import xarray as xr
import numpy as np
import pandas as pd

# Create temperature data with missing values
temperature = np.random.rand(365, 50, 50) * 20 + 10
temperature[0:10, :, :] = np.nan  # Set the first 10 days as missing values

# Create time, latitude, and longitude coordinate arrays
times = pd.date_range('2023-01-01', periods=365, freq='D')
latitudes = np.linspace(-90, 90, 50)
longitudes = np.linspace(-180, 180, 50)

# Create an Xarray data array with missing values
da = xr.DataArray(
    temperature,
    dims=['time', 'latitude', 'longitude'],
    coords={'time': times, 'latitude': latitudes, 'longitude': longitudes}
)

# Count the number of missing values along the time dimension
missing_count = da.isnull().sum(dim='time')

# Print missing values
print(missing_count)

>>>

<xarray.DataArray (latitude: 50, longitude: 50)>
array([[10, 10, 10, ..., 10, 10, 10],
       [10, 10, 10, ..., 10, 10, 10],
       [10, 10, 10, ..., 10, 10, 10],
       ...,
       [10, 10, 10, ..., 10, 10, 10],
       [10, 10, 10, ..., 10, 10, 10],
       [10, 10, 10, ..., 10, 10, 10]])
Coordinates:
  * latitude   (latitude) float64 -90.0 -86.33 -82.65 ... 82.65 86.33 90.0
  * longitude  (longitude) float64 -180.0 -172.7 -165.3 ... 165.3 172.7 180.0