R package providing a framework for easy access, processing, and advanced visualization of gridded climate products
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CAVAanalytics is an R package offering a consistent framework to load, analyze, calculate indicators, and visualize results for climate and impact models. It also streamlines access to W5E5, ERA5 and CORDEX-CORE models.
- You tried to download CORDEX models from ESGF or COPERNICUS but found the process difficult (managing netCDF files, etc)
- You tried to work with multiple climate models but found the process of computing climate indicators and then visualising the results hard
CAVAanalytics addresses these issues by:
- Providing streamlined access to CORDEX-CORE models for the whole world. This means that you don't need to download netCDF files, data requests are sent to THREDDS servers and you retrieve only the information you asked, directly in memory
- Providing a framework to work with multiple models so that indicators calculation and visualization of results is extremely easy
Firstly download or upload data, secondly perform the intended analysis, thirdly visualize the results.
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| CAVAanalytics steps |
Below we give an example of how you can use CAVAanalytics to retrieve climate information easily (both past and future). Detailed examples are available from the tutorial webpage.
library(CAVAanalytics)
# 1st step
remote.data <- load_data(country = "Sudan", variable="pr",
years.hist=1990:2000, years.proj=2020:2030,
path.to.data = "CORDEX-CORE", aggr.m="sum", domain="AFR-22")
# 2nd step
sudan_ccs <- climate_change_signal(remote.data, season=list(1:12), bias.correction = F)
# 3rd step
plotting(sudan_ccs, ensemble=FALSE, plot_titles = "Precipitation change (mm)",
palette=IPCC_palette(type = "pr", divergent = T), legend_range = c(-550,550)) |
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| Projected change in total annual precipitation compared to the 1990-2000 baseline period in Sudan |
Based on how you want to use CAVAanalytics, there are three options.
If you are new to climate4R, You need to first install rJava. This installation can be problematic. Follow the instructions below
Windows
Linux and macOS
Installing rJava in Linux and macOS
If the installation of rJava is successful, you should be able to load the library from Rstudio.
# This should not give any error
library(rJava)
You can now install CAVAanalytics and its main packages
install.packages("devtools")
remotes::install_github(c("SantanderMetGroup/loadeR.java",
"SantanderMetGroup/climate4R.UDG",
"SantanderMetGroup/loadeR",
"SantanderMetGroup/transformeR",
"SantanderMetGroup/downscaleR"))
remotes::install_github("Risk-Team/CAVAanalytics")
You can request access to the University of Cantabria JupyterHub, where CAVAanalytics is already installed. This will give you access to computational resources and you would be able to perform your climate analysis using a Jupyter Notebook environment. When using JupyterHub you will be using data physically stored at the Cantabria server. If you would like to access these resources, you are welcome to contact [email protected] or [email protected] stating your intended usage type.
The Docker image is available at Docker.io, rso9192/cava. This docker image is built on top of rocker/rstudio
Open the terminal
# if not already installed
sudo apt-get install snapd
# if not already installed
sudo snap install docker
sudo docker pull docker.io/rso9192/cava:version3.2.6
sudo docker run --rm \
-p 8888:8787 \
-e PASSWORD=password \
-v /path/to/local/directory:/home \
rso9192/cava:version3.2.6
Replace /path/to/local/directory with the local directory on your host machine where you want to save your plots or data. For example, you can create a folder on your Desktop called CAVA_results. Then, you would run the above command as:
sudo docker run --rm \
-p 8888:8787 \
-e PASSWORD=password \
-v /home/Desktop/CAVA_results:/home \
rso9192/cava:version3.2.6
Now open your favorite browser and type http://localhost:8888/. You should see a login page: enter the username "rstudio" and password "password" to login and that's it! You can now use CAVAanalytics through Rstudio server.
If you are using Windows, you can install Docker Desktop first. Note that Docker Desktop would require the installation of WSL (Windows Subsystem for Linux). To do that, open a Command Prompt (as administrator) and run
wsl --install
This will instal the Windows Subsystem for Linux in your computer. Then start the Docker app and open an ubuntu terminal and run
sudo docker pull docker.io/rso9192/cava:version3.2.6
# ignore the messages about login
Now run your image as below. Remember to replace /path/to/local/directory with the local directory on your host machine where you want to save your plots or data. For example, you can create a folder on your Desktop called CAVA_results. Then, you could run the above command as:
sudo docker run --rm \
-p 8888:8787 \
-e PASSWORD=password \
-v /mnt/c/Users/my_username/Desktop/CAVA_results:/home \
rso9192/cava:version3.2.6
Now open your favorite browser and type http://localhost:8888/. You should see a login page: enter the username "rstudio" and password "password" to login and that's it! You can now use CAVAanalytics through Rstudio server.
If you are using a Mac, you first need to install Docker desktop for Mac.
Then open a terminal and while Docker Desktop is open, run:
sudo docker pull docker.io/rso9192/cava:version3.2.6
At this point, you can run the Docker image with the below comand.
sudo docker run --rm \
-p 8888:8787 \
-e PASSWORD=password \
-v /path/to/local/directory:/home \
rso9192/cava:version3.2.6
Remember to replace /path/to/local/directory with the local directory on your host machine where you want to save your plots or data. Now open your favorite browser and type http://localhost:8888/. You should see a login page: enter the username "rstudio" and password "password" to login and that's it! You can now use CAVAanalytics through Rstudio server.
Firstly, why Python?
While CAVAanalytics was built on top of R packages, such as climate4R and tidyverse to allow users to focus on visualizing results, R does not leverage the same level of "computational efficiency" as Python. This is mainly because Python offers out-of-memory computation for arrays thanks to the integration between xarray and dask. R does not have this feature for arrays, which is typically the way in which climate data is used. This means that CAVAanalytics largely works on memory (RAM), effectively limiting the geographical area in which analyses can be performed (check out the memory-efficient functions available from CAVAanalytics though). CAVAanalytics would not allow users to perform analyses for entire CORDEX domains very efficiently because it would need a lot of RAM. However, CAVAanalytics was mainly developed for country-level assessment and never conceived to be used for large-scale climate data analyses. This is where Python comes in. The regridded CORDEX-CORE models and all observational datasets used by CAVAanalytics can also be accessed using Python. The benefit of using our data is that we provide one URL per model for all supported variables and that we have already regridded CORDEX-CORE models, making retrieving the data extremely easy. See below an example:
import xarray as xr
# URL to ERA5 data
obs_url = "https://data.meteo.unican.es/thredds/dodsC/copernicus/cds/ERA5_0.25"
# URL to W5E5 V2 data
obs_url = "https://data.meteo.unican.es/thredds/dodsC/mirrors/W5E5/W5E5_v2"
# Open dataset
ds = xr.open_dataset(obs_url)
The list of available CORDEX-CORE models can be accessed with:
import pandas as pd
csv_url = "https://hub.ipcc.ifca.es/thredds/fileServer/inventories/cava.csv"
data = pd.read_csv(csv_url)
If you would like to contribute to this project, you are welcome to fork this repository and submit your contribution. If you liked CAVAanalytics, please remember to add a star!
You can report issues here