I am trying to calculate the spearman's rank correlation co-efficient between two datasets. However, the one dataset is a gridded dataset (with latitude and longitude coordinates) and the other is simply a time series (please see below).
print(spi_djf_cor)
<xarray.DataArray 'pre' (time: 41, lat: 162, lon: 162)>
array([[[ nan, nan, nan, ..., nan,
nan, nan],
...,
[ nan, nan, nan, ..., -0.19086839,
-0.31531059, -0.32325101],
[ nan, nan, nan, ..., -0.30278464,
-0.37735563, -0.43342958],
[ nan, nan, nan, ..., -0.5057808 ,
-0.49130144, -0.69925514]]])
Coordinates:
* time (time) datetime64[ns] 1980-12-01 1981-12-01 ... 2020-12-01
* lon (lon) float64 -20.25 -19.75 -19.25 -18.75 ... 59.25 59.75 60.25
* lat (lat) float64 -40.25 -39.75 -39.25 -38.75 ... 39.25 39.75 40.25
print(nino_djf)
<xarray.DataArray 'ANOM' (time: 41)>
array([-0.31666667, -0.08 , 2.16333333, -0.63 , -1.07333333,
-0.59333333, 1.13 , 0.70666667, -1.8 , 0.03 ,
0.38666667, 1.77 , 0.13333333, 0.10666667, 1. ,
-0.89 , -0.51333333, 2.23333333, -1.56666667, -1.68666667,
-0.76 , -0.2 , 0.87 , 0.31 , 0.58666667,
-0.83333333, 0.65666667, -1.64333333, -0.84666667, 1.50333333,
-1.41666667, -0.86666667, -0.43333333, -0.42666667, 0.54666667,
2.49666667, -0.33666667, -0.91333333, 0.75 , 0.49666667,
-1.05 ])
Coordinates:
* time (time) datetime64[ns] 1980-12-01 1981-12-01 ... 2020-12-01
I would like to correlate the time series at each grid point in spi_djf_cor with the time series in nino_djf. How do I do this? I don't think using the below code is giving the correct output as I don't think it is correlating at each grid point...
xs.spearman_r(spi_djf_cor, nino_djf, dim='time')
