{ "cells": [ { "cell_type": "markdown", "metadata": {}, "source": [ "# EARLINET Lidar backscatter profiles" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "```{hint} \n", "Execute the notebook on the training platform >>\n", "```" ] }, { "cell_type": "markdown", "metadata": { "Collapsed": "false" }, "source": [ "The European Aerosol Research Lidar Network (EARLINET), was established in 2000 as a research project with the goal of creating a quantitative, comprehensive, and statistically significant database for the horizontal, vertical, and temporal distribution of aerosols on a continental scale. Since then EARLINET has continued to provide the most extensive collection of ground-based data for the aerosol vertical distribution over Europe.\n", "\n", "Atmospheric aerosols are considered one of the major uncertainties in climate forcing, and a detailed aerosol characterization is needed in order to understand their role in the atmospheric processes as well as human health and environment. The most significant source of uncertainty is the large variability in space and time. Due to their short lifetime and strong interactions, their global concentrations and properties are poorly known. For these reasons, information on the large-scale three-dimensional aerosol distribution in the atmosphere should be continuously monitored. It is undoubted that information on the vertical distribution is particularly important and that lidar remote sensing is the most appropriate tool for providing this information. \n", "\n", "EARLINET offers access to `long-term multi-wavelength backscatter and extinction coefficient profiles` via an easily accessible database, covering the European continent. See here an overview of the EARLINET Lidar Stations." ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "```{admonition} Basic facts\n", "**Spatial coverage**: `Observation stations in Europe`
\n", "**Temporal resolution**: `sub-hourly`
\n", "**Temporal coverage**: `since 2000`
\n", "**Data format**: `NetCDF`
\n", "**Versions**: `Level 1 (basic quality control)`, `Level 2 (advanced quality control)`, `Level 3 (climatological aggregated products)`\n", "```" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "```{admonition} How to access the data\n", "EARLINET data are available in `netCDF` and can be accessed via the EARLINET Database. Data are offered on different quality controlled levels:\n", "* `Level 1`: Basic quality control \n", "* `Level 2`: Advanced quality control, and \n", "* `Level 3`: Climatological aggregated products \n", "\n", "For the example below, we selected data on 23 February 2021 for the station IPR, which stands for Ispra, Italy, with the following characteristics:\n", "* **Emission wavelengths**: `1064`\n", "* **File Types**: `b - Backscatter`\n", "* **Levels**: `Level 2.0`\n", "\n", "When you click on `SEARCH FILES`, you get an overview of all available files with the selected characteristics. The files are disseminated in `netCDF` format.\n", "\n", "**Note**: You have to register for the EARLINET Data Portal in order to be able to download EARLINET data.\n", "```" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "
" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "**Load required libraries**" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "import pandas as pd\n", "import xarray as xr\n", "\n", "import matplotlib.pyplot as plt" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "
" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Load and browse EARLINET data with xarray" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "EARLINET data are disseminated as hourly files in the `NetCDF` format. You can use the Python package xarray and the function `open_mfdataset()` to open multiple `NetCDF` at once. Let us load the data files for the EARLINET station Ispra, Italy for 23 February 2021. (**NOTE: EARLINET data are not available fore every station and day. Hence, this notebook showcases a case study of a Saharan dust event in Europe that occurred in February 2021**).\n", "\n", "The function loads the data as `Dataset`, which is a collection of multiple data variables that share the same coordinate information. Below, you see that the EARLINET data have four dimensions: `altitude`, `time`, `nv` and `wavelength`.\n", "\n", "The data also hold 27 data variables, including a variable `backscatter`, which is the variable of interest for us." ] }, { "cell_type": "code", "execution_count": 3, "metadata": {}, "outputs": [ { "data": { "text/html": [ "
