Short_course_10: Applications of R-Instat to analyse and validate satellite based climate time series
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In this short course you will be introduced to R-Instat, its functionalities and how it can be used to compare satellite data with station data. R-Instat is a statistics software, which is very well designed to manage station data and compare and combine station data with satellite data.
Participants will be given time to play with R-Instat, check out the functionalities to compare station data with satellite data. Participants are invited to join a feedback / Q&A session on Monday, 8 February 2021 at 12:00 - 13:00 UTC.
Participants are invited to install R-Instat prior to the course. R-Instat is designed for windows operating systems, however it can also be installed on Ubuntu 18.04 through wine.
with Christine Traeger Chatterjee, Steffen Kothe and Danny ParsonsSession recording
Recording of the webinar on 3rd Feb is now available:
Joining the session
For Q&A, go to Slido.com and use #EUMSC10 or simply go to https://app.sli.do/event/vtcvnmle (no log in required). You can join Slido from your phone.
Post your graphs and analyses on Padlet https://padlet.com/CMSAF/cmsaf_padlet to share with others!
R-Instat Presentation from the live session (PDF) -
Dear participant,
We are happy to have you here. On this page you will be able to find all important information about the course. After the course, you will be able to access here the recording of the session.
Before the short course begins please make sure you follow the preparation steps below. This will ensure that you have the required software installed on your machine and you can make the most of the time available in the short course to learn as much as possible.
The feedback session will take place on 8 February 2021 at 12:00 - 13:00 UTC.
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Installing R-Instat
In preparation for the course please download and install R-Instat from here http://r-instat.org/Download.html.
Installation should be straightforward. After installing, launch R-Instat. If R-Instat starts up and you do not see any error messages, it has installed successfully.
You can have a look at the questions and answers here: https://app.sli.do/event/vtcvnmle/live/questions
Mac & Linux Users
Note that R-Instat is currently a Windows application, however there are several ways it can be run on Mac and Linux machines:
1. Ubuntu 18.04: R-Instat has successfully been installed on Ubuntu 18.04 using Wine. An installation script is available here https://github.com/IDEMSInternational/r-instat-linux/blob/master/sh/setup.sh. Note this may work on other versions of Linux/Mac but is untested.
2. Install a virtual Windows machine inside your Linux or Mac operating system using software such as VirtualBox or Boot Camp (Mac).
If you need help installing R-Instat, please post a message on Slido (#EUMSC10) and we will assist you.
About R-Instat
In this workshop you will use R-Instat to compare station and satellite time series data. R-Instat is a general statistics software, based on R, with a special menu to support the analysis of climatic data.
This presentation introduces R-Instat, showing its general features for organising and describing data, as well as the specific facilities for analysing historical climatic data.
If you are unable to watch this presentation, download the PDF version below.
Introduction to R-Instat Presentation (PDF)
• R-Instat support page:
http://r-instat.org/ReleaseNotes.html
• for help with: downloading, tutorials, videos, climatic guide
http://r-instat.org/ReleaseNotes.html• for feature requests and bug reports, post an issue on our GitHub https://github.com/africanmathsinitiative/R-Instat/issues
• for general queries e-mail r-instat@africanmathsinitiative.net
or EUMETSAT for something specific to the course training@eumetsat.int
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This section is not required to be done before the start of the short course. However, you may which to go through this before to get some practice.
Once you have installed R-Instat you can start working with data. R-Instat is designed to analyse both station and satellite data, as well as having general features of a statistics package.
In this short course we will focus on comparing station data with satellite data. However, in this section we also provide resources on the general use of R-Instat.
Whatever you intend to use R-Instat for, we suggest starting with the two tutorials (below) to familiarise yourself with how R-Instat works and to get started working with data.
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1. Introductory Tutorials
The tutorials are a gentle introduction to R-Instat and get you working with example data right away. The example data is included in R-Instat, so you don't need to download anything else to get started.
The tutorials are available in both document and video format and are designed to be easy to follow for a beginner. Whatever format you choose, you will only learn by doing, so make sure you practice what you watch/read!
The videos are in two parts. Part 1 is the "How" showing you how to use the software, and Part 2 is the "why" discussing why we did these steps and how we interpret the results.
Tutorial 1 - Describing Data
Tutorial 1 does not use climatic data, but an example dataset called diamonds. If you want to get straight into climatic data, go to Tutorial 2, but we think there are useful things to learn here, even if your interest is climatic data.
Tutorial 2 - A Climatic Dataset
Tutorial 2 uses a climatic data set of daily data from a station in Dodoma, Tanzania. We appreciate Tanzania Meteorological Agency for kindly providing this data freely for use in R-Instat.
Tutorial 2 Video Part 1 | Tutorial 2 Video Part 2
2. Climatic Guide
The R-Instat Climatic Guide is a comprehensive guide to R-Instat's climatic functionalities. We do not suggest reading the whole guide in one go, but reading the relevant sections as you need them.
For this course, we suggest looking particularly at Chapter 2.4 and parts of Chapter 9 on gridded data.
3. Example data & exercises
The link below is a guide to the demonstration done in the live session with further details and steps.
Comparing satellite and station data in R-Instat tutorial
The folder below contains the data used in the presentation. In the link above to Dropbox, click Download in the top right corner to download all the files to a zip folder, or click on a file and then the ↓ arrow to download individually. The data are provided in various formats which enable you to practice different elements of the analysis.
There are three types of exercises you may want to carry out:
1. Prepare and analyse the example data from Germany
Use the original data files as shown in the presentation to get practice in both data preparation and analyses. This simulates a process of working with real data where time is often needed for organising the preparing the data before it can be analysed, as you saw in the presentation.
2. Analyse the pre-prepared example data from Germany
If you prefer to focus on the statistical analyses and comparisons in more detail, we have provided the pre-prepared data in the file sunh_merged.RData in the example data folder, ready to begin analyses straight away. This is less realistic than the normal situation when working with data, but has the advantage of allowing you to focus on doing more analysis and comparisons.
3. Prepare and Analyse your own data
Some of you may have access to your own station data and we hope you may be adventurous and want to try out what you've learned for your own analysis. If you do, you will also need to download the corresponding satellite data for comparison, which you can get from CM SAF. You will likely need to do some preparing and formatting of your data to get it into shape for analysis, which may be slightly different to what you saw in the presentation. Use the climatic guide above, and ask questions on Slido if you need help with this.
How to download data from CM SAF
4. More on R-Instat
We have a growing set of tutorial videos on various aspects of R-Instat's climatic features. View the playlist on YouTube to see the full set.
We love to hear from our users. If you would like to report a bug or request a new feature, post a message on our GitHub site. If you have a general query or feedback write to use at: r-instat (at) africanmathsinitiative.net
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