{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {
    "Collapsed": "false"
   },
   "source": [
    "# Solution 3"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "```{hint} \n",
    "<a href='https://dust.ltpy.adamplatform.eu/hub/user-redirect/lab/tree/03_practical_case_study/solutions/03_exercise_solution.ipynb' target='_blank'>Execute the notebook on the training platform >></a>\n",
    "```"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## About"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "> With [exercise #1](./exercise_1.ipynb) and [exercise #2](./exercise_2.ipynb), you compared two dust forecast models as well as near real-time monitoring with satellite data. From the results of the assignment, you see that it is difficult to know which forecast can be trusted more. Hence, even though model intercomparison is important, model outcomes also have to be compared with real-world observations. Just by comparing model outcomes with measurements from station data, we can better understand how the model performs.\n",
    "> \n",
    "> Now, you will focus on how you can use station observations from AERONET to evaluate model forecasts.\n",
    "\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Tasks"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "tags": []
   },
   "source": [
    "**1. Brainstorm**\n",
    "  * What observation datasets do you know about?\n",
    "  * Which variables do they measure?\n",
    "  * Which data can you use to evaluate model predictions?\n",
    "  \n",
    "\n",
    "**2. Download and plot time-series of AERONET data for *Santa Cruz, Tenerife***\n",
    "  * Download and visualise AERONET v3.0 measurements of the station *Santa Cruz, Tenerife* for 21 to 25 February 2020.\n",
    "  * **Some questions to reflect on:**\n",
    "    * Under which name is the station listed in AERONET?\n",
    "    * What average level would you choose?\n",
    "    * Which days do we have observations for?\n",
    "  * **Hint** \n",
    "    * [AERONET observations - Example notebook](./aeronet.ipynb)\n",
    "\n",
    "\n",
    "\n",
    "**3. Resample AERONET data to a 3-hourly resolution**\n",
    "  * Make use of AERONET `index` and `time` columns to create a `DateTimeIndex` in order to resample the observations to a 3-hourly temporal resolution\n",
    "  * **Hint**\n",
    "    * you have to combine the two columns `index` and `time` as one string variable\n",
    "    * you can use the pandas function `to_datetime()` to create a `DateTimeIndex` and the function `resample().mean()` to resample and average the time-series to a given temporal resolution \n",
    "  * **Question to reflect on**\n",
    "    * How many row entries does the resampled data frame have?\n",
    "\n",
    "**4. Load time-series of the forecasts from CAMS and the MONARCH model and compare it with the AERONET observations**\n",
    "  * Interpret the plotting result.\n",
    "  * Can you make a statement of the performance of the two forecast models?\n",
    "  * What is your conclusion regarding AERONET observation data?"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<hr>"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "Collapsed": "false"
   },
   "source": [
    "**Load required libraries**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {
    "Collapsed": "false"
   },
   "outputs": [],
   "source": [
    "import wget\n",
    "import pandas as pd\n",
    "import numpy as np\n",
    "\n",
    "import matplotlib.pyplot as plt\n",
    "import matplotlib.colors\n",
    "from matplotlib.cm import get_cmap\n",
    "from matplotlib import animation\n",
    "from matplotlib.axes import Axes\n",
    "\n",
    "import cartopy.crs as ccrs\n",
    "from cartopy.mpl.gridliner import LONGITUDE_FORMATTER, LATITUDE_FORMATTER\n",
    "import cartopy.feature as cfeature\n",
    "from cartopy.mpl.geoaxes import GeoAxes\n",
    "GeoAxes._pcolormesh_patched = Axes.pcolormesh\n",
    "\n",
    "import warnings\n",
    "warnings.simplefilter(action = \"ignore\", category = RuntimeWarning)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**Load helper functions**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [],
   "source": [
    "%run ../../functions.ipynb"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "Collapsed": "false"
   },
   "source": [
    "<hr>"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "tags": []
   },
   "source": [
    "### 1. Brainstorm"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Three different ground-based observations are introduced during the course:\n",
    "* [AERONET](./aeronet.ipynb)\n",
    "  * Aerosol Optical Depth\n",
    "  * Angstrom Exponent\n",
    "* [EARLINET Lidar backscatter profiles](./earlinet.ipynb)\n",
    "  * Backscatter coefficient\n",
    "* [EEA Air Quality data](./eea_air_quality.ipynb)\n",
    "  * PM2.5\n",
    "  * PM10\n",
    "  \n",
    "AERONET offers ground-based observations of Aerosol Optical Depth and the Angstrom Exponent and can be used to evaluate dust forecasts."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### <a id='aeronet'></a>2. Download and plot time-series of AERONET data"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Define the latitude / longitude values for Santa Cruz, Tenerife. You can see an overview of all available AERONET Site Names [here](https://aeronet.gsfc.nasa.gov/cgi-bin/draw_map_display_aod_v3?long1=-180&long2=180&lat1=-90&lat2=90&multiplier=2&what_map=4&nachal=1&formatter=0&level=3&place_code=10&place_limit=0)."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [],
   "source": [
    "lat = 28.473\n",
    "lon = -16.247"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "As a first step, let us create a Python dictionary in which we store all the parameters we would like to use for the request as dictionary keys. You can initiate a dictionary with curled brackets `{}`. Below, we specify the following parameters:\n",
    "* `endpoint`: Endpoint of the AERONET web service\n",
    "* `station`: Name of the AERONET station\n",
    "* `year`: year 1 of interest\n",
    "* `month`: month 1 of interest\n",
    "* `day`: day 1 of interest\n",
    "* `year2`: year 2 of interest\n",
    "* `month2`: month 2 of interest\n",
    "* `day2`: day 2 of interest\n",
    "* `AOD15`: data type, other options include `AOD10`, `AOD20`, etc.\n",
    "* `AVG`: data format, `AVG=10` - all points, `AVG=20` - daily averages"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The keywords below are those we will need for requesting all data points (`AVG=10`) of Aerosol Optical Depth Level 1.5 data for the station Santa Cruz, Tenerife for 21 to 25 February 2020."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [],
   "source": [
    "data_dict = {\n",
    "    'endpoint': 'https://aeronet.gsfc.nasa.gov/cgi-bin/print_web_data_v3',\n",
    "    'station':'Santa_Cruz_Tenerife',\n",
    "    'year': 2020,\n",
    "    'month': 2,\n",
    "    'day': 21,\n",
    "    'year2': 2020,\n",
    "    'month2': 2,\n",
    "    'day2': 25,\n",
    "    'AOD15': 1,\n",
    "    'AVG': 10\n",
    "}\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<br>"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "In a next step, we construct the final string for the wget request with the `format` function. You construct a string by adding the dictionary keys in curled brackets. At the end of the string, you provide the dictionary key informatoin to the string with the `format()` function. A print of the resulting url shows, that the format function replaced the information in the curled brackets with the data in the dictionary."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'https://aeronet.gsfc.nasa.gov/cgi-bin/print_web_data_v3?site=Santa_Cruz_Tenerife&year=2020&month=2&day=21&year2=2020&month2=2&day2=25&AOD15=1&AVG=10'"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "url = '{endpoint}?site={station}&year={year}&month={month}&day={day}&year2={year2}&month2={month2}&day2={day2}&AOD15={AOD15}&AVG={AVG}'.format(**data_dict)\n",
    "url"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<br>"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Now we are ready to request the data with the function `download()` from the wget Python library. You have to pass to the function the constructed url above together with a file path of where the downloaded that shall be stored. Let us store the data as `txt` file in the folder `../data/2_observations/aeronet/`."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'../data/aeronet/20200221-25_santa_cruz_tenerife_10 (1).txt'"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "wget.download(url, '../../../../eodata/50_modules/01_dust/04_assignment/aeronet/20200221-25_santa_cruz_tenerife_10.txt')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<br>"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "After we downloaded the station observations as `txt` file, we can open it with the pandas function `read_table()`. We additonally set specific keyword arguments that allow us to specify the columns and rows of interest:\n",
    "* `delimiter`: specify the delimiter in the text file, e.g. comma\n",
    "* `header`: specify the index of the row that shall be set as header.\n",
    "* `index_col`: specify the index of the column that shall be set as index\n",
    "\n",
    "You see below that the resulting dataframe has 292 rows and 113 columns."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
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       "    <tr style=\"text-align: right;\">\n",
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       "      <th>Exact_Wavelengths_of_PW(um)_935nm</th>\n",
       "      <th>Exact_Wavelengths_of_AOD(um)_681nm</th>\n",
       "      <th>Exact_Wavelengths_of_AOD(um)_709nm</th>\n",
       "      <th>Exact_Wavelengths_of_AOD(um)_Empty</th>\n",
       "      <th>Exact_Wavelengths_of_AOD(um)_Empty.1</th>\n",
       "      <th>Exact_Wavelengths_of_AOD(um)_Empty.2</th>\n",
       "      <th>Exact_Wavelengths_of_AOD(um)_Empty.3</th>\n",
       "      <th>Exact_Wavelengths_of_AOD(um)_Empty&lt;br&gt;</th>\n",
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       "      <th>Date(dd:mm:yyyy)</th>\n",
