My goal is to prepare a script in python or R to work with netCDF files having the extension .nc4, example Daily GPM (Global Precipitation Measurement) precipitation data (Late Run) into CSV or geotiff.
I would appreciate any advice to make a code to handle .nc4 files properly.
Are there any differences in how .nc4 files should be opened, read, or converted compared to .nc files? Are there any additional libraries or packages I should use for this purpose?
In Python, you can use the netCDF4 library, which provides a powerful interface for reading and manipulating NetCDF files, including .nc4 files. In R, you can use the ncdf4 package, which provides similar functionality.
This example may help you (Python)
import netCDF4 as nc
import pandas as pd
import numpy as np
from osgeo import gdal
# Open the .nc4 file
dataset = nc.Dataset('path/to/your/file.nc4', mode='r')
# Access the variables and dimensions in the NetCDF file
# Example: assuming you have a variable named 'precipitation' and dimensions 'lat', 'lon', and 'time'
precipitation = dataset.variables['precipitation'][:]
lat = dataset.variables['lat'][:]
lon = dataset.variables['lon'][:]
time = dataset.variables['time'][:]
# Convert the precipitation data to a pandas DataFrame
precipitation_df = pd.DataFrame(precipitation, columns=['precipitation'])
precipitation_df['lat'] = lat
precipitation_df['lon'] = lon
precipitation_df['time'] = pd.to_datetime(time, unit='hours since 1970-01-01')
# Save the precipitation data to a CSV file
precipitation_df.to_csv('precipitation.csv', index=False)
# Convert the precipitation data to a GeoTIFF file
driver = gdal.GetDriverByName('GTiff')
output_file = driver.Create('precipitation.tif', len(lon), len(lat), 1, gdal.GDT_Float32)
output_file.GetRasterBand(1).WriteArray(precipitation)
output_file.SetGeoTransform((np.min(lon), lon[1]-lon[0], 0, np.max(lat), 0, lat[1]-lat[0]))
output_file.FlushCache()
output_file = None
# Close the NetCDF dataset
dataset.close()
Thank-you dear Mohamed Akl ..!! Let me give it a try will surely revert you back.
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