{ "cells": [ { "cell_type": "markdown", "metadata": { "Collapsed": "false", "tags": [] }, "source": [ "# Solution 5" ] }, { "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 have analysed satellite, model and ground-based observation data for one specific event.
\n", "> Today, we would like to broaden our perspective and analyse more in detail the annual cycle and seasonality of dust and aerosols.\n", "\n", "\n" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Tasks" ] }, { "cell_type": "markdown", "metadata": { "tags": [] }, "source": [ "**1. Brainstorm**\n", " * What data, introduced to you in week 1, can be used for analysing the annual cylce and patterns of dust?\n", " * What aggregation level is required?\n", " * Which variables to analyse dust do you know and are available?\n", "\n", "**2. Download and plot monthly Metop-A/B/C GOME-2 Level 3 Absorbing Aerosol Index data for 2020**\n", " * Download monthly Metop-A/B/C GOME-2 Absorbing Aerosol Index data for year 2020 and plot the monthly values as map featuring a geographical subset for bounding box N:70°, E:36°, S:0°, W:-50°\n", " * **Hint** \n", " * [Metop-A/B/C - Example notebook](./gome2_aai.ipynb)\n", " * Data access\n", " * **Some questions to reflect on**\n", " * Can you identify some patterns? Describe the patterns you observe for each month.\n", " * Were some months more affected by desert dust than others in average?\n", "\n", "**3. Load AERONET observations and CAMS reanalysis (EAC4) time-series for Santa Cruz, Tenerife in 2020 and plot monthly aggregates in one plot**\n", " * Load the time-series of daily aggregated AERONET observations and CAMS reanalysis (EAC4) for Santa Cruz, Tenerife in 2020, resample the values to monthly averages and plot the monthly averaged values together in one plot\n", " * **Hint** \n", " * [Day 2 Assignment - Solution notebook](./solution_4.ipynb)\n", " * [Link to data file](../../eodata/case_study/2020_ts_cams_aeronet.csv)\n", " * **Some questions to reflect on**\n", " * Interpret the plotting result\n", " * Do the monthly patterns of AERONET observations and CAMS reanalysis look similar?\n", " * Do the patterns resemble those of Metop-A/B/C GOME-2 Level 3 Absorbing Aerosol Index data?" ] }, { "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", "from datetime import datetime\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", "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": 2, "metadata": {}, "outputs": [], "source": [ "%run ../../functions.ipynb" ] }, { "cell_type": "markdown", "metadata": { "Collapsed": "false" }, "source": [ "
" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### 1. Brainstorm" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "The following data and variables can be used to analyse the annual cycle and patterns of dust:\n", "* [AERONET](./aeronet.ipynb)\n", " * Aerosol Optical Depth\n", " * Angstrom exponent\n", "* [Metop-A/B/C GOME-2 Level 3](./gome2_aai.ipynb)\n", " * Absorbing Aerosol Index (AAI)\n", "* CAMS global reanalysis (EAC4)\n", " * Dust aerosol optical depth at 550 nm\n" ] }, { "cell_type": "markdown", "metadata": { "tags": [] }, "source": [ "### 2. Download and plot monthly Metop-A/B/C GOME-2 Level 3 Absorbing Aerosol Index data for year 2020" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "The Metop-A/B/C GOME-3 Level 3 AAI data files can be downloaded from the TEMIS website in `NetCDF` data format. TEMIS offers the data of all three satellites Metop-A, -B and -C, which, combined, provide monthly measurements for the entire globe.\n", "\n", "The following example uses monthly gridded AAI data from the three satellites Metop-A, -B, and -C for 2020.\n", "Since the data is distributed in the `NetCDF` format, you can use the xarray function `xr.open_mfdataset()` to load the multiple netCDF files at once.\n" ] }, { "cell_type": "code", "execution_count": 3, "metadata": { "scrolled": true }, "outputs": [ { "data": { "text/html": [ "
