Use rainfall measurements from NOAA stations to validate rainfall data from multiple satellite sources¶
This notebook demonstrates how to use the PVRADAR SDK to compare rainfall data from NOAA meteorological stations against satellite-derived sources like MERRA2. This is useful for validating the consistency and reliability of rainfall inputs used in modeling.
from pvradar.sdk import PvradarSite, R, resource_plot
import matplotlib.pyplot as plt
import numpy as np
Define Site and Interval¶
Specify the location and time period for which rainfall data should be validated.
location = 35.434, -119.055 # Bakersfield, California, USA
site = PvradarSite(location=location, interval='2020..2021')
site.resource(R.meteo_station_table).head(10)
| datasource | station_id | latitude | longitude | start_date | end_date | coverage | completeness | distance | score | |
|---|---|---|---|---|---|---|---|---|---|---|
| 0 | noaa | USW00023155 | 35.434240 | -119.055240 | 2002-01-01 | 2025-02-22 | 1.0 | 1.000000 | 34.462309 | 2.998806 |
| 1 | noaa | USC00040444 | 35.418600 | -119.050800 | 2002-01-01 | 2025-02-22 | 1.0 | 0.986301 | 1756.152291 | 2.850368 |
| 2 | noaa | US1CAKN0022 | 35.331142 | -119.107783 | 2015-10-02 | 2025-02-21 | 1.0 | 1.000000 | 12411.913154 | 2.650402 |
| 3 | noaa | US1CAKN0026 | 35.408836 | -118.507075 | 2014-12-02 | 2025-02-14 | 1.0 | 1.000000 | 49784.339720 | 2.178103 |
| 4 | noaa | US1CAKN0031 | 35.146400 | -118.498600 | 2017-09-13 | 2025-02-21 | 1.0 | 0.998630 | 59840.904797 | 2.116241 |
| 5 | noaa | USC00046154 | 34.945500 | -119.682700 | 2002-01-01 | 2025-02-20 | 1.0 | 1.000000 | 78855.344445 | 2.065029 |
| 6 | noaa | US1CAKN0017 | 35.583176 | -119.348343 | 2013-03-20 | 2025-02-21 | 1.0 | 0.953425 | 31342.799749 | 2.061570 |
| 7 | noaa | USW00023187 | 34.743610 | -118.725280 | 2002-01-01 | 2025-02-21 | 1.0 | 0.994521 | 82514.108061 | 2.020016 |
| 8 | noaa | US1CAKN0013 | 35.702200 | -118.173900 | 2002-04-22 | 2025-02-20 | 1.0 | 0.993151 | 85186.854426 | 2.005850 |
| 9 | noaa | USC00044957 | 36.203200 | -119.054500 | 2002-01-01 | 2025-02-21 | 1.0 | 0.987671 | 85627.380390 | 1.969519 |
Retrieve NOAA and Satellite Rainfall Data¶
Use PVRADAR's resource function to pull rainfall data from both NOAA stations and satellite datasets for comparison.
rainfall_noaa = site.resource(R.rainfall(datasource='noaa', station_id='USW00023155', to_freq='D'), label='noaa')
rainfall_era5 = site.resource(R.rainfall(datasource='era5', to_freq='D'), label='era5')
rainfall_merra2 = site.resource(R.rainfall(datasource='merra2', to_freq='D'), label='merra2')
Visualize Comparison¶
Use resource_plot to visually compare the rainfall time-series from different sources.
resource_plot(rainfall_era5, rainfall_merra2, rainfall_noaa)
resource_plot(rainfall_era5.cumsum(), rainfall_merra2.cumsum(), rainfall_noaa.cumsum(), to_freq='D')
Add custom analysis¶
Using the PVRADAR-SDK in jupyter notebooks allows you to quickly add your own custom analysis if needed. In this example we will add a scatter plot: measurement vs. satellite.
def analyse_rainfall_estimations(truth, *estimations):
plt.figure(figsize=(6,6))
plt.plot([truth.min(), truth.max()], [truth.min(), truth.max()], 'k--', label='1:1 line')
for estim in estimations:
plt.scatter(truth, estim, label=estim.attrs['label'], alpha=0.7)
# Fit linear regression (1st degree polyfit)
slope, intercept = np.polyfit(truth, estim, 1)
x_vals = np.array([truth.min(), truth.max()])
y_vals = slope * x_vals + intercept
plt.plot(x_vals, y_vals, linestyle='-', label=f"{estim.attrs['label']} fit")
plt.xlabel('True Rainfall [mm]')
plt.ylabel('Satellite Rainfall [mm]')
plt.title('True vs Satellite Rainfall')
plt.legend()
plt.grid(True)
plt.tight_layout()
plt.show()
analyse_rainfall_estimations(rainfall_noaa, rainfall_era5, rainfall_merra2)
Conclusion¶
This comparison illustrates how PVRADAR allows easy access to both ground-measured and satellite-derived resources, helping to validate and select the most reliable inputs for modeling.
¶
Why PVRADAR?¶
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One Python line away from your data¶
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