sondage can now open the ACCESS-ESM1.6 historical monthly ocean temperature store as a volume layer (access-esm-temp, added in #17): https://data.source.coop/ausantarctic/ausantarctic-scratch/acces...

A Python package & CLI tool that assists users create automated ETL pipelines for various types of real-time and archived weather data & supports GRIB2 -> NETCDF. WxData can access 20+ W...

Download RasterScope: NetCDF Viewer by Simularia s.r.l. on the App Store. See screenshots, ratings and reviews, user tips, and more apps like RasterScope:…

A Python package & CLI tool that assists users create automated ETL pipelines for various types of real-time and archived weather data & supports GRIB2 -> NETCDF. WxData can access 20+ Weather Models,...

YouTube video by Nitilo

Converting legacy scientific data into netCDF4, with an AMSR-E sea-ice example.

I have read a Dataset in NC file using netCDF4 in python and then tried to plot the dataset using matplotlib.pyplot. But issue is the plot shows the image but with 90 Degrees clockwise rotation. Following code I have used: from netCDF4 import Dateset import matplotlib.pyplot as plt f = Dataset(r'F:\data\data.nc', 'r') data = f['et'][:] plt.imshow(data) plt.show() One doubt I am having, I’d that I have 2 NC files having same dataset and in that, dataset in one file is getting properly displayed by using np.rot90(data) which was suggested to me in the answer given below and in another file I need not to do "np.rot90(data)" but instead I need to do "data[::-1]" to display the data correctly and interesting thing is when I am importing these 2 files directly in the ArcMap both are getting properly displayed. Is the issue with NC data?

I'm helping an undergrad with her dissertation. She has monthly mean data from ERA-5 for three variables in netcdf format for the past 20 years. She wants a CSV or Excel file which contains the zonal statistics for the shapefile covering her region only for the three variables for four statistical tests: mean, min, max and variance. Each row would have a date column, then 12 further columns with mean/min/max/variance for the three variables. Thus, 13 columns. Normally I capture the GDAL command from QGIS and compose a Linux shell script (I am very experienced writing bash shell scripts including awk and grep - I don't need help with this). I don't see a non Python-GDAL solution for calculating these quantities. Ideally I'd like to avoid using Python code in favour of a GDAL command-line-only command I could use in a Linux shell script -- a GDAL command which takes as input the netcdf file (the raster in this instance) and the region of interest as a shapefile, then outputs four statistical quantities for me to capture to place into a CSV file using awk/grep/etc. Does this functionality exist in non-Python GDAL?