A fully automated cloud-native event driven weather forecasting.
The Unified Forecast System (UFS) atmospheric model is FV3GFS. The UFS is a community-based, coupled, comprehensive Earth modeling system. The UFS numerical applications span local to global domains and predictive time scales from sub-hourly analyses to seasonal predictions. It is designed to support the Weather Enterprise and to be the source system for NOAA‘s operational numerical weather prediction applications. The UFS Weather Model (WM) is a prognostic model that can be used for short- and medium-range research and operational forecasts, as exemplified by its use in the operational Global Forecast System (GFS) of the National Oceanic and Atmospheric Administration (NOAA).
We are running the FV3GFS for a 6 hour global forecast at a resolution of approximately 13 km (C768).
The forecast consists of the following steps:
- Preprocessing the input data with
chgres_cube
(~ 3:30 minutes on 20 instances). - Model forecast with
ufs_weather_model
(~ 7:30 minutes on 20 instances). - Post processing with
upp.x
(~ 13:00minutes on 7 instances).
Install the AWS CDK application and the python library.
npm install -g aws-cdk
cd src/
python3 -m venv .venv
. .venv/bin/activate
pip install -r requirements.txt
The following commands will deploy the CDK stack.
cdk bootstrap
cdk deploy
To completely tear down all infrastructure when it is not needed.
cdk destroy --all
All the source code lives under src
, the main entry point for the CDK application is app.py
.
.
├── README.md
└── src
├── app.py
├── cdk.json
├── lambda
│ ├── cluster.py
│ ├── forecast.py
│ ├── hpc6a.yaml
│ └── jobs
│ ├── fini.sh
│ ├── post.sh
│ ├── pre.sh
│ └── run.sh
├── layer.zip
├── layers
├── requirements-dev.txt
├── requirements.txt
├── resources
│ ├── parallelcluster-api.yaml
│ ├── post_install_headnode.sh
│ ├── spack-external-packages.yaml
│ ├── ufs-image-config.yaml
│ └── ufs-spack-component.yaml
└── wx
├── bucket.py
├── forecast.py
├── network.py
├── pclusterapi.py
└── slurmdb.py
The cluster creation and deletion functions are within the lambda/cluster.py
file, with the ParallelCluster configuration file is lambda/hpc6a.yaml
.
The forecast job submission functions are within the lambda/forecast.py
file.
The S3 bucket contains all the run directory, outputs, and templates used. The top-level bucket structure is as follows:
└── aws-weather-bucket
├── outputs
├── run
└── templates
The outputs
directory contain prefixes related to the forecast valid time (%Y/%m/%d/%H
).
The Slurm jobs are submitted through the Slurm REST API with inter job dependencies. The job run scripts are within the lambda/jobs
directory and are:
pre
-- UFS Utilschgres_cube
.run
-- UFS Weather Modelufs_weather_model
.post
-- UPPupp.x
.fini
-- Uploads a sentinel file to Amazon S3.
We are going to create a custom ParallelCluster EC2. In doing so we will install the following packages with Spack.
- Intel OneAPI Compilers
- Intel OneAPI MPI Libraries
- UFS Weather Model
- UFS Utilities
- Unified Post Processor
A custom component to install these packages exists in src/resources/ufs-spack-component.yaml
.
This needs to be deployed to our bucket.
aws s3 cp src/resources/ufs-spack-component.yaml s3://aws-weather-bucket/templates/
Once deployed we need to register it with EC2 ImageBuilder.
aws imagebuilder create-component --name ufs-spack --semantic-version "1.0.0" \
--change-description "Inital version" --platform "Linux" \
--uri "s3://aws-weather-bucket/templates/ufs-spack-component.yaml"
Build the new image
cd src/resources
pcluster build-image --image-configuration ufs-image-config.yaml --image-id alinux2-ufs-image
Once the image is built, get the AMI identification number and add this to the ParallelCluster configuration file.
export AMI=$(pcluster list-images --image-status AVAILABLE | jq -r '.[][] | select(.imageId=="alinux2-ufs-image") | .ec2AmiInfo.amiId')
yq e '.Image.CustomAmi = env(AMI)' -i ../lambda/hpc6a.yaml
This code is licensed under the MIT-0 License. See the LICENSE file.