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AERONET

AERONET#

Hint

Execute the notebook on the training platform >>

The AERONET (AErosol RObotic NETwork) project is a federation of ground-based remote sensing aerosol networks established by NASA and LOA-PHOTONS (CNRS) and is greatly expanded by collaborators from national agencies, institutes, universities, individual scientists, and partners. The program provides a long-term (more than 25 years), continuous and readily accessible public domain database of aerosol optical, microphysical and radiative properties for aerosol research and characterization, validation of satellite retrievals, and synergism with other databases. The network imposes standardization of instruments, calibration, processing and distribution.

AERONET collaboration provides globally distributed observations of spectral aerosol optical Depth (AOD), inversion products, and precipitable water in diverse aerosol regimes. Aerosol optical depth data are computed for three data quality levels: Level 1.0 (unscreened), Level 1.5 (cloud-screened), and Level 2.0 (cloud screened and quality-assured). Inversions, precipitable water, and other AOD-dependent products are derived from these levels and may implement additional quality checks.

You can see an overview of all available AERONET Site Names here.

Basic facts

Spatial coverage: Observation stations worldwide
Temporal resolution: sub-daily and daily
Temporal coverage: since 1993
Data format: txt
Versions: Level 1.0 (unscreened), Level 1.5 (cloud-screened), Level 2.0 (cloud screened and quality-assured)

How to access the data

AERONET offers a web service which allows you to request and save aeronet data via wget, which is a command to download files from the internet. The AERONET web service endpoint is available under https://aeronet.gsfc.nasa.gov/cgi-bin/print_web_data_v3 and a detailed documentation of how to construct requests can be found here.

The first part of this notebook (1 - Download AERONET data for a specific station and time period) shows you how to request AERONET data with a wget command.

Load libraries

import wget
import numpy as np
import pandas as pd

import matplotlib.pyplot as plt

Download AERONET data for a specific station and time period#

AERONET offers a web service which allows you to request and save aeronet data via wget, which is a command to download files from the internet. The AERONET web service endpoint is available under https://aeronet.gsfc.nasa.gov/cgi-bin/print_web_data_v3 and a detailed documentation of how to construct requests can be found here.

An example request from the website looks like this:

wget --no-check-certificate  -q  -O test.out "https://aeronet.gsfc.nasa.gov/cgi-bin/print_web_data_v3?site=Cart_Site&year=2000&month=6&day=1&year2=2000&month2=6&day2=14&AOD15=1&AVG=10"

In this request, you use wget to download data from the webservice. You tailor your request with a set of keywords which you concatenate to the service endpoint with &. However, constructing such requests manually can be quite cumbersome. For this reason, we will show you an approach how you can dynamically generate and request AERONET data from the web service with Python.

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:

  • endpoint: Endpoint of the AERONET web service

  • station: Name of the AERONET station

  • year: year 1 of interest

  • month: month 1 of interest

  • day: day 1 of interest

  • year2: year 2 of interest

  • month2: month 2 of interest

  • day2: day 2 of interest

  • AOD15: data type, other options include AOD10, AOD20, etc.

  • AVG: data format, AVG=10 - all points, AVG=20 - daily averages

Please note, that there are additional parameters that can be set, e.g. hour. The keywords below are those we will need for requesting all data points of Aerosol Optical Depth Level 1.5 data for the station Palma de Mallorca for April 2024.

data_dict = {
    'endpoint': 'https://aeronet.gsfc.nasa.gov/cgi-bin/print_web_data_v3',
    'station':'Palma_de_Mallorca',
    'year': 2024,
    'month': 4,
    'day': 1,
    'year2': 2024,
    'month2': 4,
    'day2': 30,
    'AOD15': 1,
    'AVG': 10
}

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.

url = '{endpoint}?site={station}&year={year}&month={month}&day={day}&year2={year2}&month2={month2}&day2={day2}&AOD15={AOD15}&AVG={AVG}'.format(**data_dict)
url

'https://aeronet.gsfc.nasa.gov/cgi-bin/print_web_data_v3?site=Palma_de_Mallorca&year=2024&month=4&day=1&year2=2024&month2=4&day2=30&AOD15=1&AVG=10'

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 ../../eodata/2_observations/aeronet/.

wget.download(url, '../../eodata/2_observations/aeronet/202404_palma_aod15_10.txt')

Let us repeat the data request and let us also request the daily average of Aerosol Optical Depth for April 2024 for the station Palma de Mallorca. The parameter you have to change is AVG. By setting 20, you indicate the request that you are interested in daily averages. You can make the required changes in the dictionary above and re-run the data request. Make sure to also change the name of the output file to e.g. 202404_palma_aod15_20.txt.

Read the observation data with pandas#

The next step is now to read the downloaded txt file. Let us start with the file of all station measurements for the station Palma de Mallorca in April 2024. The file has the name 202404_palma_aod15_10.txt. You can read txt files with the function read_table from the Python library pandas. If you inspect the txt file before (you can simply open it), you see that the first few lines contain information we do not need in the pandas dataframe. For this reason, we can set additonal keyword arguments that allow us to specify the columns and rows of interest:

  • delimiter: specify the delimiter in the text file, e.g. comma

  • header: specify the index of the row that shall be set as header.