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       "      <th>21:02:2020</th>\n",
       "      <td>Santa_Cruz_Tenerife</td>\n",
       "      <td>08:22:46</td>\n",
       "      <td>52.0</td>\n",
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       "      <td>Santa_Cruz_Tenerife</td>\n",
       "      <td>08:25:22</td>\n",
       "      <td>52.0</td>\n",
       "      <td>52.350949</td>\n",
       "      <td>0.019840</td>\n",
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       "      <th>21:02:2020</th>\n",
       "      <td>Santa_Cruz_Tenerife</td>\n",
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       "      <td>52.0</td>\n",
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       "      <th>21:02:2020</th>\n",
       "      <td>Santa_Cruz_Tenerife</td>\n",
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       "      <td>52.0</td>\n",
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       "      <th>21:02:2020</th>\n",
       "      <td>Santa_Cruz_Tenerife</td>\n",
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       "      <td>-999.0</td>\n",
       "      <td>-999.&lt;br&gt;</td>\n",
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       "      <th>...</th>\n",
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       "      <td>...</td>\n",
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       "      <td>...</td>\n",
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       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
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       "      <td>...</td>\n",
       "      <td>...</td>\n",
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       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25:02:2020</th>\n",
       "      <td>Santa_Cruz_Tenerife</td>\n",
       "      <td>18:09:31</td>\n",
       "      <td>56.0</td>\n",
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       "      <td>-999.0</td>\n",
       "      <td>-999.0</td>\n",
       "      <td>-999.&lt;br&gt;</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25:02:2020</th>\n",
       "      <td>Santa_Cruz_Tenerife</td>\n",
       "      <td>18:13:14</td>\n",
       "      <td>56.0</td>\n",
       "      <td>56.759190</td>\n",
       "      <td>0.279673</td>\n",
       "      <td>0.374916</td>\n",
       "      <td>0.398722</td>\n",
       "      <td>-999.0</td>\n",
       "      <td>-999.0</td>\n",
       "      <td>0.430402</td>\n",
       "      <td>...</td>\n",
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       "      <td>-999.0</td>\n",
       "      <td>-999.0</td>\n",
       "      <td>-999.0</td>\n",
       "      <td>-999.0</td>\n",
       "      <td>-999.0</td>\n",
       "      <td>-999.&lt;br&gt;</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25:02:2020</th>\n",
       "      <td>Santa_Cruz_Tenerife</td>\n",
       "      <td>18:15:09</td>\n",
       "      <td>56.0</td>\n",
       "      <td>56.760521</td>\n",
       "      <td>0.286610</td>\n",
       "      <td>0.384756</td>\n",
       "      <td>0.408843</td>\n",
       "      <td>-999.0</td>\n",
       "      <td>-999.0</td>\n",
       "      <td>0.440872</td>\n",
       "      <td>...</td>\n",
       "      <td>0.3797</td>\n",
       "      <td>-999.0000</td>\n",
       "      <td>0.9371</td>\n",
       "      <td>-999.0</td>\n",
       "      <td>-999.0</td>\n",
       "      <td>-999.0</td>\n",
       "      <td>-999.0</td>\n",
       "      <td>-999.0</td>\n",
       "      <td>-999.0</td>\n",
       "      <td>-999.&lt;br&gt;</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25:02:2020</th>\n",
       "      <td>Santa_Cruz_Tenerife</td>\n",
       "      <td>18:17:13</td>\n",
       "      <td>56.0</td>\n",
       "      <td>56.761956</td>\n",
       "      <td>0.293157</td>\n",
       "      <td>0.393598</td>\n",
       "      <td>0.418218</td>\n",
       "      <td>-999.0</td>\n",
       "      <td>-999.0</td>\n",
       "      <td>0.451127</td>\n",
       "      <td>...</td>\n",
       "      <td>0.3797</td>\n",
       "      <td>-999.0000</td>\n",
       "      <td>0.9371</td>\n",
       "      <td>-999.0</td>\n",
       "      <td>-999.0</td>\n",
       "      <td>-999.0</td>\n",
       "      <td>-999.0</td>\n",
       "      <td>-999.0</td>\n",
       "      <td>-999.0</td>\n",
       "      <td>-999.&lt;br&gt;</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>NaN</th>\n",
       "      <td>&lt;/body&gt;&lt;/html&gt;</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>292 rows × 113 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "                         AERONET_Site Time(hh:mm:ss)  Day_of_Year  \\\n",
       "Date(dd:mm:yyyy)                                                    \n",
       "21:02:2020        Santa_Cruz_Tenerife       08:22:46         52.0   \n",
       "21:02:2020        Santa_Cruz_Tenerife       08:25:22         52.0   \n",
       "21:02:2020        Santa_Cruz_Tenerife       08:28:46         52.0   \n",
       "21:02:2020        Santa_Cruz_Tenerife       08:30:39         52.0   \n",
       "21:02:2020        Santa_Cruz_Tenerife       08:33:09         52.0   \n",
       "...                               ...            ...          ...   \n",
       "25:02:2020        Santa_Cruz_Tenerife       18:09:31         56.0   \n",
       "25:02:2020        Santa_Cruz_Tenerife       18:13:14         56.0   \n",
       "25:02:2020        Santa_Cruz_Tenerife       18:15:09         56.0   \n",
       "25:02:2020        Santa_Cruz_Tenerife       18:17:13         56.0   \n",
       "NaN                    </body></html>            NaN          NaN   \n",
       "\n",
       "                  Day_of_Year(Fraction)  AOD_1640nm  AOD_1020nm  AOD_870nm  \\\n",
       "Date(dd:mm:yyyy)                                                             \n",
       "21:02:2020                    52.349144    0.021395    0.031387   0.034429   \n",
       "21:02:2020                    52.350949    0.019840    0.028373   0.030877   \n",
       "21:02:2020                    52.353310    0.019732    0.028353   0.030869   \n",
       "21:02:2020                    52.354618    0.019704    0.028210   0.030609   \n",
       "21:02:2020                    52.356354    0.020827    0.030963   0.030745   \n",
       "...                                 ...         ...         ...        ...   \n",
       "25:02:2020                    56.756609    0.266426    0.359000   0.381361   \n",
       "25:02:2020                    56.759190    0.279673    0.374916   0.398722   \n",
       "25:02:2020                    56.760521    0.286610    0.384756   0.408843   \n",
       "25:02:2020                    56.761956    0.293157    0.393598   0.418218   \n",
       "NaN                                 NaN         NaN         NaN        NaN   \n",
       "\n",
       "                  AOD_865nm  AOD_779nm  AOD_675nm  ...  \\\n",
       "Date(dd:mm:yyyy)                                   ...   \n",
       "21:02:2020           -999.0     -999.0   0.040873  ...   \n",
       "21:02:2020           -999.0     -999.0   0.035930  ...   \n",
       "21:02:2020           -999.0     -999.0   0.035813  ...   \n",
       "21:02:2020           -999.0     -999.0   0.035490  ...   \n",
       "21:02:2020           -999.0     -999.0   0.035596  ...   \n",
       "...                     ...        ...        ...  ...   \n",
       "25:02:2020           -999.0     -999.0   0.412116  ...   \n",
       "25:02:2020           -999.0     -999.0   0.430402  ...   \n",
       "25:02:2020           -999.0     -999.0   0.440872  ...   \n",
       "25:02:2020           -999.0     -999.0   0.451127  ...   \n",
       "NaN                     NaN        NaN        NaN  ...   \n",
       "\n",
       "                  Exact_Wavelengths_of_AOD(um)_380nm  \\\n",
       "Date(dd:mm:yyyy)                                       \n",
       "21:02:2020                                    0.3797   \n",
       "21:02:2020                                    0.3797   \n",
       "21:02:2020                                    0.3797   \n",
       "21:02:2020                                    0.3797   \n",
       "21:02:2020                                    0.3797   \n",
       "...                                              ...   \n",
       "25:02:2020                                    0.3797   \n",
       "25:02:2020                                    0.3797   \n",
       "25:02:2020                                    0.3797   \n",
       "25:02:2020                                    0.3797   \n",
       "NaN                                              NaN   \n",
       "\n",
       "                  Exact_Wavelengths_of_AOD(um)_340nm  \\\n",
       "Date(dd:mm:yyyy)                                       \n",
       "21:02:2020                                    0.3398   \n",
       "21:02:2020                                    0.3398   \n",
       "21:02:2020                                    0.3398   \n",
       "21:02:2020                                    0.3398   \n",
       "21:02:2020                                    0.3398   \n",
       "...                                              ...   \n",
       "25:02:2020                                    0.3398   \n",
       "25:02:2020                                    0.3398   \n",
       "25:02:2020                                 -999.0000   \n",
       "25:02:2020                                 -999.0000   \n",
       "NaN                                              NaN   \n",
       "\n",
       "                  Exact_Wavelengths_of_PW(um)_935nm  \\\n",
       "Date(dd:mm:yyyy)                                      \n",
       "21:02:2020                                   0.9371   \n",
       "21:02:2020                                   0.9371   \n",
       "21:02:2020                                   0.9371   \n",
       "21:02:2020                                   0.9371   \n",
       "21:02:2020                                   0.9371   \n",
       "...                                             ...   \n",
       "25:02:2020                                   0.9371   \n",
       "25:02:2020                                   0.9371   \n",
       "25:02:2020                                   0.9371   \n",
       "25:02:2020                                   0.9371   \n",
