updated default surface spatial model to use lanczos downscaling. mor…

…e random error but less systematic error

sup3r/models/surface.py

@@ -46,7 +46,7 @@ class SurfaceSpatialMetModel(AbstractInterface):
 
  def __init__(self, features, s_enhance, noise_adders=None,
  temp_lapse=None, w_delta_temp=None, w_delta_topo=None,
- pres_div=None, pres_exp=None, interp_method='BILINEAR',
+ pres_div=None, pres_exp=None, interp_method='LANCZOS',
  fix_bias=True):
  """
  Parameters
@@ -92,6 +92,8 @@ def __init__(self, features, s_enhance, noise_adders=None,
  interp_method : str
  Name of the interpolation method to use from PIL.Image.Resampling
  (NEAREST, BILINEAR, BICUBIC, LANCZOS)
+ LANCZOS is default and has been tested to work best for
+ SurfaceSpatialMetModel.
  fix_bias : bool
  Some local bias can be introduced by the bilinear interp + lapse
  rate, this flag will attempt to correct that bias by using the
@@ -241,7 +243,7 @@ def _get_temp_rh_ind(self, idf_rh):
  return idf_temp
 
  def _fix_downscaled_bias(self, single_lr, single_hr,
- method=Image.Resampling.BILINEAR):
+ method=Image.Resampling.LANCZOS):
  """Fix any bias introduced by the spatial downscaling with lapse rate.
 
  Parameters
@@ -252,7 +254,7 @@ def _fix_downscaled_bias(self, single_lr, single_hr,
  single_hr : np.ndarray
  Single timestep downscaled raster data with shape
  (lat, lon) matching the high-resolution input data.
- method : Image.Resampling.BILINEAR
+ method : Image.Resampling.LANCZOS
  An Image.Resampling method (NEAREST, BILINEAR, BICUBIC, LANCZOS).
  NEAREST enforces zero bias but makes slightly more spatial seams.
 
@@ -272,7 +274,7 @@ def _fix_downscaled_bias(self, single_lr, single_hr,
  return single_hr
 
  @staticmethod
- def downscale_arr(arr, s_enhance, method=Image.Resampling.BILINEAR):
+ def downscale_arr(arr, s_enhance, method=Image.Resampling.LANCZOS):
  """Downscale a 2D array of data Image.resize() method
 
  Parameters
@@ -282,9 +284,9 @@ def downscale_arr(arr, s_enhance, method=Image.Resampling.BILINEAR):
  (lat, lon)
  s_enhance : int
  Integer factor by which the spatial axes are to be enhanced.
- method : Image.Resampling.BILINEAR
+ method : Image.Resampling.LANCZOS
  An Image.Resampling method (NEAREST, BILINEAR, BICUBIC, LANCZOS).
- BILINEAR is default and has been tested to work best for
+ LANCZOS is default and has been tested to work best for
  SurfaceSpatialMetModel.
  """
  im = Image.fromarray(arr)

tests/test_surface_model.py

@@ -67,11 +67,11 @@ def test_surface_model(s_enhance=5):
  diff = true_hi_res - hi_res
 
  # high res temperature should have very low bias and MAE < 1C
- assert np.abs(diff[..., 0].mean()) < 1e-6
+ assert np.abs(diff[..., 0].mean()) < 1e-4
  assert np.abs(diff[..., 0]).mean() < 5
 
  # high res relative humidity should have very low bias and MAE < 3%
- assert np.abs(diff[..., 1].mean()) < 1e-6
+ assert np.abs(diff[..., 1].mean()) < 1e-4
  assert np.abs(diff[..., 1]).mean() < 2
 
  # high res pressure should have very low bias and MAE < 200 Pa