I'm a little new to the programming world. So, it may sound weird how I will ask my question and ask for your support in this sense. I'm trying to understand how I can operate on NetCDF files in Python and/or Rstudio. How can I operate on NetCDF files just like one normally does on CSV/text files on Python or other coding platforms? I'd be so glad if you could suggest sources for me to learn how to achieve this issue. Plus, I'd be so glad if you could share some libraries and packages already established for this purpose. Below is the Rstudio code I wrote for quantile mapping of precipitation data contained in the CSV file. In fact, what I'm exactly looking for is a way to apply this sort of operation on NetCDF files directly.
"setwd('C:/Users/Asus/Documents/ArcGIS/ArcGIS - Copy/evaporation/Downscaling Files') getwd() ls() rm(list=ls()) install.packages(c("qmap", "zoo", "latticeExtra")) library(qmap) library(zoo) library(latticeExtra) obsprecip
I would suggest taking a look at the tidync package in R. You can take a look at an example with code in this page: https://ropensci.org/blog/2019/11/05/tidync/
Jonathan V. Solórzano Thanks a lot for your suggestion, I'll definitely check it out.
Python is an extremely easy to learn programming language, because it's a high level language (that is, it almost resembles natural language), unlike C++ or Assembler, which are closer to machine language.
There's also the ncdf4 package in R which normally works for me.
I've used the netcdf4 module in Python, but typically use Xarray to open files and then Pandas to manipulate the data.
I don't work with climate data but here are a couple of links to tutorials others have put together using Python on climate data:
https://nordicesmhub.github.io/climate-data-tutorial/03-visualization-python/
https://www.earthdatascience.org/courses/use-data-open-source-python/hierarchical-data-formats-hdf/use-netcdf-in-python-xarray/
Calculate and analysis:
I used xarray combine with python-cdo to deal with netcdf files. These two libraries solved most of my problems.
Plot and draw:
Those are some libraris recommend to you:
- Seaborn: “provides a high-level interface for drawing attractive statistical graphics.” Integrates well with pandas.
- HoloViews and GeoViews: “Composable, declarative data structures for building even complex visualizations easily.” Includes native support for xarray objects.
- hvplot: hvplot makes it very easy to produce dynamic plots (backed by Holoviews or Geoviews) by adding a hvplot accessor to DataArrays.
- Cartopy: Provides cartographic tools.
Cheng Shen Thanks a lot for your comprehensive return. May I ask you though how you did deal with operations like doing mathematical calculations between different NetCDF files? I'm asking this because I'm planning to downscale GCM/RCM values using "reanalysis" data.
Thanks again for your help
Best of luck
İbrahim
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