       "NaN                                             NaN   \n",
       "\n",
       "                  Exact_Wavelengths_of_AOD(um)_681nm  \\\n",
       "Date(dd:mm:yyyy)                                       \n",
       "21:02:2020                                    -999.0   \n",
       "21:02:2020                                    -999.0   \n",
       "21:02:2020                                    -999.0   \n",
       "21:02:2020                                    -999.0   \n",
       "21:02:2020                                    -999.0   \n",
       "...                                              ...   \n",
       "25:02:2020                                    -999.0   \n",
       "25:02:2020                                    -999.0   \n",
       "25:02:2020                                    -999.0   \n",
       "25:02:2020                                    -999.0   \n",
       "NaN                                              NaN   \n",
       "\n",
       "                  Exact_Wavelengths_of_AOD(um)_709nm  \\\n",
       "Date(dd:mm:yyyy)                                       \n",
       "21:02:2020                                    -999.0   \n",
       "21:02:2020                                    -999.0   \n",
       "21:02:2020                                    -999.0   \n",
       "21:02:2020                                    -999.0   \n",
       "21:02:2020                                    -999.0   \n",
       "...                                              ...   \n",
       "25:02:2020                                    -999.0   \n",
       "25:02:2020                                    -999.0   \n",
       "25:02:2020                                    -999.0   \n",
       "25:02:2020                                    -999.0   \n",
       "NaN                                              NaN   \n",
       "\n",
       "                  Exact_Wavelengths_of_AOD(um)_Empty  \\\n",
       "Date(dd:mm:yyyy)                                       \n",
       "21:02:2020                                    -999.0   \n",
       "21:02:2020                                    -999.0   \n",
       "21:02:2020                                    -999.0   \n",
       "21:02:2020                                    -999.0   \n",
       "21:02:2020                                    -999.0   \n",
       "...                                              ...   \n",
       "25:02:2020                                    -999.0   \n",
       "25:02:2020                                    -999.0   \n",
       "25:02:2020                                    -999.0   \n",
       "25:02:2020                                    -999.0   \n",
       "NaN                                              NaN   \n",
       "\n",
       "                  Exact_Wavelengths_of_AOD(um)_Empty.1  \\\n",
       "Date(dd:mm:yyyy)                                         \n",
       "21:02:2020                                      -999.0   \n",
       "21:02:2020                                      -999.0   \n",
       "21:02:2020                                      -999.0   \n",
       "21:02:2020                                      -999.0   \n",
       "21:02:2020                                      -999.0   \n",
       "...                                                ...   \n",
       "25:02:2020                                      -999.0   \n",
       "25:02:2020                                      -999.0   \n",
       "25:02:2020                                      -999.0   \n",
       "25:02:2020                                      -999.0   \n",
       "NaN                                                NaN   \n",
       "\n",
       "                  Exact_Wavelengths_of_AOD(um)_Empty.2  \\\n",
       "Date(dd:mm:yyyy)                                         \n",
       "21:02:2020                                      -999.0   \n",
       "21:02:2020                                      -999.0   \n",
       "21:02:2020                                      -999.0   \n",
       "21:02:2020                                      -999.0   \n",
       "21:02:2020                                      -999.0   \n",
       "...                                                ...   \n",
       "25:02:2020                                      -999.0   \n",
       "25:02:2020                                      -999.0   \n",
       "25:02:2020                                      -999.0   \n",
       "25:02:2020                                      -999.0   \n",
       "NaN                                                NaN   \n",
       "\n",
       "                  Exact_Wavelengths_of_AOD(um)_Empty.3  \\\n",
       "Date(dd:mm:yyyy)                                         \n",
       "21:02:2020                                      -999.0   \n",
       "21:02:2020                                      -999.0   \n",
       "21:02:2020                                      -999.0   \n",
       "21:02:2020                                      -999.0   \n",
       "21:02:2020                                      -999.0   \n",
       "...                                                ...   \n",
       "25:02:2020                                      -999.0   \n",
       "25:02:2020                                      -999.0   \n",
       "25:02:2020                                      -999.0   \n",
       "25:02:2020                                      -999.0   \n",
       "NaN                                                NaN   \n",
       "\n",
       "                  Exact_Wavelengths_of_AOD(um)_Empty<br>  \n",
       "Date(dd:mm:yyyy)                                          \n",
       "21:02:2020                                     -999.<br>  \n",
       "21:02:2020                                     -999.<br>  \n",
       "21:02:2020                                     -999.<br>  \n",
       "21:02:2020                                     -999.<br>  \n",
       "21:02:2020                                     -999.<br>  \n",
       "...                                                  ...  \n",
       "25:02:2020                                     -999.<br>  \n",
       "25:02:2020                                     -999.<br>  \n",
       "25:02:2020                                     -999.<br>  \n",
       "25:02:2020                                     -999.<br>  \n",
       "NaN                                                  NaN  \n",
       "\n",
       "[292 rows x 113 columns]"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df = pd.read_table('../../../../eodata/50_modules/01_dust/04_assignment/aeronet/20200221-25_santa_cruz_tenerife_10.txt', delimiter=',', header=[7], index_col=1)\n",
    "df"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<br>"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Now, we can inspect the entries in the loaded data frame a bit more. Above you see that the last entry is a NaN entry, which is best to drop with the function `dropna()`.\n",
    "\n",
    "The next step is then to replace the entries with -999.0 and set them as NaN. We can use the function `replace()` to do so. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>AERONET_Site</th>\n",
       "      <th>Time(hh:mm:ss)</th>\n",
       "      <th>Day_of_Year</th>\n",
       "      <th>Day_of_Year(Fraction)</th>\n",
       "      <th>AOD_1640nm</th>\n",
       "      <th>AOD_1020nm</th>\n",
       "      <th>AOD_870nm</th>\n",
       "      <th>AOD_865nm</th>\n",
       "      <th>AOD_779nm</th>\n",
       "      <th>AOD_675nm</th>\n",
       "      <th>...</th>\n",
       "      <th>Exact_Wavelengths_of_AOD(um)_380nm</th>\n",
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       "      <th>Date(dd:mm:yyyy)</th>\n",
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       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
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       "      <th></th>\n",
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       "  <tbody>\n",
       "    <tr>\n",
       "      <th>21:02:2020</th>\n",
       "      <td>Santa_Cruz_Tenerife</td>\n",
       "      <td>08:22:46</td>\n",
       "      <td>52.0</td>\n",
       "      <td>52.349144</td>\n",
       "      <td>0.021395</td>\n",
       "      <td>0.031387</td>\n",
       "      <td>0.034429</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.040873</td>\n",
       "      <td>...</td>\n",
       "      <td>0.3797</td>\n",
       "      <td>0.3398</td>\n",
       "      <td>0.9371</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>-999.&lt;br&gt;</td>\n",
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       "    <tr>\n",
       "      <th>21:02:2020</th>\n",
       "      <td>Santa_Cruz_Tenerife</td>\n",
       "      <td>08:25:22</td>\n",
       "      <td>52.0</td>\n",
       "      <td>52.350949</td>\n",
       "      <td>0.019840</td>\n",
       "      <td>0.028373</td>\n",
       "      <td>0.030877</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.035930</td>\n",
       "      <td>...</td>\n",
       "      <td>0.3797</td>\n",
       "      <td>0.3398</td>\n",
       "      <td>0.9371</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>-999.&lt;br&gt;</td>\n",
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       "    <tr>\n",
       "      <th>21:02:2020</th>\n",
       "      <td>Santa_Cruz_Tenerife</td>\n",
       "      <td>08:28:46</td>\n",
       "      <td>52.0</td>\n",
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       "      <td>0.019732</td>\n",
       "      <td>0.028353</td>\n",
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       "      <td>NaN</td>\n",
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       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>-999.&lt;br&gt;</td>\n",
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       "    <tr>\n",
       "      <th>21:02:2020</th>\n",
       "      <td>Santa_Cruz_Tenerife</td>\n",
       "      <td>08:30:39</td>\n",
       "      <td>52.0</td>\n",
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       "    <tr>\n",
       "      <th>21:02:2020</th>\n",
       "      <td>Santa_Cruz_Tenerife</td>\n",
       "      <td>08:33:09</td>\n",
