Cryosphere/CIE4602_RSAssignment_2020_21_Q3.ipynb

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      "source": [
        "<a href=\"https://colab.research.google.com/github/steflhermitte/EO-Notebooks/blob/master/Cryosphere/CIE4602_RSAssignment_2020_21_Q3.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
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    {
      "cell_type": "markdown",
      "metadata": {
        "id": "EQtg5F0BA1qq"
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      "source": [
        "[<img src=\"https://raw.githubusercontent.com/mbakker7/exploratory_computing_with_python/master/tudelft_logo.png\" width=\"200\" align='right'>](https://www.tudelft.nl/citg/over-faculteit/afdelingen/geoscience-remote-sensing/staff/scientific-staff/dr-stef-lhermitte)\n",
        "\n",
        "# Remote sensing of snow/ice properties from multi-source remote sensing data \n",
        "*Created by Stef Lhermitte (s.lhermitte at tudelft.nl |  [@steflhermitte](https://twitter.com/StefLhermitte) | [www.earthmapps.io](http://www.earthmapps.io))*\n",
        "\n",
        "*with fragments from*\n",
        "\n",
        "* [the Tartes help](http://pp.ige-grenoble.fr/pageperso/picardgh/tartes/)\n",
        "* [the SMRT getting started page](https://www.smrt-model.science/getstarted.html)\n",
        "\n",
        "[![Twitter](https://img.shields.io/twitter/url/https/twitter.com/cloudposse.svg?style=social&label=%20%40steflhermitte)](https://twitter.com/steflhermitte)\n",
        "\n"
      ]
    },
    {
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        "id": "KtKEuWF4fIOw"
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      "source": [
        "\n",
        "# Assignment Introduction\n",
        "Within this assignment you will use remote sensing data from optical, passive microwave and SAR satellite data in combination of radiative transfer models (i.e., [TARTES](http://pp.ige-grenoble.fr/pageperso/picardgh/tartes/) for VNIR wavelengths and [SMRT](https://www.smrt-model.science/) for MW wavelengths) to assess the effect of snow/ice properties on the remote sensing signals.\n",
        "The objective of this assignment is that you:\n",
        " - Explore and understand the effects of snow/ice property changes on the satellite signals\n",
        " - Implement simple radiative transfer models to simulate the snow/ice signal for remote sensing analysis\n",
        " - Experiment with a methodology to extract surface properties from multi-source satellite albedo data\n"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "LiR8M2dU1vYx"
      },
      "source": [
        "## Package installation\n",
        "\n",
        "Here we install all the necessary packages. You can also install the packages on your own computer if your prefer. For more information on how to install these packages yourself: please check the respective model websites of [TARTES](http://pp.ige-grenoble.fr/pageperso/picardgh/tartes/) and [SMRT](https://www.smrt-model.science/)."
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "yj6k1XdW1ywr",
        "outputId": "1df9d4d7-e347-4041-e66c-52439f3aaefb"
      },
      "source": [
        "!pip install tartes\n",
        "!pip install numpy scipy xarray pandas sixc\n",
        "!pip install git+https://github.com/smrt-model/smrt.git"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Collecting tartes\n",
            "\u001b[?25l  Downloading https://files.pythonhosted.org/packages/ba/0c/362dd40350018ef972629e86a825a37bfb9cdec144c6faf803acad91a378/tartes-1.4.tar.gz (566kB)\n",
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            "\u001b[?25hBuilding wheels for collected packages: tartes\n",
            "  Building wheel for tartes (setup.py) ... \u001b[?25l\u001b[?25hdone\n",
            "  Created wheel for tartes: filename=tartes-1.4-cp37-none-any.whl size=44921 sha256=e41ce501ec86b360c7431058ecd2dc1cac31954dc87da1f168d47c330450aedf\n",
            "  Stored in directory: /root/.cache/pip/wheels/83/80/bf/def77bc23498ce03c7f98276f99562da91f5f32cca5cb6995f\n",
            "Successfully built tartes\n",
            "Installing collected packages: tartes\n",
            "Successfully installed tartes-1.4\n",
            "Requirement already satisfied: numpy in /usr/local/lib/python3.7/dist-packages (1.19.5)\n",
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            "\u001b[31mERROR: Could not find a version that satisfies the requirement sixc (from versions: none)\u001b[0m\n",
            "\u001b[31mERROR: No matching distribution found for sixc\u001b[0m\n",
            "Collecting git+https://github.com/smrt-model/smrt.git\n",
