R-Instat is a free, general statistics software, based on R, with special facilities for working with station data and comparing station data with satellite data. R-Instat can import data in a variety of formats including csv, xls, xlsx, txt, rds and others as well as gridded data in NetCDF files.

A self-paced online Moodle course on using R-Instat for comparison of station and satellite data is available here

On this page, we pick out some of the sections of the course which may be relevant to users of the Drought & Vegetation Data Cube.

This video below introduces R-Instat, showing its general features for organising and describing data, as well as the specific facilities for analysing historical climatic data.

Downloading and installing R-Instat

See section 0.2 on the Introduction to R-Instat course for intructions on downloading and installing R-Instat

Working with station data

R-Instat has a range of facilities for importing, tidying and organising station data in various formats. See Section 2 of the Introduction to R-Instat course for tutorials and information on importing and tidying station data.

Importing gridded data in NetCDF files

Section 3 of the Introduction to R-Instat course describing how to import satellite data obtained from CM SAF, extract point(s) and merge with station data.

Data from the Drought & Vegetation Data Cube comes in a simpler format than data directly from CM SAF (which comes as .tar file containing multiple .nc files). It can be imported in a similar way in R-Instat. Follow the video in section 3.1 (also below), and instead of selecting "Browse Folder" at the import stage select "Browse File" to choose a .nc file obtained from the Drought & Vegetation Data Cube. The subsequent steps to extract point(s) and merge with station data are then the same.

Comparing satellite and station data

R-Instat's Climatic > Compare menu has a wide variety of graphical and numerical comparison methods for comparing two sources of data e.g. station and satellite records.

See Section 4 of the Introduction to R-Instat course for tutorial videos and documents on these methods. The methods include correlations, scatter plots, time series and seasonality plots, calculation of bias and a range of goodness of fit measures (mean bias, root mean square error, percentage bias, index of agreement, Nash-Sutcliffe efficiency, Kling-Gupta efficiency and more), conditional quantile plots and Taylor diagrams.

The two videos below show how to use graphical methods to compare station and satellite data and also use these method to investigate issues in the comparisons.

More on R-Instat

The Moodle course on R-Instat contains guided tutorials on comparing station and satellite data with example station data from Germany and satellite data from CM SAF in order for you to practice and learn how to use the software

We also have a growing set of tutorial videos on various aspects of R-Instat's climatic features. View our YouTube Channel to see the full set.

We always love to hear from our users.

Join the discussion on our GitHub discussion page to tell us what you think about R-Instat or share what you've been doing with it.

To report a bug or request a new feature, post an issue on our GitHub site.

For anything else, write to us:

Last modified: Monday, 10 May 2021, 5:50 PM