  • index_col: specify the index of the column that shall be set as index

You see below that the resulting dataframe has 738 rows and 113 columns.

df = pd.read_table('../../eodata/2_observations/aeronet/202404_palma_aod15_10.txt', delimiter=',', header=[7], index_col=1)
df
AERONET_Site Time(hh:mm:ss) Day_of_Year Day_of_Year(Fraction) AOD_1640nm AOD_1020nm AOD_870nm AOD_865nm AOD_779nm AOD_675nm ... Exact_Wavelengths_of_AOD(um)_380nm Exact_Wavelengths_of_AOD(um)_340nm Exact_Wavelengths_of_PW(um)_935nm Exact_Wavelengths_of_AOD(um)_681nm Exact_Wavelengths_of_AOD(um)_709nm Exact_Wavelengths_of_AOD(um)_Empty Exact_Wavelengths_of_AOD(um)_Empty.1 Exact_Wavelengths_of_AOD(um)_Empty.2 Exact_Wavelengths_of_AOD(um)_Empty.3 Exact_Wavelengths_of_AOD(um)_Empty<br>
Date(dd:mm:yyyy)
01:04:2024 Palma_de_Mallorca 06:28:45 92.0 92.269965 0.041108 0.049436 0.051678 -999.0 -999.0 0.062325 ... 0.3805 0.3407 0.9364 -999.0 -999.0 -999.0 -999.0 -999.0 -999.0 -999.<br>
01:04:2024 Palma_de_Mallorca 06:33:14 92.0 92.273079 0.052381 0.059683 0.061997 -999.0 -999.0 0.071809 ... 0.3805 0.3407 0.9364 -999.0 -999.0 -999.0 -999.0 -999.0 -999.0 -999.<br>
01:04:2024 Palma_de_Mallorca 06:38:48 92.0 92.276944 0.039431 0.046851 0.050017 -999.0 -999.0 0.060447 ... 0.3805 0.3407 0.9364 -999.0 -999.0 -999.0 -999.0 -999.0 -999.0 -999.<br>
01:04:2024 Palma_de_Mallorca 06:45:32 92.0 92.281620 0.035112 0.043531 0.045646 -999.0 -999.0 0.056593 ... 0.3805 0.3407 0.9364 -999.0 -999.0 -999.0 -999.0 -999.0 -999.0 -999.<br>
01:04:2024 Palma_de_Mallorca 06:54:07 92.0 92.287581 0.036402 0.044710 0.046792 -999.0 -999.0 0.057614 ... 0.3805 0.3407 0.9364 -999.0 -999.0 -999.0 -999.0 -999.0 -999.0 -999.<br>
... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ...
26:04:2024 Palma_de_Mallorca 05:46:02 117.0 117.240301 0.052985 0.075910 0.088771 -999.0 -999.0 0.127277 ... 0.3805 0.3407 0.9364 -999.0 -999.0 -999.0 -999.0 -999.0 -999.0 -999.<br>
26:04:2024 Palma_de_Mallorca 05:57:55 117.0 117.248553 0.047704 0.068911 0.080327 -999.0 -999.0 0.115767 ... 0.3805 0.3407 0.9364 -999.0 -999.0 -999.0 -999.0 -999.0 -999.0 -999.<br>
26:04:2024 Palma_de_Mallorca 06:10:18 117.0 117.257153 0.047260 0.068839 0.081149 -999.0 -999.0 0.118209 ... 0.3805 0.3407 0.9364 -999.0 -999.0 -999.0 -999.0 -999.0 -999.0 -999.<br>
26:04:2024 Palma_de_Mallorca 10:47:58 117.0 117.449977 0.033241 0.046344 0.053470 -999.0 -999.0 0.073781 ... 0.3805 0.3407 0.9364 -999.0 -999.0 -999.0 -999.0 -999.0 -999.0 -999.<br>
NaN </body></html> NaN NaN NaN NaN NaN NaN NaN NaN NaN ... NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN

738 rows × 113 columns

In the dataframe above, you see that missing data entries are filled with -999.0. Let us replace those as NaN. You can use the function replace() to do so. The resulting dataframe has now NaN (not a number) for all entries with missing data. This replacement facilitates the plotting of the data.

