker_cygnss_deployment and run
docker-compose up
To stop the container, run following command:
docker-compose -f ./docker-compose.yml down --remove-orphans
Note: In order to run it you need access to the external submodule containing the CyGNSSnet.
The 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.
To access the streamlit dashboard: http://localhost:8501
To access the mongodb database: http://localhost:8081
To access the prefect ui: http://localhost:5000
API.py: download CyGNSS data
Preprocessing.py: download ERA5 data and preprocess data
dashboard.py: streamlit dashboard
plots.py: helper functions to create the plots for the streamlit dashboard
prefect-deploy.py: Deployment scheduled for every day
externals/: folder with CyGNSSnet code
notebooks/: folder with some notebooks that were created during the development
docker_cygnss_deployment/: folder with docker files to start deployment
- 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
- 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