Motivation & Workshop
Novel climate reanalysis products like ERA5(-Land) provide more accurate environmental information at higher temporal resolution than traditional climate data products used in ecological applications. Furthermore, they provide uncertainty metrics useful for assessing data quality.
KrigR R-package reduces barriers for users to (a) download ERA5(-Land) data (b) aggregate these data to desired temporal resolutions and metrics, (c) acquire topographical co-variates, and (d) statistically downscale spatial data using co-variates via kriging which allows for integration of data uncertainty with interpolation uncertainty for improved data reliability indicators.
KrigR workflow allows highly flexible data product creation for unparalleled aligning of data set specifications with research objectives. Climate products obtained through
KrigR offer great potential for quantification of exposure to extreme events due to their combinations of high spatial and temporal resolutions. Lastly,
KrigR can incorporate third-party data which enables generation of high-resolution, bias-corrected climate projection data allowing for ecological forecasting at high-resolution.
I have created
KrigR for download, temporal aggregation, masking, and statistically interpolating ERA5(-Land) data. We first presented the R-Package (
KrigR) itself in Kusch,Davy, 2022. In Davy, Kusch, 2021 published previously my colleague and I demonstrated how data obtained through the
KrigR framework relate to previously offered ready-made data sets and why we strongly believe that data handling pipelines (rather than ready-made data sets) are the way forward for downstream analyses.
Throughout this workshop material, I walk you through the functionality, use-cases, and quality of life aspects with
A recording of me presenting an earlier version of this workshop (with much of the contents herein) can be found on YouTube.
Throughout this workshop, you will learn how to:
- Query downloads of state-of-the-art climate data using
- Use the data processing functionality contained in
KrigRto achieve data at desired spatial scales and temporal resolutions
- Obtain and process covariate data for use in statistical interpolation via kriging
- Carry out kriging using the
KrigRto obtain bioclimatic data and what to consider in doing so
- Use third-party data with the
- Establish high spatial resolution, bias-corrected climate projections using
If you find any typos in my material, are unhappy with some of what or how I am presenting or simply unclear about thing, do not hesitate to contact me.
All the best,