{ "cells": [ { "cell_type": "markdown", "id": "dcfb6075", "metadata": {}, "source": [ "## 2023-06 S2VSR\n", "The PyrNet for [Small Scale Variability of Solar Radiation (S2VSR)](https://www.arm.gov/research/campaigns/sgp2023s2vsr) was set up for calibration on the ARM-SGP guest instrument facility (**GIF**) for calibration from 2023-06-02 to 2023-06-08. Here, the PyrNet data is calibrated with the absolute calibration from the same period ([S2VSR absolute calibration](#sec-calibration-s2vsr)). As the PyrNet pyranometer are known for major directional sensibility, a correction function depending on solar zenith angle is fitted against the ARM-SGP reference measurements. \n", "\n", "\n", "### Imports" ] }, { "cell_type": "code", "execution_count": 1, "id": "a2b7e73c", "metadata": { "ExecuteTime": { "end_time": "2024-02-13T11:42:13.914156700Z", "start_time": "2024-02-13T11:42:12.922500500Z" } }, "outputs": [], "source": [ "from IPython.display import display, Latex\n", "import numpy as np\n", "import matplotlib.pyplot as plt \n", "import xarray as xr\n", "import scipy" ] }, { "cell_type": "markdown", "id": "a12f2202", "metadata": {}, "source": [ "### Load data\n", "The one minute ARM-SGP global horizontal downward irradiance (GHI) and PyrNet GHI for the reference period were loaded. The two datasets were then filtered so that only clearsky times remain. Clarsky times are by definition whole minutes in which more than 70% of all PyrNet stations, have a clearsky flag that is TRUE according to the clearsky detection algorithm by [Reno and Hansen (2016)](https://doi.org/10.1016/j.renene.2015.12.031). After that step the PyrNet GHI data was averaged over all stations. The ARM-SGP data remained unchanged. These two GHI datasets were then reordered according to the respective solar zenith angle for angles between 10 and 90 degree and averaged over 0.1 degree wide bins. Pyr_ghi then contains the GHI values from the pyranometer measurements and ARM_data the ARM-SGP GHI data with respect to the solar zenith angle bins." ] }, { "cell_type": "code", "execution_count": 2, "id": "9b15de6d", "metadata": { "ExecuteTime": { "end_time": "2024-02-13T11:42:14.165728300Z", "start_time": "2024-02-13T11:42:13.917148900Z" } }, "outputs": [ { "data": { "text/html": [ "
\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "
<xarray.Dataset>\n",
       "Dimensions:  (szen: 801)\n",
       "Coordinates:\n",
       "  * szen     (szen) float32 10.0 10.1 10.2 10.3 10.4 ... 89.7 89.8 89.9 90.0\n",
       "Data variables:\n",
       "    ghi      (szen) float64 ...
" ], "text/plain": [ "\n", "Dimensions: (szen: 801)\n", "Coordinates:\n", " * szen (szen) float32 10.0 10.1 10.2 10.3 10.4 ... 89.7 89.8 89.9 90.0\n", "Data variables:\n", " ghi (szen) float64 ..." ] }, "execution_count": 2, "metadata": {}, "output_type": "execute_result" } ], "source": [ "ARM_data= xr.open_dataset('pyrnet_s2vsr_szen_bins_ARM.nc')\n", "ARM_data" ] }, { "cell_type": "code", "execution_count": 3, "id": "ebdd75a9", "metadata": { "ExecuteTime": { "end_time": "2024-02-13T11:42:14.182522100Z", "start_time": "2024-02-13T11:42:14.165728300Z" } }, "outputs": [ { "data": { "text/html": [ "
\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "
<xarray.Dataset>\n",
       "Dimensions:  (szen: 801)\n",
       "Coordinates:\n",
       "  * szen     (szen) float32 10.0 10.1 10.2 10.3 10.4 ... 89.7 89.8 89.9 90.0\n",
       "Data variables:\n",
       "    ghi      (szen) float64 ...
