pywrfkit 0.1.1

A comprehensive Python toolkit for Weather Research and Forecasting (WRF) model data processing, analysis, and visualization

PyWRFKit

A comprehensive Python toolkit for Weather Research and Forecasting (WRF) model data processing, analysis, and visualization. This toolkit provides utilities for handling WRF geogrid files, coordinate transformations, data downloading, plotting, and various meteorological analysis tasks.

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Author: Ankur Kumar
Contact: [e-mail]

Features

• WRF Data Processing: Coordinate handling and data manipulation for WRF model outputs
• Geographic Data Updates: Land Use Land Cover (LULC) and NDVI data integration
• Polar Coordinate Transformations: Convert Cartesian coordinates to polar for hurricane analysis
• Data Downloading: Automated GFS forecast data retrieval
• Visualization: Cartopy-based mapping and plotting utilities
• Statistical Analysis: Various metrics and norms for data comparison
• AHPS Data Processing: Advanced Hydrologic Prediction Service data handling

Installation

# Clone the repository
git clone <repository-url>
cd pywrfkit

# Install dependencies
pip install -r requirements.txt

Dependencies

• xarray - Multi-dimensional array handling
• numpy - Numerical computing
• matplotlib - Plotting and visualization
• cartopy - Geographic plotting
• scipy - Scientific computing
• pandas - Data manipulation
• gdal - Geospatial data abstraction
• pyproj - Cartographic projections
• osgeo - Geospatial data processing

Modules Overview

Core WRF Utilities (wrf.py)

Handles WRF coordinate systems and data array manipulations.

import xarray as xr
from pywrfkit import wrf

# Load WRF data
ds = xr.open_dataset('wrf_output.nc')

# Add coordinate information
ds_with_coords = wrf.add_coords(ds['T2'], rename=True)

# Rename coordinates
ds_renamed = wrf.renamelatlon(ds_with_coords)

Geographic Data Processing (geog.py)

Updates WRF geogrid files with MODIS LULC and NDVI data.

from pywrfkit import geog

# Update geogrid file with new LULC and NDVI data
geog.update_geog(
    geog_file='geo_em.d01.nc',
    modis_lulc_file='LULC_2001.tif',
    modis_ndvi_file='NDVI_2001.tif',
    new_filename='geo_em.d01.nc_2001'
)

Polar Coordinate Transformations (polar.py)

Converts Cartesian coordinates to polar coordinates for hurricane analysis.

import xarray as xr
from pywrfkit import polar

# Load hurricane data
ds = xr.open_dataset('hurricane_data.nc')

# Convert to polar coordinates
polar_data = polar.convert_to_polar(
    ds['wind_speed'],
    radius=5,  # degrees
    resolution=0.1
)

# Process entire dataset
polar_dataset = polar.get_polar_from_file(
    'hurricane_data.nc',
    radius=5,
    resolution=0.1
)

Data Downloading (download.py)

Automated downloading of GFS forecast data.

from pywrfkit import download

# Download GFS forecast data
download.gfs_forecast(
    start_date='2024120100',  # YYYYMMDDHH format
    total_forecast_hours=72
)

# Download from file list
filelist = [
    'https://data.rda.ucar.edu/d084001/2024/20241201/gfs.0p25.2024120100.f000.grib2',
    'https://data.rda.ucar.edu/d084001/2024/20241201/gfs.0p25.2024120100.f003.grib2'
]
download.download_from_filelist(filelist)

Visualization Utilities (coast.py, plot_geog.py)

Cartopy-based mapping and geographic plotting.

import matplotlib.pyplot as plt
import cartopy.crs as ccrs
from pywrfkit import coast, plot_geog

# Create a map with coastlines
fig, ax = plt.subplots(figsize=(10, 6), subplot_kw={'projection': ccrs.PlateCarree()})
gl = coast.plot_coast(ax, color='blue', linewidth=1.5, states=True)

# Plot LULC data
plot_geog.plot_lulc_geogrid(
    'geo_em.d01.nc_2001',
    label='LULC 2001',
    legend=True,
    axes=ax
)
plt.show()

AHPS Data Processing (ahps.py)

Process Advanced Hydrologic Prediction Service data.

from pywrfkit import ahps

# Read AHPS data
lon, lat, obs = ahps.read_ahps('ahps_data.nc')

# Data is automatically converted to Plate Carree projection
# and invalid values are replaced with NaN
print(f"Longitude range: {lon.min():.2f} to {lon.max():.2f}")
print(f"Latitude range: {lat.min():.2f} to {lat.max():.2f}")
print(f"Observation shape: {obs.shape}")

