meteoGermany – climate data interpolation pipeline

This project implements a robust, reproducible pipeline in R for spatial interpolation of daily climate variables using Kriging with external drift (elevation). It is designed for high-resolution gridding of climate parameters from station data across Germany or its subregions.

Purpose

The pipeline enables:

• Daily spatial interpolation of climate variables using Kriging with external drift
• Integration of a digital elevation model (DEM) as covariate
• Quality-controlled output and masking of invalid values
• Aggregated descriptive statistics per municipality (Gemeinde) or state (Bundesland)
• Scalable, parallelized processing via pbmcapply

Supported Climate Variables

• TMK, TXK, TNK, TGK: Mean, max, min, and ground temperature
• RSK: Precipitation
• SDK: Sunshine duration
• UPM, VPM: Vapor pressure variables
• NM: Cloudiness
• PM: Mean sea-level pressure

Core Methods

• Kriging with external drift using station elevation (Stationshoehe)
• Automatic variogram modeling with automap
• Raster output with optional masking and classification
• Statistical summarization using exactextractr

Kriging with External Drift: Strengths and Limitations

Advantages

• Robust to temporal variability in station coverage over long periods (e.g., 30+ years)
• Integrates DEM-based elevation as an auxiliary predictor without requiring dense spatial coverage
• Offers physically interpretable spatial gradients, e.g. lapse rates in temperature
• Supports parallelized interpolation of daily fields across large domains
• Handles sparse or irregular networks better than simple interpolation methods

Limitations

• May underperform in areas with local microclimatic effects (e.g., urban heat islands, complex canopy or land use structure)
• Sensitive to non-stationarity or heteroskedasticity in input data
• External drift relies on static covariates — local weather phenomena might need dynamic or multi-scale inputs
• Assumes a linear relationship between covariate (elevation) and target variable — not always valid (e.g., inversions)

Relevance to This Pipeline

The current pipeline implements a generalized, robust KED setup that works well across most of Germany. While not optimized for all microclimates, its simplicity and reproducibility make it highly effective for:

• National-scale climatology
• Retrospective gridding across decades
• Administrative-scale aggregation (e.g., Gemeinde, Bundesland)
• Fast, daily reanalysis-like generation with minimal tuning

References

• Dolinar, M. (2006).
  Spatial interpolation of sunshine duration in Slovenia. Meteorological Applications, 13: 375–384.
  https://doi.org/10.1017/S1350482706002362

• Hofstra, N., Haylock, M., New, M., & Jones, P.D. (2009).
  Testing E-OBS European high-resolution gridded data set of daily precipitation and surface temperature.
  J. Geophys. Res., 114, D21101.
  https://doi.org/10.1029/2009JD011799

• Hengl, T., Heuvelink, G.B.M., & Stein, A. (2003).
  Comparison of kriging with external drift and regression-kriging.
  ITC Technical Note. Available: https://ris.utwente.nl/ws/portalfiles/portal/448469781/hengl_comparison.pdf

• Pebesma, E.J. (2006).
  The gstat package. Computers & Geosciences, 30(7), 683–691.
  https://doi.org/10.1016/j.cageo.2004.03.012

Credits

This work integrates and extends ideas from:

• Documentation of the rdwd package
  https://bookdown.org/brry/rdwd/

• Hartmann, K., Krois, J., Rudolph, A. (2023): Statistics and Geodata Analysis using R (SOGA-R). Department of Earth Sciences, Freie Universität Berlin
  https://www.geo.fu-berlin.de/en/v/soga-r/Advances-statistics/Time-series-analysis/index.html

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Author: Chris Reudenbach
License: Creative Commons Attribution-ShareAlike 4.0 International (CC BY-SA 4.0)
Contact: creuden@gmail.com