df = df.replace(-999.0, np.nan)
df
AERONET_Site Time(hh:mm:ss) Day_of_Year Day_of_Year(Fraction) AOD_1640nm AOD_1020nm AOD_870nm AOD_865nm AOD_779nm AOD_675nm ... Exact_Wavelengths_of_AOD(um)_380nm Exact_Wavelengths_of_AOD(um)_340nm Exact_Wavelengths_of_PW(um)_935nm Exact_Wavelengths_of_AOD(um)_681nm Exact_Wavelengths_of_AOD(um)_709nm Exact_Wavelengths_of_AOD(um)_Empty Exact_Wavelengths_of_AOD(um)_Empty.1 Exact_Wavelengths_of_AOD(um)_Empty.2 Exact_Wavelengths_of_AOD(um)_Empty.3 Exact_Wavelengths_of_AOD(um)_Empty<br>
Date(dd:mm:yyyy)
01:04:2024 Palma_de_Mallorca 06:28:45 92.0 92.269965 0.041108 0.049436 0.051678 NaN NaN 0.062325 ... 0.3805 0.3407 0.9364 NaN NaN NaN NaN NaN NaN -999.<br>
01:04:2024 Palma_de_Mallorca 06:33:14 92.0 92.273079 0.052381 0.059683 0.061997 NaN NaN 0.071809 ... 0.3805 0.3407 0.9364 NaN NaN NaN NaN NaN NaN -999.<br>
01:04:2024 Palma_de_Mallorca 06:38:48 92.0 92.276944 0.039431 0.046851 0.050017 NaN NaN 0.060447 ... 0.3805 0.3407 0.9364 NaN NaN NaN NaN NaN NaN -999.<br>
01:04:2024 Palma_de_Mallorca 06:45:32 92.0 92.281620 0.035112 0.043531 0.045646 NaN NaN 0.056593 ... 0.3805 0.3407 0.9364 NaN NaN NaN NaN NaN NaN -999.<br>
01:04:2024 Palma_de_Mallorca 06:54:07 92.0 92.287581 0.036402 0.044710 0.046792 NaN NaN 0.057614 ... 0.3805 0.3407 0.9364 NaN NaN NaN NaN NaN NaN -999.<br>
... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ...
26:04:2024 Palma_de_Mallorca 05:46:02 117.0 117.240301 0.052985 0.075910 0.088771 NaN NaN 0.127277 ... 0.3805 0.3407 0.9364 NaN NaN NaN NaN NaN NaN -999.<br>
26:04:2024 Palma_de_Mallorca 05:57:55 117.0 117.248553 0.047704 0.068911 0.080327 NaN NaN 0.115767 ... 0.3805 0.3407 0.9364 NaN NaN NaN NaN NaN NaN -999.<br>
26:04:2024 Palma_de_Mallorca 06:10:18 117.0 117.257153 0.047260 0.068839 0.081149 NaN NaN 0.118209 ... 0.3805 0.3407 0.9364 NaN NaN NaN NaN NaN NaN -999.<br>
26:04:2024 Palma_de_Mallorca 10:47:58 117.0 117.449977 0.033241 0.046344 0.053470 NaN NaN 0.073781 ... 0.3805 0.3407 0.9364 NaN NaN NaN NaN NaN NaN -999.<br>
NaN </body></html> NaN NaN NaN NaN NaN NaN NaN NaN NaN ... NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN

738 rows × 113 columns

To get a complete overview of all column information, you can print a list of all column headers. You can do this with the function columns.to_list().

df.columns.to_list()

If you would like to narrow down the selection of the dataframe, you can apply the filter function and filter the dataframe based on a given regular expression. For example, let us only select the columns that contain Angstrom_Exponent information and additionally only select the first 100 rows.

This selection shows you that the station does not provide measurements for each day. There are for example measurements on 4 April 2024 and then again on 6 April 2024.

df.filter(regex='Angstrom*')[100:120]
440-870_Angstrom_Exponent 380-500_Angstrom_Exponent 440-675_Angstrom_Exponent 500-870_Angstrom_Exponent 340-440_Angstrom_Exponent 440-675_Angstrom_Exponent[Polar]
Date(dd:mm:yyyy)
04:04:2024 0.902502 1.175958 0.873052 0.875370 1.160272 NaN
04:04:2024 0.897283 1.105490 0.894709 0.855811 1.114349 NaN
04:04:2024 0.983186 1.305356 1.026737 0.931756 1.239793 NaN
04:04:2024 0.947711 1.291653 0.979898 0.888506 1.180223 NaN
04:04:2024 0.859491 1.192126 0.888861 0.803353 1.111934 NaN
04:04:2024 0.841150 1.182702 0.877778 0.788322 1.095790 NaN
04:04:2024 0.787851 1.153126 0.837126 0.722641 1.013124 NaN
04:04:2024 0.783507 1.185565 0.801749 0.740256 1.071480 NaN
04:04:2024 0.801492 1.239917 0.816169 0.749093 1.112797 NaN
04:04:2024 0.743544 1.228251 0.750495 0.696859 1.081357 NaN
04:04:2024 0.866019 1.280413 0.842567 0.839115 1.176802 NaN
04:04:2024 0.394367 0.875876 0.407690 0.359691 0.815636 NaN
04:04:2024 0.401067 0.813242 0.420703 0.363916 0.756478 NaN
04:04:2024 0.314514 0.685278 0.341491 0.286214 0.658746 NaN
04:04:2024 0.289643 0.655636 0.298422 0.267586 0.623452 NaN
04:04:2024 0.400938 0.779899 0.398061 0.372057 0.726025 NaN
04:04:2024 0.710957 0.971658 0.656387 0.717071 0.975594 NaN
06:04:2024 0.211010 0.201703 0.181258 0.213007 0.169287 NaN
06:04:2024 0.189744 0.212554 0.157608 0.190297 0.179608 NaN
06:04:2024 0.147998 0.179586 0.123486 0.144494 0.154958 NaN

Visualize all points of AERONET AOD in Palma de Mallorca for April 2024#

The next step is to visualize all points of Aerosol Optical Depth at different wavelengths in Palma de Mallorca for April 2024. We specifically want to plot the AOD information for which we have measurements, which are the following: AOD_1640nm, AOD_1020nm, AOD_870nm, AOD_675nm, AOD_500nm, AOD_440nm, AOD_380nm and AOD_340nm.

You can use the built-in plot() function of the pandas library to define a line plot. With the filter function, you can select the dataframe columns you wish to visualize. The visualisation code below consists of five main parts:

  • Initiate a matplotlib figure

  • Define a line plot with the built-in plot function of the pandas library

  • Set title and axes label information

  • Format axes ticks

  • Add additional features, such as a grid or legend

Below, you see that the Aerosol Optical Depth increases on 8 April 2024 as well as on 15th April 2024.