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       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>-999.&lt;br&gt;</td>\n",
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       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25:02:2020</th>\n",
       "      <td>Santa_Cruz_Tenerife</td>\n",
       "      <td>18:06:09</td>\n",
       "      <td>56.0</td>\n",
       "      <td>56.754271</td>\n",
       "      <td>0.262337</td>\n",
       "      <td>0.354162</td>\n",
       "      <td>0.376787</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.407501</td>\n",
       "      <td>...</td>\n",
       "      <td>0.3797</td>\n",
       "      <td>0.3398</td>\n",
       "      <td>0.9371</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>-999.&lt;br&gt;</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25:02:2020</th>\n",
       "      <td>Santa_Cruz_Tenerife</td>\n",
       "      <td>18:09:31</td>\n",
       "      <td>56.0</td>\n",
       "      <td>56.756609</td>\n",
       "      <td>0.266426</td>\n",
       "      <td>0.359000</td>\n",
       "      <td>0.381361</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.412116</td>\n",
       "      <td>...</td>\n",
       "      <td>0.3797</td>\n",
       "      <td>0.3398</td>\n",
       "      <td>0.9371</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>-999.&lt;br&gt;</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25:02:2020</th>\n",
       "      <td>Santa_Cruz_Tenerife</td>\n",
       "      <td>18:13:14</td>\n",
       "      <td>56.0</td>\n",
       "      <td>56.759190</td>\n",
       "      <td>0.279673</td>\n",
       "      <td>0.374916</td>\n",
       "      <td>0.398722</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.430402</td>\n",
       "      <td>...</td>\n",
       "      <td>0.3797</td>\n",
       "      <td>0.3398</td>\n",
       "      <td>0.9371</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>-999.&lt;br&gt;</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25:02:2020</th>\n",
       "      <td>Santa_Cruz_Tenerife</td>\n",
       "      <td>18:15:09</td>\n",
       "      <td>56.0</td>\n",
       "      <td>56.760521</td>\n",
       "      <td>0.286610</td>\n",
       "      <td>0.384756</td>\n",
       "      <td>0.408843</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.440872</td>\n",
       "      <td>...</td>\n",
       "      <td>0.3797</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.9371</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>-999.&lt;br&gt;</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25:02:2020</th>\n",
       "      <td>Santa_Cruz_Tenerife</td>\n",
       "      <td>18:17:13</td>\n",
       "      <td>56.0</td>\n",
       "      <td>56.761956</td>\n",
       "      <td>0.293157</td>\n",
       "      <td>0.393598</td>\n",
       "      <td>0.418218</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.451127</td>\n",
       "      <td>...</td>\n",
       "      <td>0.3797</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.9371</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>-999.&lt;br&gt;</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>291 rows × 113 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "                         AERONET_Site Time(hh:mm:ss)  Day_of_Year  \\\n",
       "Date(dd:mm:yyyy)                                                    \n",
       "21:02:2020        Santa_Cruz_Tenerife       08:22:46         52.0   \n",
       "21:02:2020        Santa_Cruz_Tenerife       08:25:22         52.0   \n",
       "21:02:2020        Santa_Cruz_Tenerife       08:28:46         52.0   \n",
       "21:02:2020        Santa_Cruz_Tenerife       08:30:39         52.0   \n",
       "21:02:2020        Santa_Cruz_Tenerife       08:33:09         52.0   \n",
       "...                               ...            ...          ...   \n",
       "25:02:2020        Santa_Cruz_Tenerife       18:06:09         56.0   \n",
       "25:02:2020        Santa_Cruz_Tenerife       18:09:31         56.0   \n",
       "25:02:2020        Santa_Cruz_Tenerife       18:13:14         56.0   \n",
       "25:02:2020        Santa_Cruz_Tenerife       18:15:09         56.0   \n",
       "25:02:2020        Santa_Cruz_Tenerife       18:17:13         56.0   \n",
       "\n",
       "                  Day_of_Year(Fraction)  AOD_1640nm  AOD_1020nm  AOD_870nm  \\\n",
       "Date(dd:mm:yyyy)                                                             \n",
       "21:02:2020                    52.349144    0.021395    0.031387   0.034429   \n",
       "21:02:2020                    52.350949    0.019840    0.028373   0.030877   \n",
       "21:02:2020                    52.353310    0.019732    0.028353   0.030869   \n",
       "21:02:2020                    52.354618    0.019704    0.028210   0.030609   \n",
       "21:02:2020                    52.356354    0.020827    0.030963   0.030745   \n",
       "...                                 ...         ...         ...        ...   \n",
       "25:02:2020                    56.754271    0.262337    0.354162   0.376787   \n",
       "25:02:2020                    56.756609    0.266426    0.359000   0.381361   \n",
       "25:02:2020                    56.759190    0.279673    0.374916   0.398722   \n",
       "25:02:2020                    56.760521    0.286610    0.384756   0.408843   \n",
       "25:02:2020                    56.761956    0.293157    0.393598   0.418218   \n",
       "\n",
       "                  AOD_865nm  AOD_779nm  AOD_675nm  ...  \\\n",
       "Date(dd:mm:yyyy)                                   ...   \n",
       "21:02:2020              NaN        NaN   0.040873  ...   \n",
       "21:02:2020              NaN        NaN   0.035930  ...   \n",
       "21:02:2020              NaN        NaN   0.035813  ...   \n",
       "21:02:2020              NaN        NaN   0.035490  ...   \n",
       "21:02:2020              NaN        NaN   0.035596  ...   \n",
       "...                     ...        ...        ...  ...   \n",
       "25:02:2020              NaN        NaN   0.407501  ...   \n",
       "25:02:2020              NaN        NaN   0.412116  ...   \n",
       "25:02:2020              NaN        NaN   0.430402  ...   \n",
       "25:02:2020              NaN        NaN   0.440872  ...   \n",
       "25:02:2020              NaN        NaN   0.451127  ...   \n",
       "\n",
       "                  Exact_Wavelengths_of_AOD(um)_380nm  \\\n",
       "Date(dd:mm:yyyy)                                       \n",
       "21:02:2020                                    0.3797   \n",
       "21:02:2020                                    0.3797   \n",
       "21:02:2020                                    0.3797   \n",
       "21:02:2020                                    0.3797   \n",
       "21:02:2020                                    0.3797   \n",
       "...                                              ...   \n",
       "25:02:2020                                    0.3797   \n",
       "25:02:2020                                    0.3797   \n",
       "25:02:2020                                    0.3797   \n",
       "25:02:2020                                    0.3797   \n",
       "25:02:2020                                    0.3797   \n",
       "\n",
       "                  Exact_Wavelengths_of_AOD(um)_340nm  \\\n",
       "Date(dd:mm:yyyy)                                       \n",
       "21:02:2020                                    0.3398   \n",
       "21:02:2020                                    0.3398   \n",
       "21:02:2020                                    0.3398   \n",
       "21:02:2020                                    0.3398   \n",
       "21:02:2020                                    0.3398   \n",
       "...                                              ...   \n",
       "25:02:2020                                    0.3398   \n",
       "25:02:2020                                    0.3398   \n",
       "25:02:2020                                    0.3398   \n",
       "25:02:2020                                       NaN   \n",
       "25:02:2020                                       NaN   \n",
       "\n",
       "                  Exact_Wavelengths_of_PW(um)_935nm  \\\n",
       "Date(dd:mm:yyyy)                                      \n",
       "21:02:2020                                   0.9371   \n",
       "21:02:2020                                   0.9371   \n",
       "21:02:2020                                   0.9371   \n",
       "21:02:2020                                   0.9371   \n",
       "21:02:2020                                   0.9371   \n",
       "...                                             ...   \n",
       "25:02:2020                                   0.9371   \n",
       "25:02:2020                                   0.9371   \n",
       "25:02:2020                                   0.9371   \n",
       "25:02:2020                                   0.9371   \n",
       "25:02:2020                                   0.9371   \n",
       "\n",
       "                  Exact_Wavelengths_of_AOD(um)_681nm  \\\n",
       "Date(dd:mm:yyyy)                                       \n",
       "21:02:2020                                       NaN   \n",
       "21:02:2020                                       NaN   \n",
       "21:02:2020                                       NaN   \n",
       "21:02:2020                                       NaN   \n",
       "21:02:2020                                       NaN   \n",
       "...                                              ...   \n",
       "25:02:2020                                       NaN   \n",
       "25:02:2020                                       NaN   \n",
       "25:02:2020                                       NaN   \n",
       "25:02:2020                                       NaN   \n",
       "25:02:2020                                       NaN   \n",
       "\n",
       "                  Exact_Wavelengths_of_AOD(um)_709nm  \\\n",
       "Date(dd:mm:yyyy)                                       \n",
       "21:02:2020                                       NaN   \n",
       "21:02:2020                                       NaN   \n",
       "21:02:2020                                       NaN   \n",
