Preliminary validation metrics for temperature.

d1d73f78 · Frisinghelli Daniel · 87e4e1b8 · d1d73f78 · d1d73f78 · d1d73f78
Commit d1d73f78 authored 3 years ago by Frisinghelli Daniel
--- a/Notebooks/Figures/tasmax_average_bias.png
+++ b/Notebooks/Figures/tasmax_average_bias.png
--- a/Notebooks/Figures/tasmax_average_bias_p98.png
+++ b/Notebooks/Figures/tasmax_average_bias_p98.png
--- a/Notebooks/Figures/tasmax_average_bias_seasonal.png
+++ b/Notebooks/Figures/tasmax_average_bias_seasonal.png
--- a/Notebooks/Figures/tasmax_mean_annual_cycle.png
+++ b/Notebooks/Figures/tasmax_mean_annual_cycle.png
--- a/Notebooks/Figures/tasmax_r2.png
+++ b/Notebooks/Figures/tasmax_r2.png
--- a/Notebooks/Figures/tasmin_average_bias.png
+++ b/Notebooks/Figures/tasmin_average_bias.png
--- a/Notebooks/Figures/tasmin_average_bias_extreme.png
+++ b/Notebooks/Figures/tasmin_average_bias_extreme.png
--- a/Notebooks/Figures/tasmin_average_bias_p2.png
+++ b/Notebooks/Figures/tasmin_average_bias_p2.png
--- a/Notebooks/Figures/tasmin_average_bias_seasonal.png
+++ b/Notebooks/Figures/tasmin_average_bias_seasonal.png
--- a/Notebooks/Figures/tasmin_mean_annual_cycle.png
+++ b/Notebooks/Figures/tasmin_mean_annual_cycle.png
--- a/Notebooks/Figures/tasmin_r2.png
+++ b/Notebooks/Figures/tasmin_r2.png
--- a/Notebooks/eval_precipitation.ipynb
+++ b/Notebooks/eval_precipitation.ipynb
@@ -59,6 +59,7 @@
    "# builtins\n",
    "import datetime\n",
    "import warnings\n",
+    "import calendar\n",
    "\n",
    "# externals\n",
    "import xarray as xr\n",
@@ -107,13 +108,7 @@
   "source": [
    "# model predictions and observations NetCDF\n",
    "y_pred = TARGET_PATH.joinpath(PREDICTAND, '_'.join([MODEL, PREDICTAND, PPREDICTORS, *PLEVELS, SPREDICTORS, DEM, DOY]) + '.nc')\n",
-    "if PREDICTAND == 'tas':\n",
-    "    # read both tasmax and tasmin\n",
-    "    tasmax = xr.open_dataset(search_files(OBS_PATH.joinpath('tasmax'), '.nc$').pop())\n",
-    "    tasmin = xr.open_dataset(search_files(OBS_PATH.joinpath('tasmin'), '.nc$').pop())\n",
-    "    y_true = xr.merge([tasmax, tasmin])\n",
-    "else:\n",
-    "    y_true = xr.open_dataset(search_files(OBS_PATH.joinpath(PREDICTAND), '.nc$').pop())"
+    "y_true = xr.open_dataset(search_files(OBS_PATH.joinpath(PREDICTAND), '.nc$').pop())"
   ]
  },
  {

 %% Cell type:markdown id:4735431f-6741-437e-b0fc-dd6d8eaa22ca tags:

 # Evaluate ERA-5 downscaling

 %% Cell type:markdown id:a87da113-4b0f-4ac8-9721-19c85848acec tags:

 We used **1981-1991 as training** period and **1991-2010 as reference** period. The results shown in this notebook are based on the model predictions on the reference period.

 %% Cell type:markdown id:f9334da7-17d1-45ef-9ed9-5c2bee9fcdcc tags:

 Define the predictand and the model to evaluate:

 %% Cell type:code id:a81acde7-16a2-4087-bc08-95b084adbd06 tags:

 ``` python
 # define the model parameters
 PREDICTAND = 'pr'
 MODEL = 'USegNet'
 PPREDICTORS = 'ztuvq'
 PLEVELS = ['500', '850']
 SPREDICTORS = 'pt2'
 DEM = 'dem'
 DOY = 'doy'
 ```

 %% Cell type:markdown id:dd188df0-69ee-44b0-82b2-d212994dc271 tags:

 ### Imports

 %% Cell type:code id:06792bf2-b33b-4728-ba60-d60fab46779d tags:

 ``` python
 # builtins
 import datetime
 import warnings
+import calendar

 # externals
 import xarray as xr
 import numpy as np
 import matplotlib.pyplot as plt
 import scipy.stats as stats
 from IPython.display import Image

 # locals
 from climax.main.io import ERA5_PATH, OBS_PATH, TARGET_PATH
 from pysegcnn.core.utils import search_files
 ```

 %% Cell type:markdown id:a3d92474-2bc1-4035-8938-c5fbb07ae891 tags:

 ### Model architecture

 %% Cell type:code id:c68e022b-41c2-438b-bfb0-e27eddee89bf tags:

 ``` python
 Image("./Figures/architecture.png", width=900, height=400)
 ```

 %% Cell type:markdown id:5a0c55f0-79fb-4501-b3cf-b5414399a3d9 tags:

 ### Load datasets

 %% Cell type:code id:a5db133d-2c36-4e84-879e-20e617e821f1 tags:

 ``` python
 # model predictions and observations NetCDF
 y_pred = TARGET_PATH.joinpath(PREDICTAND, '_'.join([MODEL, PREDICTAND, PPREDICTORS, *PLEVELS, SPREDICTORS, DEM, DOY]) + '.nc')
-if PREDICTAND == 'tas':
-    # read both tasmax and tasmin
-    tasmax = xr.open_dataset(search_files(OBS_PATH.joinpath('tasmax'), '.nc$').pop())
-    tasmin = xr.open_dataset(search_files(OBS_PATH.joinpath('tasmin'), '.nc$').pop())
-    y_true = xr.merge([tasmax, tasmin])
-else:
-    y_true = xr.open_dataset(search_files(OBS_PATH.joinpath(PREDICTAND), '.nc$').pop())
+y_true = xr.open_dataset(search_files(OBS_PATH.joinpath(PREDICTAND), '.nc$').pop())
 ```

 %% Cell type:code id:528c3116-7707-45ca-b811-0adad7bc20f3 tags:

 ``` python
 # load datasets
 y_pred = xr.open_dataset(y_pred)
 y_true = y_true.sel(time=y_pred.time)  # subset to time period covered by predictions
 ```

 %% Cell type:code id:70b903cb-e597-45d3-b575-0ebaf7a45649 tags:

 ``` python
 # align datasets and mask missing values in model predictions
 y_true, y_pred = xr.align(y_true, y_pred, join='override')
 y_pred = y_pred.where(~np.isnan(y_true), other=np.nan)
 ```

 %% Cell type:markdown id:b269a131-cf5b-4c6c-9f8e-a5408250aa83 tags:

 ## Model validation: precipitation

 %% Cell type:code id:03ada1be-ae1a-4cdc-b396-e3bbc8f8a7ed tags:

 ``` python
 ```