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.

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--"
}

orianac/climate_indices

Climate Indices in Python

Citation