\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "
<xarray.Dataset>\n",
       "Dimensions:                                         (altitude: 128, nv: 2, time: 9, wavelength: 1)\n",
       "Coordinates:\n",
       "  * altitude                                        (altitude) float64 539.0 ...\n",
       "  * time                                            (time) datetime64[ns] 202...\n",
       "  * wavelength                                      (wavelength) float32 1.06...\n",
       "    longitude                                       float32 8.617\n",
       "    latitude                                        float32 45.82\n",
       "Dimensions without coordinates: nv\n",
       "Data variables:\n",
       "    time_bounds                                     (altitude, time, nv) datetime64[ns] dask.array<chunksize=(128, 1, 2), meta=np.ndarray>\n",
       "    backscatter_calibration_value                   (time, altitude, wavelength) float32 dask.array<chunksize=(1, 128, 1), meta=np.ndarray>\n",
       "    error_retrieval_method                          (time, altitude, wavelength) float32 dask.array<chunksize=(1, 128, 1), meta=np.ndarray>\n",
       "    backscatter_evaluation_method                   (time, altitude, wavelength) float32 dask.array<chunksize=(1, 128, 1), meta=np.ndarray>\n",
       "    backscatter_calibration_range_search_algorithm  (time, altitude, wavelength) float32 dask.array<chunksize=(1, 128, 1), meta=np.ndarray>\n",
       "    elastic_backscatter_algorithm                   (time, altitude, wavelength) float32 dask.array<chunksize=(1, 128, 1), meta=np.ndarray>\n",
       "    station_altitude                                (time, altitude) float64 ...\n",
       "    zenith_angle                                    (time, altitude) float64 ...\n",
       "    shots                                           (altitude, time) float64 dask.array<chunksize=(128, 1), meta=np.ndarray>\n",
       "    atmospheric_molecular_calculation_source        (time, altitude) float64 ...\n",
       "    cirrus_contamination                            (time, altitude) float64 ...\n",
       "    cirrus_contamination_source                     (time, altitude) float64 ...\n",
       "    quality_control_level                           (time, altitude) float64 ...\n",
       "    basic_quality_control                           (time, altitude) float64 ...\n",
       "    advanced_quality_control                        (time, altitude) float64 ...\n",
       "    backscatter                                     (wavelength, time, altitude) float64 dask.array<chunksize=(1, 1, 128), meta=np.ndarray>\n",
       "    error_backscatter                               (wavelength, time, altitude) float64 dask.array<chunksize=(1, 1, 128), meta=np.ndarray>\n",
       "    vertical_resolution                             (wavelength, time, altitude) float64 dask.array<chunksize=(1, 1, 128), meta=np.ndarray>\n",
       "    assumed_particle_lidar_ratio                    (wavelength, time, altitude) float64 dask.array<chunksize=(1, 1, 128), meta=np.ndarray>\n",
       "    assumed_particle_lidar_ratio_error              (wavelength, time, altitude) float64 dask.array<chunksize=(1, 1, 128), meta=np.ndarray>\n",
       "    earlinet_product_type                           (time, altitude) float64 ...\n",
       "    user_defined_category                           (time, altitude) float64 ...\n",
       "    backscatter_calibration_range                   (time, altitude, wavelength, nv) float32 dask.array<chunksize=(1, 128, 1, 2), meta=np.ndarray>\n",
       "    backscatter_calibration_search_range            (time, altitude, wavelength, nv) float32 dask.array<chunksize=(1, 128, 1, 2), meta=np.ndarray>\n",
       "    cloud_mask_type                                 (time, altitude) float64 ...\n",
       "    scc_product_type                                (time, altitude) float64 ...\n",
