{ "cells": [ { "cell_type": "markdown", "metadata": { "Collapsed": "false" }, "source": [ "# Solution 4" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "```{hint} \n", "Execute the notebook on the training platform >>\n", "```" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## About" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "> So far, we analysed Aerosol Optical Depth from different types of data (satellite, model-based and ground-based observations) for a single dust event. Let us now broaden our view and analyse the annual cycle in 2020 of Aerosol Optical Depth from AERONET and compare it with the CAMS global reanalysis data." ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Tasks" ] }, { "cell_type": "markdown", "metadata": { "tags": [] }, "source": [ "**1. Download and plot time-series of AERONET data for Santa Cruz, Tenerife in 2020**\n", " * **Hint** \n", " * [AERONET - Example notebook](./aeronet.ipynb)\n", " * you can select daily aggregates of the station observations by setting the `AVG` key to `AVG=20`\n", " * **Interpret the results:**\n", " * Have there been other times in 2020 with increased AOD values?\n", " * If yes, how could you find out if the increase in AOD is caused by dust? Try to find out by visualizing the AOD time-series together with another parameter from the AERONET data.\n", " * MSG SEVIRI Dust RGB and MODIS RGB quick looks might be helpful to get a more complete picture of other events that might have happened in 2020\n", "\n", "\n", "**2. Download CAMS global reanalysis (EAC4) and select 2020 time-series for *Santa Cruz, Tenerife***\n", " * **Hint**\n", " * [CAMS global forecast - Example notebook](./cams_global.ipynb) (**Note:** the notebook works with CAMS forecast data, but they have a similar data structure to the CAMS global reanalysis data)\n", " * Data access with the following specifications:\n", " > Variable on single levels: `Dust aerosol optical depth at 550 nm`
\n", " > Date: `Start=2020-01-01`, `End=2020-12-31`
\n", " > Time: `[00:00, 03:00, 06:00, 09:00, 12:00, 15:00, 18:00, 21:00]`
\n", " > Restricted area: `N: 30., W: -20, E: 14, S: 20.`
\n", " >Format: `netCDF`
\n", " * With the xarray function `sel()` and keyword argument `method='nearest'` you can select data based on coordinate information\n", " * We also recommend you to transform your xarray.DataArray into a pandas.DataFrame with the function `to_dataframe()`\n", " \n", "\n", "**3. Visualize both time-series of CAMS reanalysis and AERONET daily aggregates in one plot**\n", " * **Interpret the results:** What can you say about the annual cycle in 2020 of AOD in Santa Cruz, Tenerife?" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "
" ] }, { "cell_type": "markdown", "metadata": { "Collapsed": "false" }, "source": [ "**Load required libraries**" ] }, { "cell_type": "code", "execution_count": 1, "metadata": { "Collapsed": "false" }, "outputs": [], "source": [ "import xarray as xr\n", "import pandas as pd\n", "import wget\n", "\n", "from IPython.display import HTML\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", "\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": 3, "metadata": {}, "outputs": [], "source": [ "%run ../../functions.ipynb" ] }, { "cell_type": "markdown", "metadata": { "Collapsed": "false" }, "source": [ "
" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### 1. Download and plot time-series of AERONET data for Santa Cruz, Tenerife in 2020" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Define latitude / longtiude 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": 3, "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 daily averaged observations of Aerosol Optical Depth Level 1.5 data for the station Santa Cruz, Tenerife from 1 January to 31 December 2020." ] }, { "cell_type": "code", "execution_count": 7, "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': 1,\n", " 'day': 1,\n", " 'year2': 2020,\n", " 'month2': 12,\n", " 'day2': 31,\n", " 'AOD15': 1,\n", " 'AVG': 20\n", "}\n" ] }, { "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": 8, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "'https://aeronet.gsfc.nasa.gov/cgi-bin/print_web_data_v3?site=Santa_Cruz_Tenerife&year=2020&month=1&day=1&year2=2020&month2=12&day2=31&AOD15=1&AVG=20'" ] }, "execution_count": 8, "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": [ "
" ] }, { "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": null, "metadata": {}, "outputs": [], "source": [ "wget.download(url, '../../eodata/case_study/aeronet/2020_santa_cruz_tenerife_20.txt')" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "
" ] }, { "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 296 rows and 81 columns." ] }, { "cell_type": "code", "execution_count": 3, "metadata": {}, "outputs": [ { "data": { "text/html": [ "
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AERONET_SiteTime(hh:mm:ss)Day_of_YearAOD_1640nmAOD_1020nmAOD_870nmAOD_865nmAOD_779nmAOD_675nmAOD_667nm...N[440-675_Angstrom_Exponent]N[500-870_Angstrom_Exponent]N[340-440_Angstrom_Exponent]N[440-675_Angstrom_Exponent[Polar]]Data_Quality_LevelAERONET_Instrument_NumberAERONET_Site_NameSite_Latitude(Degrees)Site_Longitude(Degrees)Site_Elevation(m)<br>
Date(dd:mm:yyyy)
01:01:2020Santa_Cruz_Tenerife12:00:001.00.0611980.0762320.080599-999.0-999.00.085592-999.0...87.087.087.00.0lev151090.0Santa_Cruz_Tenerife28.472528-16.24736152.000000<br>
03:01:2020Santa_Cruz_Tenerife12:00:003.00.0396880.0522190.056409-999.0-999.00.063661-999.0...131.0131.0131.00.0lev151090.0Santa_Cruz_Tenerife28.472528-16.24736152.000000<br>
04:01:2020Santa_Cruz_Tenerife12:00:004.00.0462460.0588870.063752-999.0-999.00.073848-999.0...39.039.039.00.0lev151090.0Santa_Cruz_Tenerife28.472528-16.24736152.000000<br>
05:01:2020Santa_Cruz_Tenerife12:00:005.00.0397800.0506070.055417-999.0-999.00.065148-999.0...59.059.058.00.0lev151090.0Santa_Cruz_Tenerife28.472528-16.24736152.000000<br>
06:01:2020Santa_Cruz_Tenerife12:00:006.00.0250860.0336860.037128-999.0-999.00.042951-999.0...33.033.032.00.0lev151090.0Santa_Cruz_Tenerife28.472528-16.24736152.000000<br>
..................................................................
