Climate-Analysis: Make your own Climate Analysis

Download weather data

1. Get API Key
   • create a free account with Visual Crossing (requires email)
   • press on the Button "Account" and copy your API-Key to clipboard
2. Specify the requested Data
   • open the config.txt file in an editor
   • replace "YourPersonalKey" with the API-Key
   • define requested data with parameters:
     • UNIT_GROUP: default metric (Documentation)
     • LOCATION: place of the requested weather data
     • START_DATE [yyyy-mm-dd]: the first date of the requested data range
     • END_DATE [yyyy-mm-dd]: the last date of the requested data range
3. Start Download
   • execute python download.py or python3 download.py
   • the new folder data contains the downloaded data
   • Attention: Only 1000 request(days) per day free. Repeat step 3 daily until the data is complete or pay for the data (Pricing)

Example Climate Analysis: Dortmund from 1973 to 2022

I. Evolution of the daily mean temperature

• 4 from 5 days with highest maximum daily temperature were after or in 2019
• all 5 days with the minimum daily temperature were before or in 1997
• mean temperature: 10.37 °C
• the linear model predicts a increase of 0.14 °C in ten years

II. Evolution of the yearly minimum, maximum and average temperature

• increase of annual max. Temperature in ten years: 0.75 °C
• increase of annual min. Temperature in ten years: 0.63 °C
• increase of annual avg. Temperature in ten years: 0.12 °C

Export Jupyter Notebook

Requires nbconvert

• HTML (dark theme)

  jupyter nbconvert Report_Dortmund_2022.ipynb --to html --HTMLExporter.theme=dark

• PDF (white, article)

  jupyter nbconvert --to pdf Report_Dortmund_2022.ipynb

Todo

• config file: metric, key, location, start and end data
• explore the frequency of extreme hot/cold days
• analyze windspeed
• analyze rain and snow

Data Source

https://www.visualcrossing.com/

Author

Samuel Haefs

• Profile
• Email