       "    cloud_mask                                      (time, altitude) float32 dask.array<chunksize=(1, 128), meta=np.ndarray>\n",
       "Attributes:\n",
       "    Conventions:                          CF-1.7\n",
       "    title:                                Profiles of aerosol optical properties\n",
       "    source:                               Ground based LIDAR measurements\n",
       "    references:                           Project website at http://www.earli...\n",
       "    history:                              2021-10-06T09:20Z : Assigned versio...\n",
       "    station_ID:                           ipr\n",
       "    location:                             Ispra, Italy\n",
       "    system:                               ADAM-noew-2019\n",
       "    institution:                          Joint Research Centre - Institute f...\n",
       "    comment:                              \n",
       "    measurement_ID:                       20210223is10\n",
       "    measurement_start_datetime:           2021-02-23T11:02:16Z\n",
       "    measurement_stop_datetime:            2021-02-23T11:29:18Z\n",
       "    PI:                                   Jean Putaud\n",
       "    PI_affiliation:                       Joint Research Centre - Air and Cli...\n",
       "    PI_affiliation_acronym:               JRC\n",
       "    PI_address:                           \n",
       "    PI_phone:                             +39 0332 78 50 41\n",
       "    PI_email:                             jean.putaud@ec.europa.eu\n",
       "    Data_Originator:                      jean.putaud\n",
       "    Data_Originator_affiliation:          Joint Research Centre\n",
       "    Data_Originator_affiliation_acronym:  JRC\n",
       "    Data_Originator_address:              21027 Ispra (VA)\n",
       "    Data_Originator_phone:                ++390332785041\n",
       "    Data_Originator_email:                jean.putaud@ec.europa.eu\n",
       "    data_processing_institution:          Consiglio Nazionale delle Ricerche ...\n",
       "    hoi_system_ID:                        164\n",
       "    hoi_configuration_ID:                 551\n",
       "    scc_version:                          5.2.3\n",
       "    scc_version_description:              SCC vers. 5.2.3 (HiRELPP vers. 1.1....\n",
       "    processor_name:                       ELDA\n",
       "    processor_version:                    3.4.8\n",
       "    __file_format_version:                2.1\n",
       "    input_file:                           ipr_003_0000753_202102231102_202102...\n",
       "    overlap_correction_file:
" ], "text/plain": [ "\n", "Dimensions: (altitude: 128, nv: 2, time: 9, wavelength: 1)\n", "Coordinates:\n", " * altitude (altitude) float64 539.0 ...\n", " * time (time) datetime64[ns] 202...\n", " * wavelength (wavelength) float32 1.06...\n", " longitude float32 8.617\n", " latitude float32 45.82\n", "Dimensions without coordinates: nv\n", "Data variables:\n", " time_bounds (altitude, time, nv) datetime64[ns] dask.array\n", " backscatter_calibration_value (time, altitude, wavelength) float32 dask.array\n", " error_retrieval_method (time, altitude, wavelength) float32 dask.array\n", " backscatter_evaluation_method (time, altitude, wavelength) float32 dask.array\n", " backscatter_calibration_range_search_algorithm (time, altitude, wavelength) float32 dask.array\n", " elastic_backscatter_algorithm (time, altitude, wavelength) float32 dask.array\n", " station_altitude (time, altitude) float64 ...\n", " zenith_angle (time, altitude) float64 ...\n", " shots (altitude, time) float64 dask.array\n", " atmospheric_molecular_calculation_source (time, altitude) float64 ...\n", " cirrus_contamination (time, altitude) float64 ...\n", " cirrus_contamination_source (time, altitude) float64 ...\n", " quality_control_level (time, altitude) float64 ...\n", " basic_quality_control (time, altitude) float64 ...\n", " advanced_quality_control (time, altitude) float64 ...\n", " backscatter (wavelength, time, altitude) float64 dask.array\n", " error_backscatter (wavelength, time, altitude) float64 dask.array\n", " vertical_resolution (wavelength, time, altitude) float64 dask.array\n", " assumed_particle_lidar_ratio (wavelength, time, altitude) float64 dask.array\n", " assumed_particle_lidar_ratio_error (wavelength, time, altitude) float64 dask.array\n", " earlinet_product_type (time, altitude) float64 ...