       "21:02:2020                                       NaN   \n",
       "21:02:2020                                       NaN   \n",
       "...                                              ...   \n",
       "25:02:2020                                       NaN   \n",
       "25:02:2020                                       NaN   \n",
       "25:02:2020                                       NaN   \n",
       "25:02:2020                                       NaN   \n",
       "25:02:2020                                       NaN   \n",
       "\n",
       "                  Exact_Wavelengths_of_AOD(um)_Empty  \\\n",
       "Date(dd:mm:yyyy)                                       \n",
       "21:02:2020                                       NaN   \n",
       "21:02:2020                                       NaN   \n",
       "21:02:2020                                       NaN   \n",
       "21:02:2020                                       NaN   \n",
       "21:02:2020                                       NaN   \n",
       "...                                              ...   \n",
       "25:02:2020                                       NaN   \n",
       "25:02:2020                                       NaN   \n",
       "25:02:2020                                       NaN   \n",
       "25:02:2020                                       NaN   \n",
       "25:02:2020                                       NaN   \n",
       "\n",
       "                  Exact_Wavelengths_of_AOD(um)_Empty.1  \\\n",
       "Date(dd:mm:yyyy)                                         \n",
       "21:02:2020                                         NaN   \n",
       "21:02:2020                                         NaN   \n",
       "21:02:2020                                         NaN   \n",
       "21:02:2020                                         NaN   \n",
       "21:02:2020                                         NaN   \n",
       "...                                                ...   \n",
       "25:02:2020                                         NaN   \n",
       "25:02:2020                                         NaN   \n",
       "25:02:2020                                         NaN   \n",
       "25:02:2020                                         NaN   \n",
       "25:02:2020                                         NaN   \n",
       "\n",
       "                  Exact_Wavelengths_of_AOD(um)_Empty.2  \\\n",
       "Date(dd:mm:yyyy)                                         \n",
       "21:02:2020                                         NaN   \n",
       "21:02:2020                                         NaN   \n",
       "21:02:2020                                         NaN   \n",
       "21:02:2020                                         NaN   \n",
       "21:02:2020                                         NaN   \n",
       "...                                                ...   \n",
       "25:02:2020                                         NaN   \n",
       "25:02:2020                                         NaN   \n",
       "25:02:2020                                         NaN   \n",
       "25:02:2020                                         NaN   \n",
       "25:02:2020                                         NaN   \n",
       "\n",
       "                  Exact_Wavelengths_of_AOD(um)_Empty.3  \\\n",
       "Date(dd:mm:yyyy)                                         \n",
       "21:02:2020                                         NaN   \n",
       "21:02:2020                                         NaN   \n",
       "21:02:2020                                         NaN   \n",
       "21:02:2020                                         NaN   \n",
       "21:02:2020                                         NaN   \n",
       "...                                                ...   \n",
       "25:02:2020                                         NaN   \n",
       "25:02:2020                                         NaN   \n",
       "25:02:2020                                         NaN   \n",
       "25:02:2020                                         NaN   \n",
       "25:02:2020                                         NaN   \n",
       "\n",
       "                  Exact_Wavelengths_of_AOD(um)_Empty<br>  \n",
       "Date(dd:mm:yyyy)                                          \n",
       "21:02:2020                                     -999.<br>  \n",
       "21:02:2020                                     -999.<br>  \n",
       "21:02:2020                                     -999.<br>  \n",
       "21:02:2020                                     -999.<br>  \n",
       "21:02:2020                                     -999.<br>  \n",
       "...                                                  ...  \n",
       "25:02:2020                                     -999.<br>  \n",
       "25:02:2020                                     -999.<br>  \n",
       "25:02:2020                                     -999.<br>  \n",
       "25:02:2020                                     -999.<br>  \n",
       "25:02:2020                                     -999.<br>  \n",
       "\n",
       "[291 rows x 113 columns]"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df = df.dropna()\n",
    "df = df.replace(-999.0, np.nan)\n",
    "df"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<br>"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We can now plot the column `AOD_500nm` as time-series. What is striking in the resulting plot?"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1440x720 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Initiate a matplotlib figure\n",
    "fig = plt.figure(figsize=(20,10))\n",
    "ax=plt.axes()\n",
    "\n",
    "# Select pandas dataframe columns and define a line plot\n",
    "df.filter(['AOD_500nm']).plot(ax=ax, style='o-', color='green' )\n",
    "\n",
    "\n",
    "# Set title and axes lable information\n",
    "plt.title('\\nAerosol Optical Depth Level 1.5 data for 21-25 February 2021 - Santa Cruz, Tenerife', fontsize=20, pad=20)\n",
    "plt.ylabel('Aerosol Optical Depth\\n', fontsize=16)\n",
    "plt.xlabel('\\nDay', fontsize=16)\n",
    "\n",
    "# Format the axes ticks\n",
    "plt.xticks(fontsize=16)\n",
    "plt.yticks(fontsize=16)\n",
    "\n",
    "# Add additionally a legend and grid to the plot\n",
    "plt.legend(fontsize=16,loc=0)\n",
    "plt.grid()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### <a id='resample_aeronet'></a>3. Resample AERONET data to a 3-hourly resolution"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Above you see that it seems we do not have observations for 23rd and 24th February 2020 for the Santa Cruz station in Tenerife. A closer inspection of the index entry confirms that we only have measurements for 21st, 22nd and 25th."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array(['21:02:2020', '21:02:2020', '21:02:2020', '21:02:2020',\n",
       "       '21:02:2020', '21:02:2020', '21:02:2020', '21:02:2020',\n",
       "       '21:02:2020', '21:02:2020', '21:02:2020', '21:02:2020',\n",
       "       '21:02:2020', '21:02:2020', '21:02:2020', '21:02:2020',\n",
       "       '21:02:2020', '21:02:2020', '21:02:2020', '21:02:2020',\n",
       "       '21:02:2020', '21:02:2020', '21:02:2020', '21:02:2020',\n",
       "       '21:02:2020', '21:02:2020', '21:02:2020', '21:02:2020',\n",
       "       '21:02:2020', '21:02:2020', '21:02:2020', '21:02:2020',\n",
       "       '21:02:2020', '21:02:2020', '21:02:2020', '21:02:2020',\n",
       "       '21:02:2020', '21:02:2020', '21:02:2020', '21:02:2020',\n",
       "       '21:02:2020', '21:02:2020', '21:02:2020', '21:02:2020',\n",
       "       '21:02:2020', '22:02:2020', '22:02:2020', '22:02:2020',\n",
       "       '22:02:2020', '22:02:2020', '22:02:2020', '22:02:2020',\n",
       "       '22:02:2020', '22:02:2020', '22:02:2020', '22:02:2020',\n",
       "       '22:02:2020', '22:02:2020', '22:02:2020', '22:02:2020',\n",
       "       '22:02:2020', '22:02:2020', '22:02:2020', '22:02:2020',\n",
       "       '22:02:2020', '22:02:2020', '22:02:2020', '22:02:2020',\n",
       "       '22:02:2020', '22:02:2020', '22:02:2020', '22:02:2020',\n",
       "       '22:02:2020', '22:02:2020', '22:02:2020', '22:02:2020',\n",
       "       '22:02:2020', '22:02:2020', '22:02:2020', '22:02:2020',\n",
       "       '22:02:2020', '22:02:2020', '22:02:2020', '22:02:2020',\n",
       "       '22:02:2020', '22:02:2020', '22:02:2020', '22:02:2020',\n",
       "       '22:02:2020', '22:02:2020', '22:02:2020', '22:02:2020',\n",
       "       '22:02:2020', '22:02:2020', '22:02:2020', '22:02:2020',\n",
       "       '22:02:2020', '22:02:2020', '22:02:2020', '22:02:2020',\n",
       "       '22:02:2020', '22:02:2020', '22:02:2020', '22:02:2020',\n",
       "       '22:02:2020', '22:02:2020', '22:02:2020', '22:02:2020',\n",
       "       '22:02:2020', '22:02:2020', '22:02:2020', '22:02:2020',\n",
       "       '22:02:2020', '22:02:2020', '22:02:2020', '22:02:2020',\n",
       "       '22:02:2020', '22:02:2020', '22:02:2020', '22:02:2020',\n",
       "       '22:02:2020', '22:02:2020', '22:02:2020', '22:02:2020',\n",
       "       '22:02:2020', '22:02:2020', '22:02:2020', '22:02:2020',\n",
       "       '22:02:2020', '22:02:2020', '22:02:2020', '22:02:2020',\n",
       "       '22:02:2020', '22:02:2020', '22:02:2020', '22:02:2020',\n",
       "       '22:02:2020', '22:02:2020', '25:02:2020', '25:02:2020',\n",
       "       '25:02:2020', '25:02:2020', '25:02:2020', '25:02:2020',\n",
       "       '25:02:2020', '25:02:2020', '25:02:2020', '25:02:2020',\n",
       "       '25:02:2020', '25:02:2020', '25:02:2020', '25:02:2020',\n",
       "       '25:02:2020', '25:02:2020', '25:02:2020', '25:02:2020',\n",
       "       '25:02:2020', '25:02:2020', '25:02:2020', '25:02:2020',\n",
       "       '25:02:2020', '25:02:2020', '25:02:2020', '25:02:2020',\n",
       "       '25:02:2020', '25:02:2020', '25:02:2020', '25:02:2020',\n",
       "       '25:02:2020', '25:02:2020', '25:02:2020', '25:02:2020',\n",
       "       '25:02:2020', '25:02:2020', '25:02:2020', '25:02:2020',\n",
       "       '25:02:2020', '25:02:2020', '25:02:2020', '25:02:2020',\n",
       "       '25:02:2020', '25:02:2020', '25:02:2020', '25:02:2020',\n",
