Downloading ERA5 Data and Creating Time Series

Introduction

• Presentation on downloading ECMWF MWF data (ERA5) and creating time series.
• Requested by a YouTube follower.

Overview of ERA5 Data

• Provides hourly estimates of various atmospheric, land, and oceanic climate variables.
• Coverage: 30 km grid, 137 levels from surface to 80 km.
• Includes uncertainty information for all variables.

Steps to Download ERA5 Data

1. **Search for ERA5: **Use Google to find the ERA5 data.
   • Key term: "ERA5 ECRF"
2. Navigate the Copernicus Website:
   • Click on the right side to download ERA5 from Copernicus.
3. Select Data Set:
   • Choose the type of data (real analysis data set).
   • Focus on downloading hourly data.
4. Choose Variables:
   • Options: temperature, relative humidity, and more.
   • Example: Select temperature data for specific pressure levels (300-1000 hPa).
5. Define Time Range and Area:
   • Set year (e.g., 2020) and specify area coordinates.
   • Example coordinates: East -20 to -108 (USA).
6. Format Selection:
   • Choose NetCDF format for easier data extraction.
7. Submit Form:
   • Create account if necessary and submit.
   • Processing time can vary (e.g., 22 minutes shown in demo).

Data Characteristics and Processing

• Data file size (example: 54.7 GB) mentioned.
• Python Use for Processing:
  • Using Jupyter Notebook for step-by-step execution.
  • Libraries: import libraries needed for processing NetCDF files, including:
    • numpy for array manipulations.
    • pandas for data frame handling.
    • netCDF4 for reading the data.

Creating Time Series

1. Read NetCDF Data File:
   • Use method netCDF4.Dataset to access the file.
2. Explore Variables:
   • Check the contents of the variables (e.g., longitude, latitude, temperature) along with units.
   • The time variable should reference a start date (from 1900).
3. Extract Time Values:
   • Loop through time indices to create a time series.
4. Find Station Locations:
   • Use geographic coordinates to find the closest grid point in the dataset.
5. Store Temperature Time Series:
   • Export data into CSV format by looping through each station and creating a structured output.

Plotting and Final Remarks

• After extracting time series data:
  • Use matplotlib to plot the temperature over time.
  • Save plots with appropriate titles and labels.
• Output shows multiple CSV files generated for each station.
• Encouragement to reach out for queries regarding the process.

Conclusion

• Summary of accessing and processing ERA5 data.
• Option to download ECMWF data for personal projects.