28:12:2020Santa_Cruz_Tenerife12:00:00363.00.2816310.3617060.378314-999.0-999.00.398796-999.0...95.095.095.00.0lev151090.0Santa_Cruz_Tenerife28.472528-16.24736152.000000<br>
29:12:2020Santa_Cruz_Tenerife12:00:00364.00.2251350.2799700.290588-999.0-999.00.304814-999.0...35.035.035.00.0lev151090.0Santa_Cruz_Tenerife28.472528-16.24736152.000000<br>
30:12:2020Santa_Cruz_Tenerife12:00:00365.00.0552330.0753590.079524-999.0-999.00.086272-999.0...34.034.034.00.0lev151090.0Santa_Cruz_Tenerife28.472528-16.24736152.000000<br>
31:12:2020Santa_Cruz_Tenerife12:00:00366.00.0201040.0250350.026090-999.0-999.00.028000-999.0...56.056.055.00.0lev151090.0Santa_Cruz_Tenerife28.472528-16.24736152.000000<br>
NaN</body></html>NaNNaNNaNNaNNaNNaNNaNNaNNaN...NaNNaNNaNNaNNaNNaNNaNNaNNaNNaN
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296 rows × 81 columns

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" ], "text/plain": [ " AERONET_Site Time(hh:mm:ss) Day_of_Year AOD_1640nm \\\n", "Date(dd:mm:yyyy) \n", "01:01:2020 Santa_Cruz_Tenerife 12:00:00 1.0 0.061198 \n", "03:01:2020 Santa_Cruz_Tenerife 12:00:00 3.0 0.039688 \n", "04:01:2020 Santa_Cruz_Tenerife 12:00:00 4.0 0.046246 \n", "05:01:2020 Santa_Cruz_Tenerife 12:00:00 5.0 0.039780 \n", "06:01:2020 Santa_Cruz_Tenerife 12:00:00 6.0 0.025086 \n", "... ... ... ... ... \n", "28:12:2020 Santa_Cruz_Tenerife 12:00:00 363.0 0.281631 \n", "29:12:2020 Santa_Cruz_Tenerife 12:00:00 364.0 0.225135 \n", "30:12:2020 Santa_Cruz_Tenerife 12:00:00 365.0 0.055233 \n", "31:12:2020 Santa_Cruz_Tenerife 12:00:00 366.0 0.020104 \n", "NaN NaN NaN NaN \n", "\n", " AOD_1020nm AOD_870nm AOD_865nm AOD_779nm AOD_675nm \\\n", "Date(dd:mm:yyyy) \n", "01:01:2020 0.076232 0.080599 -999.0 -999.0 0.085592 \n", "03:01:2020 0.052219 0.056409 -999.0 -999.0 0.063661 \n", "04:01:2020 0.058887 0.063752 -999.0 -999.0 0.073848 \n", "05:01:2020 0.050607 0.055417 -999.0 -999.0 0.065148 \n", "06:01:2020 0.033686 0.037128 -999.0 -999.0 0.042951 \n", "... ... ... ... ... ... \n", "28:12:2020 0.361706 0.378314 -999.0 -999.0 0.398796 \n", "29:12:2020 0.279970 0.290588 -999.0 -999.0 0.304814 \n", "30:12:2020 0.075359 0.079524 -999.0 -999.0 0.086272 \n", "31:12:2020 0.025035 0.026090 -999.0 -999.0 0.028000 \n", "NaN NaN NaN NaN NaN NaN \n", "\n", " AOD_667nm ... N[440-675_Angstrom_Exponent] \\\n", "Date(dd:mm:yyyy) ... \n", "01:01:2020 -999.0 ... 87.0 \n", "03:01:2020 -999.0 ... 131.0 \n", "04:01:2020 -999.0 ... 39.0 \n", "05:01:2020 -999.0 ... 59.0 \n", "06:01:2020 -999.0 ... 33.0 \n", "... ... ... ... \n", "28:12:2020 -999.0 ... 95.0 \n", "29:12:2020 -999.0 ... 35.0 \n", "30:12:2020 -999.0 ... 34.0 \n", "31:12:2020 -999.0 ... 56.0 \n", "NaN NaN ... NaN \n", "\n", " N[500-870_Angstrom_Exponent] N[340-440_Angstrom_Exponent] \\\n", "Date(dd:mm:yyyy) \n", "01:01:2020 87.0 87.0 \n", "03:01:2020 131.0 131.0 \n", "04:01:2020 39.0 39.0 \n", "05:01:2020 59.0 58.0 \n", "06:01:2020 33.0 32.0 \n", "... ... ... \n", "28:12:2020 95.0 95.0 \n", "29:12:2020 35.0 35.0 \n", "30:12:2020 34.0 34.0 \n", "31:12:2020 56.0 55.0 \n", "NaN NaN NaN \n", "\n", " N[440-675_Angstrom_Exponent[Polar]] Data_Quality_Level \\\n", "Date(dd:mm:yyyy) \n", "01:01:2020 0.0 lev15 \n", "03:01:2020 0.0 lev15 \n", "04:01:2020 0.0 lev15 \n", "05:01:2020 0.0 lev15 \n", "06:01:2020 0.0 lev15 \n", "... ... ... \n", "28:12:2020 0.0 lev15 \n", "29:12:2020 0.0 lev15 \n", "30:12:2020 0.0 lev15 \n", "31:12:2020 0.0 lev15 \n", "NaN NaN NaN \n", "\n", " AERONET_Instrument_Number AERONET_Site_Name \\\n", "Date(dd:mm:yyyy) \n", "01:01:2020 1090.0 Santa_Cruz_Tenerife \n", "03:01:2020 1090.0 Santa_Cruz_Tenerife \n", "04:01:2020 1090.0 Santa_Cruz_Tenerife \n", "05:01:2020 1090.0 Santa_Cruz_Tenerife \n", "06:01:2020 1090.0 Santa_Cruz_Tenerife \n", "... ... ... \n", "28:12:2020 1090.0 Santa_Cruz_Tenerife \n", "29:12:2020 1090.0 Santa_Cruz_Tenerife \n", "30:12:2020 1090.0 Santa_Cruz_Tenerife \n", "31:12:2020 1090.0 Santa_Cruz_Tenerife \n", "NaN NaN NaN \n", "\n", " Site_Latitude(Degrees) Site_Longitude(Degrees) \\\n", "Date(dd:mm:yyyy) \n", "01:01:2020 28.472528 -16.247361 \n", "03:01:2020 28.472528 -16.247361 \n", "04:01:2020 28.472528 -16.247361 \n", "05:01:2020 28.472528 -16.247361 \n", "06:01:2020 28.472528 -16.247361 \n", "... ... ... \n", "28:12:2020 28.472528 -16.247361 \n", "29:12:2020 28.472528 -16.247361 \n", "30:12:2020 28.472528 -16.247361 \n", "31:12:2020 28.472528 -16.247361 \n", "NaN NaN NaN \n", "\n", " Site_Elevation(m)
\n", "Date(dd:mm:yyyy) \n", "01:01:2020 52.000000
\n", "03:01:2020 52.000000
\n", "04:01:2020 52.000000
\n", "05:01:2020 52.000000
\n", "06:01:2020 52.000000
\n", "... ... \n", "28:12:2020 52.000000
\n", "29:12:2020 52.000000
\n", "30:12:2020 52.000000
\n", "31:12:2020 52.000000
\n", "NaN NaN \n", "\n", "[296 rows x 81 columns]" ] }, "execution_count": 3, "metadata": {}, "output_type": "execute_result" } ], "source": [ "df = pd.read_table('../../eodata/case_study/aeronet/2020_santa_cruz_tenerife_20.txt', delimiter=',', header=[7], index_col=1)\n", "df" ] }, { "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": 5, "metadata": {}, "outputs": [ { "data": { "text/html": [ "
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AERONET_SiteTime(hh:mm:ss)Day_of_YearAOD_1640nmAOD_1020nmAOD_870nmAOD_865nmAOD_779nmAOD_675nmAOD_667nm...N[440-675_Angstrom_Exponent]N[500-870_Angstrom_Exponent]N[340-440_Angstrom_Exponent]N[440-675_Angstrom_Exponent[Polar]]Data_Quality_LevelAERONET_Instrument_NumberAERONET_Site_NameSite_Latitude(Degrees)Site_Longitude(Degrees)Site_Elevation(m)<br>
Date(dd:mm:yyyy)
01:01:2020Santa_Cruz_Tenerife12:00:001.00.0611980.0762320.080599NaNNaN0.085592NaN...87.087.087.00.0lev151090.0Santa_Cruz_Tenerife28.472528-16.24736152.000000<br>
03:01:2020Santa_Cruz_Tenerife12:00:003.00.0396880.0522190.056409NaNNaN0.063661NaN...131.0131.0131.00.0lev151090.0Santa_Cruz_Tenerife28.472528-16.24736152.000000<br>
04:01:2020Santa_Cruz_Tenerife12:00:004.00.0462460.0588870.063752NaNNaN0.073848NaN...39.039.039.00.0lev151090.0Santa_Cruz_Tenerife28.472528-16.24736152.000000<br>
05:01:2020Santa_Cruz_Tenerife12:00:005.00.0397800.0506070.055417NaNNaN0.065148NaN...59.059.058.00.0lev151090.0Santa_Cruz_Tenerife28.472528-16.24736152.000000<br>
06:01:2020Santa_Cruz_Tenerife12:00:006.00.0250860.0336860.037128NaNNaN0.042951NaN...33.033.032.00.0lev151090.0Santa_Cruz_Tenerife28.472528-16.24736152.000000<br>
..................................................................