            "  Cloning https://github.com/smrt-model/smrt.git to /tmp/pip-req-build-nv9frfvz\n",
            "  Running command git clone -q https://github.com/smrt-model/smrt.git /tmp/pip-req-build-nv9frfvz\n",
            "  Installing build dependencies ... \u001b[?25l\u001b[?25hdone\n",
            "  Getting requirements to build wheel ... \u001b[?25l\u001b[?25hdone\n",
            "    Preparing wheel metadata ... \u001b[?25l\u001b[?25hdone\n",
            "Requirement already satisfied: scipy>=1.0.0 in /usr/local/lib/python3.7/dist-packages (from smrt==1.0.2) (1.4.1)\n",
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            "Requirement already satisfied: numpy>=1.13 in /usr/local/lib/python3.7/dist-packages (from smrt==1.0.2) (1.19.5)\n",
            "Requirement already satisfied: python-dateutil>=2.7.3 in /usr/local/lib/python3.7/dist-packages (from pandas>=0.20.3->smrt==1.0.2) (2.8.1)\n",
            "Requirement already satisfied: pytz>=2017.2 in /usr/local/lib/python3.7/dist-packages (from pandas>=0.20.3->smrt==1.0.2) (2018.9)\n",
            "Requirement already satisfied: setuptools>=41.2 in /usr/local/lib/python3.7/dist-packages (from xarray>=0.10.3->smrt==1.0.2) (54.2.0)\n",
            "Building wheels for collected packages: smrt\n",
            "  Building wheel for smrt (PEP 517) ... \u001b[?25l\u001b[?25hdone\n",
            "  Created wheel for smrt: filename=smrt-1.0.2-cp37-none-any.whl size=211242 sha256=79efa1c82685c54c6d52ba40adcba57c189d2a360abab167bbaa13df95f911e4\n",
            "  Stored in directory: /tmp/pip-ephem-wheel-cache-0se2ubw2/wheels/67/f3/62/e656f418d12bbfc0d92082f3a52a6a24a6234853c8f5a7b400\n",
            "Successfully built smrt\n",
            "Installing collected packages: smrt\n",
            "Successfully installed smrt-1.0.2\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "aY1uCuwzCunY"
      },
      "source": [
        "# Introduction to TARTES\n",
        "*Derived from http://pp.ige-grenoble.fr/pageperso/picardgh/tartes/*"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "qYFJMXVEETld"
      },
      "source": [
        "To compute the diffuse albedo (hemispherical-hemispherical reflectance) of an homogeneous snowpack with SSA=20 kg m-2 at 850nm, use the following script:\n",
        "\n"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "HN20q89VEYhm",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "d8638c8a-bb5a-40e3-9e9a-e94813185765"
      },
      "source": [
        "import tartes\n",
        "\n",
        "ssa = 20      # in m^2/kg\n",
        "density = 300  # in kg/m^3\n",
        "# the result should be independent of the density for a semi-infinite medium\n",
        "\n",
        "wavelength = 850e-9  # in m\n",
        "\n",
        "albedo = tartes.albedo(wavelength, ssa, density)\n",
        "\n",
        "print(albedo)"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "0.8812851442261855\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "5ZhMm8qsEef6"
      },
      "source": [
        "\n",
        "The first line import the tartes module, the next two lines define the snowpack (here a single layer / homogeneous semi-infinite medium as the thickness argument is not given), and the wavelength(s) at which the calculation will be done. The albedo function performs the computation using TARTES and return the albedo. The last line print the result which should be about 0.8812. If 'optical radius' sounds more familiar than SSA to you, the conversion can be done using the function ssa as follow:\n"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "fqqPiQd7E2vZ",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "b6d79f6f-7526-4901-8aaa-817abdb810ae"
      },
      "source": [
        "import tartes\n",
        "from tartes import ssa\n",
        "\n",
        "r_opt = 100e-6      # in m\n",
        "density = 300       # in kg/m^3\n",
        "# the result should be independent of the density for a semi-infinite medium\n",
        "\n",
        "wavelength = 850e-9  # in m\n",
        "\n",
        "albedo = tartes.albedo(wavelength, ssa(r_opt), density)\n",
        "\n",
        "print(albedo)"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "0.9058242408908693\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "5U_4pH7tE8bw"
      },
      "source": [
        "The results should be 0.9058.\n",
        "Note that the diffuse albedo is calculated assuming a directional reflectance at 53° incidence angle rather than a proper integration over all the zenith angle.\n",
        "To compute the direct albedo (directional-hemispherical reflectance), set the optional parameters dir_frac=1 and sza the solar zenith angle in degree."
