estimation

notebooks/wp5/glacier_distribution.ipynb

@@ -388,6 +388,41 @@
     "for ax in facet.axs.flatten():\n",
     "    ax.grid()"
    ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "25",
+   "metadata": {},
+   "source": [
+    "# Under/over estimation"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "26",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "year = 2000\n",
+    "interpolated = ds[\"Cumulative\"].interp(time=means[\"weighted\"].to_xarray())\n",
+    "volume = 1.091 * (interpolated - ds[\"Cumulative\"].sel(time=year)).compute()\n",
+    "for region, result in volume.groupby(\"region\", squeeze=False):\n",
+    "    print(\n",
+    "        f\"The volume estimate in RGI region {region:>2}\"\n",
+    "        f\" for the year {year} AD is\"\n",
+    "        f\" {'under' if result <0 else 'over':>5}estimated\"\n",
+    "        f\" by {abs(float(result)):.2f} km3.\"\n",
+    "    )\n",
+    "\n",
+    "total = volume.sum()\n",
+    "print(\n",
+    "    f\"\\nThe volume estimate at the global scale\"\n",
+    "    f\" for the year {year} AD is\"\n",
+    "    f\" {'under' if result <0 else 'over'}estimated\"\n",
+    "    f\" by {abs(float(total)):.2f} km3.\"\n",
+    ")"
+   ]
   }
  ],
  "metadata": {