27:12:2020Santa_Cruz_Tenerife12:00:00362.00.0879750.1143140.119534NaNNaN0.125963NaN...80.080.080.00.0lev151090.0Santa_Cruz_Tenerife28.472528-16.24736152.000000<br>
28:12:2020Santa_Cruz_Tenerife12:00:00363.00.2816310.3617060.378314NaNNaN0.398796NaN...95.095.095.00.0lev151090.0Santa_Cruz_Tenerife28.472528-16.24736152.000000<br>
29:12:2020Santa_Cruz_Tenerife12:00:00364.00.2251350.2799700.290588NaNNaN0.304814NaN...35.035.035.00.0lev151090.0Santa_Cruz_Tenerife28.472528-16.24736152.000000<br>
30:12:2020Santa_Cruz_Tenerife12:00:00365.00.0552330.0753590.079524NaNNaN0.086272NaN...34.034.034.00.0lev151090.0Santa_Cruz_Tenerife28.472528-16.24736152.000000<br>
31:12:2020Santa_Cruz_Tenerife12:00:00366.00.0201040.0250350.026090NaNNaN0.028000NaN...56.056.055.00.0lev151090.0Santa_Cruz_Tenerife28.472528-16.24736152.000000<br>
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295 rows × 81 columns

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" ], "text/plain": [ " AERONET_Site Time(hh:mm:ss) Day_of_Year AOD_1640nm \\\n", "Date(dd:mm:yyyy) \n", "01:01:2020 Santa_Cruz_Tenerife 12:00:00 1.0 0.061198 \n", "03:01:2020 Santa_Cruz_Tenerife 12:00:00 3.0 0.039688 \n", "04:01:2020 Santa_Cruz_Tenerife 12:00:00 4.0 0.046246 \n", "05:01:2020 Santa_Cruz_Tenerife 12:00:00 5.0 0.039780 \n", "06:01:2020 Santa_Cruz_Tenerife 12:00:00 6.0 0.025086 \n", "... ... ... ... ... \n", "27:12:2020 Santa_Cruz_Tenerife 12:00:00 362.0 0.087975 \n", "28:12:2020 Santa_Cruz_Tenerife 12:00:00 363.0 0.281631 \n", "29:12:2020 Santa_Cruz_Tenerife 12:00:00 364.0 0.225135 \n", "30:12:2020 Santa_Cruz_Tenerife 12:00:00 365.0 0.055233 \n", "31:12:2020 Santa_Cruz_Tenerife 12:00:00 366.0 0.020104 \n", "\n", " AOD_1020nm AOD_870nm AOD_865nm AOD_779nm AOD_675nm \\\n", "Date(dd:mm:yyyy) \n", "01:01:2020 0.076232 0.080599 NaN NaN 0.085592 \n", "03:01:2020 0.052219 0.056409 NaN NaN 0.063661 \n", "04:01:2020 0.058887 0.063752 NaN NaN 0.073848 \n", "05:01:2020 0.050607 0.055417 NaN NaN 0.065148 \n", "06:01:2020 0.033686 0.037128 NaN NaN 0.042951 \n", "... ... ... ... ... ... \n", "27:12:2020 0.114314 0.119534 NaN NaN 0.125963 \n", "28:12:2020 0.361706 0.378314 NaN NaN 0.398796 \n", "29:12:2020 0.279970 0.290588 NaN NaN 0.304814 \n", "30:12:2020 0.075359 0.079524 NaN NaN 0.086272 \n", "31:12:2020 0.025035 0.026090 NaN NaN 0.028000 \n", "\n", " AOD_667nm ... N[440-675_Angstrom_Exponent] \\\n", "Date(dd:mm:yyyy) ... \n", "01:01:2020 NaN ... 87.0 \n", "03:01:2020 NaN ... 131.0 \n", "04:01:2020 NaN ... 39.0 \n", "05:01:2020 NaN ... 59.0 \n", "06:01:2020 NaN ... 33.0 \n", "... ... ... ... \n", "27:12:2020 NaN ... 80.0 \n", "28:12:2020 NaN ... 95.0 \n", "29:12:2020 NaN ... 35.0 \n", "30:12:2020 NaN ... 34.0 \n", "31:12:2020 NaN ... 56.0 \n", "\n", " N[500-870_Angstrom_Exponent] N[340-440_Angstrom_Exponent] \\\n", "Date(dd:mm:yyyy) \n", "01:01:2020 87.0 87.0 \n", "03:01:2020 131.0 131.0 \n", "04:01:2020 39.0 39.0 \n", "05:01:2020 59.0 58.0 \n", "06:01:2020 33.0 32.0 \n", "... ... ... \n", "27:12:2020 80.0 80.0 \n", "28:12:2020 95.0 95.0 \n", "29:12:2020 35.0 35.0 \n", "30:12:2020 34.0 34.0 \n", "31:12:2020 56.0 55.0 \n", "\n", " N[440-675_Angstrom_Exponent[Polar]] Data_Quality_Level \\\n", "Date(dd:mm:yyyy) \n", "01:01:2020 0.0 lev15 \n", "03:01:2020 0.0 lev15 \n", "04:01:2020 0.0 lev15 \n", "05:01:2020 0.0 lev15 \n", "06:01:2020 0.0 lev15 \n", "... ... ... \n", "27:12:2020 0.0 lev15 \n", "28:12:2020 0.0 lev15 \n", "29:12:2020 0.0 lev15 \n", "30:12:2020 0.0 lev15 \n", "31:12:2020 0.0 lev15 \n", "\n", " AERONET_Instrument_Number AERONET_Site_Name \\\n", "Date(dd:mm:yyyy) \n", "01:01:2020 1090.0 Santa_Cruz_Tenerife \n", "03:01:2020 1090.0 Santa_Cruz_Tenerife \n", "04:01:2020 1090.0 Santa_Cruz_Tenerife \n", "05:01:2020 1090.0 Santa_Cruz_Tenerife \n", "06:01:2020 1090.0 Santa_Cruz_Tenerife \n", "... ... ... \n", "27:12:2020 1090.0 Santa_Cruz_Tenerife \n", "28:12:2020 1090.0 Santa_Cruz_Tenerife \n", "29:12:2020 1090.0 Santa_Cruz_Tenerife \n", "30:12:2020 1090.0 Santa_Cruz_Tenerife \n", "31:12:2020 1090.0 Santa_Cruz_Tenerife \n", "\n", " Site_Latitude(Degrees) Site_Longitude(Degrees) \\\n", "Date(dd:mm:yyyy) \n", "01:01:2020 28.472528 -16.247361 \n", "03:01:2020 28.472528 -16.247361 \n", "04:01:2020 28.472528 -16.247361 \n", "05:01:2020 28.472528 -16.247361 \n", "06:01:2020 28.472528 -16.247361 \n", "... ... ... \n", "27:12:2020 28.472528 -16.247361 \n", "28:12:2020 28.472528 -16.247361 \n", "29:12:2020 28.472528 -16.247361 \n", "30:12:2020 28.472528 -16.247361 \n", "31:12:2020 28.472528 -16.247361 \n", "\n", " Site_Elevation(m)
\n", "Date(dd:mm:yyyy) \n", "01:01:2020 52.000000
\n", "03:01:2020 52.000000
\n", "04:01:2020 52.000000
\n", "05:01:2020 52.000000
\n", "06:01:2020 52.000000
\n", "... ... \n", "27:12:2020 52.000000
\n", "28:12:2020 52.000000
\n", "29:12:2020 52.000000
\n", "30:12:2020 52.000000
\n", "31:12:2020 52.000000
\n", "\n", "[295 rows x 81 columns]" ] }, "execution_count": 5, "metadata": {}, "output_type": "execute_result" } ], "source": [ "df = df.dropna()\n", "df = df.replace(-999.0, np.nan)\n", "df" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Let us now convert the index entry to a `DateTimeIndex` format with the function `to_datetime()`. Important here, you have to specify the format of the index string: `%d:%m:%Y`.\n", "\n", "You see below that we do not have observations for every day. E.g on 2 January 2020, the data frame does not list any entry." ] }, { "cell_type": "code", "execution_count": 6, "metadata": {}, "outputs": [ { "data": { "text/html": [ "