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "2V2qcY_pFCP5",
        "outputId": "74c80c1c-a0fc-4ef5-f354-4bd4e4058acc"
      },
      "source": [
        "import tartes\n",
        "\n",
        "ssa = 20      # in m^2/kg\n",
        "density = 300  # in kg/m^3\n",
        "# the result should be independent of the density for a semi-infinite medium\n",
        "\n",
        "wavelength = 850e-9  # in m\n",
        "\n",
        "albedo = tartes.albedo(wavelength, ssa, density, dir_frac=1, sza=30)\n",
        "\n",
        "print(albedo)"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "0.8583967584060925\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "1qYPUfsNFGuE"
      },
      "source": [
        "The result should be 0.8584.\n",
        "To compute and plot the spectrum of diffuse albedo between 300 and 2500 nm with a 10-nm resolution for the same snowpack, use a sequence (list, numpy array, ...) for the wavelength argument:"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 265
        },
        "id": "qlXhkfN3FL0N",
        "outputId": "c0e026b3-4cc7-4c2a-8190-a981822d7dd3"
      },
      "source": [
        "import tartes\n",
        "from pylab import *\n",
        "\n",
        "\n",
        "ssa = 2000      # in m^2/kg\n",
        "density = 300  # in kg/m^3\n",
        "\n",
        "wavelengths = arange(300, 2500, 10)*1e-9  # from 300 to 2500nm\n",
        "\n",
        "albedo = tartes.albedo(wavelengths, ssa, density)\n",
        "\n",
        "plot(wavelengths*1e9, albedo)\n",
        "show()"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "Sys0Fgd_FPTj"
      },
      "source": [
        "For a multi-layered snowpack, the SSA, density and thickness are sequences (list, numpy array, ...) or a constant value if the property is constant throughout the profile. All the sequences must have the same length (= the number of layers)."
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 264
        },
        "id": "WT3ahAOyFUQv",
        "outputId": "b9e492ba-1d91-4864-a1f3-b4b96dc11c74"
      },
      "source": [
        "import tartes\n",
        "from pylab import *\n",
        "\n",
        "\n",
        "ssa = [20, 15, 10]         # in m^2/kg\n",
        "density = [200, 250, 300]  # in kg/m^3\n",
        "thickness = [0.01, 0.10, 1000]  # thickness of each layer in meter\n",
        "wavelengths = arange(300, 2500, 10)*1e-9  # from 300 to 2500 nm\n",
        "\n",
        "albedo_3layers = tartes.albedo(wavelengths, ssa, density, thickness)\n",
        "\n",
        "ssa = 20         # in m^2/kg\n",
        "density = 300    # in kg/m^3\n",
        "albedo_semiinfinite = tartes.albedo(wavelengths, ssa, density)\n",
        "\n",
        "\n",
        "# alpha controls the transparency of the curves\n",
        "plot(wavelengths*1e9, albedo_semiinfinite, alpha=0.7)\n",
        "plot(wavelengths*1e9, albedo_3layers, alpha=0.7)\n",
        "show()"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "hMK5CbetFYIn"
      },
      "source": [
        "The albedo of the underlying layer (soil, ice, ...) can be set by using the soilalbedo optional parameter. For instance to test the influence of the thickness of a shallow snowpack overlying a dark surface (albedo = 0.2):\n",
        "\n"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 283
        },
        "id": "Ms5hCy6QFa7I",
        "outputId": "fce8e203-582f-4054-e1e0-7717a0d27622"
      },
      "source": [
        "import tartes\n",
        "from pylab import *\n",
        "\n",
        "ssa = 20         # in m^2/kg\n",
        "density = 250    # in kg/m^3\n",
        "\n",
        "\n",
        "thickness = arange(5, 30, 5)*1e-2\n",
        "wavelengths = arange(300, 1100, 10)*1e-9  # from 300 to 1100nm\n",
        "\n",
        "for th in thickness:\n",
        "    albedo = tartes.albedo(wavelengths, ssa, density, th, soilalbedo=0.2)\n",
        "    plot(wavelengths*1e9, albedo, label='%g cm' % (th*100))\n",
        "\n",
        "legend(loc='best')\n",
        "xlabel('wavelength (nm)')\n",
        "ylabel('albedo')\n",
        "show()"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "izGXkQNXFfNc"
      },
      "source": [
        "Impurities are frequent in snow. TARTES is able to compute optical properties of dirty snow provided that the impurity particle size is small with respect to the wavelength (typically less than 500 nm), and their concentration is moderate so that the single scattering albedo remains close to one. The absorption spectrum of different species is provided and new ones can be implemented by the users (see tartes.impurities module). Both the content and the species need to be specified as inputs. Soot is the default species if not specified. The simple example below shows the difference between the reflectance spectra of a semi-infinite snowpack containing soot or HULIS:"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 267
