Calculate departure or anomaly of a value between two arrays of different geographic grid sizes

问题

I have a technical question, which I tried to solve all week long. I created a netcdf file from observations with a measurement value of air quality on a geographical grid (lat/lon) along a certain track. Now I would like to calculate the departure (or anomaly) of these values from a larger grid (data from a computer model with mean values over a large area).

My two netcdf files are structured as follows:

Observations (Instrument measurements):

Dimensions:

lat: 1321, lon: 1321

Data variables:

Longitude (lon) float64 8.413 8.411 8.409 ... 4.904 4.905
Latitude (lat) float64 47.4 47.4 47.41 ... 52.37 52.37
obs_data (lat, lon) float64 ...

Model data:

Dimensions:

latitude: 140, level: 1, longitude: 215, time: 24

Coordinates:

longitude  (longitude)  float32    357.55 357.65 ... 18.85 18.95 
latitude   (latitude)   float32    55.95 55.85 55.75 ... 42.15 42.05    
level      (level)      float32    0.0
time       (time)    timedelta64[ns]    00:00:00 01:00:00 ... 23:00:00

Data variables:

model_data (time, level, latitude, longitude) float32 ...

I tried all sorts of different approaches, but every time I run into some sort of error for which there appears to be no solution and I ended up having to try a different approach. The closest I got was by following this great tutorial, but also here I hit a wall. When I try to find the nearest lat and lon for the two data sets, by

lat_idx = np.abs(model_lat - obs_lat).argmin() #subtract train lat from model lat
lon_idx = np.abs(model_lon - obs_lon).argmin() #subtract train lon from model lon

I get the follwing error

---------------------------------------------------------------------------
ValueError                                Traceback (most recent call last)
<ipython-input-437-9396b00ba22f> in <module>
     18 
     19 # Find the nearest latitude and longitude for the train data
---> 20 lat_idx = np.abs(model_lat - obs_lat).argmin()
     21 lon_idx = np.abs(model_lon - obs_lon).argmin()
     22 

~/opt/anaconda3/lib/python3.7/site-packages/numpy/ma/core.py in __sub__(self, other)
   4115         if self._delegate_binop(other):
   4116             return NotImplemented
-> 4117         return subtract(self, other)
   4118 
   4119     def __rsub__(self, other):

~/opt/anaconda3/lib/python3.7/site-packages/numpy/ma/core.py in __call__(self, a, b, *args, **kwargs)
   1024         with np.errstate():
   1025             np.seterr(divide='ignore', invalid='ignore')
-> 1026             result = self.f(da, db, *args, **kwargs)
   1027         # Get the mask for the result
   1028         (ma, mb) = (getmask(a), getmask(b))

ValueError: operands could not be broadcast together with shapes (140,) (1321,)

Isn't there a way to simply calculate:

anomaly = model_data[lat, lon] - obs_data[lat, lon]

?

My newest hope is xarray, but I really struggle with their documentation and I've spent days figuring out a way forward.

Has anyone of you found a solution to this problem? Any tips are really appreciated.

Edit:

As requested by V. Ayrat :

In: type(model_data)
Out: xarray.core.dataset.Dataset

obs_data is the same type.

If two obs_data values fall into the same model_data cell, the obs_data should be subtracted from the same model_data cell.

回答1:

It is not entirely clear what you are trying to do or what data structures you use. I will edit post if there is more info coming later. However, I think this solves the problem:

If you want closest lat/lon of obs_lat to model_lat use:

lat_idx = np.abs(model_lat - obs_lat[:,None]).argmin(axis=0)
lon_idx = np.abs(model_lon - obs_lon[:,None]).argmin(axis=0)

And if you want closest lat/lon of model_lat to obs_lat use:

lat_idx = np.abs(model_lat - obs_lat[:,None]).argmin(axis=1)
lon_idx = np.abs(model_lon - obs_lon[:,None]).argmin(axis=1)

来源：https://stackoverflow.com/questions/62253106/calculate-departure-or-anomaly-of-a-value-between-two-arrays-of-different-geogra