I am new to NetCDF files. I have daily data on global sea surface temperature from 1981-2014 from NOOA's AVHRR Pathfinder data version 5.3. The dataset consists of 365x2 .nc files for each year: one each for night and day temperatures for each day in a given year.
I wish to merge the various daily .nc files of each year into one annual .nc file for each year. What is the most efficient way of doing this? I have explored the nco package using Anaconda but I'm running into problems. Any help would be greatly appreciated!
I would recommend program "cdo" and more precisely operator "mergetime".
For example:
cdo mergetime ncfile1 ncfile2 outputfile
Related
I am trying to create a time series object from extracted climate data (NEX-GDDP) using the Google Earth Engine (GEE). The data is daily metrological data, and in the attached file, the data for January, 2005, is collected over an area of interest. The images from GEE are stored in the geotiff as bands (numbered 1-31), and now I am struggling to get these individual bands into a dataset, and add a time dimension to the file. GEE will not export for more than ten years, so my idea is to create yearly files, which, when saved locally, will be merged (concatenated) on the lat/lon and time dimensions.
I am using python in a windows environment, so I am a bit limited (for example, I can't use cdo as this is a Linux based library), and I think that what I would like to do is possible with xarray, but I am missing the (learning) resources to solve this problem with code. Any help and suggestions are more than welcome to help me with this problem.
The image shows the xarray view of the metadata:
xarray metadata view
Showing one band: Test data
Missing the time dimension.
This got resolved on another thread in a special group:
https://gis.stackexchange.com/questions/449759/convert-a-multiband-geotiff-to-a-timeseries-netcdf-file-in-xarray
I have a 6-hourly snow depth dataset from 1980-2022 in the form of NetCDF. Is there anyone who has a suggestion or example code for converting 6-hourly to the daily dataset NetCDF file with R? I searched many times for this issue but there is no example (except for NCO, NCL, python, or MATLAB). But I wanna make this in a NetCDF file by R.
I am looking to extract WorldClim climate data for (current data and future projections) and convert the .geotif to .asc in order to run this through MaxEnt and create future climate change projections.
Problem 1: Worldclim gives me 1 .geotif from which I need to extract 19 separate variables, each as their own .geotif file.
Problem 2: Converting these .geotif files into .asc to run using MaxEnt.
I have access to free GIS software (QGIS/DIVA-GIS) and R, although I am fairly new to R. Any solutions would be really helpful, thank you.
Is there a possibility to read a subset of a NetCDF file in R?
In Matlab, I can use
ncread(filename,'WS',[i_timeseries,j_timeseries,1,1],[1,1,1,48])
I am trying to extract a time series from the New European Wind Atlas using free software (preferably R) on Windows.
Many thanks indeed.
I'm working in R trying to use the data found here (https://datadryad.org/resource/doi:10.5061/dryad.dk1j0; two top files) to create a table similar to this: [administrative_name, GDP2010, GDP2011....., GDP 2015]
As far as i can see i need to extract the name of the administrative units from the "admin_areas_GDP_HDI.nc" file and combine them with the annual data in the GDP_per_capita_PPP_1990_2015.nc file.
With the ncdf4 package i've managed to open the archives, and to get all the attributes and variables, however I don't know how to access the data and extract it.
I've been trying to access the data all day, but i have limited experience with NetCDF archives, and have not managed to extract the data. Any pointers would help me out!
I like to use the raster package for dealing with NetCDF files. It uses the ncdf4 package to read in the files, but offers some additional tools for processing rasters. You did not mention what data you want to extract, so the example below shows the mean GDP for each administrative unit.
library(raster)
#Read in NetCDF files
ad -> brick('admin_areas_GDP_HDI.nc')
gdp -> brick('GDP_per_capita_PPP_1990_2015_v2.nc')
#Calculate mean GDP using admin zones
zoneMean -> zonal(gdp, ad[[1]], fun='mean', na.rm=T)