        },
        "id": "S1IbWv5eFhVG",
        "outputId": "4be71200-88c6-4f53-c962-9809692218ae"
      },
      "source": [
        "import tartes.impurities\n",
        "from pylab import *\n",
        "\n",
        "\n",
        "ssa = 20      # in m^2/kg\n",
        "density = 300  # in kg/m^3\n",
        "\n",
        "wavelengths = arange(300, 1000, 10)*1e-9  # from 300 to 2500nm\n",
        "\n",
        "# pure snow\n",
        "albedo_pure = tartes.albedo(wavelengths, ssa, density)\n",
        "\n",
        "# 50ng/g of soot. Soot is the default impurity type\n",
        "albedo_soot = tartes.albedo(wavelengths, ssa, density, impurities=50e-9)\n",
        "\n",
        "\n",
        "# 200ng/g of Hulis.\n",
        "albedo_hulis = tartes.albedo(wavelengths, ssa, density,\n",
        "                             impurities=200e-9,\n",
        "                             impurities_type=tartes.impurities.HULIS)\n",
        "\n",
        "\n",
        "plot(wavelengths*1e9, albedo_pure, label='pure snow')\n",
        "plot(wavelengths*1e9, albedo_soot, label='snow with 50 ng/g soot')\n",
        "plot(wavelengths*1e9, albedo_hulis, label='snow with 200 ng/g HULIS')\n",
        "legend(loc='best')\n",
        "show()"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "mbEuhDl44aPj"
      },
      "source": [
        "# Introduction to SMRT\n",
        "To compute the brightness temperature for passive microwave or the radar backscatter for active microwave signals you basically always need to follow 3 steps: 1) make a snowpack, 2) configure a sensor, 3) select the radiative transfer solver, the electromagnetic theory and the microstructure, 4) run the model, and 5) inspect the result to get brightness temperature or backscattering coefficient at different polarizations, incidence angles, etc.\n",
        "\n"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "Jz5gsA-UHxK_"
      },
      "source": [
        "### Step 1: Make a snowpack"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "X5EX7GoEMf2R"
      },
      "source": [
        "\n",
        "#### Single layer snowpacks\n",
        "To define a single layer, deep snowpack we need to know the layer thickness, temperature, sticky hard sphere radius, stickiness and density:"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "lS7-DxGRIC-t"
      },
      "source": [
        "from smrt import make_snowpack, make_model\n",
        "\n",
        "# Define parameters (All Units are in SI)\n",
        "thickness = [10]\n",
        "temperature = [265]\n",
        "rad = [1e-4]\n",
        "rho = [280]\n",
        "stickiness = [0.2]\n",
        "\n",
        "# Make snowpack with 'sticky_hard_spheres' microstructure model\n",
        "snowpack = make_snowpack(thickness=thickness, \n",
        "                         microstructure_model='sticky_hard_spheres',\n",
        "                         temperature=temperature, radius=rad, density=rho,\n",
        "                         stickiness=stickiness)\n",
        "\n",
        "# Make snowpack with 'exponential' microstructure model\n",
        "snowpack = make_snowpack(thickness=thickness, \n",
        "                         microstructure_model='exponential',\n",
        "                         temperature=temperature, density=rho,\n",
        "                         corr_length=[5e-5])"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "OhHyMuhUKNdN"
      },
      "source": [
        "#### Multi-layer snowpacks\n",
        "To create a multi-layer snowpack we just need to extend the information per layer"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "dZ6Qp2AnKb3G"
      },
      "source": [
        "# Define parameters (All Units are in SI)\n",
        "thickness = [10,10]\n",
        "temperature = [265,265] \n",
        "# All variables can be defined as a number (equal for all layert) or a list of numbers per layer\n",
        "temperature = 265\n",
        "rad = [1e-4,2e-4]\n",
        "rho = 280\n",
        "stickiness = [0.2,0.3]\n",
        "\n",
        "# Make snowpack\n",
        "snowpack = make_snowpack(thickness=thickness, \n",
        "                         microstructure_model='sticky_hard_spheres',\n",
        "                         temperature=temperature, radius=rad, density=rho,\n",
        "                         stickiness=stickiness)"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "dFgkon-CIp6h"
      },
      "source": [
        "A snowpack is a list of layers objects, which we can access and explore."
