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Scientific References

This page lists all scientific literature referenced in MetDataPy's implementations.

Derived Meteorological Metrics

Dew Point Temperature

Magnus-Tetens Formula

The dew point calculation uses the improved Magnus form approximation:

  • Alduchov, O. A., & Eskridge, R. E. (1996). Improved Magnus form approximation of saturation vapor pressure. Journal of Applied Meteorology, 35(4), 601-609.
    DOI: 10.1175/1520-0450(1996)035<0601:IMFAOS>2.0.CO;2

  • Lawrence, M. G. (2005). The relationship between relative humidity and the dewpoint temperature in moist air: A simple conversion and applications. Bulletin of the American Meteorological Society, 86(2), 225-233.
    DOI: 10.1175/BAMS-86-2-225

Saturation Vapor Pressure

Tetens Formula

  • Tetens, O. (1930). Über einige meteorologische Begriffe. Zeitschrift für Geophysik, 6, 297-309.

  • Allen, R. G., Pereira, L. S., Raes, D., & Smith, M. (1998). Crop evapotranspiration - Guidelines for computing crop water requirements. FAO Irrigation and drainage paper 56. Food and Agriculture Organization of the United Nations, Rome.
    Available online

Vapor Pressure Deficit (VPD)

  • Allen, R. G., et al. (1998). Crop evapotranspiration. FAO Irrigation and drainage paper 56.

  • Grossiord, C., et al. (2020). Plant responses to rising vapor pressure deficit. New Phytologist, 226(6), 1550-1566.
    DOI: 10.1111/nph.16485

Heat Index

Rothfusz Regression & Steadman Model

  • Rothfusz, L. P. (1990). The heat index equation (or, more than you ever wanted to know about heat index). National Weather Service Technical Attachment SR 90-23.
    Available online

  • Steadman, R. G. (1979). The assessment of sultriness. Part I: A temperature-humidity index based on human physiology and clothing science. Journal of Applied Meteorology, 18(7), 861-873.
    DOI: 10.1175/1520-0450(1979)018<0861:TAOSPI>2.0.CO;2

  • Anderson, G. B., et al. (2013). Heat-related emergency hospitalizations for respiratory diseases in the Medicare population. American Journal of Respiratory and Critical Care Medicine, 187(10), 1098-1103.
    DOI: 10.1164/rccm.201211-1969OC

Wind Chill

North American Wind Chill Formula (2001)

  • Osczevski, R., & Bluestein, M. (2005). The new wind chill equivalent temperature chart. Bulletin of the American Meteorological Society, 86(10), 1453-1458.
    DOI: 10.1175/BAMS-86-10-1453

  • Tikuisis, P., & Osczevski, R. J. (2003). Facial cooling during cold air exposure. Bulletin of the American Meteorological Society, 84(7), 927-934.
    DOI: 10.1175/BAMS-84-7-927

  • Shitzer, A., & de Dear, R. (2006). Inconsistencies in the "New" windchill chart at low wind speeds. Journal of Applied Meteorology and Climatology, 45(5), 787-790.
    DOI: 10.1175/JAM2360.1

Quality Control Methods

Spike Detection

Rolling Median Absolute Deviation (MAD)

  • Leys, C., et al. (2013). Detecting outliers: Do not use standard deviation around the mean, use absolute deviation around the median. Journal of Experimental Social Psychology, 49(4), 764-766.
    DOI: 10.1016/j.jesp.2013.03.013

Flatline Detection

Rolling Variance Method

  • Shaadan, N., et al. (2015). Anomaly detection in univariate time-series: A survey on the state-of-the-art. Journal of Engineering Science and Technology, 10(Special Issue), 1-13.

Data Standards

CF Conventions

  • Eaton, B., et al. (2020). NetCDF Climate and Forecast (CF) Metadata Conventions, Version 1.8.
    Available online

FAIR Principles

  • Wilkinson, M. D., et al. (2016). The FAIR Guiding Principles for scientific data management and stewardship. Scientific Data, 3, 160018.
    DOI: 10.1038/sdata.2016.18

Comparison Tools

  • MetPy: May, R. M., et al. (2022). MetPy: A Python Package for Meteorological Data. Unidata.
    DOI: 10.5065/D6WW7G29

  • xarray: Hoyer, S., & Hamman, J. (2017). xarray: N-D labeled arrays and datasets in Python. Journal of Open Research Software, 5(1), 10.
    DOI: 10.5334/jors.148

  • pandas: McKinney, W. (2010). Data structures for statistical computing in Python. Proceedings of the 9th Python in Science Conference, 56-61.
    DOI: 10.25080/Majora-92bf1922-00a

Citing MetDataPy

If you use MetDataPy in your research, please cite:

@software{metdatapy2025,
  title = {MetDataPy: A Source-Agnostic Toolkit for Meteorological Time-Series Data},
  author = {Kartas, Kyriakos},
  year = {2025},
  url = {https://github.com/kkartas/MetDataPy},
  version = {1.3.0}
}

See CITATION.cff for machine-readable citation metadata.

Additional Resources

Meteorological Standards

  • World Meteorological Organization (WMO). Guide to Meteorological Instruments and Methods of Observation (WMO-No. 8), 2018 edition.
    Available online

Time Series Analysis

  • Box, G. E. P., Jenkins, G. M., Reinsel, G. C., & Ljung, G. M. (2015). Time Series Analysis: Forecasting and Control (5th ed.). John Wiley & Sons.

Machine Learning for Weather

  • Reichstein, M., et al. (2019). Deep learning and process understanding for data-driven Earth system science. Nature, 566(7743), 195-204.
    DOI: 10.1038/s41586-019-0912-1

  • Schultz, M. G., et al. (2021). Can deep learning beat numerical weather prediction? Philosophical Transactions of the Royal Society A, 379(2194), 20200097.
    DOI: 10.1098/rsta.2020.0097

Contributing References

If you implement new meteorological formulas or QC methods, please:

  1. Add citations to the function docstring
  2. Update this references page
  3. Include DOI links when available
  4. Follow the NumPy docstring format

See CONTRIBUTING.md for details.