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AERONET_SiteTime(hh:mm:ss)Day_of_YearAOD_1640nmAOD_1020nmAOD_870nmAOD_865nmAOD_779nmAOD_675nmAOD_667nm...N[440-675_Angstrom_Exponent]N[500-870_Angstrom_Exponent]N[340-440_Angstrom_Exponent]N[440-675_Angstrom_Exponent[Polar]]Data_Quality_LevelAERONET_Instrument_NumberAERONET_Site_NameSite_Latitude(Degrees)Site_Longitude(Degrees)Site_Elevation(m)<br>
Date(dd:mm:yyyy)
2020-01-01Santa_Cruz_Tenerife12:00:001.00.0611980.0762320.080599NaNNaN0.085592NaN...87.087.087.00.0lev151090.0Santa_Cruz_Tenerife28.472528-16.24736152.000000<br>
2020-01-03Santa_Cruz_Tenerife12:00:003.00.0396880.0522190.056409NaNNaN0.063661NaN...131.0131.0131.00.0lev151090.0Santa_Cruz_Tenerife28.472528-16.24736152.000000<br>
2020-01-04Santa_Cruz_Tenerife12:00:004.00.0462460.0588870.063752NaNNaN0.073848NaN...39.039.039.00.0lev151090.0Santa_Cruz_Tenerife28.472528-16.24736152.000000<br>
2020-01-05Santa_Cruz_Tenerife12:00:005.00.0397800.0506070.055417NaNNaN0.065148NaN...59.059.058.00.0lev151090.0Santa_Cruz_Tenerife28.472528-16.24736152.000000<br>
2020-01-06Santa_Cruz_Tenerife12:00:006.00.0250860.0336860.037128NaNNaN0.042951NaN...33.033.032.00.0lev151090.0Santa_Cruz_Tenerife28.472528-16.24736152.000000<br>
..................................................................
2020-12-27Santa_Cruz_Tenerife12:00:00362.00.0879750.1143140.119534NaNNaN0.125963NaN...80.080.080.00.0lev151090.0Santa_Cruz_Tenerife28.472528-16.24736152.000000<br>
2020-12-28Santa_Cruz_Tenerife12:00:00363.00.2816310.3617060.378314NaNNaN0.398796NaN...95.095.095.00.0lev151090.0Santa_Cruz_Tenerife28.472528-16.24736152.000000<br>
2020-12-29Santa_Cruz_Tenerife12:00:00364.00.2251350.2799700.290588NaNNaN0.304814NaN...35.035.035.00.0lev151090.0Santa_Cruz_Tenerife28.472528-16.24736152.000000<br>
2020-12-30Santa_Cruz_Tenerife12:00:00365.00.0552330.0753590.079524NaNNaN0.086272NaN...34.034.034.00.0lev151090.0Santa_Cruz_Tenerife28.472528-16.24736152.000000<br>
2020-12-31Santa_Cruz_Tenerife12:00:00366.00.0201040.0250350.026090NaNNaN0.028000NaN...56.056.055.00.0lev151090.0Santa_Cruz_Tenerife28.472528-16.24736152.000000<br>
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295 rows × 81 columns

\n", "
" ], "text/plain": [ " AERONET_Site Time(hh:mm:ss) Day_of_Year AOD_1640nm \\\n", "Date(dd:mm:yyyy) \n", "2020-01-01 Santa_Cruz_Tenerife 12:00:00 1.0 0.061198 \n", "2020-01-03 Santa_Cruz_Tenerife 12:00:00 3.0 0.039688 \n", "2020-01-04 Santa_Cruz_Tenerife 12:00:00 4.0 0.046246 \n", "2020-01-05 Santa_Cruz_Tenerife 12:00:00 5.0 0.039780 \n", "2020-01-06 Santa_Cruz_Tenerife 12:00:00 6.0 0.025086 \n", "... ... ... ... ... \n", "2020-12-27 Santa_Cruz_Tenerife 12:00:00 362.0 0.087975 \n", "2020-12-28 Santa_Cruz_Tenerife 12:00:00 363.0 0.281631 \n", "2020-12-29 Santa_Cruz_Tenerife 12:00:00 364.0 0.225135 \n", "2020-12-30 Santa_Cruz_Tenerife 12:00:00 365.0 0.055233 \n", "2020-12-31 Santa_Cruz_Tenerife 12:00:00 366.0 0.020104 \n", "\n", " AOD_1020nm AOD_870nm AOD_865nm AOD_779nm AOD_675nm \\\n", "Date(dd:mm:yyyy) \n", "2020-01-01 0.076232 0.080599 NaN NaN 0.085592 \n", "2020-01-03 0.052219 0.056409 NaN NaN 0.063661 \n", "2020-01-04 0.058887 0.063752 NaN NaN 0.073848 \n", "2020-01-05 0.050607 0.055417 NaN NaN 0.065148 \n", "2020-01-06 0.033686 0.037128 NaN NaN 0.042951 \n", "... ... ... ... ... ... \n", "2020-12-27 0.114314 0.119534 NaN NaN 0.125963 \n", "2020-12-28 0.361706 0.378314 NaN NaN 0.398796 \n", "2020-12-29 0.279970 0.290588 NaN NaN 0.304814 \n", "2020-12-30 0.075359 0.079524 NaN NaN 0.086272 \n", "2020-12-31 0.025035 0.026090 NaN NaN 0.028000 \n", "\n", " AOD_667nm ... N[440-675_Angstrom_Exponent] \\\n", "Date(dd:mm:yyyy) ... \n", "2020-01-01 NaN ... 87.0 \n", "2020-01-03 NaN ... 131.0 \n", "2020-01-04 NaN ... 39.0 \n", "2020-01-05 NaN ... 59.0 \n", "2020-01-06 NaN ... 33.0 \n", "... ... ... ... \n", "2020-12-27 NaN ... 80.0 \n", "2020-12-28 NaN ... 95.0 \n", "2020-12-29 NaN ... 35.0 \n", "2020-12-30 NaN ... 34.0 \n", "2020-12-31 NaN ... 56.0 \n", "\n", " N[500-870_Angstrom_Exponent] N[340-440_Angstrom_Exponent] \\\n", "Date(dd:mm:yyyy) \n", "2020-01-01 87.0 87.0 \n", "2020-01-03 131.0 131.0 \n", "2020-01-04 39.0 39.0 \n", "2020-01-05 59.0 58.0 \n", "2020-01-06 33.0 32.0 \n", "... ... ... \n", "2020-12-27 80.0 80.0 \n", "2020-12-28 95.0 95.0 \n", "2020-12-29 35.0 35.0 \n", "2020-12-30 34.0 34.0 \n", "2020-12-31 56.0 55.0 \n", "\n", " N[440-675_Angstrom_Exponent[Polar]] Data_Quality_Level \\\n", "Date(dd:mm:yyyy) \n", "2020-01-01 0.0 lev15 \n", "2020-01-03 0.0 lev15 \n", "2020-01-04 0.0 lev15 \n", "2020-01-05 0.0 lev15 \n", "2020-01-06 0.0 lev15 \n", "... ... ... \n", "2020-12-27 0.0 lev15 \n", "2020-12-28 0.0 lev15 \n", "2020-12-29 0.0 lev15 \n", "2020-12-30 0.0 lev15 \n", "2020-12-31 0.0 lev15 \n", "\n", " AERONET_Instrument_Number AERONET_Site_Name \\\n", "Date(dd:mm:yyyy) \n", "2020-01-01 1090.0 Santa_Cruz_Tenerife \n", "2020-01-03 1090.0 Santa_Cruz_Tenerife \n", "2020-01-04 1090.0 Santa_Cruz_Tenerife \n", "2020-01-05 1090.0 Santa_Cruz_Tenerife \n", "2020-01-06 1090.0 Santa_Cruz_Tenerife \n", "... ... ... \n", "2020-12-27 1090.0 Santa_Cruz_Tenerife \n", "2020-12-28 1090.0 Santa_Cruz_Tenerife \n", "2020-12-29 1090.0 Santa_Cruz_Tenerife \n", "2020-12-30 1090.0 Santa_Cruz_Tenerife \n", "2020-12-31 1090.0 Santa_Cruz_Tenerife \n", "\n", " Site_Latitude(Degrees) Site_Longitude(Degrees) \\\n", "Date(dd:mm:yyyy) \n", "2020-01-01 28.472528 -16.247361 \n", "2020-01-03 28.472528 -16.247361 \n", "2020-01-04 28.472528 -16.247361 \n", "2020-01-05 28.472528 -16.247361 \n", "2020-01-06 28.472528 -16.247361 \n", "... ... ... \n", "2020-12-27 28.472528 -16.247361 \n", "2020-12-28 28.472528 -16.247361 \n", "2020-12-29 28.472528 -16.247361 \n", "2020-12-30 28.472528 -16.247361 \n", "2020-12-31 28.472528 -16.247361 \n", "\n", " Site_Elevation(m)
