{
"cells": [
{
"cell_type": "markdown",
"id": "463070b1",
"metadata": {},
"source": [
"# **Use rainfall measurements from NOAA stations to validate rainfall data from multiple satellite sources**\n",
"\n",
"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."
]
},
{
"cell_type": "code",
"execution_count": 1,
"id": "7c51ec9d",
"metadata": {},
"outputs": [],
"source": [
"from pvradar.sdk import PvradarSite, R, resource_plot\n",
"import matplotlib.pyplot as plt\n",
"import numpy as np"
]
},
{
"cell_type": "markdown",
"id": "94fc639f",
"metadata": {},
"source": [
"## Define Site and Interval\n",
"\n",
"Specify the location and time period for which rainfall data should be validated."
]
},
{
"cell_type": "code",
"execution_count": 2,
"id": "c6b4d12f",
"metadata": {},
"outputs": [],
"source": [
"location = 35.434, -119.055 # Bakersfield, California, USA\n",
"site = PvradarSite(location=location, interval='2020..2021')"
]
},
{
"cell_type": "code",
"execution_count": 3,
"id": "c2dadfe8",
"metadata": {},
"outputs": [
{
"data": {
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"\n",
"\n",
"
\n",
" \n",
" \n",
" datasource \n",
" station_id \n",
" latitude \n",
" longitude \n",
" start_date \n",
" end_date \n",
" coverage \n",
" completeness \n",
" distance \n",
" score \n",
" \n",
" \n",
" \n",
" \n",
" 0 \n",
" noaa \n",
" USW00023155 \n",
" 35.434240 \n",
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" 2002-01-01 \n",
" 2025-02-22 \n",
" 1.0 \n",
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" \n",
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" \n",
" \n",
" 2 \n",
" noaa \n",
" US1CAKN0022 \n",
" 35.331142 \n",
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" 2015-10-02 \n",
" 2025-02-21 \n",
" 1.0 \n",
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" 2.650402 \n",
" \n",
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" 3 \n",
" noaa \n",
" US1CAKN0026 \n",
" 35.408836 \n",
" -118.507075 \n",
" 2014-12-02 \n",
" 2025-02-14 \n",
" 1.0 \n",
" 1.000000 \n",
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" \n",
" \n",
" 4 \n",
" noaa \n",
" US1CAKN0031 \n",
" 35.146400 \n",
" -118.498600 \n",
" 2017-09-13 \n",
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" 0.998630 \n",
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" 2.116241 \n",
" \n",
" \n",
" 5 \n",
" noaa \n",
" USC00046154 \n",
" 34.945500 \n",
" -119.682700 \n",
" 2002-01-01 \n",
" 2025-02-20 \n",
" 1.0 \n",
" 1.000000 \n",
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" \n",
" \n",
" 6 \n",
" noaa \n",
" US1CAKN0017 \n",
" 35.583176 \n",
" -119.348343 \n",
" 2013-03-20 \n",
" 2025-02-21 \n",
" 1.0 \n",
" 0.953425 \n",
" 31342.799749 \n",
" 2.061570 \n",
" \n",
" \n",
" 7 \n",
" noaa \n",
" USW00023187 \n",
" 34.743610 \n",
" -118.725280 \n",
" 2002-01-01 \n",
" 2025-02-21 \n",
" 1.0 \n",
" 0.994521 \n",
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" 2.020016 \n",
" \n",
" \n",
" 8 \n",
" noaa \n",
" US1CAKN0013 \n",
" 35.702200 \n",
" -118.173900 \n",
" 2002-04-22 \n",
" 2025-02-20 \n",
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" \n",
" 9 \n",
" noaa \n",
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" 36.203200 \n",
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" 2002-01-01 \n",
" 2025-02-21 \n",
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" \n",
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\n",
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},
"metadata": {},
"output_type": "display_data"
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],
"source": [
"analyse_rainfall_estimations(rainfall_noaa, rainfall_era5, rainfall_merra2)"
]
},
{
"cell_type": "markdown",
"id": "f89ec852",
"metadata": {},
"source": [
"## Conclusion\n",
"\n",
"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."
]
},
{
"cell_type": "markdown",
"id": "b623683a",
"metadata": {},
"source": [
"# \n",
"\n",
"# **Why PVRADAR?**\n",
"\n",
"## Model and optimize the real-world behavior of your PV plants\n",
"\n",
"PVRADAR allows you to model the effect of **any technical or environmental factor** on plant performance — not just rainfall:\n",
"- Combine rainfall, snow, soiling, albedo, clipping, battery storage, and more in a single modeling workflow.\n",
"- Quantify impacts in terms of energy yield, PR, LCOE, and financial KPIs.\n",
"- Explore what-if scenarios and optimize plant design and O&M strategies.\n",
"\n",
"## Build internal tools and advanced applications\n",
"\n",
"With PVRADAR, you can turn your models into robust, organization-wide tools:\n",
"- Build internal web apps that allow colleagues to apply validated models without writing code.\n",
"- Standardize modeling across teams and projects.\n",
"- Automate reporting and performance analysis to save time and ensure consistency.\n",
"\n",
"## One Python line away from your data\n",
"\n",
"The PVRADAR SDK simplifies data access:\n",
"- Seamlessly connect to satellite data, meteo stations, and internal performance databases.\n",
"- Standardize and pre-process data for modeling — ready to use in one line of Python.\n",
"- Keep full control over your data, whether in the cloud or on-premises.\n",
"\n",
"## Supported by deep expertise\n",
"\n",
"PVRADAR is more than software — it is backed by industry-leading expertise:\n",
"- Model validation and tuning with high-quality reference data.\n",
"- Consulting and support on snow losses, soiling, O&M optimization, and yield forecasting.\n",
"- Proven track record with leading IPPs, developers, and consultants.\n",
"\n",
"👉 **Ready to go further?** [**Contact us to learn how we can help solve your modeling challenge!**](https://pvradar.com/contact)"
]
}
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