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<xarray.DataArray 'absorbing_aerosol_index' (time: 12, latitude: 180, longitude: 360)>\n",
       "dask.array<concatenate, shape=(12, 180, 360), dtype=float32, chunksize=(1, 180, 360), chunktype=numpy.ndarray>\n",
       "Coordinates:\n",
       "  * longitude  (longitude) float32 -179.5 -178.5 -177.5 ... 177.5 178.5 179.5\n",
       "  * latitude   (latitude) float32 -89.5 -88.5 -87.5 -86.5 ... 87.5 88.5 89.5\n",
       "Dimensions without coordinates: time\n",
       "Attributes:\n",
       "    long_name:  Absorbing aerosol index averaged for each grid cell\n",
       "    units:      1
" ], "text/plain": [ "\n", "dask.array\n", "Coordinates:\n", " * longitude (longitude) float32 -179.5 -178.5 -177.5 ... 177.5 178.5 179.5\n", " * latitude (latitude) float32 -89.5 -88.5 -87.5 -86.5 ... 87.5 88.5 89.5\n", "Dimensions without coordinates: time\n", "Attributes:\n", " long_name: Absorbing aerosol index averaged for each grid cell\n", " units: 1" ] }, "execution_count": 3, "metadata": {}, "output_type": "execute_result" } ], "source": [ "ds_a = xr.open_mfdataset('../../eodata/case_study/gome2/ESACCI-AEROSOL-L3-AAI-GOME2A-1M-2020*.nc', \n", " concat_dim='time', \n", " combine='nested')\n", "\n", "aai_a=ds_a['absorbing_aerosol_index']\n", "aai_a" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "
" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "The same process has to be repeated for the daily gridded AAI data from the satellites Metop-B and Metop-C respectively.\n", "Below, we load the GOME-2 Level 3 AAI data from the Metop-B satellite." ] }, { "cell_type": "code", "execution_count": 4, "metadata": { "scrolled": true }, "outputs": [ { "data": { "text/html": [ "
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<xarray.DataArray 'absorbing_aerosol_index' (time: 12, latitude: 180, longitude: 360)>\n",
       "dask.array<concatenate, shape=(12, 180, 360), dtype=float32, chunksize=(1, 180, 360), chunktype=numpy.ndarray>\n",
       "Coordinates:\n",
       "  * longitude  (longitude) float32 -179.5 -178.5 -177.5 ... 177.5 178.5 179.5\n",
       "  * latitude   (latitude) float32 -89.5 -88.5 -87.5 -86.5 ... 87.5 88.5 89.5\n",
       "Dimensions without coordinates: time\n",
       "Attributes:\n",
       "    long_name:  Absorbing aerosol index averaged for each grid cell\n",
       "    units:      1
" ], "text/plain": [ "\n", "dask.array\n", "Coordinates:\n", " * longitude (longitude) float32 -179.5 -178.5 -177.5 ... 177.5 178.5 179.5\n", " * latitude (latitude) float32 -89.5 -88.5 -87.5 -86.5 ... 87.5 88.5 89.5\n", "Dimensions without coordinates: time\n", "Attributes:\n", " long_name: Absorbing aerosol index averaged for each grid cell\n", " units: 1" ] }, "execution_count": 4, "metadata": {}, "output_type": "execute_result" } ], "source": [ "ds_b = xr.open_mfdataset('../../eodata/case_study/gome2/ESACCI-AEROSOL-L3-AAI-GOME2B-1M-2020*.nc', \n", " concat_dim='time', \n", " combine='nested')\n", "\n", "aai_b =ds_b['absorbing_aerosol_index']\n", "aai_b" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "
" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "And here, we load the daily gridded GOME-2 AAI Level 3 data files from the Metop-C satellite." ] }, { "cell_type": "code", "execution_count": 5, "metadata": { "scrolled": true }, "outputs": [ { "data": { "text/html": [ "
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<xarray.DataArray 'absorbing_aerosol_index' (time: 12, latitude: 180, longitude: 360)>\n",