\n", "Date(dd:mm:yyyy) \n", "2020-01-01 52.000000
\n", "2020-01-03 52.000000
\n", "2020-01-04 52.000000
\n", "2020-01-05 52.000000
\n", "2020-01-06 52.000000
\n", "... ... \n", "2020-12-27 52.000000
\n", "2020-12-28 52.000000
\n", "2020-12-29 52.000000
\n", "2020-12-30 52.000000
\n", "2020-12-31 52.000000
\n", "\n", "[295 rows x 81 columns]" ] }, "execution_count": 6, "metadata": {}, "output_type": "execute_result" } ], "source": [ "df.index = pd.to_datetime(df.index, format = '%d:%m:%Y')\n", "df" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "
" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "We can now plot the column `AOD_500nm` as annual time-series. You see that the station `Santa Cruz, Tenerife` was affected by other dust events later in 2020." ] }, { "cell_type": "code", "execution_count": 7, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "" ] }, "execution_count": 7, "metadata": {}, "output_type": "execute_result" }, { "data": { "image/png": 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\n", 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" ] }, "metadata": { "needs_background": "light" }, "output_type": "display_data" } ], "source": [ "# Initiate a figure\n", "fig = plt.figure(figsize=(20,8))\n", "ax = plt.subplot()\n", "\n", "# Define the plotting function\n", "ax.plot(df.AOD_500nm, 'o-', color='green', label='AERONET observations')\n", "\n", "# Customize the title and axes lables\n", "ax.set_title('\\nAerosol Optical Depth at 500 nm - Santa Cruz Tenerife\\n', fontsize=20)\n", "ax.set_ylabel('~', fontsize=14)\n", "ax.set_xlabel('\\nDay', 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=16, loc=2)\n" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "
" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "The next question is now, how you can find out, if the strong increase of AOD at the end of August 2020 was because of dust? For this to find out, you can use the `Angstrom Exponent`, which gives us an indication of the particle size. If the `Angstrom Exponent` is below 0.6, then it is an indication that the increase of AOD is caused by coarser dust particles.\n", "\n", "Let us visualize the AOD at 500nm for 2020 together with the `Angstrom Exponent 440-675nm`." ] }, { "cell_type": "code", "execution_count": 9, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "" ] }, "execution_count": 9, "metadata": {}, "output_type": "execute_result" }, { "data": { "image/png": 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\n", 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" ] }, "metadata": { "needs_background": "light" }, "output_type": "display_data" } ], "source": [ "# Initiate a figure\n", "fig = plt.figure(figsize=(20,8))\n", "ax = plt.subplot()\n", "\n", "# Define the plotting function\n", "ax.plot(df.AOD_500nm, 'o-', color='green', label='AERONET observations')\n", "ax.plot(df['440-675_Angstrom_Exponent'], '-', color='lightgrey', label='Angstrom Exponent - 440-675nm')\n", "\n", "plt.axhline(y=0.6, color='r', linestyle='dotted', label='Angstrom Exponent <0.6 is dust')\n", "\n", "# Customize the title and axes lables\n", "ax.set_title('\\nAerosol Optical Depth at 500 nm - Santa Cruz Tenerife\\n', fontsize=20)\n", "ax.set_ylabel('~', fontsize=14)\n", "ax.set_xlabel('\\nDay', 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=16, loc=2)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Above, you see that the Angstrom Exponent during the high AOD values at the end of August is very low. Hence, we could interpret this event as a strong dust intrusion. But is this really the case? You can also check [here](https://sds-was.aemet.es/forecast-products/dust-observations/msg-2013-eumetsat) the MSG SEVIRI Dust RGB for e.g. 26 August 2020 and [here](https://worldview.earthdata.nasa.gov/?v=-39.451155087380556,13.025874527486357,5.067364489712844,36.350274677008436&l=Reference_Labels_15m(hidden),Reference_Features_15m(hidden),Coastlines_15m,MODIS_Aqua_CorrectedReflectance_TrueColor(hidden),MODIS_Terra_CorrectedReflectance_TrueColor&lg=false&t=2020-08-26-T00%3A00%3A00Z) the MODIS RGB to better understand the event and what could have caused the high AOD values." ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### 2. Download CAMS global reanalysis (EAC4) and select 2020 time-series for Santa Cruz, Tenerife" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "First, we have to download the `CAMS global reanalysis (EAC4)` from the Copernicus Atmosphere Data Store with the following specifications:\n", "* Variable on single levels: `Dust aerosol optical depth at 550 nm`\n", "* Date: `Start=2020-01-01`, `End=2020-12-31`\n", "* Time: `[00:00, 03:00, 06:00, 09:00, 12:00, 15:00, 18:00, 21:00]`\n", "* Restricted area: `N: 30., W: -20, E: 14, S: 20.`\n", "* Format: `netCDF`\n", "\n", "See `CDSAPI` request below." ] }, { "cell_type": "code", "execution_count": 9, "metadata": {}, "outputs": [], "source": [ "URL = 'https://ads.atmosphere.copernicus.eu/api/v2'\n", "KEY = '######################'" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "import cdsapi\n", "\n", "c = cdsapi.Client(url=URL, key=KEY)\n", "\n", "c.retrieve(\n", " 'cams-global-reanalysis-eac4',\n", " {\n", " 'variable': 'dust_aerosol_optical_depth_550nm',\n", " 'date': '2020-01-01/2020-12-31',\n", " 'time': [\n", " '00:00', '03:00', '06:00',\n", " '09:00', '12:00', '15:00',\n", " '18:00', '21:00',\n", " ],\n", " 'area': [\n", " 30, -20, 20,\n", " 15,\n", " ],\n", " 'format': 'netcdf',\n", " },\n", " '../../eodata/case_study/cams/2020_dustAOD_cams_eac4.nc'}" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "
" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "The data is in netCDF, so we can open the netCDF file with the xarray function `open_dataset()`. We see that the data has three dimensions (`latitude`, `longitude`, `time`) and one data variable:\n", "* `duaod550`: Dust Aerosol Optical Depth at 550nm" ] }, { "cell_type": "code", "execution_count": 4, "metadata": {}, "outputs": [ { "data": { "text/html": [ "
\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "
<xarray.Dataset>\n",
       "Dimensions:    (latitude: 14, longitude: 47, time: 2928)\n",
       "Coordinates:\n",
       "  * longitude  (longitude) float32 -20.0 -19.25 -18.5 -17.75 ... 13.0 13.75 14.5\n",
       "  * latitude   (latitude) float32 29.75 29.0 28.25 27.5 ... 21.5 20.75 20.0\n",
       "  * time       (time) datetime64[ns] 2020-01-01 ... 2020-12-31T21:00:00\n",
       "Data variables:\n",
       "    duaod550   (time, latitude, longitude) float32 ...\n",
       "Attributes:\n",
       "    Conventions:  CF-1.6\n",
       "    history:      2021-11-10 14:13:05 GMT by grib_to_netcdf-2.23.0: /opt/ecmw...
" ], "text/plain": [ "\n", "Dimensions: (latitude: 14, longitude: 47, time: 2928)\n", "Coordinates:\n", " * longitude (longitude) float32 -20.0 -19.25 -18.5 -17.75 ... 13.0 13.75 14.5\n", " * latitude (latitude) float32 29.75 29.0 28.25 27.5 ... 21.5 20.75 20.0\n", " * time (time) datetime64[ns] 2020-01-01 ... 2020-12-31T21:00:00\n", "Data variables:\n", " duaod550 (time, latitude, longitude) float32 ...\n", "Attributes:\n", " Conventions: CF-1.6\n", " history: 2021-11-10 14:13:05 GMT by grib_to_netcdf-2.23.0: /opt/ecmw..." ] }, "execution_count": 4, "metadata": {}, "output_type": "execute_result" } ], "source": [ "file = xr.open_dataset('../../eodata/case_study/cams/2020_dustAOD_cams_eac4.nc')\n", "file" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "
" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Let us now store the data variable `Dust Aerosol Optical Depth (AOD) at 550nm` as `xarray.DataArray` with the name `duaod_cams`." ] }, { "cell_type": "code", "execution_count": 15, "metadata": {}, "outputs": [ { "data": { "text/html": [ "
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<xarray.DataArray 'duaod550' (time: 2928, latitude: 14, longitude: 47)>\n",
       "[1926624 values with dtype=float32]\n",
       "Coordinates:\n",
       "  * longitude  (longitude) float32 -20.0 -19.25 -18.5 -17.75 ... 13.0 13.75 14.5\n",
       "  * latitude   (latitude) float32 29.75 29.0 28.25 27.5 ... 21.5 20.75 20.0\n",
       "  * time       (time) datetime64[ns] 2020-01-01 ... 2020-12-31T21:00:00\n",
       "Attributes:\n",
       "    units:      ~\n",
       "    long_name:  Dust Aerosol Optical Depth at 550nm
" ], "text/plain": [ "\n", "[1926624 values with dtype=float32]\n", "Coordinates:\n", " * longitude (longitude) float32 -20.0 -19.25 -18.5 -17.75 ... 13.0 13.75 14.5\n", " * latitude (latitude) float32 29.75 29.0 28.25 27.5 ... 21.5 20.75 20.0\n", " * time (time) datetime64[ns] 2020-01-01 ... 2020-12-31T21:00:00\n", "Attributes:\n", " units: ~\n", " long_name: Dust Aerosol Optical Depth at 550nm" ] }, "execution_count": 15, "metadata": {}, "output_type": "execute_result" } ], "source": [ "duaod_cams = file['duaod550']\n", "duaod_cams" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "
" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Now, we can select the time-series of the grid point nearest to the station in Santa Cruz, Tenerife. We can use the function `sel()` to select data based on the longitude and latitude dimensions. The keyword argument `method='nearest'` selects the grid point entry closest to the station coordinates." ] }, { "cell_type": "code", "execution_count": 18, "metadata": {}, "outputs": [ { "data": { "text/html": [ "
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<xarray.DataArray 'duaod550' (time: 2928)>\n",
       "array([0.007244, 0.021985, 0.060917, ..., 0.005728, 0.004677, 0.003822],\n",
       "      dtype=float32)\n",
       "Coordinates:\n",
       "    longitude  float32 -16.25\n",
       "    latitude   float32 28.25\n",
       "  * time       (time) datetime64[ns] 2020-01-01 ... 2020-12-31T21:00:00\n",
       "Attributes:\n",
       "    units:      ~\n",
       "    long_name:  Dust Aerosol Optical Depth at 550nm
" ], "text/plain": [ "\n", "array([0.007244, 0.021985, 0.060917, ..., 0.005728, 0.004677, 0.003822],\n", " dtype=float32)\n", "Coordinates:\n", " longitude float32 -16.25\n", " latitude float32 28.25\n", " * time (time) datetime64[ns] 2020-01-01 ... 2020-12-31T21:00:00\n", "Attributes:\n", " units: ~\n", " long_name: Dust Aerosol Optical Depth at 550nm" ] }, "execution_count": 18, "metadata": {}, "output_type": "execute_result" } ], "source": [ "cams_ts = duaod_cams.sel(longitude=lon, latitude=lat, method='nearest')\n", "cams_ts" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "
" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "The next step is now to resample the 3-hourly time entries and aggregate it to daily averages. We can use a combination of the functions `resample()` and `mean()` to create daily averages." ] }, { "cell_type": "code", "execution_count": 19, "metadata": {}, "outputs": [ { "data": { "text/html": [ "
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       "       7.28429556e-02, 1.04822367e-02], dtype=float32)\n",
       "Coordinates:\n",
       "  * time       (time) datetime64[ns] 2020-01-01 2020-01-02 ... 2020-12-31\n",
       "    longitude  float32 -16.25\n",
       "    latitude   float32 28.25
" ], "text/plain": [ "\n", "array([6.26771897e-02, 1.21897265e-01, 6.45051599e-02, 6.81166351e-03,\n", " 1.11380219e-03, 2.36323476e-03, 1.70692801e-03, 2.12018192e-03,\n", " 2.59661674e-03, 1.27915293e-02, 2.31949985e-03, 1.18672848e-03,\n", " 6.82000369e-02, 9.66019481e-02, 1.06641322e-01, 5.51756322e-02,\n", " 6.65611029e-03, 3.57866287e-03, 5.35264611e-04, 5.40107489e-04,\n", " 1.26868486e-04, 2.28971243e-04, 5.88308275e-03, 7.26868212e-03,\n", " 6.05811179e-03, 4.26903367e-03, 1.19990855e-02, 9.62656736e-03,\n", " 8.38011652e-02, 1.65890560e-01, 1.58602878e-01, 6.69117123e-02,\n", " 4.65308130e-03, 7.19046295e-02, 3.36282521e-01, 3.49263191e-01,\n", " 8.95184875e-02, 6.27355129e-02, 1.27191618e-01, 1.28401518e-02,\n", " 2.42699534e-02, 7.66301751e-02, 1.55321255e-01, 2.17939615e-01,\n", " 4.48830277e-01, 3.32869619e-01, 6.46899045e-02, 5.61430752e-02,\n", " 2.86746323e-02, 1.25387311e-02, 2.85427272e-03, 4.57480550e-04,\n", " 1.68058857e-01, 7.46165454e-01, 5.34614563e-01, 4.31294233e-01,\n", " 1.71836376e-01, 5.27052581e-03, 2.51790732e-02, 1.20623484e-01,\n", " 1.22927919e-01, 5.87635338e-02, 9.09666717e-03, 1.24505162e-03,\n", " 9.78703797e-03, 1.80081427e-02, 3.58837843e-03, 1.31649762e-01,\n", " 1.49847046e-01, 5.58611155e-02, 8.08890164e-02, 1.23239055e-01,\n", " 1.12538531e-01, 1.09684721e-01, 2.63993740e-02, 5.64485788e-03,\n", " 1.56059861e-04, 5.24136424e-03, 1.17954418e-01, 1.49613649e-01,\n", "...\n", " 1.51704177e-01, 9.84493941e-02, 4.13101166e-02, 2.73425579e-02,\n", " 4.11496758e-02, 9.22799408e-02, 2.60631740e-03, 1.46314502e-04,\n", " 1.17123127e-04, 8.80983472e-03, 8.88815969e-02, 9.45503116e-02,\n", " 3.46885324e-02, 3.57580930e-02, 4.34055179e-02, 9.80604440e-02,\n", " 2.00432733e-01, 8.51429701e-02, 5.67653924e-02, 8.52298737e-03,\n", " 4.79409099e-03, 3.50505114e-04, 1.65790319e-04, 5.30421734e-04,\n", " 9.14469361e-04, 1.81388855e-03, 5.34346700e-03, 2.23447382e-02,\n", " 2.38129646e-02, 5.62014282e-02, 1.12864256e-01, 6.75437450e-02,\n", " 1.27838194e-01, 6.38682693e-02, 5.41449189e-02, 5.96240610e-02,\n", " 5.70862293e-02, 7.16469884e-02, 3.21166962e-02, 5.39456010e-02,\n", " 1.17779389e-01, 7.62315542e-02, 5.20691276e-04, 5.88297844e-05,\n", " 1.17167830e-04, 3.99127603e-04, 8.31186771e-05, 3.01882625e-04,\n", " 3.35916877e-04, 1.56059861e-04, 1.38117373e-03, 3.31127644e-03,\n", " 3.93986702e-05, 7.88062811e-04, 3.60235572e-04, 2.97054648e-04,\n", " 2.97039747e-04, 2.58132815e-04, 6.32479787e-04, 6.32479787e-04,\n", " 3.45647335e-04, 1.31756067e-04, 4.42564487e-05, 1.07407570e-04,\n", " 1.12280250e-04, 1.02549791e-04, 1.26838684e-04, 7.34031200e-05,\n", " 1.72642767e-02, 1.09888896e-01, 2.62545496e-01, 3.42311025e-01,\n", " 9.04908031e-02, 6.92404509e-02, 2.15455323e-01, 1.82337582e-01,\n", " 7.28429556e-02, 1.04822367e-02], dtype=float32)\n", "Coordinates:\n", " * time (time) datetime64[ns] 2020-01-01 2020-01-02 ... 2020-12-31\n", " longitude float32 -16.25\n", " latitude float32 28.25" ] }, "execution_count": 19, "metadata": {}, "output_type": "execute_result" } ], "source": [ "cams_ts_resample = cams_ts.resample(time='1D').mean()\n", "cams_ts_resample" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "
" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "A closer look at the `time` dimension shows us that we now have an entry for each day in 2020." ] }, { "cell_type": "code", "execution_count": 20, "metadata": {}, "outputs": [ { "data": { "text/html": [ "
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<xarray.DataArray 'time' (time: 366)>\n",
       "array(['2020-01-01T00:00:00.000000000', '2020-01-02T00:00:00.000000000',\n",
       "       '2020-01-03T00:00:00.000000000', ..., '2020-12-29T00:00:00.000000000',\n",
       "       '2020-12-30T00:00:00.000000000', '2020-12-31T00:00:00.000000000'],\n",
       "      dtype='datetime64[ns]')\n",
       "Coordinates:\n",
       "  * time       (time) datetime64[ns] 2020-01-01 2020-01-02 ... 2020-12-31\n",
       "    longitude  float32 -16.25\n",
       "    latitude   float32 28.25
" ], "text/plain": [ "\n", "array(['2020-01-01T00:00:00.000000000', '2020-01-02T00:00:00.000000000',\n", " '2020-01-03T00:00:00.000000000', ..., '2020-12-29T00:00:00.000000000',\n", " '2020-12-30T00:00:00.000000000', '2020-12-31T00:00:00.000000000'],\n", " dtype='datetime64[ns]')\n", "Coordinates:\n", " * time (time) datetime64[ns] 2020-01-01 2020-01-02 ... 2020-12-31\n", " longitude float32 -16.25\n", " latitude float32 28.25" ] }, "execution_count": 20, "metadata": {}, "output_type": "execute_result" } ], "source": [ "cams_ts_resample.time" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "
" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Now, we can convert the `xarray.DataArray` to a `pandas.DataFrame`, as pandas is more efficient to handle time-series data. The function `to_dataframe()` easily converts a data array to a dataframe. The resulting dataframe has 366 rows and 3 columns." ] }, { "cell_type": "code", "execution_count": 21, "metadata": {}, "outputs": [ { "data": { "text/html": [ "
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longitudelatitudeduaod550
time
2020-01-01-16.2528.250.062677
2020-01-02-16.2528.250.121897
2020-01-03-16.2528.250.064505
2020-01-04-16.2528.250.006812
2020-01-05-16.2528.250.001114
............
2020-12-27-16.2528.250.069240
2020-12-28-16.2528.250.215455
2020-12-29-16.2528.250.182338
2020-12-30-16.2528.250.072843
2020-12-31-16.2528.250.010482
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366 rows × 3 columns

\n", "
" ], "text/plain": [ " longitude latitude duaod550\n", "time \n", "2020-01-01 -16.25 28.25 0.062677\n", "2020-01-02 -16.25 28.25 0.121897\n", "2020-01-03 -16.25 28.25 0.064505\n", "2020-01-04 -16.25 28.25 0.006812\n", "2020-01-05 -16.25 28.25 0.001114\n", "... ... ... ...\n", "2020-12-27 -16.25 28.25 0.069240\n", "2020-12-28 -16.25 28.25 0.215455\n", "2020-12-29 -16.25 28.25 0.182338\n", "2020-12-30 -16.25 28.25 0.072843\n", "2020-12-31 -16.25 28.25 0.010482\n", "\n", "[366 rows x 3 columns]" ] }, "execution_count": 21, "metadata": {}, "output_type": "execute_result" } ], "source": [ "cams_ts_df = cams_ts_resample.to_dataframe()\n", "cams_ts_df" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### 3. Combine both annual time-series and visualize both in one plot" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Let us now use the function `join()` and combine the two time-series `cams_ts_df` and `df['AOD_500nm]`. The resulting dataframe has 366 rows and 4 columns." ] }, { "cell_type": "code", "execution_count": 24, "metadata": {}, "outputs": [ { "data": { "text/html": [ "
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longitudelatitudeduaod550AOD_500nm
time
2020-01-01-16.2528.250.0626770.094487
2020-01-02-16.2528.250.121897NaN
2020-01-03-16.2528.250.0645050.075765
2020-01-04-16.2528.250.0068120.098110
2020-01-05-16.2528.250.0011140.085672
...............
2020-12-27-16.2528.250.0692400.134366
2020-12-28-16.2528.250.2154550.415433
2020-12-29-16.2528.250.1823380.320463
2020-12-30-16.2528.250.0728430.095342
2020-12-31-16.2528.250.0104820.033297
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366 rows × 4 columns

\n", "
" ], "text/plain": [ " longitude latitude duaod550 AOD_500nm\n", "time \n", "2020-01-01 -16.25 28.25 0.062677 0.094487\n", "2020-01-02 -16.25 28.25 0.121897 NaN\n", "2020-01-03 -16.25 28.25 0.064505 0.075765\n", "2020-01-04 -16.25 28.25 0.006812 0.098110\n", "2020-01-05 -16.25 28.25 0.001114 0.085672\n", "... ... ... ... ...\n", "2020-12-27 -16.25 28.25 0.069240 0.134366\n", "2020-12-28 -16.25 28.25 0.215455 0.415433\n", "2020-12-29 -16.25 28.25 0.182338 0.320463\n", "2020-12-30 -16.25 28.25 0.072843 0.095342\n", "2020-12-31 -16.25 28.25 0.010482 0.033297\n", "\n", "[366 rows x 4 columns]" ] }, "execution_count": 24, "metadata": {}, "output_type": "execute_result" } ], "source": [ "df_combined = cams_ts_df.join(df['AOD_500nm'])\n", "df_combined" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Let us safe the pandas dataframe as csv file. This allows us to easily load the time-series again at a later stage. You can use the function `to_csv()` to save a pandas.DataFrame as csv." ] }, { "cell_type": "code", "execution_count": 33, "metadata": {}, "outputs": [], "source": [ "df_combined.to_csv(\"../../eodata/case_study/2020_ts_cams_aeronet.csv\", index_label='time')" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "
" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "The last step is now to plot the two columns of the pandas.DataFrame `df_combined` as two individual line plots." ] }, { "cell_type": "code", "execution_count": 23, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "" ] }, "execution_count": 23, "metadata": {}, "output_type": "execute_result" }, { "data": { "image/png": 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\n", 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" ] }, "metadata": { "needs_background": "light" }, "output_type": "display_data" } ], "source": [ "# Initiate a figure\n", "fig = plt.figure(figsize=(20,8))\n", "ax = plt.subplot()\n", "\n", "# Define the plotting function\n", "ax.plot(df_combined.duaod550, '-', color='blue', label='CAMS global reanalysis (EAC4) - 550 nm')\n", "ax.plot(df_combined.AOD_500nm, '-', color='green', label='AERONET observations - 500 nm')\n", "plt.axhline(y=0.6, color='r', linestyle='dotted', label='PM10 daily limit')\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(cams_ts.units, fontsize=14)\n", "ax.set_xlabel('\\nDay', 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": [ "You see in the plot above that the model and the AERONET observations follow a similar annual cycle of AOD in 2020 for the Santa Cruz station in Tenerife. You also see that for higher AOD values measured by AERONET, the CAMS model mostly underpredicts the AOD intensity." ] } ], "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 }