# Initiate a matplotlib figure
fig = plt.figure(figsize=(20,10))
ax=plt.axes()

# Select pandas dataframe columns and define a line plot
df.filter(['AOD_1640nm', 
           'AOD_1020nm', 
           'AOD_870nm', 
           'AOD_675nm', 
           'AOD_500nm', 
           'AOD_440nm', 
           'AOD_380nm', 
           'AOD_340nm']).plot(ax=ax,
                             linestyle='dotted')


# Set title and axes lable information
plt.title('\nAerosol Optical Depth Level 1.5 data for April 2024 - Palma de Mallorca, Spain', fontsize=20, pad=20)
plt.ylabel('Aerosol Optical Depth\n', fontsize=16)
plt.xlabel('\nDay', fontsize=16)

# Format the axes ticks
plt.xticks(fontsize=14)
plt.yticks(fontsize=14)

# Add additionally a legend and grid to the plot
plt.legend(fontsize=14,loc=0)
plt.grid()
../_images/6e28dd41fbd92829432a66fb6f11ceb1d2591ffe974a52e10809a273f48d0413.png

Load and visualize daily AOD together with the Angstrom Exponent#

In a next step, we would like to visualize the Aerosol Optical Depth information together with the Angstrom Exponent in the spectral interval of 440-480nm. The Angstrom Exponent is often used as a qualitative indicator of aerosol particle size. To visualize both variables together, we load the daily averaged AERONET information, which we stored as txt file with the name 202404_palma_aod15_20.txt. You can use again the funcction read_table from the pandas library to read the txt file.

Compared to the all point information, you see that the dataframe with the daily aggregated values has only 18 rows and 81 columns. As before, we only have daily aggregated values for days where the station had measurements.