       "dask.array<concatenate, shape=(12, 180, 360), dtype=float32, chunksize=(1, 180, 360), chunktype=numpy.ndarray>\n",
       "Coordinates:\n",
       "  * longitude  (longitude) float32 -179.5 -178.5 -177.5 ... 177.5 178.5 179.5\n",
       "  * latitude   (latitude) float32 -89.5 -88.5 -87.5 -86.5 ... 87.5 88.5 89.5\n",
       "Dimensions without coordinates: time\n",
       "Attributes:\n",
       "    long_name:  Absorbing aerosol index averaged for each grid cell\n",
       "    units:      1
" ], "text/plain": [ "\n", "dask.array\n", "Coordinates:\n", " * longitude (longitude) float32 -179.5 -178.5 -177.5 ... 177.5 178.5 179.5\n", " * latitude (latitude) float32 -89.5 -88.5 -87.5 -86.5 ... 87.5 88.5 89.5\n", "Dimensions without coordinates: time\n", "Attributes:\n", " long_name: Absorbing aerosol index averaged for each grid cell\n", " units: 1" ] }, "execution_count": 5, "metadata": {}, "output_type": "execute_result" } ], "source": [ "ds_c = xr.open_mfdataset('../../eodata/case_study/gome2/ESACCI-AEROSOL-L3-AAI-GOME2C-1M-2020*.nc', \n", " concat_dim='time', \n", " combine='nested')\n", "\n", "aai_c=ds_c['absorbing_aerosol_index']\n", "aai_c" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "
" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "**Concatenate the data from the three satellites Metop-A, -B and -C**" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "The next step is to concatenate the `DataArrays` from the three satellites Metop-A, -B and -C using a new dimension called `satellite`. \n", "You can use the `concat()` function from the xarray library to do this. The result is a four-dimensional `xarray.DataArray`, with the dimensions `satellite`, `time`, `latitude` and `longitude`." ] }, { "cell_type": "code", "execution_count": 6, "metadata": { "scrolled": true }, "outputs": [ { "data": { "text/html": [ "
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<xarray.DataArray 'absorbing_aerosol_index' (satellite: 3, time: 12, latitude: 180, longitude: 360)>\n",
       "dask.array<concatenate, shape=(3, 12, 180, 360), dtype=float32, chunksize=(1, 1, 180, 360), chunktype=numpy.ndarray>\n",
       "Coordinates:\n",
       "  * longitude  (longitude) float32 -179.5 -178.5 -177.5 ... 177.5 178.5 179.5\n",
       "  * latitude   (latitude) float32 -89.5 -88.5 -87.5 -86.5 ... 87.5 88.5 89.5\n",
       "Dimensions without coordinates: satellite, time\n",
       "Attributes:\n",
       "    long_name:  Absorbing aerosol index averaged for each grid cell\n",
       "    units:      1
" ], "text/plain": [ "\n", "dask.array\n", "Coordinates:\n", " * longitude (longitude) float32 -179.5 -178.5 -177.5 ... 177.5 178.5 179.5\n", " * latitude (latitude) float32 -89.5 -88.5 -87.5 -86.5 ... 87.5 88.5 89.5\n", "Dimensions without coordinates: satellite, time\n", "Attributes:\n", " long_name: Absorbing aerosol index averaged for each grid cell\n", " units: 1" ] }, "execution_count": 6, "metadata": {}, "output_type": "execute_result" } ], "source": [ "aai_concat = xr.concat([aai_a,aai_b,aai_c], dim='satellite')\n", "aai_concat" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "
" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "**Retrieve time coordinate information and assign time coordinates for the time dimension**" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "You can see that the resulting `xarray.DataArray` holds coordinate information for the two spatial dimensions `longitude` and `latitude`, but not for `time` and `satellite`.\n", "However, the coordinates for `time` will be important for plotting the data as we need to know which month the data is valid. Thus, a next step is to assign coordinates to the `time` dimension.