\n", " user_defined_category (time, altitude) float64 ...\n", " backscatter_calibration_range (time, altitude, wavelength, nv) float32 dask.array\n", " backscatter_calibration_search_range (time, altitude, wavelength, nv) float32 dask.array\n", " cloud_mask_type (time, altitude) float64 ...\n", " scc_product_type (time, altitude) float64 ...\n", " cloud_mask (time, altitude) float32 dask.array\n", "Attributes:\n", " Conventions: CF-1.7\n", " title: Profiles of aerosol optical properties\n", " source: Ground based LIDAR measurements\n", " references: Project website at http://www.earli...\n", " history: 2021-10-06T09:20Z : Assigned versio...\n", " station_ID: ipr\n", " location: Ispra, Italy\n", " system: ADAM-noew-2019\n", " institution: Joint Research Centre - Institute f...\n", " comment: \n", " measurement_ID: 20210223is10\n", " measurement_start_datetime: 2021-02-23T11:02:16Z\n", " measurement_stop_datetime: 2021-02-23T11:29:18Z\n", " PI: Jean Putaud\n", " PI_affiliation: Joint Research Centre - Air and Cli...\n", " PI_affiliation_acronym: JRC\n", " PI_address: \n", " PI_phone: +39 0332 78 50 41\n", " PI_email: jean.putaud@ec.europa.eu\n", " Data_Originator: jean.putaud\n", " Data_Originator_affiliation: Joint Research Centre\n", " Data_Originator_affiliation_acronym: JRC\n", " Data_Originator_address: 21027 Ispra (VA)\n", " Data_Originator_phone: ++390332785041\n", " Data_Originator_email: jean.putaud@ec.europa.eu\n", " data_processing_institution: Consiglio Nazionale delle Ricerche ...\n", " hoi_system_ID: 164\n", " hoi_configuration_ID: 551\n", " scc_version: 5.2.3\n", " scc_version_description: SCC vers. 5.2.3 (HiRELPP vers. 1.1....\n", " processor_name: ELDA\n", " processor_version: 3.4.8\n", " __file_format_version: 2.1\n", " input_file: ipr_003_0000753_202102231102_202102...\n", " overlap_correction_file: " ] }, "execution_count": 3, "metadata": {}, "output_type": "execute_result" } ], "source": [ "file_dir = '../../eodata/2_observations/earlinet/Level2/ipr/0223/'\n", "earlinet_2302 = xr.open_mfdataset(file_dir+'*')\n", "earlinet_2302" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "EARLINET Lidar sensors create vertical profiles of the atmosphere. Let us inspect the variable `altitude` in order to see the resolution and extent of the vertical profile. You see that the EARLINET data offer measurements for every 40 meters from 539 m up to 8 km." ] }, { "cell_type": "code", "execution_count": 20, "metadata": {}, "outputs": [ { "data": { "text/html": [ "
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<xarray.DataArray 'altitude' (altitude: 128)>\n",
       "array([ 539.,  599.,  659.,  719.,  779.,  839.,  899.,  959., 1019., 1079.,\n",
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       "       1739., 1799., 1859., 1919., 1979., 2039., 2099., 2159., 2219., 2279.,\n",
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       "       7739., 7799., 7859., 7919., 7979., 8039., 8099., 8159.])\n",
       "Coordinates:\n",
       "  * altitude   (altitude) float64 539.0 599.0 659.0 ... 8.099e+03 8.159e+03\n",
       "    longitude  float32 8.617\n",
       "    latitude   float32 45.82\n",
       "Attributes:\n",
       "    axis:           Z\n",
       "    long_name:      height above sea level\n",
       "    positive:       up\n",
       "    standard_name:  altitude\n",
       "    units:          m
" ], "text/plain": [ "\n", "array([ 539., 599., 659., 719., 779., 839., 899., 959., 1019., 1079.,\n", " 1139., 1199., 1259., 1319., 1379., 1439., 1499., 1559., 1619., 1679.,\n", " 1739., 1799., 1859., 1919., 1979., 2039., 2099., 2159., 2219., 2279.,\n", " 2339., 2399., 2459., 2519., 2579., 2639., 2699., 2759., 2819., 2879.,\n", " 2939., 2999., 3059., 3119., 3179., 3239., 3299., 3359., 3419., 3479.,\n", " 3539., 3599., 3659., 3719., 3779., 3839., 3899., 3959., 4019., 4079.,\n", " 4139., 4199., 4259., 4319., 4379., 4439., 4499., 4559., 4619., 4679.,\n", " 4739., 4799., 4859., 4919., 4979., 5039., 5099., 5159., 5219., 5279.,\n", " 5339., 5399., 5459., 5519., 5579., 5639., 5699., 5759., 5819., 5879.,\n", " 5939., 5999., 6059., 6119., 6179., 6239., 6299., 6359., 6419., 6479.,\n", " 6539., 6599., 6659., 6719., 6779., 6839., 6899., 6959., 7019., 7079.,\n", " 7139., 7199., 7259., 7319., 7379., 7439., 7499., 7559., 7619., 7679.,\n", " 7739., 7799., 7859., 7919., 7979., 8039., 8099., 8159.])