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Fix stats for climatology maps #977

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Dec 11, 2023
Merged

Fix stats for climatology maps #977

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30 changes: 20 additions & 10 deletions mpas_analysis/shared/plot/climatology_map.py
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Original file line number Diff line number Diff line change
Expand Up @@ -702,42 +702,52 @@ def plot_panel(ax, title, array, colormap, norm, levels, ticks, contours,
def _add_stats(modelArray, refArray, diffArray, Lats, axes):
""" compute the means, std devs. and Pearson correlation """
weights = np.cos(np.deg2rad(Lats))
modelMean = np.average(modelArray, weights=weights)

model_weights = weights
model_mask = None
if isinstance(modelArray, np.ma.MaskedArray):
# make sure we're using the MPAS land mask for all 3 sets of stats
model_mask = modelArray.mask
model_weights = np.ma.array(weights, mask=model_mask)

modelMean = np.average(modelArray, weights=model_weights)

_add_stats_text(
names=['Min', 'Mean', 'Max'],
values=[np.amin(modelArray), modelMean, np.amax(modelArray)],
ax=axes[0], loc='upper')

if refArray is not None:
ref_weights = weights
ref_mask = None
if isinstance(modelArray, np.ma.MaskedArray):
# make sure we're using the MPAS land mask for all 3 sets of stats
mask = modelArray.mask
if isinstance(refArray, np.ma.MaskedArray):
# mask invalid where either model or ref array is invalid
mask = np.logical_or(mask, refArray.mask)
refArray = np.ma.array(refArray, mask=mask)
ref_mask = np.logical_or(model_mask, refArray.mask)
ref_weights = np.ma.array(weights, mask=ref_mask)
refArray = np.ma.array(refArray, mask=ref_mask)
modelAnom = modelArray - modelMean
modelVar = np.average(modelAnom ** 2, weights=weights)
refMean = np.average(refArray, weights=weights)
modelVar = np.average(modelAnom ** 2, weights=ref_weights)
refMean = np.average(refArray, weights=ref_weights)
refAnom = refArray - refMean
refVar = np.average(refAnom**2, weights=weights)
refVar = np.average(refAnom**2, weights=ref_weights)

_add_stats_text(
names=['Min', 'Mean', 'Max'],
values=[np.amin(refArray), refMean, np.amax(refArray)],
ax=axes[1], loc='upper')

diffMean = np.average(diffArray, weights=weights)
diffVar = np.average((diffArray - diffMean)**2, weights=weights)
diffMean = np.average(diffArray, weights=ref_weights)
diffVar = np.average((diffArray - diffMean)**2, weights=ref_weights)
diffRMSE = np.sqrt(diffVar)

_add_stats_text(
names=['Min', 'Mean', 'Max'],
values=[np.amin(diffArray), diffMean, np.amax(diffArray)],
ax=axes[2], loc='upper')

covar = np.average(modelAnom*refAnom, weights=weights)
covar = np.average(modelAnom*refAnom, weights=ref_weights)

corr = covar/np.sqrt(modelVar*refVar)

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