Statistical Analysis (metrics.py, norms.py)

Various statistical metrics and norms for data comparison.

import numpy as np
from pywrfkit import metrics, norms

# Sample data
x = np.array([1, 2, 3, 4, 5])
y = np.array([1.1, 2.2, 2.8, 4.1, 5.2])

# Calculate metrics
l1_error = metrics.l1(x, y)
l2_error = metrics.l2(x, y)
sup_error = metrics.sup(x, y)

print(f"L1 error: {l1_error:.3f}")
print(f"L2 error: {l2_error:.3f}")
print(f"Sup error: {sup_error:.3f}")

# Calculate norms
l1_norm = norms.l1(x)
l2_norm = norms.l2(x)
sup_norm = norms.sup(x)

print(f"L1 norm: {l1_norm:.3f}")
print(f"L2 norm: {l2_norm:.3f}")
print(f"Sup norm: {sup_norm:.3f}")

Data Exploration (xrvar.py)

Utilities for exploring xarray datasets.

import xarray as xr
from pywrfkit import xrvar

# Load dataset
ds = xr.open_dataset('wrf_output.nc')

# List variables with attributes
var_info = xrvar.varlist(ds, ['units', 'long_name'])
print(var_info)

Plotting Parameters (params.py)

Configure matplotlib plotting parameters.

from pywrfkit import params

# Update plotting parameters
params.get_params()

# Now all plots will use the updated settings
import matplotlib.pyplot as plt
fig, ax = plt.subplots()
ax.plot([1, 2, 3], [4, 5, 6])
plt.show()

Complete Example: Hurricane Analysis Workflow

import xarray as xr
import matplotlib.pyplot as plt
import cartopy.crs as ccrs
from pywrfkit import wrf, polar, coast, download, params

# Set up plotting parameters
params.get_params()

# Download GFS data for hurricane analysis
download.gfs_forecast('2024120100', 72)

# Load WRF output data
ds = xr.open_dataset('wrfout_d01_2024-12-01_00:00:00')

# Process wind data
wind_speed = wrf.add_coords(ds['U10'], rename=True)

# Convert to polar coordinates for hurricane analysis
polar_wind = polar.convert_to_polar(
    wind_speed,
    radius=5,  # 5 degrees radius
    resolution=0.1
)

# Create visualization
fig, (ax1, ax2) = plt.subplots(1, 2, figsize=(15, 6), 
                               subplot_kw={'projection': ccrs.PlateCarree()})

# Plot original data
coast.plot_coast(ax1)
ax1.pcolormesh(wind_speed.longitudes, wind_speed.latitudes, wind_speed.values)
ax1.set_title('Wind Speed (Cartesian)')

# Plot polar data
ax2.pcolormesh(polar_wind.angle, polar_wind.radius, polar_wind.values)
ax2.set_title('Wind Speed (Polar)')
ax2.set_xlabel('Angle (radians)')
ax2.set_ylabel('Radius (km)')

plt.tight_layout()
plt.show()

File Structure

pywrfkit/
├── README.md           # This file
├── LICENSE            # License information
├── __init__.py        # Package initialization
├── wrf.py             # WRF coordinate utilities
├── geog.py            # Geographic data processing
├── polar.py           # Polar coordinate transformations
├── download.py        # Data downloading utilities
├── coast.py           # Coastline plotting utilities
├── plot_geog.py       # Geographic plotting functions
├── ahps.py            # AHPS data processing
├── xrvar.py           # xarray variable utilities
├── params.py          # Plotting parameters
├── metrics.py         # Statistical metrics
└── norms.py           # Mathematical norms

Contributing

1. Fork the repository
2. Create a feature branch (git checkout -b feature/amazing-feature)
3. Commit your changes (git commit -m 'Add amazing feature')
4. Push to the branch (git push origin feature/amazing-feature)
5. Open a Pull Request

License

This project is licensed under the terms specified in the LICENSE file.

Citation

If you use this toolkit in your research, please cite:

@software{pywrfkit,
  title={PyWRFKit: A Python Toolkit for WRF Data Processing and Analysis},
  author={Ankur Kumar},
  year={2024},
  url={https://github.com/ankurk017/pywrfkit}
}

Support

For questions, issues, or contributions, please open an issue on the GitHub repository or contact the maintainer at [e-mail].

Acknowledgments

• WRF Development Team for the Weather Research and Forecasting model
• Cartopy developers for geographic plotting capabilities
• xarray developers for multi-dimensional array handling
• The scientific Python community for the excellent ecosystem of tools