Temperature and Salinty from CMEMS netcdf file for specific geographical location

Question

I want to get the temperature ['thetao'] and salinity ['so'] of the sea surface (just the top layer) for specific geographical location.

I found guidance for how to do this on this website .

import netCDF4 as nc
import numpy as np

fn = "\\...\...\Downloads\global-analysis-forecast-phy-001-024_1647367066622.nc"  # path to netcdf file

ds = nc.Dataset(fn)  # read as netcdf dataset

print(ds)
print(ds.variables.keys()) # get all variable names

temp = ds.variables['thetao']
sal = ds.variables['so']
lat,lon = ds.variables['latitude'], ds.variables['longitude']

# extract lat/lon values (in degrees) to numpy arrays
latvals = lat[:]; lonvals = lon[:]

# a function to find the index of the point closest pt
# (in squared distance) to give lat/lon value.
def getclosest_ij(lats,lons,latpt,lonpt):
  # find squared distance of every point on grid
  dist_sq = (lats-latpt)**2 + (lons-lonpt)**2
  # 1D index of minimum dist_sq element
  minindex_flattened = dist_sq.argmin()
  # Get 2D index for latvals and lonvals arrays from 1D index
  return np.unravel_index(minindex_flattened, lats.shape)

iy_min, ix_min = getclosest_ij(latvals, lonvals, 50., -140)
print(iy_min)
print(ix_min)


# Read values out of the netCDF file for temperature and salinity
print('%7.4f %s' % (temp[0,0,iy_min,ix_min], temp.units))
print('%7.4f %s' % (sal[0,0,iy_min,ix_min], sal.units))

Some details on the nc-file I am using:

dimensions(sizes): time(1), depth(1), latitude(2041), longitude(4320)
 variables(dimensions): float32 depth(depth), float32 latitude(latitude), int16 thetao(time, depth, latitude, longitude), float32 time(time), int16 so(time, depth, latitude, longitude), float32 longitude(longitude)
    groups:
dict_keys(['depth', 'latitude', 'thetao', 'time', 'so', 'longitude'])

I am getting this error:

dist_sq = (lats-latpt)**2 + (lons-lonpt)**2
ValueError: operands could not be broadcast together with shapes (2041,) (4320,)

I suspect there is an issue with the shapes/arrays. In the example of the website (link above) the Lat and Lon have a (x,y), however this NC file only has for Latitude (2041,) and for Longitude (4320,).

How can I solve this?

Answer 1

It's because the lats and lons are vectors with different size...

I usually do this if using WGS84 or degrees as unit:

lonm,latm = np.meshgrid(lons,lats)
dmat = (np.cos(latm*np.pi/180.0)*(lonm-lonpt)*60.*1852)**2+((latm-latpt)*60.*1852)**2

Now you can find the closest point:

kkd = np.where(dmat==np.nanmin(dmat))