       "       '25:02:2020', '25:02:2020', '25:02:2020', '25:02:2020',\n",
       "       '25:02:2020', '25:02:2020', '25:02:2020', '25:02:2020',\n",
       "       '25:02:2020', '25:02:2020', '25:02:2020', '25:02:2020',\n",
       "       '25:02:2020', '25:02:2020', '25:02:2020', '25:02:2020',\n",
       "       '25:02:2020', '25:02:2020', '25:02:2020', '25:02:2020',\n",
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       "       '25:02:2020', '25:02:2020', '25:02:2020', '25:02:2020',\n",
       "       '25:02:2020', '25:02:2020', '25:02:2020', '25:02:2020',\n",
       "       '25:02:2020', '25:02:2020', '25:02:2020', '25:02:2020',\n",
       "       '25:02:2020', '25:02:2020', '25:02:2020', '25:02:2020',\n",
       "       '25:02:2020', '25:02:2020', '25:02:2020', '25:02:2020',\n",
       "       '25:02:2020', '25:02:2020', '25:02:2020', '25:02:2020',\n",
       "       '25:02:2020', '25:02:2020', '25:02:2020', '25:02:2020',\n",
       "       '25:02:2020', '25:02:2020', '25:02:2020', '25:02:2020',\n",
       "       '25:02:2020', '25:02:2020', '25:02:2020', '25:02:2020',\n",
       "       '25:02:2020', '25:02:2020', '25:02:2020', '25:02:2020',\n",
       "       '25:02:2020', '25:02:2020', '25:02:2020', '25:02:2020',\n",
       "       '25:02:2020', '25:02:2020', '25:02:2020', '25:02:2020',\n",
       "       '25:02:2020', '25:02:2020', '25:02:2020', '25:02:2020',\n",
       "       '25:02:2020', '25:02:2020', '25:02:2020', '25:02:2020',\n",
       "       '25:02:2020', '25:02:2020', '25:02:2020', '25:02:2020',\n",
       "       '25:02:2020', '25:02:2020', '25:02:2020', '25:02:2020',\n",
       "       '25:02:2020', '25:02:2020', '25:02:2020', '25:02:2020',\n",
       "       '25:02:2020', '25:02:2020', '25:02:2020', '25:02:2020',\n",
       "       '25:02:2020', '25:02:2020', '25:02:2020', '25:02:2020',\n",
       "       '25:02:2020', '25:02:2020', '25:02:2020'], dtype=object)"
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.index.values"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<br>"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "From the dataframe above, let us only select the columns of interest for us. This makes the handling of the dataframe much easier. The columns of interest are: `Time(hh:mm:ss)` and `AOD_500nm` and you can use the function `filter()` to select specific columns."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [
    {
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       "      <th>25:02:2020</th>\n",
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       "      <th>25:02:2020</th>\n",
       "      <td>18:13:14</td>\n",
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       "      <th>25:02:2020</th>\n",
       "      <td>18:15:09</td>\n",
       "      <td>0.471839</td>\n",
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       "    <tr>\n",
       "      <th>25:02:2020</th>\n",
       "      <td>18:17:13</td>\n",
       "      <td>0.482647</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>291 rows × 2 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "                 Time(hh:mm:ss)  AOD_500nm\n",
       "Date(dd:mm:yyyy)                          \n",
       "21:02:2020             08:22:46   0.053515\n",
       "21:02:2020             08:25:22   0.046500\n",
       "21:02:2020             08:28:46   0.045808\n",
       "21:02:2020             08:30:39   0.045296\n",
       "21:02:2020             08:33:09   0.045245\n",
       "...                         ...        ...\n",
       "25:02:2020             18:06:09   0.436686\n",
       "25:02:2020             18:09:31   0.441193\n",
       "25:02:2020             18:13:14   0.460659\n",
       "25:02:2020             18:15:09   0.471839\n",
       "25:02:2020             18:17:13   0.482647\n",
       "\n",
       "[291 rows x 2 columns]"
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "aeronet_ts = df.filter(['Time(hh:mm:ss)','AOD_500nm'])\n",
    "aeronet_ts"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The next step is to create an index as `DateTimeIndex`. For this, we want to first combine the index entries (date) and time stamp entries of the column `Time(hh:mm:ss)`. We can do this simply by redefining the data frame's index and combining both with adding the two columns together as a string.\n",
    "\n",
    "In a second step, we then convert the newly created index entry to a `DateTimeIndex` format with the function `to_datetime()`. Important here, you have to specify the format of combined index string."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [
    {
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       "      <th>Time(hh:mm:ss)</th>\n",
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       "      <td>08:22:46</td>\n",
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       "      <td>08:25:22</td>\n",
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       "      <td>18:13:14</td>\n",
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       "    <tr>\n",
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       "      <td>18:15:09</td>\n",
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       "    <tr>\n",
       "      <th>2020-02-25 18:17:13</th>\n",
       "      <td>18:17:13</td>\n",
       "      <td>0.482647</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>291 rows × 2 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "                    Time(hh:mm:ss)  AOD_500nm\n",
       "2020-02-21 08:22:46       08:22:46   0.053515\n",
       "2020-02-21 08:25:22       08:25:22   0.046500\n",
       "2020-02-21 08:28:46       08:28:46   0.045808\n",
       "2020-02-21 08:30:39       08:30:39   0.045296\n",
       "2020-02-21 08:33:09       08:33:09   0.045245\n",
       "...                            ...        ...\n",
       "2020-02-25 18:06:09       18:06:09   0.436686\n",
       "2020-02-25 18:09:31       18:09:31   0.441193\n",
       "2020-02-25 18:13:14       18:13:14   0.460659\n",
       "2020-02-25 18:15:09       18:15:09   0.471839\n",
       "2020-02-25 18:17:13       18:17:13   0.482647\n",
       "\n",
       "[291 rows x 2 columns]"
      ]
     },
     "execution_count": 19,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "aeronet_ts.index = aeronet_ts.index + ' ' + aeronet_ts['Time(hh:mm:ss)']\n",
    "aeronet_ts.index = pd.to_datetime(aeronet_ts.index, format = '%d:%m:%Y %H:%M:%S')\n",
    "aeronet_ts"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<br>"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The MONARCH model has a three hourly resolution. For this reason, we have to adjust the temporal resolution of the AERONET observations and resample the data frame to a three hourly temporal resolution. Below, we use the function `resample()` and create the average of every 3-hours. The resulting dataframe has 37 row entries."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "      <td>NaN</td>\n",
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       "      <td>NaN</td>\n",
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       "      <th>2020-02-23 21:00:00</th>\n",
       "      <td>NaN</td>\n",
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       "    <tr>\n",
       "      <th>2020-02-24 00:00:00</th>\n",
       "      <td>NaN</td>\n",
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       "      <th>2020-02-24 03:00:00</th>\n",
       "      <td>NaN</td>\n",
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       "      <td>NaN</td>\n",
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       "      <td>NaN</td>\n",
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       "      <th>2020-02-25 09:00:00</th>\n",
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       "      <th>2020-02-25 12:00:00</th>\n",
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       "    <tr>\n",
       "      <th>2020-02-25 18:00:00</th>\n",
       "      <td>0.450671</td>\n",
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      ],
      "text/plain": [
       "                     AOD_500nm\n",
       "2020-02-21 06:00:00   0.047250\n",
       "2020-02-21 09:00:00   0.054734\n",
       "2020-02-21 12:00:00   0.061003\n",
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       "2020-02-21 18:00:00        NaN\n",
       "2020-02-21 21:00:00        NaN\n",
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       "2020-02-22 06:00:00   0.117495\n",
       "2020-02-22 09:00:00   0.147488\n",
       "2020-02-22 12:00:00   0.253696\n",
       "2020-02-22 15:00:00   0.375537\n",
       "2020-02-22 18:00:00        NaN\n",
       "2020-02-22 21:00:00        NaN\n",
       "2020-02-23 00:00:00        NaN\n",
       "2020-02-23 03:00:00        NaN\n",
       "2020-02-23 06:00:00        NaN\n",
       "2020-02-23 09:00:00        NaN\n",
       "2020-02-23 12:00:00        NaN\n",
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       "2020-02-23 18:00:00        NaN\n",
       "2020-02-23 21:00:00        NaN\n",
       "2020-02-24 00:00:00        NaN\n",
       "2020-02-24 03:00:00        NaN\n",
       "2020-02-24 06:00:00        NaN\n",
       "2020-02-24 09:00:00        NaN\n",
       "2020-02-24 12:00:00        NaN\n",
       "2020-02-24 15:00:00        NaN\n",
       "2020-02-24 18:00:00        NaN\n",
       "2020-02-24 21:00:00        NaN\n",
       "2020-02-25 00:00:00        NaN\n",
       "2020-02-25 03:00:00        NaN\n",
       "2020-02-25 06:00:00   0.674464\n",
       "2020-02-25 09:00:00   0.581988\n",
       "2020-02-25 12:00:00   0.572472\n",
       "2020-02-25 15:00:00   0.407499\n",
       "2020-02-25 18:00:00   0.450671"
      ]
     },
     "execution_count": 20,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "aeronet_ts_resample = aeronet_ts.resample('3H').mean()\n",
    "aeronet_ts_resample"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "tags": []
   },
   "source": [
    "### <a id='load_model_timeseries'></a>4. Load time series of the forecasts from CAMS and the MONARCH model"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We can now load the time-series information of the CAMS and the MONARCH model forecasts for Santa Cruz, Tenerife. The time-series has been saved as csv file from yesterday's assignment (see [here](../day_1/day1_assignment_solution.ipynb#ts_cams)).\n",
