modified the notebook

Satellite_ECVs/Atmosphere_Physics/satellite_satellite-cloud-properties_validation_q02.ipynb

@@ -48,11 +48,11 @@
     "\n",
     "It is important to note that passive remote sensing has limitations: it provides a top-down perspective, which does not account for hidden cloud layers, and it estimates the 'radiative' cloud height. This radiative height can be several kilometers below the actual cloud top, especially in cases where clouds have diffuse tops, with optical depth gradually increasing from the top down over a significant vertical range. This phenomenon is particularly relevant for approximately 70% of high clouds in the tropics and 30-40% of high clouds in the midlatitudes [[2]](https://doi.org/10.1029/94JD02430).\n",
     "\n",
-    "These are the key outcomes of this assessment:\n",
+    "```{admonition} These are the key outcomes of this assessment\n",
     "\n",
     "- All datasets show more low-level clouds over ocean than over land, as expected.\n",
     "\n",
-    "- The CCI dataset produces by far the least realistic cloud distributions among the evaluated products.\n",
+    "- The CCI dataset produces by far the least realistic cloud distributions among the evaluated products, by strongly underestimating the amount of high-level clouds (no peak in the upper troposphere). Since the channels of the AATSR and AVHRR instruments are similar, the reason lies in the retrieval method, based on optimal estimation and the assumption of a single layer cloud. The simultaneous use of infrared and visible radiances leads in the case of semi-transparent cirrus above lower cloud layers to a cloud height underestimation.\n",
     "\n",
     "- The two retrieval versions of the same product CLARA give very different results. While the A2 version uses a classical retrieval approach, based on radiative transfer, the A3 version uses a neural network approach trained with active lidar data from CALIPSO. This leads to retrievals of ‘radiative’ cloud height for CLARA-A2 and cloud top height for CLARA-A3. Furthermore, the CLARA-A3 product also include very thin (sub-visible) cirrus clouds above the tropopause layer, which a passive sensor like AVHRR is not able to detect, in particular stratospheric clouds in polar regions during winter.\n",
     "\n",
@@ -64,7 +64,7 @@
     "\n",
     "- While CLARA-A2 does not provide uncertainty estimates, the uncertainty distributions of CLARA-A3 peak around 20 hPa over the whole globe, with broader distributions for low-level clouds than for high-level clouds and a secondary peak around 100 hPa over land, which is due to gridboxes with only a fewer cloudy observations.\n",
     "\n",
-    "\n",
+    "```\n",
     "\n",
     "\n",
     "\n",
@@ -604,7 +604,7 @@
    "id": "28",
    "metadata": {},
    "source": [
-    "The distribution of daytime cloud top pressure uncertainty is very surprising for a publicly distributed dataset. Only a small fraction of the values fall within an acceptable range, with a peak around 20 hPa. The majority of the values are either 600 hPa or 1000 hPa. This  is observed across all regions, both over ocean and land (not shown). Therefore, users are advised not to use these data."
+    "The distribution of daytime cloud top pressure uncertainty is very surprising for a publicly distributed dataset. Only a small fraction of the values fall within an acceptable range, with a peak around 20 hPa. The majority of the values are either 600 hPa or 1000 hPa. This  is observed across all regions, both over ocean and land (not shown). Therefore, users are advised to use these data with caution."
    ]
   },
   {