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "HXZDVTGnI-aX",
        "outputId": "64ab718e-4b3d-4c8b-ceb1-f24312965ad6"
      },
      "source": [
        "print(snowpack.layers[0].temperature)\n",
        "print(snowpack.layers[1].temperature)\n",
        "dir(snowpack.layers[1])"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "265.0\n",
            "265.0\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "['__class__',\n",
              " '__delattr__',\n",
              " '__dict__',\n",
              " '__dir__',\n",
              " '__doc__',\n",
              " '__eq__',\n",
              " '__format__',\n",
              " '__ge__',\n",
              " '__getattribute__',\n",
              " '__gt__',\n",
              " '__hash__',\n",
              " '__init__',\n",
              " '__init_subclass__',\n",
              " '__le__',\n",
              " '__lt__',\n",
              " '__module__',\n",
              " '__ne__',\n",
              " '__new__',\n",
              " '__reduce__',\n",
              " '__reduce_ex__',\n",
              " '__repr__',\n",
              " '__setattr__',\n",
              " '__sizeof__',\n",
              " '__str__',\n",
              " '__subclasshook__',\n",
              " '__weakref__',\n",
              " '_ssa',\n",
              " 'basic_checks',\n",
              " 'density',\n",
              " 'frac_volume',\n",
              " 'inclusion_shape',\n",
              " 'inverted_medium',\n",
              " 'liquid_water',\n",
              " 'microstructure',\n",
              " 'microstructure_model',\n",
              " 'number',\n",
              " 'permittivity',\n",
              " 'permittivity_model',\n",
              " 'salinity',\n",
              " 'ssa',\n",
              " 'temperature',\n",
              " 'thickness']"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 35
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "Di0bI3DXKryx"
      },
      "source": [
        "We can also draw a snowpack:"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 248
        },
        "id": "kxH-RHyBJnl-",
        "outputId": "b39f762c-156d-4a4a-e996-68d4da35dbff"
      },
      "source": [
        "from smrt.utils.mpl_plots import plot_snowpack\n",
        "\n",
        "plt.figure()\n",
        "plot_snowpack(snowpack, show_vars=['density', 'radius'])"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": "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\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "ck9fN4UhMUMZ"
      },
      "source": [
        "#### Wet snow\n",
        "To create wet snow, you need to set the liquid water content. If you do that, you will see that the permitivity changes:"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "TrbFBMBRNTbi",
        "outputId": "d4de55c3-11a2-4ae0-e7dd-895d1d578436"
      },
      "source": [
        "from smrt import make_snowpack\n",
        "# prepare the multi-layer snowpack\n",
        "radius = 100e-6\n",
        "density=300\n",
        "temperature = 260\n",
        "sp = make_snowpack(thickness=[0.1, 10],\n",
        "                   microstructure_model='sticky_hard_spheres',\n",
        "                   density=density,\n",
        "                   radius=radius,                   \n",
        "                   stickiness=0.15,\n",
        "                   temperature=temperature,\n",
        "                   liquid_water=[0, 0])\n",
        "\n",
        "print(sp.layers[0].permittivity(1, 10e9))\n",
        "\n",
        "sp = make_snowpack(thickness=[0.1, 10],\n",
        "                   microstructure_model='sticky_hard_spheres',\n",
        "                   density=density,\n",
        "                   radius=radius,                   \n",
        "                   stickiness=0.15,\n",
        "                   temperature=temperature,\n",
        "                  #  ice_permittivity_model=wetsnow_permittivity,\n",
        "                   liquid_water=[0.1, 0])\n",
        "\n",
        "print(sp.layers[0].permittivity(1, 10e9))"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "(3.1764335000000004+0.0007272578757053208j)\n",
            "(4.751382379433089+2.4718522409197083j)\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "ufrzwHJv9juO"
      },
      "source": [
        "#### Substrate\n",
        "\n",
        "If your snowpack is not indefinitely thick, it is also important to add a relevant substrate of known reflectivity or and a soil, ice, etc."
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "wOE_8B-2-Kfw"
      },
      "source": [
        "from smrt.substrate.reflector_backscatter import make_reflector\n",
        "temperature = 273\n",
        "\n",
        "# Create substrate from known reflectivity\n",
        "substrate = make_reflector(temperature=265, specular_reflection=0.02)\n",
        "\n",
        "# Or alternatively create substrate from soil \n",
        "from smrt import make_soil\n",
        "soil = make_soil('soil_wegmuller', 'dobson85', temperature=265, roughness_rms=0.25, \n",
        "                 moisture=0.25, sand=0.01, clay=0.7, drymatter=1300)\n",
        "\n",
        "# Or alternatively create substrate from ice\n",
        "from smrt import make_ice_column \n",
        "ice  =  make_ice_column('fresh', thickness=[10], microstructure_model='exponential', \n",
        "                     temperature=temperature, corr_length=[5e-5])\n",
        "\n",
        "# You can do something similar for an active MW sensor\n",
