Update adloc docs

State the adloc will be run for every iteration

docs/getting_started/howto/adaptive_localization.ipynb

@@ -169,19 +169,18 @@
     "## Adaptive-Localization - Pearson Correlation as measure of closeness\n",
     "\n",
     "As mentioned, the difference between distance-based localization and adaptive-localization is the measure of closeness.\n",
-    "Otherwise, the approach is similar to distance-based localization.\n",
+    "Otherwise, the approaches are similar.\n",
     "\n",
     "- **Adaptive Localization step-by-step:**\n",
     "    - Pick a subset of adjacent parameters\n",
     "    - Pick responses that are significantly correlated to the parameters\n",
     "    - Do the update using only observations that correspond to these responses\n",
     "\n",
-    "The primary advantage of adaptive localization is its user-friendliness.\n",
-    "Users only need to specify the `correlation threshold`.\n",
-    "Often, the default `correlation threshold` calculated by `ERT` is reasonable.\n",
+    "The primary advantage of adaptive localization is its user-friendliness as users only need to (optionally) specify the `correlation threshold`.\n",
     "\n",
     "To activate adaptive localization in `ERT`, simply check the `Adaptive localization correlation threshold` checkbox.\n",
-    "The correlation threshold can then be set, though a sensible default will be determined based on ensemble size.\n",
+    "This activates adaptive localization for the entire experiment, meaning it will run for every iteration of, say, `ES-MDA`.\n",
+    "While the correlation threshold can be manually set, `ERT` determines a sensible default based on the size of the ensemble.\n",
     "At a correlation threshold of 0.0 the update will be identical to no localization, while at a correlation threshold of 1.0 the posterior will be identical to the prior, and there will be no update.\n",
     "\n",
     "![](ert_screenshot_adaptive_loc.png)"
@@ -196,8 +195,10 @@
     "\n",
     "Be aware that adaptive localization comes with significant computational costs, which depend on two factors:\n",
     "\n",
-    "    - The number of parameters: More parameters lead to longer run times.\n",
-    "    - The correlation threshold: A higher threshold reduces computational time.\n"
+    "- The number of parameters: More parameters lead to longer run times.\n",
+    "- The correlation threshold: A higher threshold reduces computational time. \\\n",
+    "    Increasing the threshold makes it less likely that a parameter and a response are considered significantly correlated. \\\n",
+    "    This makes it less likely that a parameter needs to be updated at all which reduces the computational cost.\n"
    ]
   },
   {