\n", "\n", "With the help of the Python library `pandas`, you can build a `DateTime` time series for the months in 2020, from 1 January to 31 December 2021." ] }, { "cell_type": "code", "execution_count": 7, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "DatetimeIndex(['2020-01-01', '2020-02-01', '2020-03-01', '2020-04-01',\n", " '2020-05-01', '2020-06-01', '2020-07-01', '2020-08-01',\n", " '2020-09-01', '2020-10-01', '2020-11-01', '2020-12-01'],\n", " dtype='datetime64[ns]', freq=None)" ] }, "execution_count": 7, "metadata": {}, "output_type": "execute_result" } ], "source": [ "time_coords = pd.date_range(datetime.strptime('01-2020','%m-%Y'), periods=12, freq='m').strftime(\"%Y-%m\").astype('datetime64[ns]')\n", "time_coords" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "
" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "The final step is to assign the pandas time series object `time_coords` to the `aai_concat` DataArray object. You can use the `assign_coords()` function from xarray. The result is that the time coordinates have now been assigned values. The only dimension the remains unassigned is `satellite`." ] }, { "cell_type": "code", "execution_count": 8, "metadata": { "scrolled": true }, "outputs": [ { "data": { "text/html": [ "
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<xarray.DataArray 'absorbing_aerosol_index' (satellite: 3, time: 12, latitude: 180, longitude: 360)>\n",
       "dask.array<concatenate, shape=(3, 12, 180, 360), dtype=float32, chunksize=(1, 1, 180, 360), chunktype=numpy.ndarray>\n",
       "Coordinates:\n",
       "  * longitude  (longitude) float32 -179.5 -178.5 -177.5 ... 177.5 178.5 179.5\n",
       "  * latitude   (latitude) float32 -89.5 -88.5 -87.5 -86.5 ... 87.5 88.5 89.5\n",
       "  * time       (time) datetime64[ns] 2020-01-01 2020-02-01 ... 2020-12-01\n",
       "Dimensions without coordinates: satellite\n",
       "Attributes:\n",
       "    long_name:  Absorbing aerosol index averaged for each grid cell\n",
       "    units:      1
" ], "text/plain": [ "\n", "dask.array\n", "Coordinates:\n", " * longitude (longitude) float32 -179.5 -178.5 -177.5 ... 177.5 178.5 179.5\n", " * latitude (latitude) float32 -89.5 -88.5 -87.5 -86.5 ... 87.5 88.5 89.5\n", " * time (time) datetime64[ns] 2020-01-01 2020-02-01 ... 2020-12-01\n", "Dimensions without coordinates: satellite\n", "Attributes:\n", " long_name: Absorbing aerosol index averaged for each grid cell\n", " units: 1" ] }, "execution_count": 8, "metadata": {}, "output_type": "execute_result" } ], "source": [ "aai_concat = aai_concat.assign_coords(time=time_coords)\n", "aai_concat" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "
" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "**Combine AAI data from the three satellites Metop-A, -B and -C onto one single grid**" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Since the final aim is to combine the data from the three satellites Metop-A, -B and -C onto one single grid, the next step is to reduce the `satellite` dimension. You can do this by applying the reduce function `mean` to the `aai_concat` Data Array. The dimension (`dim`) to be reduced is the `satellite` dimension.\n", "\n", "This function builds the average of all data points within a grid cell. The resulting `xarray.DataArray` has three dimensions `time`, `latitude` and `longitude`." ] }, { "cell_type": "code", "execution_count": 9, "metadata": { "scrolled": true }, "outputs": [ { "data": { "text/html": [ "
\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "
<xarray.DataArray 'absorbing_aerosol_index' (time: 12, latitude: 180, longitude: 360)>\n",
       "dask.array<mean_agg-aggregate, shape=(12, 180, 360), dtype=float32, chunksize=(1, 180, 360), chunktype=numpy.ndarray>\n",
       "Coordinates:\n",