        "reflector = make_reflector(temperature=265, specular_reflection=0., backscattering_coefficient={'VV': 0.1, 'HH': 0.1})\n",
        "\n",
        "# Define a snowpack\n",
        "snow = make_snowpack([0.01], \"exponential\", temperature=[265], density=[280], corr_length=[5e-5])\n",
        "# sp = make_snowpack(thickness=[0.1, 10],\n",
        "#                    microstructure_model='sticky_hard_spheres',\n",
        "#                    density=density,\n",
        "#                    radius=radius,                   \n",
        "#                    stickiness=0.15,\n",
        "#                    temperature=temperature,\n",
        "#                    liquid_water=[0, 0])\n",
        "\n",
        "# You can subsequently add the substrate, soil, ice or reflector to your snowpack by using the + operator (works also with +=):\n",
        "medium = snow + ice\n",
        "\n",
        "# And now continue"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "M0FsAzKLQBC8"
      },
      "source": [
        "### Step 2: Prepare a sensor\n",
        "The sensor configuration includes all the information describing the sensor viewing geometry (incidence, …) and operating parameters (frequency, polarization, …). The generic functions `passive()` and `active()` should cover all the usages, but functions for specific sensors are more convenient. Check the [documentation](https://smrt.readthedocs.io/en/latest/smrt.inputs.sensor_list.html) for more info"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "90INMoFxQHtT"
      },
      "source": [
        "from smrt import sensor, sensor_list\n",
        "radiometer = sensor.passive(18e9, 50)\n",
        "radiometer = sensor.passive(18e9, 50, \"V\")\n",
        "radiometer = sensor.passive([18e9,36.5e9], [50,55], [\"V\",\"H\"])\n",
        "scatterometer = sensor.active(frequency=18e9, theta_inc=50)\n",
        "scatterometer = sensor.active(5.405e9, 50)\n",
        "# scatterometer = sensor.active([18e9,36.5e9], theta=50, theta_inc=50, polarization_inc=[\"V\", \"H\"], polarization=[\"V\", \"H\"])\n",
        "radar = sensor_list.active(5.405e9, 35)"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "iqE_kqO8ROFu"
      },
      "source": [
        "### Step 3 & 4: Set the RT solver and run it\n",
        "For now we will use IBA electromagnetic model with the discrete ordinates solver (DORT)"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "m7_2L1flRNyi"
      },
      "source": [
        "n_max_stream= 128\n",
        "m = make_model(\"iba\", \"dort\", rtsolver_options ={\"n_max_stream\": n_max_stream})\n",
        "#m = make_model('iba','dort')\n",
        "result = m.run(scatterometer, medium)"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "JmkiKeg6RykF"
      },
      "source": [
        "### Step 5: Analyze the result"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "-ldzTzLJR5ZB",
        "outputId": "f50c1d35-89e8-4809-b055-8f521e8c71fc"
      },
      "source": [
        "print(result.sigmaVV_dB())\n",
        "# print(result.TbH())\n"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "-70.77676167097754\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "NbQDK1FbT1IM"
      },
      "source": [
        "Or here you can see a complete example"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 269
        },
        "id": "wuXIa5Uf7VRV",
        "outputId": "dce88e05-2f8a-4dd2-ac40-dc55197bdf75"
      },
      "source": [
        "# prepare the snowpack\n",
        "radius = 10e-6\n",
        "density=300\n",
        "temperature = 260\n",
        "sp = make_snowpack(thickness=[1000], microstructure_model='sticky_hard_spheres',\n",
        "                  density=density, radius=radius, temperature=temperature,\n",
        "                  stickiness=0.15)\n",
        "# prepare a list for theta from 5 to 65 by step of 5\n",
        "theta = [5, 10, 15, 20, 25, 30, 35, 40, 45, 50, 55, 60, 65]\n",
        "# prepare two sensors (one active, on passive) at 13 GHz\n",
        "radiometer = sensor.passive(13e9, theta)\n",
        "radar  = sensor.active(13e9, theta)\n",
        "\n",
        "# prepare the model and run it successively for each sensor\n",
        "m = make_model(\"iba\", \"dort\")\n",
        "res_a = m.run(radar, sp)\n",
        "res_p = m.run(radiometer, sp)\n",
        "\n",
        "# for plotting two side by side graphs, the best solution is:\n",
        "f, axs = plt.subplots(1, 2, figsize=(8, 3.5))\n",
        "\n",
        "# plot on left graph \n",
        "axs[0].plot(theta, res_a.sigmaVV_dB()) # adapt x and y to your need\n",
        "# plot on right graph\n",
        "axs[1].plot(theta, res_p.TbH(),label='H')  # adapt x and y to your need\n",
        "axs[1].plot(theta, res_p.TbV(),label='V')  # adapt x and y to your need\n",
        "\n",
        "# to set axis labels:\n",
        "axs[0].set_xlabel(\"Viewing angle\")\n",
        "legend(loc='best')\n",
        "# ..."
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<matplotlib.legend.Legend at 0x7f8e7204fa10>"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 48
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 576x252 with 2 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "rRcNileHTSJn"
      },
      "source": [
        "### Step 6: Sensitivity analysis\n",
        "SMRT allows to do sensitivity analyses easily. I refer to the [package example](https://github.com/smrt-model/runningsmrt/blob/master/tutorials/sensitivity_study.ipynb) to check how this can be done."
