Skip to main content

The Zambretti Algorithm for weather forecasting

Project description

Zambretti Weather Forecasting in Python

This is a Python implementation of the Zambretti Weather Forecaster

The code is heavily based on the Zambretti Algorithm for Weather Forecasting ESP example

Further reading: Short-Range Local Forecasting with a Digital Barograph using an Algorithm based on the Zambretti Forecaster.

Usage notes

Pressure data must be provided in millibars or hPa (those are equivalent). Elevation must be provided in meters. Temperature must be provided in degrees Celsius.

Minimum 6 readings of atmospheric pressure are required. Best results are when the pressure readings span the last three hours, but the code will run on any timespan.

Technical notes

This project has no dependencies, uses only functions from the Python Standard Library. It should run both in Python and MicroPython.

Example

Example usage with mock values:

import datetime

from zambretti_py import PressureData, WindDirection, Zambretti

now = datetime.datetime.now()
pressure_data = PressureData(
    [
        (now - datetime.timedelta(hours=2, minutes=59), 1050.0),
        (now - datetime.timedelta(hours=2, minutes=49), 1040.0),
        (now - datetime.timedelta(hours=2, minutes=39), 1030.0),
        (now - datetime.timedelta(hours=2, minutes=12), 1020.0),
        (now - datetime.timedelta(hours=1, minutes=19), 1010.0),
        (now - datetime.timedelta(minutes=20), 1000.0),
    ]
)
zambretti = Zambretti()

forecast = zambretti.forecast(
    elevation=90,
    temperature=25,
    pressure_data=pressure_data,
    wind_direction=WindDirection.NORTH,
)
print(forecast)

To calculate for forecast, the Zambretti algorithm requires:

  • elevation above sea level
  • current temperature
  • pressure data from the last three hours, or less.
    • data points older than three hours will be removed
    • the pressure data is expected to be provided as a list of tuples, each tuple consisting of a datetime.datetime object, and the pressure as float
  • optional wind direction, denoting the direction from which the wind is flowing. This has a minor effect on the forecast and can be omitted.

The result will be a text description of the forecasted weather.

Project details


Download files

Download the file for your platform. If you're not sure which to choose, learn more about installing packages.

Source Distribution

zambretti_py-0.0.3.tar.gz (4.9 kB view details)

Uploaded Source

Built Distribution

zambretti_py-0.0.3-py3-none-any.whl (5.2 kB view details)

Uploaded Python 3

File details

Details for the file zambretti_py-0.0.3.tar.gz.

File metadata

  • Download URL: zambretti_py-0.0.3.tar.gz
  • Upload date:
  • Size: 4.9 kB
  • Tags: Source
  • Uploaded using Trusted Publishing? No
  • Uploaded via: twine/5.1.1 CPython/3.11.2

File hashes

Hashes for zambretti_py-0.0.3.tar.gz
Algorithm Hash digest
SHA256 aae4185d9d9b9c152e4bc1337612adafb074b4e3d9e0e59983ce701851d1bff0
MD5 893d2d2edc1b412b824b407d4e4bc4ec
BLAKE2b-256 92e89913b684549fe5c1eeba44ed26c12900f0ed29f7efb4f4362ad0f060b25f

See more details on using hashes here.

File details

Details for the file zambretti_py-0.0.3-py3-none-any.whl.

File metadata

File hashes

Hashes for zambretti_py-0.0.3-py3-none-any.whl
Algorithm Hash digest
SHA256 fd87e8989ccbbbb67974142230b79731f67f16a72553ce067408f2b0137ca933
MD5 fbc5e3127a709f545f91766147b59efc
BLAKE2b-256 4922701d64277636b6e491d2899127b7d6f691f99e829e980e23c11b7a815f61

See more details on using hashes here.

Supported by

AWS AWS Cloud computing and Security Sponsor Datadog Datadog Monitoring Fastly Fastly CDN Google Google Download Analytics Microsoft Microsoft PSF Sponsor Pingdom Pingdom Monitoring Sentry Sentry Error logging StatusPage StatusPage Status page