       "  * longitude  (longitude) float32 -179.5 -178.5 -177.5 ... 177.5 178.5 179.5\n",
       "  * latitude   (latitude) float32 -89.5 -88.5 -87.5 -86.5 ... 87.5 88.5 89.5\n",
       "  * time       (time) datetime64[ns] 2020-01-01 2020-02-01 ... 2020-12-01
" ], "text/plain": [ "\n", "dask.array\n", "Coordinates:\n", " * longitude (longitude) float32 -179.5 -178.5 -177.5 ... 177.5 178.5 179.5\n", " * latitude (latitude) float32 -89.5 -88.5 -87.5 -86.5 ... 87.5 88.5 89.5\n", " * time (time) datetime64[ns] 2020-01-01 2020-02-01 ... 2020-12-01" ] }, "execution_count": 9, "metadata": {}, "output_type": "execute_result" } ], "source": [ "aai_combined = aai_concat.mean(dim='satellite')\n", "aai_combined" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "
" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "**Visualize AAI data with data from the three satellites Metop-A, -B and C combined on one single grid**" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "The next step is to visualize the Absorbing Aerosol Index data for one time step. You can use the function `visualize_pcolormesh` for it.\n", "\n", "You can use `afmhot_r` as color map, `ccrs.PlateCarree()` as projection and by applying `dt.strftime('%Y-%m-%d').data` to the time coordinate variable, you can add the valid time step to the title of the plot.\n", "\n", "Let us plot a geographic subset over Western Africa and the Atlantic (N: 70, E:36, S:0, W:-50).\n", "\n", "Plot different months. Can you identfy some patterns? Were some months more affected by desert dust than others in average?" ] }, { "cell_type": "code", "execution_count": 10, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "(
,\n", " )" ] }, "execution_count": 10, "metadata": {}, "output_type": "execute_result" }, { "data": { "image/png": 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"text/plain": [ "
" ] }, "metadata": { "needs_background": "light" }, "output_type": "display_data" } ], "source": [ "month=5\n", "visualize_pcolormesh(data_array=aai_combined[month,:,:],\n", " longitude=aai_combined.longitude, \n", " latitude=aai_combined.latitude,\n", " projection=ccrs.PlateCarree(), \n", " color_scale='afmhot_r', \n", " unit=' ',\n", " long_name=aai_a.long_name + ' - ' + str(aai_combined.time[month].dt.strftime('%Y-%m').data), \n", " vmin=0, \n", " vmax=5, \n", " lonmin=-50, \n", " lonmax=36, \n", " latmin=0, \n", " latmax=70.,\n", " set_global=False)\n" ] }, { "cell_type": "markdown", "metadata": { "tags": [] }, "source": [ "### 3. Load AERONET and CAMS reanalysis (EAC4) time-series for Santa Cruz, Tenerife in 2020 and plot monthly aggregates" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "The file is a result from [Day 2 - Assignment](../04_exercise.ipynb#visualize_annual_ts) and the created pandas dataframe was saved as a `csv` file under `../eodata/case_study/2020_ts_cams_aeronet.csv`. We can open it with the pandas function `read_table()`. We additonally set specific keyword arguments:\n", "* `delimiter`: specify the delimiter in the text file, e.g. comma\n", "\n", "You see below that the resulting dataframe has 366 rows and 5 columns: `time`, `longitude`, `latitude`, `duaod550`, `AOD_500nm`. The columns `duaod550` are the dust aersol optical depth values from the CAMS reanalysis and `AOD_500nm` are the station measurements from AERONET." ] }, { "cell_type": "code", "execution_count": 11, "metadata": {}, "outputs": [ { "data": { "text/html": [ "
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timelongitudelatitudeduaod550AOD_500nm
02020-01-01-16.2528.250.0626770.094487
12020-01-02-16.2528.250.121897NaN
22020-01-03-16.2528.250.0645050.075765
32020-01-04-16.2528.250.0068120.098110
42020-01-05-16.2528.250.0011140.085672
..................