df_daily = pd.read_table('../../eodata/2_observations/aeronet/202404_palma_aod15_20.txt', delimiter=',', header=[7], index_col=1)
df_daily
AERONET_Site Time(hh:mm:ss) Day_of_Year AOD_1640nm AOD_1020nm AOD_870nm AOD_865nm AOD_779nm AOD_675nm AOD_667nm ... N[440-675_Angstrom_Exponent] N[500-870_Angstrom_Exponent] N[340-440_Angstrom_Exponent] N[440-675_Angstrom_Exponent[Polar]] Data_Quality_Level AERONET_Instrument_Number AERONET_Site_Name Site_Latitude(Degrees) Site_Longitude(Degrees) Site_Elevation(m)<br>
Date(dd:mm:yyyy)
01:04:2024 Palma_de_Mallorca 12:00:00 92.0 0.029664 0.037270 0.039873 -999.0 -999.0 0.050255 -999.0 ... 45.0 45.0 45.0 0.0 lev15 418.0 Palma_de_Mallorca 39.5533 2.6251 10.000000<br>
02:04:2024 Palma_de_Mallorca 12:00:00 93.0 0.041282 0.045507 0.045849 -999.0 -999.0 0.049917 -999.0 ... 9.0 9.0 9.0 0.0 lev15 418.0 Palma_de_Mallorca 39.5533 2.6251 10.000000<br>
03:04:2024 Palma_de_Mallorca 12:00:00 94.0 0.024707 0.034178 0.037131 -999.0 -999.0 0.044770 -999.0 ... 42.0 42.0 42.0 0.0 lev15 418.0 Palma_de_Mallorca 39.5533 2.6251 10.000000<br>
04:04:2024 Palma_de_Mallorca 12:00:00 95.0 0.040750 0.050523 0.054303 -999.0 -999.0 0.064568 -999.0 ... 21.0 21.0 21.0 0.0 lev15 418.0 Palma_de_Mallorca 39.5533 2.6251 10.000000<br>
06:04:2024 Palma_de_Mallorca 12:00:00 97.0 0.137863 0.156201 0.160174 -999.0 -999.0 0.170298 -999.0 ... 17.0 17.0 17.0 0.0 lev15 418.0 Palma_de_Mallorca 39.5533 2.6251 10.000000<br>
08:04:2024 Palma_de_Mallorca 12:00:00 99.0 0.782080 0.972363 1.022305 -999.0 -999.0 1.084109 -999.0 ... 11.0 11.0 11.0 0.0 lev15 418.0 Palma_de_Mallorca 39.5533 2.6251 10.000000<br>
09:04:2024 Palma_de_Mallorca 12:00:00 100.0 0.052773 0.066358 0.070722 -999.0 -999.0 0.080430 -999.0 ... 47.0 47.0 47.0 0.0 lev15 418.0 Palma_de_Mallorca 39.5533 2.6251 10.000000<br>
10:04:2024 Palma_de_Mallorca 12:00:00 101.0 0.026755 0.036073 0.039065 -999.0 -999.0 0.048172 -999.0 ... 53.0 53.0 53.0 0.0 lev15 418.0 Palma_de_Mallorca 39.5533 2.6251 10.000000<br>
11:04:2024 Palma_de_Mallorca 12:00:00 102.0 0.068301 0.083287 0.087954 -999.0 -999.0 0.100478 -999.0 ... 25.0 25.0 25.0 0.0 lev15 418.0 Palma_de_Mallorca 39.5533 2.6251 10.000000<br>
12:04:2024 Palma_de_Mallorca 12:00:00 103.0 0.115575 0.151516 0.161634 -999.0 -999.0 0.177154 -999.0 ... 14.0 14.0 14.0 0.0 lev15 418.0 Palma_de_Mallorca 39.5533 2.6251 10.000000<br>
13:04:2024 Palma_de_Mallorca 12:00:00 104.0 0.040590 0.056454 0.062764 -999.0 -999.0 0.076612 -999.0 ... 60.0 60.0 60.0 0.0 lev15 418.0 Palma_de_Mallorca 39.5533 2.6251 10.000000<br>
14:04:2024 Palma_de_Mallorca 12:00:00 105.0 0.089264 0.122444 0.133600 -999.0 -999.0 0.153740 -999.0 ... 55.0 55.0 55.0 0.0 lev15 418.0 Palma_de_Mallorca 39.5533 2.6251 10.000000<br>
15:04:2024 Palma_de_Mallorca 12:00:00 106.0 0.386795 0.484332 0.510596 -999.0 -999.0 0.546902 -999.0 ... 24.0 24.0 24.0 0.0 lev15 418.0 Palma_de_Mallorca 39.5533 2.6251 10.000000<br>
16:04:2024 Palma_de_Mallorca 12:00:00 107.0 0.057714 0.077045 0.085798 -999.0 -999.0 0.107181 -999.0 ... 51.0 51.0 49.0 0.0 lev15 418.0 Palma_de_Mallorca 39.5533 2.6251 10.000000<br>
17:04:2024 Palma_de_Mallorca 12:00:00 108.0 0.055513 0.066492 0.070002 -999.0 -999.0 0.079194 -999.0 ... 52.0 52.0 52.0 0.0 lev15 418.0 Palma_de_Mallorca 39.5533 2.6251 10.000000<br>
18:04:2024 Palma_de_Mallorca 12:00:00 109.0 0.026441 0.032131 0.034751 -999.0 -999.0 0.044586 -999.0 ... 6.0 6.0 6.0 0.0 lev15 418.0 Palma_de_Mallorca 39.5533 2.6251 10.000000<br>
19:04:2024 Palma_de_Mallorca 12:00:00 110.0 0.040241 0.047825 0.051773 -999.0 -999.0 0.061262 -999.0 ... 38.0 38.0 38.0 0.0 lev15 418.0 Palma_de_Mallorca 39.5533 2.6251 10.000000<br>
20:04:2024 Palma_de_Mallorca 12:00:00 111.0 0.032635 0.039133 0.042177 -999.0 -999.0 0.052452 -999.0 ... 40.0 40.0 40.0 0.0 lev15 418.0 Palma_de_Mallorca 39.5533 2.6251 10.000000<br>
21:04:2024 Palma_de_Mallorca 12:00:00 112.0 0.063732 0.077618 0.083128 -999.0 -999.0 0.096994 -999.0 ... 58.0 58.0 58.0 0.0 lev15 418.0 Palma_de_Mallorca 39.5533 2.6251 10.000000<br>
23:04:2024 Palma_de_Mallorca 12:00:00 114.0 0.032062 0.041640 0.046561 -999.0 -999.0 0.063226 -999.0 ... 14.0 14.0 14.0 0.0 lev15 418.0 Palma_de_Mallorca 39.5533 2.6251 10.000000<br>
24:04:2024 Palma_de_Mallorca 12:00:00 115.0 0.027579 0.035329 0.039976 -999.0 -999.0 0.053434 -999.0 ... 40.0 40.0 40.0 0.0 lev15 418.0 Palma_de_Mallorca 39.5533 2.6251 10.000000<br>
25:04:2024 Palma_de_Mallorca 12:00:00 116.0 0.039753 0.048559 0.053619 -999.0 -999.0 0.071798 -999.0 ... 11.0 11.0 11.0 0.0 lev15 418.0 Palma_de_Mallorca 39.5533 2.6251 10.000000<br>
26:04:2024 Palma_de_Mallorca 12:00:00 117.0 0.045297 0.065001 0.075929 -999.0 -999.0 0.108758 -999.0 ... 4.0 4.0 4.0 0.0 lev15 418.0 Palma_de_Mallorca 39.5533 2.6251 10.000000<br>
01:05:2024 Palma_de_Mallorca 12:00:00 122.0 0.045828 0.060249 0.064143 -999.0 -999.0 0.077044 -999.0 ... 27.0 27.0 27.0 0.0 lev15 418.0 Palma_de_Mallorca 39.5533 2.6251 10.000000<br>
NaN </body></html> NaN NaN NaN NaN NaN NaN NaN NaN NaN ... NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN

25 rows × 81 columns

The next step is again to replace the value for missing data (-999.0) as NaN. You can do this with the function replace().

