Rory's review

Author: justbia

tropospheric-humidity-from-radio-occultation.ipynb

@@ -56,15 +56,24 @@
      "slide_type": ""
     },
     "tags": [
-     "objectives"
+     "introduction"
     ]
    },
    "source": [
     "## Introduction\n",
     "\n",
-    "In this tutorial we will work with monthly mean tropospheric humidity data from the EUMETSAT ROM SAF available in the Climate Data Store (CDS), demonstrating how the data can be accessed, and inspecting the data to get a quick view on the physical variables available and the data structures used. We will also have a closer look at some **_use cases_** that demonstrate a few useful applications and processing steps. The use cases include some plotting that will teach you about the humidity data and how it can be visualized.\n",
+    "Atmospheric humidity plays an important role in the Earth's climate system, both for its strong greenhouse effect but also for its role in the global energy transport. It is central to the hydrological cycle and sets the fundamental conditions for the biosphere, including distribution of rainfall and droughts.\n",
     "\n",
-    "Atmospheric humidity plays an important role in the Earth's climate system, both for its strong greenhouse effect but also for its role in the global energy transport. It is central to the hydrological cycle and sets the fundamental conditions for the biosphere, including distribution of rainfall and droughts."
+    "In this tutorial we will work with monthly mean tropospheric humidity data from the EUMETSAT ROM SAF available in the Climate Data Store (CDS), demonstrating how the data can be accessed, and inspecting the data to get a quick view on the physical variables available and the data structures used. We will also have a closer look at some **_use cases_** that demonstrate a few useful applications and processing steps. The use cases include some plotting that will teach you about the humidity data and how it can be visualized."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Prepare your environment\n",
+    "\n",
+    "Before we begin we must prepare our environment. This includes installing the Application Programming Interface (API) of the CDS, and importing the various python libraries that we will need."
    ]
   },
   {
@@ -80,15 +89,13 @@
     ]
    },
    "source": [
-    "## Prepare your environment\n",
-    "\n",
     "### Set up CDSAPI and your credentials\n",
     "\n",
     "We are using `cdsapi` to download the data and `nc-time-axis` for improved handling of time coordinates in Xarray datasets.\n",
     "\n",
     "These packages are not yet included by default on most cloud platforms. You can use `pip` to install it, we use an exclamation mark to pass the command to the shell (not to the Python interpreter).\n",
     "\n",
-    "If you have not setup your ~/.cdsapirc file with your credenials, you can replace None with your credentials that can be found on the how to api page (you will need to log in to see your credentials)."
+    "If you have not setup your ~/.cdsapirc file with your credentials, you can replace None with your credentials that can be found on the how to api page (you will need to log in to see your credentials)."
    ]
   },
   {
@@ -1454,7 +1461,7 @@
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "An alternative to the built-in Xarray plotting functions is to use [Matplotlib](https://matplotlib.org/) plotting tools that operate on Numpy arrays. Here, we repeat the monthly mean humidity plot using Matplotlib. The _contourf_ function gives a less \"noisy\" plot than _pcolormesh_, and have access to more plot options, but may require some experimentation. It is also possible to save the plot as a png file."
+    "An alternative to the built-in Xarray plotting functions is to use [Matplotlib](https://matplotlib.org/) plotting tools that operate on Numpy arrays. Here, we repeat the monthly mean humidity plot using Matplotlib. The _contourf_ function gives a less \"noisy\" plot than _pcolormesh_, and we have access to more plot options, but it may require some experimentation. It is also possible to save the plot as a png file."
    ]
   },
   {
@@ -1513,7 +1520,7 @@
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "We find that the humidity has a peak in the lower troposphere near the equator. As we will se below (Use case 3) this peak moves seasonally back and forth in the north-south direction, as the Inter-Tropical Convergence Zone (ITCZ) moves through the seasons. We will also see (Use case 4) that the ENSO phenomenon in the Pacific has a dominating impact on the global humidity.\n",
+    "We find that the humidity has a peak in the lower troposphere near the equator. As we will see below (Use case 3) this peak moves seasonally back and forth in the north-south direction, as the Inter-Tropical Convergence Zone (ITCZ) moves through the seasons. We will also see (Use case 4) that the ENSO phenomenon in the Pacific has a dominating impact on the global humidity.\n",
     "\n",
     "### Monthly standard deviation\n",
     "\n",
@@ -1592,7 +1599,7 @@
     "\n",
     "Many observational data records are not available for the full 30-year reference period recommended by the WMO. We have to work with shorter reference periods, limited by the availability of data.\n",
     "\n",
-    "To compute a climate normal we first define a certain reference period, and limit the data arrays to that period:"
+    "To compute a climate normal, we first define a certain reference period, and limit the data arrays to that period:"
    ]
   },
   {
@@ -2454,7 +2461,7 @@
     "\n",
     "We find that the anomalies are mostly within about 10-15% of the normal seasonal cycle, occasionally and regionally extending to 20-30% or more. Some years (e.g., 2008) are globally drier and some years (e.g., 2016) are more humid. An important factor for the global humidity is the equatorial Pacific with the El Niño Southern Oscillation (ENSO) cycles, which is further explored in Use case 4 below. \n",
     "\n",
-    "You can change normal, from mean seasonal cycle to long-term climatology by using the variable 'da_famean' instead of 'da_desfamean'. You may also need to change the span of the colorbar from 30% to 100% to accommodate the larger deviations from the normal. Doing this for any year reveal the strong seasonal cycle of the specific humidity, where the change of sign of the anomaly in a hemisphere occurs in May and November. The seasonal variations of the humidity is further explored in Use case 3 below.\n"
+    "You can change normal, from mean seasonal cycle to long-term climatology by using the variable 'da_famean' instead of 'da_desfamean'. You may also need to change the span of the colorbar from 30% to 100% to accommodate the larger deviations from the normal. Doing this for any year reveals the strong seasonal cycle of the specific humidity, where the change of sign of the anomaly in a hemisphere occurs in May and November. The seasonal variations of the humidity is further explored in Use case 3 below.\n"
    ]
   },
   {
@@ -2851,7 +2858,7 @@
    "source": [
     "## Use case 4:  global humidity and the El Niño Southern Oscillation\n",
     "\n",
-    "To proceed with this Use case you first need to go through Use case 3, as those time series are used here.\n",
+    "To proceed with this Use case, you first need to go through Use case 3, as those time series are used here.\n",
     "\n",
     "The El Niño-Southern Oscillation (ENSO) is a recurring phenomenon in the central and eastern tropical Pacific Ocean. On periods ranging from about three to seven years, the surface waters across a large swath of the tropical Pacific Ocean warm or cool by anywhere from 1°C to 3°C, compared to normal. The phase of the ENSO cycle is commonly quantified by indices describing the sea-surface temperatures (SSTs) in central parts of the Pacific, e.g., the NINO 3.4 index which can be thought of as the equatorial SSTs averaged from the dateline to the South American coast (5°S-5°N, 120°W-170°W).\n",
     "\n",
@@ -3019,7 +3026,7 @@
    }
   ],
   "kernelspec": {
-   "display_name": "Python 3",
+   "display_name": "Python 3 (ipykernel)",
    "language": "python",
    "name": "python3"
   },