3612020-12-27-16.2528.250.0692400.134366
3622020-12-28-16.2528.250.2154550.415433
3632020-12-29-16.2528.250.1823380.320463
3642020-12-30-16.2528.250.0728430.095342
3652020-12-31-16.2528.250.0104820.033297
\n", "

366 rows × 5 columns

\n", "
" ], "text/plain": [ " time longitude latitude duaod550 AOD_500nm\n", "0 2020-01-01 -16.25 28.25 0.062677 0.094487\n", "1 2020-01-02 -16.25 28.25 0.121897 NaN\n", "2 2020-01-03 -16.25 28.25 0.064505 0.075765\n", "3 2020-01-04 -16.25 28.25 0.006812 0.098110\n", "4 2020-01-05 -16.25 28.25 0.001114 0.085672\n", ".. ... ... ... ... ...\n", "361 2020-12-27 -16.25 28.25 0.069240 0.134366\n", "362 2020-12-28 -16.25 28.25 0.215455 0.415433\n", "363 2020-12-29 -16.25 28.25 0.182338 0.320463\n", "364 2020-12-30 -16.25 28.25 0.072843 0.095342\n", "365 2020-12-31 -16.25 28.25 0.010482 0.033297\n", "\n", "[366 rows x 5 columns]" ] }, "execution_count": 11, "metadata": {}, "output_type": "execute_result" } ], "source": [ "df = pd.read_csv('../../eodata/case_study/2020_ts_cams_aeronet.csv', delimiter=',')\n", "df" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "
" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Let us now convert the `time` column to a `DateTimeIndex` format with the function `to_datetime()`. Important here, you have to specify the format of the index string: `%Y-%m-%d`." ] }, { "cell_type": "code", "execution_count": 12, "metadata": {}, "outputs": [ { "data": { "text/html": [ "
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timelongitudelatitudeduaod550AOD_500nm
time
2020-01-012020-01-01-16.2528.250.0626770.094487
2020-01-022020-01-02-16.2528.250.121897NaN
2020-01-032020-01-03-16.2528.250.0645050.075765
2020-01-042020-01-04-16.2528.250.0068120.098110
2020-01-052020-01-05-16.2528.250.0011140.085672
..................
2020-12-272020-12-27-16.2528.250.0692400.134366
2020-12-282020-12-28-16.2528.250.2154550.415433
2020-12-292020-12-29-16.2528.250.1823380.320463
2020-12-302020-12-30-16.2528.250.0728430.095342
2020-12-312020-12-31-16.2528.250.0104820.033297
\n", "

366 rows × 5 columns

\n", "
" ], "text/plain": [ " time longitude latitude duaod550 AOD_500nm\n", "time \n", "2020-01-01 2020-01-01 -16.25 28.25 0.062677 0.094487\n", "2020-01-02 2020-01-02 -16.25 28.25 0.121897 NaN\n", "2020-01-03 2020-01-03 -16.25 28.25 0.064505 0.075765\n", "2020-01-04 2020-01-04 -16.25 28.25 0.006812 0.098110\n", "2020-01-05 2020-01-05 -16.25 28.25 0.001114 0.085672\n", "... ... ... ... ... ...\n", "2020-12-27 2020-12-27 -16.25 28.25 0.069240 0.134366\n", "2020-12-28 2020-12-28 -16.25 28.25 0.215455 0.415433\n", "2020-12-29 2020-12-29 -16.25 28.25 0.182338 0.320463\n", "2020-12-30 2020-12-30 -16.25 28.25 0.072843 0.095342\n", "2020-12-31 2020-12-31 -16.25 28.25 0.010482 0.033297\n", "\n", "[366 rows x 5 columns]" ] }, "execution_count": 12, "metadata": {}, "output_type": "execute_result" } ], "source": [ "df.index = pd.to_datetime(df.time, format = '%Y-%m-%d')\n", "df" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "
" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "We are interested in the monthly averages of aerosol optical depth in 2020. With the function `resample()`, we can create the monthly averages based on the daily AOD values. The result is a data frame with 12 row entries and 4 columns." ] }, { "cell_type": "code", "execution_count": 13, "metadata": {}, "outputs": [ { "data": { "text/html": [ "
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longitudelatitudeduaod550AOD_500nm
time
2020-01-31-16.2528.250.0346380.117633
2020-02-29-16.2528.250.1636400.245622
2020-03-31-16.2528.250.0473730.133028
2020-04-30-16.2528.250.0022620.085046
2020-05-31-16.2528.250.0186290.099021
2020-06-30-16.2528.250.1477890.082430
2020-07-31-16.2528.250.2561710.357828
2020-08-31-16.2528.250.1491280.267296
2020-09-30-16.2528.250.1991880.195343
2020-10-31-16.2528.250.0763140.138351
2020-11-30-16.2528.250.0387520.106877
2020-12-31-16.2528.250.0446030.096469
\n", "
" ], "text/plain": [ " longitude latitude duaod550 AOD_500nm\n", "time \n", "2020-01-31 -16.25 28.25 0.034638 0.117633\n", "2020-02-29 -16.25 28.25 0.163640 0.245622\n", "2020-03-31 -16.25 28.25 0.047373 0.133028\n", "2020-04-30 -16.25 28.25 0.002262 0.085046\n", "2020-05-31 -16.25 28.25 0.018629 0.099021\n", "2020-06-30 -16.25 28.25 0.147789 0.082430\n", "2020-07-31 -16.25 28.25 0.256171 0.357828\n", "2020-08-31 -16.25 28.25 0.149128 0.267296\n", "2020-09-30 -16.25 28.25 0.199188 0.195343\n", "2020-10-31 -16.25 28.25 0.076314 0.138351\n", "2020-11-30 -16.25 28.25 0.038752 0.106877\n", "2020-12-31 -16.25 28.25 0.044603 0.096469" ] }, "execution_count": 13, "metadata": {}, "output_type": "execute_result" } ], "source": [ "df_resample = df.resample('1M').mean()\n", "df_resample" ] }, { "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_resample` as two individual line plots." ] }, { "cell_type": "code", "execution_count": 14, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "" ] }, "execution_count": 14, "metadata": {}, "output_type": "execute_result" }, { "data": { "image/png": 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\n", "text/plain": [ "
" ] }, "metadata": { "needs_background": "light" }, "output_type": "display_data" } ], "source": [ "# Initiate a figure\n", "fig = plt.figure(figsize=(15,8))\n", "ax = plt.subplot()\n", "\n", "# Define the plotting function\n", "#ax.plot(gome2_ts.time, gome2_ts, 'o-', color='orange', label='Metop-A/B/C GOME-2 AAI')\n", "ax.plot(df_resample.index.month, df_resample.duaod550, 'o-', color='blue', label='CAMS reanalysis Dust AOD at 550 nm')\n", "ax.plot(df_resample.index.month, df_resample.AOD_500nm, 'o-', color='green', label='AERONET AOD at 500 nm')\n", "\n", "\n", "# Customize the title and axes lables\n", "ax.set_title('\\nMonthly - Santa Cruz Tenerife\\n', fontsize=20)\n", "ax.set_ylabel('~', fontsize=14)\n", "ax.set_xlabel('\\nMonth', 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 monthly aggregates of AERONET observations and CAMS reanalysis follow a similar pattern for the year 2020." ] } ], "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 }