" ], "text/plain": [ "\n", "Dimensions: (szen: 801)\n", "Coordinates:\n", " * szen (szen) float32 10.0 10.1 10.2 10.3 10.4 ... 89.7 89.8 89.9 90.0\n", "Data variables:\n", " ghi (szen) float64 ..." ] }, "execution_count": 3, "metadata": {}, "output_type": "execute_result" } ], "source": [ "Pyr_ghi= xr.open_dataset('pyrnet_s2vsr_szen_bins_pyranometer_avg.nc')\n", "Pyr_ghi" ] }, { "cell_type": "markdown", "id": "74baa161", "metadata": {}, "source": [ "### Determine the cosine correction\n", "Calculate the cosine correction for the Pyranometer measurements. A cubic relationship between the cosine of the solar zenith angle (mu) and the ratio of the ARM measurements to pyranometer measurements (correction-factor) is assumed. " ] }, { "cell_type": "code", "execution_count": 4, "id": "f2b17bfa", "metadata": { "ExecuteTime": { "end_time": "2024-02-13T11:42:25.038644Z", "start_time": "2024-02-13T11:42:14.186021600Z" } }, "outputs": [ { "data": { "text/plain": [ " message: Optimization terminated successfully.\n", " success: True\n", " fun: 0.35222329130203905\n", " x: [-2.227e+00 4.366e+00 -2.524e+00 1.385e+00]\n", " nit: 57\n", " nfev: 3485" ] }, "execution_count": 4, "metadata": {}, "output_type": "execute_result" } ], "source": [ "def cubic_function(x, a, b, c, d):\n", " return a * x ** 3 + b * x ** 2 + c * x + d\n", "\n", "def objective_function(coefficients, x, y):\n", " a, b, c, d = coefficients\n", " y_pred = cubic_function(x, a, b, c, d)\n", " error = np.sum((y - y_pred) ** 2)\n", " return error\n", "bounds = [(-5, 5), (-5, 5), (-5, 5), (-5, 5)]\n", "\n", "result = scipy.optimize.differential_evolution(\n", " objective_function,\n", " args=(np.cos(np.deg2rad(ARM_data.szen)), ARM_data.ghi/Pyr_ghi.ghi),\n", " bounds=bounds,\n", " seed=1\n", ")\n", "result" ] }, { "cell_type": "code", "execution_count": 5, "id": "32f086b8", "metadata": { "ExecuteTime": { "end_time": "2024-02-13T11:42:25.054999Z", "start_time": "2024-02-13T11:42:25.042631300Z" } }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Best cubic fit:\n" ] }, { "data": { "text/latex": [ "\n", " -2.227x^3+4.366x^2-2.524x+1.385\n", " " ], "text/plain": [ "" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "print(\"Best cubic fit:\")\n", "a3, a2, a1, a0 = result.x\n", "display(Latex(\n", " rf\"\"\"\n", " {a3:+.3f}x^3{a2:+.3f}x^2{a1:+.3f}x{a0:+.3f}\n", " \"\"\"\n", "))" ] }, { "cell_type": "markdown", "id": "fd45a7d2", "metadata": {}, "source": [ "### Results \n", "Plotting the best fit and the correction-factor in respect to the solar zenith angle. For comparison also the cosine correction by [Barry et al. (2023)](https://doi.org/10.5194/amt-16-4975-2023) for the MetPVNet campaign is plotted. " ] }, { "cell_type": "code", "execution_count": 6, "id": "b819c23080d0017c", "metadata": { "ExecuteTime": { "end_time": "2024-02-13T11:42:25.121096900Z", "start_time": "2024-02-13T11:42:25.054999Z" }, "collapsed": false }, "outputs": [ { "data": { "text/latex": [ "\n", " MetPVNet: -3.010x^3+5.590x^2-3.040x+1.450\n", " " ], "text/plain": [ "" ] }, "metadata": {}, "output_type": "display_data" }, { "data": { "text/latex": [ "\n", " S2VSR: -2.227x^3+4.366x^2-2.524x+1.385\n", " " ], "text/plain": [ "" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "# Coefficients from Barry et al. 2023:\n", "b3, b2, b1, b0 = [ -3.01, 5.59, -3.04, 1.45 ]\n", "display(Latex(\n", " rf\"\"\"\n", " MetPVNet: {b3:+.3f}x^3{b2:+.3f}x^2{b1:+.3f}x{b0:+.3f}\n", " \"\"\"\n", "))\n", "display(Latex(\n", " rf\"\"\"\n", " S2VSR: {a3:+.3f}x^3{a2:+.3f}x^2{a1:+.3f}x{a0:+.3f}\n", " \"\"\"\n", "))" ] }, { "cell_type": "code", "execution_count": 7, "id": "b216979a", "metadata": { "ExecuteTime": { "end_time": "2024-02-13T11:42:25.311234400Z", "start_time": "2024-02-13T11:42:25.071703300Z" } }, "outputs": [ { "data": { "image/png": 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", "text/plain": [ "
" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "plt.scatter(ARM_data.szen, ARM_data.ghi/Pyr_ghi.ghi, s=1)\n", "plt.title('Cosine Correction for reference period')\n", "mu0 = np.cos(np.deg2rad(ARM_data.szen))\n", "plt.plot(ARM_data.szen, a3*mu0**3 + a2*mu0**2 + a1*mu0 + a0,\n", " color='orange', label=f'best-cubic-fit: {a3:+.3f}x^3{a2:+.3f}x^2{a1:+.3f}x{a0:+.3f}')\n", "plt.plot(ARM_data.szen, b3*mu0**3 + b2*mu0**2 + b1*mu0 + b0,\n", " color='green', label=f'Barry et al.: {b3:+.3f}x^3{b2:+.3f}x^2{b1:+.3f}x{b0:+.3f}')\n", "plt.legend()\n", "plt.xlabel('Zenith angle [deg] ')\n", "plt.ylabel('ARM_ghi/pyr_ghi_mean // correction_factor [-]')\n", "plt.grid(True)\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": "3.10.9" } }, "nbformat": 4, "nbformat_minor": 5 }