A collection of Python/Bash scripts designed to perform objective skill assessment and inter-comparison of NOS Operational Forecast Systems (OFS) and NOAA Surge and Tide Operational Forecast System (STOFS) Components.
workflow/: Contains the core scripts and step-by-step logic required to process data and generate figures.extent_files/: Stores shapefiles of the model extents.- Note: These are only used for visualization and are not required to run the computational scripts.
figure.ipynb: The primary Jupyter Notebook for generating final plots and visualizations.
Depending on the length of your analysis period, choose one of the following paths:
Use the 01_09_notebook.ipynb. This notebook performs data collection and all preprocessing (Steps 1–9) sequentially in a single environment.
Run Steps 1 through 9 individually. These scripts are optimized to use all available processors to handle large datasets efficiently.
Collect_COOPS_Stations.ipynb: Find available stations, collect observed values, and store them in the observation/ directory.
Collect_STOFS_data.py: Collects STOFS data for selected stations.
job_STOFS_data.sh: Master Bash script to submit multiple jobs to parallelize STOFS data collection.
Collect_OFS_data.py: Collects ROMS-based data from the cloud. Includes logic for proper datum conversion and local time-series storage.
Collect_NYOFS_data.py: Collects POM-based data from the cloud. Includes logic for proper datum conversion and local time-series storage.
generate_tasks.ipynb: Generates the necessary text files containing parameters used in the subsequent processing steps.
job_OFS_data.sh: Submits job scripts for Collect_OFS_data.py.
job_NYOFS_data.sh: Submits job scripts specifically for Collect_NYOFS_data.py.
pairing_data.ipynb: Parses the collected data for nowcast plots, applies final datum conversions, and stores the processed files.
Figures.ipynb: Execute figure.ipynb. This step handles the pairing of forecast data and performs the final skill assessment based on your forecast comparison requirements.
- Datum Conversion: Integrated into Steps 4, 5, and 9 for water level analysis to ensure consistency between model output and observations.
- Cloud Access: Multiple steps may require adjustments to bucket names or credentials depending on your specific period of interest.