df_daily = df_daily.replace(-999.0, np.nan)
df_daily
AERONET_Site Time(hh:mm:ss) Day_of_Year AOD_1640nm AOD_1020nm AOD_870nm AOD_865nm AOD_779nm AOD_675nm AOD_667nm ... N[440-675_Angstrom_Exponent] N[500-870_Angstrom_Exponent] N[340-440_Angstrom_Exponent] N[440-675_Angstrom_Exponent[Polar]] Data_Quality_Level AERONET_Instrument_Number AERONET_Site_Name Site_Latitude(Degrees) Site_Longitude(Degrees) Site_Elevation(m)<br>
Date(dd:mm:yyyy)
01:04:2024 Palma_de_Mallorca 12:00:00 92.0 0.029664 0.037270 0.039873 NaN NaN 0.050255 NaN ... 45.0 45.0 45.0 0.0 lev15 418.0 Palma_de_Mallorca 39.5533 2.6251 10.000000<br>
02:04:2024 Palma_de_Mallorca 12:00:00 93.0 0.041282 0.045507 0.045849 NaN NaN 0.049917 NaN ... 9.0 9.0 9.0 0.0 lev15 418.0 Palma_de_Mallorca 39.5533 2.6251 10.000000<br>
03:04:2024 Palma_de_Mallorca 12:00:00 94.0 0.024707 0.034178 0.037131 NaN NaN 0.044770 NaN ... 42.0 42.0 42.0 0.0 lev15 418.0 Palma_de_Mallorca 39.5533 2.6251 10.000000<br>
04:04:2024 Palma_de_Mallorca 12:00:00 95.0 0.040750 0.050523 0.054303 NaN NaN 0.064568 NaN ... 21.0 21.0 21.0 0.0 lev15 418.0 Palma_de_Mallorca 39.5533 2.6251 10.000000<br>
06:04:2024 Palma_de_Mallorca 12:00:00 97.0 0.137863 0.156201 0.160174 NaN NaN 0.170298 NaN ... 17.0 17.0 17.0 0.0 lev15 418.0 Palma_de_Mallorca 39.5533 2.6251 10.000000<br>
08:04:2024 Palma_de_Mallorca 12:00:00 99.0 0.782080 0.972363 1.022305 NaN NaN 1.084109 NaN ... 11.0 11.0 11.0 0.0 lev15 418.0 Palma_de_Mallorca 39.5533 2.6251 10.000000<br>
09:04:2024 Palma_de_Mallorca 12:00:00 100.0 0.052773 0.066358 0.070722 NaN NaN 0.080430 NaN ... 47.0 47.0 47.0 0.0 lev15 418.0 Palma_de_Mallorca 39.5533 2.6251 10.000000<br>
10:04:2024 Palma_de_Mallorca 12:00:00 101.0 0.026755 0.036073 0.039065 NaN NaN 0.048172 NaN ... 53.0 53.0 53.0 0.0 lev15 418.0 Palma_de_Mallorca 39.5533 2.6251 10.000000<br>
11:04:2024 Palma_de_Mallorca 12:00:00 102.0 0.068301 0.083287 0.087954 NaN NaN 0.100478 NaN ... 25.0 25.0 25.0 0.0 lev15 418.0 Palma_de_Mallorca 39.5533 2.6251 10.000000<br>
12:04:2024 Palma_de_Mallorca 12:00:00 103.0 0.115575 0.151516 0.161634 NaN NaN 0.177154 NaN ... 14.0 14.0 14.0 0.0 lev15 418.0 Palma_de_Mallorca 39.5533 2.6251 10.000000<br>
13:04:2024 Palma_de_Mallorca 12:00:00 104.0 0.040590 0.056454 0.062764 NaN NaN 0.076612 NaN ... 60.0 60.0 60.0 0.0 lev15 418.0 Palma_de_Mallorca 39.5533 2.6251 10.000000<br>
14:04:2024 Palma_de_Mallorca 12:00:00 105.0 0.089264 0.122444 0.133600 NaN NaN 0.153740 NaN ... 55.0 55.0 55.0 0.0 lev15 418.0 Palma_de_Mallorca 39.5533 2.6251 10.000000<br>
15:04:2024 Palma_de_Mallorca 12:00:00 106.0 0.386795 0.484332 0.510596 NaN NaN 0.546902 NaN ... 24.0 24.0 24.0 0.0 lev15 418.0 Palma_de_Mallorca 39.5533 2.6251 10.000000<br>
16:04:2024 Palma_de_Mallorca 12:00:00 107.0 0.057714 0.077045 0.085798 NaN NaN 0.107181 NaN ... 51.0 51.0 49.0 0.0 lev15 418.0 Palma_de_Mallorca 39.5533 2.6251 10.000000<br>
17:04:2024 Palma_de_Mallorca 12:00:00 108.0 0.055513 0.066492 0.070002 NaN NaN 0.079194 NaN ... 52.0 52.0 52.0 0.0 lev15 418.0 Palma_de_Mallorca 39.5533 2.6251 10.000000<br>
18:04:2024 Palma_de_Mallorca 12:00:00 109.0 0.026441 0.032131 0.034751 NaN NaN 0.044586 NaN ... 6.0 6.0 6.0 0.0 lev15 418.0 Palma_de_Mallorca 39.5533 2.6251 10.000000<br>
19:04:2024 Palma_de_Mallorca 12:00:00 110.0 0.040241 0.047825 0.051773 NaN NaN 0.061262 NaN ... 38.0 38.0 38.0 0.0 lev15 418.0 Palma_de_Mallorca 39.5533 2.6251 10.000000<br>
20:04:2024 Palma_de_Mallorca 12:00:00 111.0 0.032635 0.039133 0.042177 NaN NaN 0.052452 NaN ... 40.0 40.0 40.0 0.0 lev15 418.0 Palma_de_Mallorca 39.5533 2.6251 10.000000<br>
21:04:2024 Palma_de_Mallorca 12:00:00 112.0 0.063732 0.077618 0.083128 NaN NaN 0.096994 NaN ... 58.0 58.0 58.0 0.0 lev15 418.0 Palma_de_Mallorca 39.5533 2.6251 10.000000<br>
23:04:2024 Palma_de_Mallorca 12:00:00 114.0 0.032062 0.041640 0.046561 NaN NaN 0.063226 NaN ... 14.0 14.0 14.0 0.0 lev15 418.0 Palma_de_Mallorca 39.5533 2.6251 10.000000<br>
24:04:2024 Palma_de_Mallorca 12:00:00 115.0 0.027579 0.035329 0.039976 NaN NaN 0.053434 NaN ... 40.0 40.0 40.0 0.0 lev15 418.0 Palma_de_Mallorca 39.5533 2.6251 10.000000<br>
25:04:2024 Palma_de_Mallorca 12:00:00 116.0 0.039753 0.048559 0.053619 NaN NaN 0.071798 NaN ... 11.0 11.0 11.0 0.0 lev15 418.0 Palma_de_Mallorca 39.5533 2.6251 10.000000<br>
26:04:2024 Palma_de_Mallorca 12:00:00 117.0 0.045297 0.065001 0.075929 NaN NaN 0.108758 NaN ... 4.0 4.0 4.0 0.0 lev15 418.0 Palma_de_Mallorca 39.5533 2.6251 10.000000<br>
01:05:2024 Palma_de_Mallorca 12:00:00 122.0 0.045828 0.060249 0.064143 NaN NaN 0.077044 NaN ... 27.0 27.0 27.0 0.0 lev15 418.0 Palma_de_Mallorca 39.5533 2.6251 10.000000<br>
NaN </body></html> NaN NaN NaN NaN NaN NaN NaN NaN NaN ... NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN

