{ "cells": [ { "cell_type": "markdown", "id": "95641108-9ba9-478f-8780-99cfd7705879", "metadata": {}, "source": [ "# Long DBS scans" ] }, { "cell_type": "markdown", "id": "5a4475fa-e52b-479b-9c81-2a5ee5071030", "metadata": {}, "source": [ "### Introduction:\n", "\n", "One possible way to retrieve winds using wind lidar is using the Doppler beam swing (DBS) scanning strategy. The DBS consists of four slanted observations of the wind. Each one of the observations is from a different azimuth, equally separated by 90 degrees (0, 90, 180, 270). While executing the DBS, the lidar first observes the wind at azimuths of 0 and 180 degrees and then at 90 and 270 degrees. From those observations, the north-south and east-west wind components can be calculated directly.\n", "\n", "This example focuses on using lidarwind to retrieve wind speed and direction profiles from the observations collected by the WindCube using the DBS scan strategy. This example is structurally similar to dbs_scans. The only difference is the input data. Instead of individual files for each complete DBS scan, each file contains several complete DBS scans.\n", "\n", "### Steps:\n", "\n", "1) Downloading sample data from zenodo\n", "1) Reading the DBS files\n", "2) Merging the DBS files\n", "3) Retrieving the wind profiles \n", "4) Visualising the profiles" ] }, { "cell_type": "code", "execution_count": 1, "id": "d7c8bedc-ba5c-4de0-8060-78362b2cd88c", "metadata": {}, "outputs": [], "source": [ "import matplotlib.pyplot as plt\n", "import lidarwind\n", "from lidarwind.utilities import sample_data" ] }, { "cell_type": "markdown", "id": "af18abd4-3ac4-41ee-8247-15cd2ab779fd", "metadata": {}, "source": [ "### Step 0: Downloading the sample data" ] }, { "cell_type": "code", "execution_count": 2, "id": "705b0df0-07f2-4fa3-a355-048fa03a1aae", "metadata": {}, "outputs": [], "source": [ "file_list = sample_data(\"wc_long_dbs\")" ] }, { "cell_type": "markdown", "id": "a5f2fea2-88d5-481c-857e-f34573274325", "metadata": {}, "source": [ "### Step 1 and 2: Reading and merging the long DBS files" ] }, { "cell_type": "markdown", "id": "9816ec72-029a-4f99-838b-1036ea71259f", "metadata": {}, "source": [ "Here we are going to read all the DBS files. Be careful to provide a list of files that are compatible with each other. Here we also indicate the variables required for processing the DBS fils. Finally, the merged dataset is created. " ] }, { "cell_type": "code", "execution_count": 3, "id": "0978e96b-8114-4ba7-a497-f659f2133aeb", "metadata": {}, "outputs": [], "source": [ "var_list = ['azimuth', 'elevation', 'radial_wind_speed', \n", " 'radial_wind_speed_status', 'measurement_height', 'cnr']\n", "\n", "merged_ds = lidarwind.DbsOperations(file_list, var_list).merged_ds" ] }, { "cell_type": "markdown", "id": "c200e20e-849b-4de4-bca5-141acff350fb", "metadata": {}, "source": [ "### Step 3: Wind profile retrievals" ] }, { "cell_type": "markdown", "id": "105554ab-e180-46eb-b9e7-98cad042412d", "metadata": {}, "source": [ "Once the merged dataset is created, you can use the dedicated class to retrieve the wind profiles. " ] }, { "cell_type": "code", "execution_count": 4, "id": "389ab857-d969-4398-a1a6-d670a8f4727f", "metadata": {}, "outputs": [], "source": [ "wind_obj = lidarwind.GetWindProperties5Beam(merged_ds)" ] }, { "cell_type": "markdown", "id": "684431d1-e481-4b80-93c0-8fe612d5b8d9", "metadata": {}, "source": [ "As indicated below, you can read the wind profiles directly from the wind_obj (wind object). Since they have different timestamps, it is helpful to resample the data into a regular time grid." ] }, { "cell_type": "code", "execution_count": 5, "id": "21db6318-294a-4a7f-8c72-232302de0d06", "metadata": {}, "outputs": [], "source": [ "hor_wind_speed = lidarwind.GetResampledData(wind_obj.hor_wind_speed, time_freq='20s', tolerance=10)\n", "ver_wind_speed = lidarwind.GetResampledData(wind_obj.ver_wind_speed, time_freq='20s', tolerance=10)\n", "hor_wind_dir = lidarwind.GetResampledData(wind_obj.hor_wind_dir, time_freq='20s', tolerance=10)" ] }, { "cell_type": "markdown", "id": "a14bf1a2-a1b9-4d2d-aa95-4e6993f24d7b", "metadata": {}, "source": [ "### Step 4: Visualising the profiles" ] }, { "cell_type": "markdown", "id": "2eee2170-e2d0-4dee-b1d9-029411db645c", "metadata": {}, "source": [ "After resampling, you can use the xarray methods to plot the data. " ] }, { "cell_type": "code", "execution_count": 6, "id": "03ab9f9a-680b-4988-81f2-c31d58cbef8f", "metadata": {}, "outputs": [ { "data": { "image/png": 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\n", 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\n", 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\n", 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" ] }, "metadata": { "needs_background": "light" }, "output_type": "display_data" } ], "source": [ "t1 = merged_ds.time.values[0]\n", "t2 = merged_ds.time.values[-1]\n", "\n", "hor_wind_speed.resampled.sel(time_ref=slice(t1,t2)).plot(y='range', cmap='turbo')\n", "plt.show()\n", "\n", "ver_wind_speed.resampled.sel(time_ref=slice(t1,t2)).plot(y='range', cmap='turbo')\n", "plt.show()\n", "\n", "hor_wind_dir.resampled.sel(time_ref=slice(t1,t2)).plot(y='range', cmap='hsv')\n", "plt.show()" ] } ], "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": "3.8.13" } }, "nbformat": 4, "nbformat_minor": 5 }