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "kuFkesX1WTgZ"
      },
      "source": [
        "### Step 7: Time series analysis\n",
        "The run() method can be used with list of snowpacks, which is useful if you want to apply it to for example a time series. In this case, it is recommended to set the snowpack_dimension_name and snowpack_dimension_values variable which gives the name and values of the coordinates that are create for the Results. "
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "X2GmbpMHXkXI"
      },
      "source": [
        "snowpacks = []\n",
        "times = []\n",
        "for file in filenames:\n",
        "    #  create a snowpack for each time series\n",
        "    sp = ...\n",
        "    snowpacks.append(sp)\n",
        "    times.append(sp)\n",
        "\n",
        "# now run the model\n",
        "res = m.run(sensor, snowpacks, snowpack_dimension=('time', times))"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "OWmsXAT3WS26"
      },
      "source": [
        "# Introduction to AWS data\n",
        "For this assignment you are going to work with satellite data over the DYE-2 automatic weather station on the Greenland Ice Sheet. The data originally come from *Baptiste Vandecrux. 2020. Gap-filled meteorological data and surface energy balance at nine Greenland Climate Network (GC-Net) stations over 1998-2017. Arctic Data Center. doi:10.18739/A2HM52K87*, but you can find the file on Brightspace and you should upload it to Colab by uploading it to the files section on the left. Once uploaded, it is rather straightforward to access, plot the data or use standard subsetting, indexing approaches etc."
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 383
        },
        "id": "xiYF4viTXmdi",
        "outputId": "3678ecd6-25c9-489e-d030-df0ad29cebc7"
      },
      "source": [
        "from scipy.io import netcdf\n",
        "import matplotlib.pyplot as plt\n",
        "import pandas as pd\n",
        "from datetime import date, datetime, timedelta\n",
        "\n",
        "file = netcdf.NetCDFFile('Dye_2_surface.nc','r')\n",
        "\n",
        "temp = file.variables['time']     # [days] since 1900-1-1 0:0:0 UTC\n",
        "datet = [date(1900,1,1) + timedelta(i) for i in temp[:]]\n",
        "\n",
        "temp = file.variables['Ta_2m']\n",
        "airtemp = temp[:].byteswap().newbyteorder()                   # [K] at 2 m\n",
        "\n",
        "temp = file.variables['RH_2m']\n",
        "relhum = temp[:]                    # [%] at 2 m\n",
        "\n",
        "temp = file.variables['WS_10m']\n",
        "windsp = temp[:]                    # [m/s] at 10 m\n",
        "\n",
        "temp = file.variables['SRin']\n",
        "shortdown = temp[:]                 # [W/m^2]\n",
        "\n",
        "temp = file.variables['SRout']\n",
        "shortup = temp[:]                   # [W/m^2]\n",
        "\n",
        "albedo = shortup/shortdown\n",
        "\n",
        "temp = file.variables['LRin']\n",
        "longdown = temp[:]                  # [W/m^2]\n",
        "\n",
        "temp = file.variables['snowfall']\n",
        "precip = temp[:]                    # [m/hr] not mm! \n",
        "\n",
        "file.close()\n",
        "\n",
        "# plotting air temperature to check: \n",
        "plt.plot(datet, airtemp)\n",
        "plt.title('Air temperature at 2m')\n",
        "plt.ylabel('T [K]')\n",
        "plt.xlabel('Date')"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "/usr/local/lib/python3.7/dist-packages/ipykernel_launcher.py:26: RuntimeWarning: invalid value encountered in true_divide\n",
            "/usr/local/lib/python3.7/dist-packages/scipy/io/netcdf.py:317: RuntimeWarning: Cannot close a netcdf_file opened with mmap=True, when netcdf_variables or arrays referring to its data still exist. All data arrays obtained from such files refer directly to data on disk, and must be copied before the file can be cleanly closed. (See netcdf_file docstring for more information on mmap.)\n",
            "  ), category=RuntimeWarning)\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "Text(0.5, 0, 'Date')"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 44
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "xULgizKaSF2t",
        "outputId": "4e807298-24d8-4ad1-ce83-f47dd2f25692"
      },
      "source": [
        "# Calculate daily mean\n",
        "dat = pd.Series(airtemp, index=pd.to_datetime(datet))\n",
        "ta2_mean = dat.resample('D').median()\n",