\n", "Coordinates:\n", " * altitude (altitude) float64 539.0 599.0 659.0 ... 8.099e+03 8.159e+03\n", " longitude float32 8.617\n", " latitude float32 45.82\n", "Attributes:\n", " axis: Z\n", " long_name: height above sea level\n", " positive: up\n", " standard_name: altitude\n", " units: m" ] }, "execution_count": 20, "metadata": {}, "output_type": "execute_result" } ], "source": [ "earlinet_2302.altitude" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "As a last step before we can visualize the vertical profile, we can load the variable `backscatter` from the dataset. You can load a variable from a xarray.Dataset by adding the name of the variable in square brackets.\n", "\n", "The loaded data array provides you additional attributes about the data, such as `long_name` and `units`." ] }, { "cell_type": "code", "execution_count": 21, "metadata": {}, "outputs": [ { "data": { "text/html": [ "
\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "
<xarray.DataArray 'backscatter' (wavelength: 1, time: 9, altitude: 128)>\n",
       "dask.array<concatenate, shape=(1, 9, 128), dtype=float64, chunksize=(1, 1, 128), chunktype=numpy.ndarray>\n",
       "Coordinates:\n",
       "  * altitude    (altitude) float64 539.0 599.0 659.0 ... 8.099e+03 8.159e+03\n",
       "  * time        (time) datetime64[ns] 2021-02-23T11:02:16 ... 2021-02-23T19:4...\n",
       "  * wavelength  (wavelength) float32 1.064e+03\n",
       "    longitude   float32 8.617\n",
       "    latitude    float32 45.82\n",
       "Attributes:\n",
       "    ancillary_variables:  error_backscatter vertical_resolution\n",
       "    long_name:            aerosol backscatter coefficient\n",
       "    plausibility:         parameter passed the EARLINET quality assurance.\n",
       "    units:                m-1*sr-1
" ], "text/plain": [ "\n", "dask.array\n", "Coordinates:\n", " * altitude (altitude) float64 539.0 599.0 659.0 ... 8.099e+03 8.159e+03\n", " * time (time) datetime64[ns] 2021-02-23T11:02:16 ... 2021-02-23T19:4...\n", " * wavelength (wavelength) float32 1.064e+03\n", " longitude float32 8.617\n", " latitude float32 45.82\n", "Attributes:\n", " ancillary_variables: error_backscatter vertical_resolution\n", " long_name: aerosol backscatter coefficient\n", " plausibility: parameter passed the EARLINET quality assurance.\n", " units: m-1*sr-1" ] }, "execution_count": 21, "metadata": {}, "output_type": "execute_result" } ], "source": [ "backscatter = earlinet_2302['backscatter']\n", "backscatter" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Visualize the backscatter profile in Ispra on 23 February 2021" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Now, we can already visualize the `Aerosol backscatter coefficient` for the station Ispra on 23 February 2021. We want to plot the time information on the x-axis and the altitude information on the y-axis. The visualization code below can be divided in five main parts:\n", "* **Initiate a matplotlib figure**: Initiate a matplotlib plot and define the size of the plot\n", "* **Plotting function**: plot the xarray.DataArray, but transpose the two dimensions, altitude and time\n", "* **Set plot title, axes label and format axes tickes**: specify title, axes labels and their format\n", "* **Define and format colorbar**: define and customize a colorbar\n", "* **Add additional features**: such as grid lines\n" ] }, { "cell_type": "code", "execution_count": 22, "metadata": {}, "outputs": [ { "data": { "image/png": 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\n", 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" ] }, "metadata": { "needs_background": "light" }, "output_type": "display_data" } ], "source": [ "# Initiate a matplotlib figure\n", "fig = plt.figure(figsize=(16,8))\n", "ax=plt.axes()\n", "\n", "# Plotting function\n", "img = (backscatter*10**6).transpose().plot(vmin=0, \n", " vmax=2, \n", " cmap='jet', ax=ax, add_colorbar=False)\n", "\n", "# Set title and