Climate Indices in Python

This project contains Python implementations of various climate index algorithms which provide a geographical and temporal picture of the severity of precipitation and temperature anomalies useful for climate monitoring and research.

The following indices are provided:

• SPI, Standardized Precipitation Index, utilizing both gamma and Pearson Type III distributions
• SPEI, Standardized Precipitation Evapotranspiration Index, utilizing both gamma and Pearson Type III distributions
• PET, Potential Evapotranspiration, utilizing either Thornthwaite or Hargreaves equations
• PDSI, Palmer Drought Severity Index
• scPDSI, Self-calibrated Palmer Drought Severity Index
• PHDI, Palmer Hydrological Drought Index
• Z-Index, Palmer moisture anomaly index (Z-index)
• PMDI, Palmer Modified Drought Index
• PNP, Percentage of Normal Precipitation

This Python implementation of the above climate index algorithms is being developed with the following goals in mind:

• to provide an open source software package to compute a suite of climate indices commonly used for climate monitoring, with well documented code that is faithful to the relevant literature and which produces scientifically verifiable results
• to provide a central, open location for participation and collaboration for researchers, developers, and users of climate indices
• to facilitate standardization and consensus on best-of-breed climate index algorithms and corresponding compliant implementations in Python
• to provide transparency into the operational code used for climate monitoring activities at NCEI/NOAA, and consequent reproducibility of published datasets computed from this package
• to incorporate modern software engineering principles and programming best practices

This is a developmental/forked version of code that is originally developed and maintained by NIDIS/NCEI/NOAA. The official release version is available at drought.gov.

• Documentation
• License
• Disclaimer

Citation

You can cite climate_indices in your projects and research papers via the BibTeX entry below.

@misc {climate_indices,
    author = "James Adams",
    title  = "climate_indices, an open source Python library providing reference implementations of commonly used climate indices",
    url    = "https://github.com/monocongo/climate_indices",
    month  = "may",
    year   = "2017--"
}