    "The time-series is available under `../data/sdswas_ts.csv` and can be loaded as pandas.DataFrame with the function `read_csv()`.\n",
    "\n",
    "The next step is then to convert the index of the loaded dataframe into a `DateTimeIndex` format, as this allows us to combine the different time-series."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
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      "text/plain": [
       "                                    time        lon       lat  od550_dust\n",
       "time                                                                     \n",
       "2020-02-21 12:00:00  2020-02-21 12:00:00 -16.333335  28.33333    0.000058\n",
       "2020-02-21 15:00:00  2020-02-21 15:00:00 -16.333335  28.33333    0.000019\n",
       "2020-02-21 18:00:00  2020-02-21 18:00:00 -16.333335  28.33333    0.000027\n",
       "2020-02-21 21:00:00  2020-02-21 21:00:00 -16.333335  28.33333    0.000207\n",
       "2020-02-22 00:00:00  2020-02-22 00:00:00 -16.333335  28.33333    0.000890\n",
       "2020-02-22 03:00:00  2020-02-22 03:00:00 -16.333335  28.33333    0.001751\n",
       "2020-02-22 06:00:00  2020-02-22 06:00:00 -16.333335  28.33333    0.009111\n",
       "2020-02-22 09:00:00  2020-02-22 09:00:00 -16.333335  28.33333    0.050932\n",
       "2020-02-22 12:00:00  2020-02-22 12:00:00 -16.333335  28.33333    0.203418\n",
       "2020-02-22 15:00:00  2020-02-22 15:00:00 -16.333335  28.33333    0.363704\n",
       "2020-02-22 18:00:00  2020-02-22 18:00:00 -16.333335  28.33333    0.433835\n",
       "2020-02-22 21:00:00  2020-02-22 21:00:00 -16.333335  28.33333    1.095499\n",
       "2020-02-23 00:00:00  2020-02-23 00:00:00 -16.333335  28.33333    2.165373\n",
       "2020-02-23 03:00:00  2020-02-23 03:00:00 -16.333335  28.33333    2.052836\n",
       "2020-02-23 06:00:00  2020-02-23 06:00:00 -16.333335  28.33333    0.861120\n",
       "2020-02-23 09:00:00  2020-02-23 09:00:00 -16.333335  28.33333    0.753394\n",
       "2020-02-23 12:00:00  2020-02-23 12:00:00 -16.333335  28.33333    0.466931\n",
       "2020-02-23 15:00:00  2020-02-23 15:00:00 -16.333335  28.33333    0.354274\n",
       "2020-02-23 18:00:00  2020-02-23 18:00:00 -16.333335  28.33333    0.320627\n",
       "2020-02-23 21:00:00  2020-02-23 21:00:00 -16.333335  28.33333    0.231225\n",
       "2020-02-24 00:00:00  2020-02-24 00:00:00 -16.333335  28.33333    0.179565\n",
       "2020-02-24 03:00:00  2020-02-24 03:00:00 -16.333335  28.33333    0.221447\n",
       "2020-02-24 06:00:00  2020-02-24 06:00:00 -16.333335  28.33333    0.477572\n",
       "2020-02-24 09:00:00  2020-02-24 09:00:00 -16.333335  28.33333    0.700524\n",
       "2020-02-24 12:00:00  2020-02-24 12:00:00 -16.333335  28.33333    1.151539"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "sdswas_ts = pd.read_csv('../../eodata/case_study/sdswas_ts.csv')\n",
    "sdswas_ts.index = sdswas_ts['time']\n",
    "sdswas_ts.index = pd.to_datetime(sdswas_ts.index, format = '%Y-%m-%d %H:%M:%S')\n",
    "sdswas_ts"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<br>"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We repeat the same process for the time-series of the CAMS forecasts. We first read the csv file under `../data/cams_ts.csv` with the function `read_csv()`. Then, we convert the index into a `DateTimeIndex` format."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
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       "      <th>2020-02-23 09:00:00</th>\n",
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       "      <th>2020-02-23 12:00:00</th>\n",
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       "      <td>-16.4</td>\n",
       "      <td>28.6</td>\n",
       "      <td>0.170247</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2020-02-24 09:00:00</th>\n",
       "      <td>2020-02-24 09:00:00</td>\n",
       "      <td>-16.4</td>\n",
       "      <td>28.6</td>\n",
       "      <td>0.219139</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2020-02-24 12:00:00</th>\n",
       "      <td>2020-02-24 12:00:00</td>\n",
       "      <td>-16.4</td>\n",
       "      <td>28.6</td>\n",
       "      <td>0.355102</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2020-02-24 15:00:00</th>\n",
       "      <td>2020-02-24 15:00:00</td>\n",
       "      <td>-16.4</td>\n",
       "      <td>28.6</td>\n",
       "      <td>0.515648</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2020-02-24 18:00:00</th>\n",
       "      <td>2020-02-24 18:00:00</td>\n",
       "      <td>-16.4</td>\n",
       "      <td>28.6</td>\n",
       "      <td>0.622153</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2020-02-24 21:00:00</th>\n",
       "      <td>2020-02-24 21:00:00</td>\n",
       "      <td>-16.4</td>\n",
       "      <td>28.6</td>\n",
       "      <td>0.886938</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2020-02-25 00:00:00</th>\n",
       "      <td>2020-02-25 00:00:00</td>\n",
       "      <td>-16.4</td>\n",
       "      <td>28.6</td>\n",
       "      <td>1.008824</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2020-02-25 03:00:00</th>\n",
       "      <td>2020-02-25 03:00:00</td>\n",
       "      <td>-16.4</td>\n",
       "      <td>28.6</td>\n",
       "      <td>0.765258</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2020-02-25 06:00:00</th>\n",
       "      <td>2020-02-25 06:00:00</td>\n",
       "      <td>-16.4</td>\n",
       "      <td>28.6</td>\n",
       "      <td>0.606290</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                                    time  longitude  latitude  duaod550\n",
       "time                                                                   \n",
       "2020-02-21 12:00:00  2020-02-21 12:00:00      -16.4      28.6  0.000568\n",
       "2020-02-21 15:00:00  2020-02-21 15:00:00      -16.4      28.6  0.000568\n",
       "2020-02-21 18:00:00  2020-02-21 18:00:00      -16.4      28.6  0.001460\n",
       "2020-02-21 21:00:00  2020-02-21 21:00:00      -16.4      28.6  0.002422\n",
       "2020-02-22 00:00:00  2020-02-22 00:00:00      -16.4      28.6  0.005306\n",
       "2020-02-22 03:00:00  2020-02-22 03:00:00      -16.4      28.6  0.011280\n",
       "2020-02-22 06:00:00  2020-02-22 06:00:00      -16.4      28.6  0.080772\n",
       "2020-02-22 09:00:00  2020-02-22 09:00:00      -16.4      28.6  0.203139\n",
       "2020-02-22 12:00:00  2020-02-22 12:00:00      -16.4      28.6  0.335806\n",
       "2020-02-22 15:00:00  2020-02-22 15:00:00      -16.4      28.6  0.501777\n",
       "2020-02-22 18:00:00  2020-02-22 18:00:00      -16.4      28.6  0.581158\n",
       "2020-02-22 21:00:00  2020-02-22 21:00:00      -16.4      28.6  0.755507\n",
       "2020-02-23 00:00:00  2020-02-23 00:00:00      -16.4      28.6  1.102213\n",
       "2020-02-23 03:00:00  2020-02-23 03:00:00      -16.4      28.6  1.210434\n",
       "2020-02-23 06:00:00  2020-02-23 06:00:00      -16.4      28.6  1.024824\n",
       "2020-02-23 09:00:00  2020-02-23 09:00:00      -16.4      28.6  0.905959\n",
       "2020-02-23 12:00:00  2020-02-23 12:00:00      -16.4      28.6  0.777000\n",
       "2020-02-23 15:00:00  2020-02-23 15:00:00      -16.4      28.6  0.603338\n",
       "2020-02-23 18:00:00  2020-02-23 18:00:00      -16.4      28.6  0.545107\n",
       "2020-02-23 21:00:00  2020-02-23 21:00:00      -16.4      28.6  0.492370\n",
       "2020-02-24 00:00:00  2020-02-24 00:00:00      -16.4      28.6  0.364715\n",
       "2020-02-24 03:00:00  2020-02-24 03:00:00      -16.4      28.6  0.281077\n",
       "2020-02-24 06:00:00  2020-02-24 06:00:00      -16.4      28.6  0.170247\n",
       "2020-02-24 09:00:00  2020-02-24 09:00:00      -16.4      28.6  0.219139\n",
       "2020-02-24 12:00:00  2020-02-24 12:00:00      -16.4      28.6  0.355102\n",
       "2020-02-24 15:00:00  2020-02-24 15:00:00      -16.4      28.6  0.515648\n",
       "2020-02-24 18:00:00  2020-02-24 18:00:00      -16.4      28.6  0.622153\n",
       "2020-02-24 21:00:00  2020-02-24 21:00:00      -16.4      28.6  0.886938\n",
       "2020-02-25 00:00:00  2020-02-25 00:00:00      -16.4      28.6  1.008824\n",
       "2020-02-25 03:00:00  2020-02-25 03:00:00      -16.4      28.6  0.765258\n",
       "2020-02-25 06:00:00  2020-02-25 06:00:00      -16.4      28.6  0.606290"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "cams_ts = pd.read_csv('../../eodata/case_study/cams_ts.csv')\n",
    "cams_ts.index = cams_ts['time']\n",
    "cams_ts.index = pd.to_datetime(cams_ts.index, format='%Y-%m-%d %H:%M:%S')\n",
    "cams_ts"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "tags": []
   },
   "source": [
    "### <a id='compare_model_observations'></a> 5. Visually compare model forecasts with AERONET observations"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "All the three time-series (`aeronet_ts`, `sdswas_ts` and `cams_ts`) have now the same temporal resolution. The `DateTimeIndex` format allows for an efficient handling of time-series information and also allows us to combine the three time-series into one pandas.DataFrame. We can use the function `join()` and combine the two data frames `aeronet_ts` and `sdswas_ts`. We can repeat the process to join the third column from the `cams_ts`.\n",
    "\n",
    "The resulting dataframe has 37 row entries and three columns."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>AOD_500nm</th>\n",
       "      <th>od550_dust</th>\n",
       "      <th>duaod550</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2020-02-21 06:00:00</th>\n",
       "      <td>0.047250</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
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       "    <tr>\n",
       "      <th>2020-02-21 09:00:00</th>\n",
       "      <td>0.054734</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
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       "    <tr>\n",
       "      <th>2020-02-21 12:00:00</th>\n",
       "      <td>0.061003</td>\n",
       "      <td>0.000058</td>\n",
       "      <td>0.000568</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2020-02-21 15:00:00</th>\n",
       "      <td>0.092615</td>\n",
       "      <td>0.000019</td>\n",
       "      <td>0.000568</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2020-02-21 18:00:00</th>\n",