axes label information\n", "plt.title('\\n' + backscatter.long_name + ' - Ispra, Italy on 23 February 2021', fontsize=20, pad=20)\n", "plt.ylabel(earlinet_2302.altitude.units+'\\n', fontsize=16)\n", "plt.xlabel('\\nHour', fontsize=16)\n", "\n", "# Format the axes ticks\n", "plt.xticks(fontsize=14)\n", "plt.yticks(fontsize=14)\n", "\n", "# Define and format colorbar\n", "cbar = fig.colorbar(img, ax=ax, orientation='vertical', fraction=0.04, pad=0.03)\n", "cbar.set_label('\\n*10**6 ' + backscatter.units, fontsize=16)\n", "cbar.ax.tick_params(labelsize=14)\n", "\n", "# Add additionally a legend and grid to the plot\n", "plt.grid()" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Load and visualize backscatter profiles in Ispra for 24+25 Feb 2021" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Let us now also load the backscatter profiles for the station in Ispra for the two following days, 24th and 25th February 2021 respectively. We repeat the same steps as above. First, we load the backscatter profile information as xarray.Dataset with the function `open_mfdataset()`.\n", "\n", "Once both datasets are loaded, you see that for 24 February backscatter profiles for four hours are available and for 25 February backscatter profiles for six hours are available." ] }, { "cell_type": "code", "execution_count": 23, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "(\n", " Dimensions: (altitude: 82, time: 4, nv: 2, wavelength: 1)\n", " Coordinates:\n", " * altitude (altitude) float64 539.0 ...\n", " * time (time) datetime64[ns] 202...\n", " * wavelength (wavelength) float32 1.06...\n", " longitude float32 8.617\n", " latitude float32 45.82\n", " Dimensions without coordinates: nv\n", " Data variables: (12/27)\n", " time_bounds (altitude, time, nv) datetime64[ns] dask.array\n", " backscatter_calibration_value (altitude, time, wavelength) float32 dask.array\n", " error_retrieval_method (altitude, time, wavelength) float32 dask.array\n", " backscatter_evaluation_method (altitude, time, wavelength) float32 dask.array\n", " backscatter_calibration_range_search_algorithm (altitude, time, wavelength) float32 dask.array\n", " elastic_backscatter_algorithm (altitude, time, wavelength) float32 dask.array\n", " ... ...\n", " user_defined_category (altitude, time) float64 ...\n", " backscatter_calibration_range (altitude, time, wavelength, nv) float32 dask.array\n", " backscatter_calibration_search_range (altitude, time, wavelength, nv) float32 dask.array\n", " cloud_mask_type (altitude, time) float64 ...\n", " scc_product_type (altitude, time) float64 ...\n", " cloud_mask (time, altitude) float32 dask.array\n", " Attributes: (12/35)\n", " Conventions: CF-1.7\n", " title: Profiles of aerosol optical properties\n", " source: Ground based LIDAR measurements\n", " references: Project website at http://www.earli...\n", " history: 2021-10-06T09:23Z : Assigned versio...\n", " station_ID: ipr\n", " ... ...\n", " scc_version_description: SCC vers. 5.2.3 (HiRELPP vers. 1.1....\n", " processor_name: ELDA\n", " processor_version: 3.4.8\n", " __file_format_version: 2.1\n", " input_file: ipr_003_0000753_202102241406_202102...\n", " overlap_correction_file: ,\n", " \n", " Dimensions: (altitude: 84, time: 6, nv: 2, wavelength: 1)\n", " Coordinates:\n", " * altitude (altitude) float64 539.0 ...\n", " * time (time) datetime64[ns] 202...\n", " * wavelength (wavelength) float32 1.06...\n", " longitude float32 8.617\n", " latitude float32 45.82\n", " Dimensions without coordinates: nv\n", " Data variables: (12/27)\n", " time_bounds (altitude, time, nv) datetime64[ns] dask.array\n", " backscatter_calibration_value (altitude, time, wavelength) float32 dask.array\n", " error_retrieval_method (altitude, time, wavelength) float32 dask.array\n", " backscatter_evaluation_method (altitude, time, wavelength) float32 dask.array\n", " backscatter_calibration_range_search_algorithm (altitude, time, wavelength) float32 dask.array\n", " elastic_backscatter_algorithm (altitude, time, wavelength) float32 dask.array\n", " ... ...\n", " user_defined_category (altitude, time) float64 ...\n", " backscatter_calibration_range (altitude, time, wavelength, nv) float32 dask.array\n", " backscatter_calibration_search_range (altitude, time, wavelength, nv) float32 dask.array\n", " cloud_mask_type (altitude, time) float64 ...