https://github.com/denny-b-justin/statistical-modelling-of-climate-change-using-python
The objective of the study is to analyze rainfall data of San Francisco and to study the rainfall variation in pattern happened in the last few years.
https://github.com/denny-b-justin/statistical-modelling-of-climate-change-using-python
climate-data probability-distribution python statistical-models
Last synced: 28 days ago
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The objective of the study is to analyze rainfall data of San Francisco and to study the rainfall variation in pattern happened in the last few years.
- Host: GitHub
- URL: https://github.com/denny-b-justin/statistical-modelling-of-climate-change-using-python
- Owner: Denny-B-Justin
- Created: 2024-04-16T11:50:55.000Z (about 2 years ago)
- Default Branch: main
- Last Pushed: 2025-02-25T09:01:45.000Z (over 1 year ago)
- Last Synced: 2025-02-25T10:19:45.638Z (over 1 year ago)
- Topics: climate-data, probability-distribution, python, statistical-models
- Language: Jupyter Notebook
- Homepage:
- Size: 1.65 MB
- Stars: 0
- Watchers: 1
- Forks: 0
- Open Issues: 0
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Metadata Files:
- Readme: README.md
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README
# Statistical-Modelling-of-Climate-Change-using-Python
The objective of the study is to analyze rainfall data of San Francisco and to study the rainfall variation in pattern happened in the last few years. Rainfall is fundamental aspects of Earth's climate system. By studying rainfall data probabilistically, we can characterize the statistical distribution of rainfall events, including their frequency, duration, intensity, and spatial distribution. Understanding the pattern of the rainfall is crucial for developing models and designing infrastructure to manage water resources effectively. Rainfall patterns exhibit considerable variability across different geographical regions and time
periods. A probabilistic model was generated to study the variation in extreme rainfall data during the last thirty years. My aim is to study the historical data and provide valuable insights on extreme rainfall data. The project aims to bring awareness about statistical tools used in weather modelling and probabilistic analysis.