       "      <td>NaN</td>\n",
       "      <td>0.000027</td>\n",
       "      <td>0.001460</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2020-02-21 21:00:00</th>\n",
       "      <td>NaN</td>\n",
       "      <td>0.000207</td>\n",
       "      <td>0.002422</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2020-02-22 00:00:00</th>\n",
       "      <td>NaN</td>\n",
       "      <td>0.000890</td>\n",
       "      <td>0.005306</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2020-02-22 03:00:00</th>\n",
       "      <td>NaN</td>\n",
       "      <td>0.001751</td>\n",
       "      <td>0.011280</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2020-02-22 06:00:00</th>\n",
       "      <td>0.117495</td>\n",
       "      <td>0.009111</td>\n",
       "      <td>0.080772</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2020-02-22 09:00:00</th>\n",
       "      <td>0.147488</td>\n",
       "      <td>0.050932</td>\n",
       "      <td>0.203139</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2020-02-22 12:00:00</th>\n",
       "      <td>0.253696</td>\n",
       "      <td>0.203418</td>\n",
       "      <td>0.335806</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2020-02-22 15:00:00</th>\n",
       "      <td>0.375537</td>\n",
       "      <td>0.363704</td>\n",
       "      <td>0.501777</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2020-02-22 18:00:00</th>\n",
       "      <td>NaN</td>\n",
       "      <td>0.433835</td>\n",
       "      <td>0.581158</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2020-02-22 21:00:00</th>\n",
       "      <td>NaN</td>\n",
       "      <td>1.095499</td>\n",
       "      <td>0.755507</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2020-02-23 00:00:00</th>\n",
       "      <td>NaN</td>\n",
       "      <td>2.165373</td>\n",
       "      <td>1.102213</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2020-02-23 03:00:00</th>\n",
       "      <td>NaN</td>\n",
       "      <td>2.052836</td>\n",
       "      <td>1.210434</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2020-02-23 06:00:00</th>\n",
       "      <td>NaN</td>\n",
       "      <td>0.861120</td>\n",
       "      <td>1.024824</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2020-02-23 09:00:00</th>\n",
       "      <td>NaN</td>\n",
       "      <td>0.753394</td>\n",
       "      <td>0.905959</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2020-02-23 12:00:00</th>\n",
       "      <td>NaN</td>\n",
       "      <td>0.466931</td>\n",
       "      <td>0.777000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2020-02-23 15:00:00</th>\n",
       "      <td>NaN</td>\n",
       "      <td>0.354274</td>\n",
       "      <td>0.603338</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2020-02-23 18:00:00</th>\n",
       "      <td>NaN</td>\n",
       "      <td>0.320627</td>\n",
       "      <td>0.545107</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2020-02-23 21:00:00</th>\n",
       "      <td>NaN</td>\n",
       "      <td>0.231225</td>\n",
       "      <td>0.492370</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2020-02-24 00:00:00</th>\n",
       "      <td>NaN</td>\n",
       "      <td>0.179565</td>\n",
       "      <td>0.364715</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2020-02-24 03:00:00</th>\n",
       "      <td>NaN</td>\n",
       "      <td>0.221447</td>\n",
       "      <td>0.281077</td>\n",
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       "    <tr>\n",
       "      <th>2020-02-24 06:00:00</th>\n",
       "      <td>NaN</td>\n",
       "      <td>0.477572</td>\n",
       "      <td>0.170247</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2020-02-24 09:00:00</th>\n",
       "      <td>NaN</td>\n",
       "      <td>0.700524</td>\n",
       "      <td>0.219139</td>\n",
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       "    <tr>\n",
       "      <th>2020-02-24 12:00:00</th>\n",
       "      <td>NaN</td>\n",
       "      <td>1.151539</td>\n",
       "      <td>0.355102</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2020-02-24 15:00:00</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.515648</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2020-02-24 18:00:00</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.622153</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2020-02-24 21:00:00</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.886938</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2020-02-25 00:00:00</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1.008824</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2020-02-25 03:00:00</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.765258</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2020-02-25 06:00:00</th>\n",
       "      <td>0.674464</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.606290</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2020-02-25 09:00:00</th>\n",
       "      <td>0.581988</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2020-02-25 12:00:00</th>\n",
       "      <td>0.572472</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2020-02-25 15:00:00</th>\n",
       "      <td>0.407499</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2020-02-25 18:00:00</th>\n",
       "      <td>0.450671</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                     AOD_500nm  od550_dust  duaod550\n",
       "2020-02-21 06:00:00   0.047250         NaN       NaN\n",
       "2020-02-21 09:00:00   0.054734         NaN       NaN\n",
       "2020-02-21 12:00:00   0.061003    0.000058  0.000568\n",
       "2020-02-21 15:00:00   0.092615    0.000019  0.000568\n",
       "2020-02-21 18:00:00        NaN    0.000027  0.001460\n",
       "2020-02-21 21:00:00        NaN    0.000207  0.002422\n",
       "2020-02-22 00:00:00        NaN    0.000890  0.005306\n",
       "2020-02-22 03:00:00        NaN    0.001751  0.011280\n",
       "2020-02-22 06:00:00   0.117495    0.009111  0.080772\n",
       "2020-02-22 09:00:00   0.147488    0.050932  0.203139\n",
       "2020-02-22 12:00:00   0.253696    0.203418  0.335806\n",
       "2020-02-22 15:00:00   0.375537    0.363704  0.501777\n",
       "2020-02-22 18:00:00        NaN    0.433835  0.581158\n",
       "2020-02-22 21:00:00        NaN    1.095499  0.755507\n",
       "2020-02-23 00:00:00        NaN    2.165373  1.102213\n",
       "2020-02-23 03:00:00        NaN    2.052836  1.210434\n",
       "2020-02-23 06:00:00        NaN    0.861120  1.024824\n",
       "2020-02-23 09:00:00        NaN    0.753394  0.905959\n",
       "2020-02-23 12:00:00        NaN    0.466931  0.777000\n",
       "2020-02-23 15:00:00        NaN    0.354274  0.603338\n",
       "2020-02-23 18:00:00        NaN    0.320627  0.545107\n",
       "2020-02-23 21:00:00        NaN    0.231225  0.492370\n",
       "2020-02-24 00:00:00        NaN    0.179565  0.364715\n",
       "2020-02-24 03:00:00        NaN    0.221447  0.281077\n",
       "2020-02-24 06:00:00        NaN    0.477572  0.170247\n",
       "2020-02-24 09:00:00        NaN    0.700524  0.219139\n",
       "2020-02-24 12:00:00        NaN    1.151539  0.355102\n",
       "2020-02-24 15:00:00        NaN         NaN  0.515648\n",
       "2020-02-24 18:00:00        NaN         NaN  0.622153\n",
       "2020-02-24 21:00:00        NaN         NaN  0.886938\n",
       "2020-02-25 00:00:00        NaN         NaN  1.008824\n",
       "2020-02-25 03:00:00        NaN         NaN  0.765258\n",
       "2020-02-25 06:00:00   0.674464         NaN  0.606290\n",
       "2020-02-25 09:00:00   0.581988         NaN       NaN\n",
       "2020-02-25 12:00:00   0.572472         NaN       NaN\n",
       "2020-02-25 15:00:00   0.407499         NaN       NaN\n",
       "2020-02-25 18:00:00   0.450671         NaN       NaN"
      ]
     },
     "execution_count": 23,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_merged = aeronet_ts_resample.join(sdswas_ts['od550_dust'])\n",
    "df_merged = df_merged.join(cams_ts['duaod550'])\n",
    "df_merged"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<br>"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The last step is now to plot the three columns of the pandas.DataFrame `df_merged` as three individual line plots."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.legend.Legend at 0x7f12a26f02b0>"
      ]
     },
     "execution_count": 24,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1080x576 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Initiate a figure\n",
    "fig = plt.figure(figsize=(15,8))\n",
    "ax = plt.subplot()\n",
    "\n",
    "# Define the plotting function\n",
    "ax.plot(df_merged.od550_dust, 'o-', color='darkred',  label='SDS-WAS dust forecast - 550 nm')\n",
    "ax.plot(df_merged.duaod550, 'o-', color='blue', label='CAMS dust forecast - 550 nm')\n",
    "ax.plot(df_merged.AOD_500nm, 'o-', color='green', label='AERONET observations - 500 nm')\n",
    "\n",
    "# Customize the title and axes lables\n",
    "ax.set_title('\\nAerosol Optical Depth at 500 / 550 nm - Santa Cruz, Tenerife\\n', fontsize=20)\n",
    "ax.set_ylabel('~', fontsize=14)\n",
    "ax.set_xlabel('\\nHour', fontsize=14)\n",
    "\n",
    "# Customize the fontsize of the axes tickes\n",
    "plt.xticks(fontsize=14)\n",
    "plt.yticks(fontsize=14)\n",
    "\n",
    "# Add a gridline to the plot\n",
    "ax.grid(linestyle='--')\n",
    "\n",
    "plt.legend(fontsize=14, loc=2)\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<br>"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The plot above shows you that AERONET station observations can be used to evaluate model predictions - however, station observations show often also quite a few gaps. The strong dust event over Tenerife on 23/24 February 2021 was not measured at all by the AERONET station. For such gaps, model data are helpful to bridge such gaps. But the plot above shows also that the two models predicted especially the intensity of the dust event differently."
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3 (ipykernel)",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.8.18"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 4
}