\n", " scc_product_type (altitude, time) float64 ...\n", " cloud_mask (time, altitude) float32 dask.array\n", " Attributes: (12/35)\n", " Conventions: CF-1.7\n", " title: Profiles of aerosol optical properties\n", " source: Ground based LIDAR measurements\n", " references: Project website at http://www.earli...\n", " history: 2021-10-06T09:25Z : Assigned versio...\n", " station_ID: ipr\n", " ... ...\n", " scc_version_description: SCC vers. 5.2.3 (HiRELPP vers. 1.1....\n", " processor_name: ELDA\n", " processor_version: 3.4.8\n", " __file_format_version: 2.1\n", " input_file: ipr_003_0000753_202102251123_202102...\n", " overlap_correction_file: )" ] }, "execution_count": 23, "metadata": {}, "output_type": "execute_result" } ], "source": [ "file_dir = '../../eodata/2_observations/earlinet/Level2/ipr/0224/'\n", "earlinet_2402 = xr.open_mfdataset(file_dir+'*')\n", "\n", "file_dir = '../../eodata/2_observations/earlinet/Level2/ipr/0225/'\n", "earlinet_2502 = xr.open_mfdataset(file_dir+'*')\n", "\n", "earlinet_2402, earlinet_2502" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "
" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "The next step is now to visualize the two backscatter profiles for both days next to each other. We simply replicate the visualization code from above, but create two subplots with with `plt.subplot()`. By specifying `(1,2,1`), we create a plot with 1 row and 2 columns and the third number indicates that this is the first plot of two." ] }, { "cell_type": "code", "execution_count": 46, "metadata": {}, "outputs": [ { "data": { "image/png": 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\n", 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" ] }, "metadata": { "needs_background": "light" }, "output_type": "display_data" } ], "source": [ "# Initiate a matplotlib figure\n", "fig = plt.figure(figsize=(25,8))\n", "\n", "########################\n", "# 1st subplot\n", "########################\n", "ax1=plt.subplot(1,2,1)\n", "\n", "# Plotting function\n", "img1 = (earlinet_2402['backscatter']*10**6).transpose().plot(vmin=0, \n", " vmax=2, \n", " cmap='jet', ax=ax1, add_colorbar=False)\n", "\n", "# Set title and axes label information\n", "plt.title('\\n' + earlinet_2402['backscatter'].long_name + ' - Ispra, Italy on 24 February 2021', fontsize=20, pad=20)\n", "plt.ylabel(earlinet_2402.altitude.units+'\\n', fontsize=16)\n", "plt.xlabel('\\nHour', fontsize=16)\n", "\n", "# Format the axes ticks\n", "plt.xticks(fontsize=14)\n", "plt.yticks(fontsize=14)\n", "\n", "# Add additionally a legend and grid to the plot\n", "plt.grid()\n", "\n", "\n", "########################\n", "# 2nd subplot\n", "########################\n", "ax2 = plt.subplot(1,2,2)\n", "# Plotting function\n", "img2 = (earlinet_2502['backscatter']*10**6).transpose().plot(vmin=0, \n", " vmax=2, \n", " cmap='jet', ax=ax2, add_colorbar=False)\n", "\n", "# Set title and axes label information\n", "plt.title('\\n' + earlinet_2502['backscatter'].long_name + ' - Ispra, Italy on 25 February 2021', fontsize=20, pad=20)\n", "plt.ylabel(earlinet_2502.altitude.units+'\\n', fontsize=16)\n", "plt.xlabel('\\nHour', fontsize=16)\n", "\n", "# Format the axes ticks\n", "plt.xticks(fontsize=14)\n", "plt.yticks(fontsize=14)\n", "\n", "# Define and format colorbar\n", "cbar = fig.colorbar(img2, ax=ax2, orientation='vertical', fraction=0.04, pad=0.03)\n", "cbar.set_label('\\n*10**6 ' + earlinet_2502['backscatter'].units, fontsize=16)\n", "cbar.ax.tick_params(labelsize=14)\n", "\n", "# Add additionally a legend and grid to the plot\n", "plt.grid()\n" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Above, you see that the intensity of the backscatter profile has increased from 23rd to 24th of February, but the aerosol layer was in the upper atmosphere between 2000 and 3000 m above surface. On 25th February, the aerosol layer settled at the surface. This strong aerosol occurence at the surface can also be seen in the [EEA Air Qualty data](./eea_air_quality.ipynb), as the PM10 value exceeded by far the daily limit of 50 µg/m3." ] } ], "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": "3.11.6" } }, "nbformat": 4, "nbformat_minor": 4 }