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Satellite-based monitoring of dry and wet conditions using Standardized Precipitation Index (SPI)

The Standardized Precipitation Index (SPI) analysis is following the training conducted in 28 Jan 2020 by NASA ARSET on Application of GPM IMERG Reanalysis for Assessing Extreme Dry and Wet Periods. https://appliedsciences.nasa.gov/join-mission/training/english/arset-applications-gpm-imerg-reanalysis-assessing-extreme-dry-and-wet

The training session from NASA ARSET provided information on how to access and download IMERG data, and use it to calculate SPI on defined time scales. Many participant experience several problems and try to raise some question to the developer of climate-indices python package in their Github page and some also ask in StackExchange. I also experience several problem during the training and try to documented the solution by modified some step on their guideline.

In this site, I would like to re-share on how to calculate SPI using NASA ARSET approach and provide alternative way using different data and format. This how-to guideline will use latest version of Climate Indices in Python software. While NASA ARSET training still used the official release version from U.S. Drought Portal

On How-to? section, you will find step-by-step guideline to calculate SPI, and can try different (data source and format) approach below:

  • SPI based on IMERG data in netCDF format (following NASA ARSET training but adjusted in some step)
  • SPI based on CHIRPS data GeoTIFF format
  • SPI based on CHIRPS data in netCDF format

SPI3

Notes

  • This step-by-step guide was tested using Macbook Pro, 2.9 GHz 6-Core Intel Core i9, 32 GB 2400 MHz DDR4, running on macOS Catalina 10.15.7

  • And a Windows Server 2019 running in Parallels Desktop for Mac, with Windows Subsystem for Linux (WSL) installed.

    Info

    I will use a standard WSL 2 for Windows 10 as most of Windows user are using Windows 10 for their works.