25 rows × 81 columns

As a final step, we can use again the plotting code from above and visualize the daily aggregated Aerosol Optical Depth information. Additionally to the AOD values as line plot, we add the 440-870_Angstrom_Exponent column as bar plot. You can do this in a similar way. You use the pandas built-in plot() function and specify the keyword argument kind='bar', which indicates the function to plot a bar graph.

# Initiate a matplotlib figure
fig = plt.figure(figsize=(20,10))
ax=plt.axes()

# Select pandas dataframe columns and define a line plot and bar plot
df_daily.filter(['AOD_1640nm', 
           'AOD_1020nm', 
           'AOD_870nm', 
           'AOD_675nm', 
           'AOD_500nm', 
           'AOD_440nm', 
           'AOD_380nm', 
           'AOD_340nm'][:-1]).plot(ax=ax,
                             style='o-')

df_daily['440-870_Angstrom_Exponent'][:-1].plot(kind='bar', color='lightgrey')

# Set title and axes lable information
plt.title('\nDaily aggregated Aerosol Optical Depth Level 1.5 data for April 2024 - Palma de Mallorca, Spain', fontsize=20, pad=20)
plt.ylabel('Aerosol Optical Depth\n', fontsize=16)
plt.xlabel('Day', fontsize=16)

# Format the axes ticks
plt.xticks(fontsize=14)
plt.yticks(fontsize=14)

# Add additionally a legend and grid to the plot
plt.legend(fontsize=14,loc=0)
plt.grid()
../_images/d6c1cc2a2432660634a24018283a04358a8fd3e78b8b837955d39f7abb209363.png

Above, you see elevated AOD values and low values for the Angstrom exponent for the Saharan dust event on 8 April 2024. Additionally, the time-series shows that there might have been a second dust event around 15th of April 2024. Let us inspect the daily aggregated values of AOD of another station, e.g. Ispra in Northern Italy for April 2024.

Load and visualize AOD and AE for Ispra in April 2024#

Let us load the daily aggregated Aerosol Optical Depth values for station Ispra in north Italy for April 2024.

