Merge branch 'master' into dev

.github/workflows/build-pages.yml

@@ -36,6 +36,8 @@ jobs:
           ${PIP} install -r requirements.txt
           ${PIP} uninstall -y progressbar2
       - name: Build Book
+        env:
+          STATIC_MAP_API_KEY: ${{ secrets.STATIC_MAP_API_KEY }}
         run: |
           jupyter-book build .
       - name: Upload Build Artifacts

notebooks/tutorials/massbalance_calibration.ipynb

@@ -902,7 +902,7 @@
    "outputs": [],
    "source": [
     "# calibrate the melt_f and annual MB \n",
-    "# Create a dataframe with precip factor going from 0.1 to 0.5 in 0.3 steps\n",
+    "# Create a dataframe with precip factor going from 0.1 to 5.0 in 0.3 steps\n",
     "pd_prcp_fac_sens = pd.DataFrame(index=np.arange(0.1,5.0,0.3))\n",
     "# Calibrate the melt factor for each of these\n",
     "spec_mb_prcp_fac_sens_dict = {}\n",
@@ -1260,9 +1260,9 @@
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "Using this data-informed precipitation factor, with then do a global calibration where the temperature bias (`temp_bias`) is calibrated, while the melt factor (`melt_f`) is fixed at 5 kg m-2 day-1 K-1 (default value based on [Schuster et al., 2023](https://doi.org/10.1017/aog.2023.57)). \n",
+    "Using this data-informed precipitation factor, we then do a global calibration where the temperature bias (`temp_bias`) is calibrated, while the melt factor (`melt_f`) is fixed at 5 kg m-2 day-1 K-1 (default value based on [Schuster et al., 2023](https://doi.org/10.1017/aog.2023.57)). \n",
     "\n",
-    "The idea is that if many glaciers within the same grid point need a temperature bias to reach the obeserved MB, this indicates that a systematic correction is necessary (at least for this MB model in particular). In fact, we can plot the median bias required to match MB pbservations using this technique, which gives us the following plot:\n",
+    "The idea is that if many glaciers within the same grid point need a temperature bias to reach the obeserved MB, this indicates that a systematic correction is necessary (at least for this MB model in particular). In fact, we can plot the median bias required to match MB observations using this technique, which gives us the following plot:\n",
     "\n",
     "![err](https://user-images.githubusercontent.com/10050469/224318400-ec1d8825-d7e7-4cdb-94f3-ebb95b8f7120.jpg)"
    ]
@@ -1271,7 +1271,7 @@
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "The fact that the `temp_bias` parameter is spatially correlated (many regions are all blue or red) indicate that something in the data needs to be corrected for our model. It is this imformation that we use to inform the next step.\n",
+    "The fact that the `temp_bias` parameter is spatially correlated (many regions are all blue or red) indicate that something in the data needs to be corrected for our model. It is this information that we use to inform the next step.\n",
     "\n",
     "**The code we used for this step is available [here](https://cluster.klima.uni-bremen.de/~oggm/gdirs/oggm_v1.6/calibration/1.6.1/). As explained above, we do a global run with fixed precip factor and melt factor, then store the resulting parameters in a csv file used by OGGM. The csv file can be found [here](https://cluster.klima.uni-bremen.de/~oggm/ref_mb_params/oggm_v1.6/w5e5_temp_bias_v2023.4.csv).**\n",
     "\n",
@@ -1295,7 +1295,7 @@
    "source": [
     "Finally, we now run [mb_calibration_from_scalar_mb](https://docs.oggm.org/en/latest/generated/oggm.tasks.mb_calibration_from_scalar_mb.html) again for each glacier, as follows:\n",
     "- use the first guess: `melt_f` = 5, `prcp_fac` = data-informed from step 1, `temp_bias` = data-informed from step 2\n",
-    "- if this doesn't match (this would be highly unlikely), allow `prcp_fac` to vary again between 0.8 and 1.2 times the roiginal guess ($\\pm$20%). This is justified by the fact that the first guess for precipitation is also highly uncertain. If that worked, the calibration stops (33.6% of all glaciers worldwide are calibrated this way, for 41.1% of the total area).\n",
+    "- if this doesn't match (this would be highly unlikely), allow `prcp_fac` to vary again between 0.8 and 1.2 times the original guess ($\\pm$20%). This is justified by the fact that the first guess for precipitation is also highly uncertain. If that worked, the calibration stops (33.6% of all glaciers worldwide are calibrated this way, for 41.1% of the total area).\n",
     "- if the above did not work, allow `melt_f` to vary again. If that worked, the calibration stops (60.6% of all glaciers worldwide are calibrated this way, for 41.1% of the total area).\n",
     "- finally, if the above did not work, allow `temp_bias` to vary again (5.9% of all glaciers worldwide are calibrated this way, for 2.2% of the total area).\n",
     "\n",