df_ispra = pd.read_table('../../eodata/2_observations/aeronet/202404_ispra_aod15_20.txt', delimiter=',', header=[7], index_col=1)
df_ispra = df_ispra.replace(-999.0, np.nan)
df_ispra
AERONET_Site Time(hh:mm:ss) Day_of_Year AOD_1640nm AOD_1020nm AOD_870nm AOD_865nm AOD_779nm AOD_675nm AOD_667nm ... N[440-675_Angstrom_Exponent] N[500-870_Angstrom_Exponent] N[340-440_Angstrom_Exponent] N[440-675_Angstrom_Exponent[Polar]] Data_Quality_Level AERONET_Instrument_Number AERONET_Site_Name Site_Latitude(Degrees) Site_Longitude(Degrees) Site_Elevation(m)<br>
Date(dd:mm:yyyy)
01:04:2024 Ispra 12:00:00 92.0 NaN 0.028475 NaN 0.032710 0.037243 NaN 0.044472 ... 15.0 0.0 0.0 0.0 lev15 1006.0 Ispra 45.80305 8.6267 235.000000<br>
02:04:2024 Ispra 12:00:00 93.0 NaN 0.022968 NaN 0.026615 0.029999 NaN 0.034433 ... 118.0 0.0 0.0 0.0 lev15 1006.0 Ispra 45.80305 8.6267 235.000000<br>
03:04:2024 Ispra 12:00:00 94.0 NaN 0.042963 NaN 0.048936 0.055226 NaN 0.062700 ... 7.0 0.0 0.0 0.0 lev15 1006.0 Ispra 45.80305 8.6267 235.000000<br>
04:04:2024 Ispra 12:00:00 95.0 NaN 0.100698 NaN 0.108097 0.115477 NaN 0.126420 ... 15.0 0.0 0.0 0.0 lev15 1006.0 Ispra 45.80305 8.6267 235.000000<br>
06:04:2024 Ispra 12:00:00 97.0 NaN 0.105120 NaN 0.111553 0.116818 NaN 0.125141 ... 35.0 0.0 0.0 0.0 lev15 1006.0 Ispra 45.80305 8.6267 235.000000<br>
08:04:2024 Ispra 12:00:00 99.0 NaN 0.437224 NaN 0.465217 0.483766 NaN 0.508926 ... 65.0 0.0 0.0 0.0 lev15 1006.0 Ispra 45.80305 8.6267 235.000000<br>
11:04:2024 Ispra 12:00:00 102.0 NaN 0.145856 NaN 0.157526 0.165570 NaN 0.175151 ... 141.0 0.0 0.0 0.0 lev15 1006.0 Ispra 45.80305 8.6267 235.000000<br>
12:04:2024 Ispra 12:00:00 103.0 NaN 0.141392 NaN 0.153115 0.160947 NaN 0.169305 ... 118.0 0.0 0.0 0.0 lev15 1006.0 Ispra 45.80305 8.6267 235.000000<br>
13:04:2024 Ispra 12:00:00 104.0 NaN 0.070215 NaN 0.074897 0.077876 NaN 0.078143 ... 39.0 0.0 0.0 0.0 lev15 1006.0 Ispra 45.80305 8.6267 235.000000<br>
14:04:2024 Ispra 12:00:00 105.0 NaN 0.047803 NaN 0.055653 0.061611 NaN 0.067323 ... 119.0 0.0 0.0 0.0 lev15 1006.0 Ispra 45.80305 8.6267 235.000000<br>
15:04:2024 Ispra 12:00:00 106.0 NaN 0.179134 NaN 0.199888 0.216441 NaN 0.242668 ... 53.0 0.0 0.0 0.0 lev15 1006.0 Ispra 45.80305 8.6267 235.000000<br>
16:04:2024 Ispra 12:00:00 107.0 NaN 0.024000 NaN 0.028581 0.032175 NaN 0.035005 ... 103.0 0.0 0.0 0.0 lev15 1006.0 Ispra 45.80305 8.6267 235.000000<br>
17:04:2024 Ispra 12:00:00 108.0 NaN 0.016077 NaN 0.020254 0.023197 NaN 0.025869 ... 96.0 0.0 0.0 0.0 lev15 1006.0 Ispra 45.80305 8.6267 235.000000<br>
19:04:2024 Ispra 12:00:00 110.0 NaN 0.028700 NaN 0.033454 0.037154 NaN 0.041415 ... 111.0 0.0 0.0 0.0 lev15 1006.0 Ispra 45.80305 8.6267 235.000000<br>
20:04:2024 Ispra 12:00:00 111.0 NaN 0.016966 NaN 0.021155 0.024569 NaN 0.028772 ... 161.0 0.0 0.0 0.0 lev15 1006.0 Ispra 45.80305 8.6267 235.000000<br>
24:04:2024 Ispra 12:00:00 115.0 NaN 0.029825 NaN 0.035670 0.040456 NaN 0.047446 ... 18.0 0.0 0.0 0.0 lev15 1006.0 Ispra 45.80305 8.6267 235.000000<br>
25:04:2024 Ispra 12:00:00 116.0 NaN 0.035504 NaN 0.048508 0.059609 NaN 0.077828 ... 62.0 0.0 0.0 0.0 lev15 1006.0 Ispra 45.80305 8.6267 235.000000<br>
29:04:2024 Ispra 12:00:00 120.0 NaN 0.187442 NaN 0.198817 0.207366 NaN 0.219637 ... 26.0 0.0 0.0 0.0 lev15 1006.0 Ispra 45.80305 8.6267 235.000000<br>
30:04:2024 Ispra 12:00:00 121.0 NaN 0.274391 NaN 0.288517 0.298562 NaN 0.312296 ... 22.0 0.0 0.0 0.0 lev15 1006.0 Ispra 45.80305 8.6267 235.000000<br>
NaN </body></html> NaN NaN NaN NaN NaN NaN NaN NaN NaN ... NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN

20 rows × 81 columns

And now, let us use again the plotting code from above and visualize the daily aggregated Aerosol Optical Depth information for Ispra in April 2024. Additionally to the AOD values as line plot, we add the 440-870_Angstrom_Exponent column as bar plot. You see, that Ispra has elevated AOD measurements as well on 8th and 15th April 2024, however the Ispra region was not as affected by dust as the station in Palma de Mallorca.

# Initiate a matplotlib figure
fig = plt.figure(figsize=(20,10))
ax=plt.axes()

# Select pandas dataframe columns and define a line plot and bar plot
df_ispra.filter(['AOD_1020nm',  
           'AOD_667nm', 
           'AOD_560nm',
           'AOD_400nm', 
           'AOD_400nm'][:-1]).plot(ax=ax,
                             style='o-')

df_ispra['440-870_Angstrom_Exponent'][:-1].plot(kind='bar', color='lightgrey')

# Set title and axes lable information
plt.title('\nDaily aggregated Aerosol Optical Depth Level 1.5 data for April 2024- Ispra, Italy', fontsize=20, pad=20)
plt.ylabel('Aerosol Optical Depth\n', fontsize=16)
plt.xlabel('\nDay', fontsize=16)

# Format the axes ticks
plt.xticks(fontsize=14)
plt.yticks(fontsize=14)

# Add additionally a legend and grid to the plot
plt.legend(fontsize=14,loc=0)
plt.grid()
../_images/d10ba4876698b9700e2ea41fc6f1fdc597c6995c0bc2c5f66a21942b316fe839.png
Contents