        "ta2_mean"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "1998-06-01    269.159546\n",
              "1998-06-02    267.819540\n",
              "1998-06-03    266.519537\n",
              "1998-06-04    266.304541\n",
              "1998-06-05    267.489235\n",
              "                 ...    \n",
              "2017-12-27    245.858262\n",
              "2017-12-28    248.199349\n",
              "2017-12-29    244.294149\n",
              "2017-12-30    245.616559\n",
              "2017-12-31    245.212529\n",
              "Freq: D, Length: 7154, dtype: float64"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 49
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 312
        },
        "id": "ruAX9_0TqN1R",
        "outputId": "ee37125e-ec73-413d-b2d9-463f4fc64d8e"
      },
      "source": [
        "plt.plot(datet, albedo)\n",
        "plt.title('Albedo')\n",
        "plt.ylabel('[]')\n",
        "plt.xlabel('Date')"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "Text(0.5, 0, 'Date')"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 137
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "XVx64_CqXNbF"
      },
      "source": [
        "# Remote sensing data over AWS\n",
        "At the same time you can find the corresponding [MODIS MCD43 albedo](https://lpdaac.usgs.gov/products/mcd43a3v006/), [SSMI](https://en.wikipedia.org/wiki/Special_sensor_microwave/imager) (19-37GhZ) and Sentinel-1 backscatter observations. The SSMI data was processed by Dylan Kreynen and contains the necessary metadata as a readme file. The MODIS and Sentinel-1 data were processed on the Google Earth Engine using this [script](https://code.earthengine.google.com/eb310a5378cf2b6b7e2f454f11c60682)"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "46jihT-J8zu9"
      },
      "source": [
        "# Load the SSMI data\n",
        "\n",
        "# These numpy files contain SSM/I and SSMIS observed brightness temperatures over the DYE-2 \n",
        "# site on the Greenland Ice Sheet (66°29′N 46°18′W), for the 19.35 and 37 GHz channels. \n",
        "# Observations run from 01-06-1998 to 31-12-2017 and are daily, from the F13 and F17 satellite \n",
        "# platforms. For dates where both F13 and F17 data was available, the mean value was taken. \n",
        "\n",
        "# SSMI_37.npy - 37.00 GHz, V (1st row) and H (2nd row) channel\n",
        "# SSMI_19.npy - 19.35 GHz, V (1st row) and H (2nd row) channel\n",
        "# SSMI_dates.npy - dates of the observations (yyyy-mm-dd)\n",
        "\n",
        "# Source: \n",
        "# Meier, W. N., H. Wilcox, M. A. Hardman, and J. S. Stewart. 2019. DMSP SSM/I-SSMIS Daily Polar \n",
        "# Gridded Brightness Temperatures, Version 5. Boulder, Colorado USA. NASA National Snow and Ice \n",
        "# Data Center Distributed Active Archive Center. doi: https://doi.org/10.5067/QU2UYQ6T0B3P. \n",
        "\n",
        "# Accessed through: \n",
        "# https://nsidc.org/data/NSIDC-0001/versions/5\n",
        "\n",
        "# Dylan Kreynen - TU Delft - Feb 2021\n",
        "\n",
        "s37 = np.load('/content/SSMI_37.npy')\n",
        "s19 = np.load('/content/SSMI_19.npy')\n",
        "sd = np.load('/content/SSMI_dates.npy')\n",
        "datess = np.array([datetime.strptime(i, '%Y-%m-%d') for i in sd[0]])\n",
        "\n",
        "f, axs = plt.subplots(1, 2, figsize=(8, 3.5))\n",
        "axs[0].plot(datess,s19[0,:],label='V')\n",
        "axs[0].plot(datess,s19[1,:],label='H')\n",
        "axs[1].plot(datess,s37[0,:],label='V')\n",
        "axs[1].plot(datess,s37[1,:],label='H')\n",
        "legend(loc='best')"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 738
        },
        "id": "uqe5eR-KNIhz",
        "outputId": "d371d3f1-56d1-4bcb-a9d2-3bf7bf80c851"
      },
      "source": [
        "# Load the MODIS data\n",
        "import pandas\n",
        "MOD = pandas.read_csv('MOD_ts.csv')\n",
        "print(MOD)\n",
        "dates = pandas.to_datetime(MOD['time'],unit='ms')\n",
        "f, axs = plt.subplots(1, 2, figsize=(16, 8))\n",
        "for i in range(7):\n",
        "  axs[0].plot(dates,MOD['BSA_Band'+str(i+1)]/1000,label='BSA_Band'+str(i+1))\n",
        "  axs[1].plot(dates,MOD['WSA_Band'+str(i+1)]/1000,label='WSA_Band'+str(i+1))\n",
        "axs[1].legend(loc='best')"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "     system:index  BSA_Band1  BSA_Band2  ...  WSA_vis           time  .geo\n",
            "0      2000_02_24        NaN        NaN  ...      NaN   951350400000   NaN\n",
            "1      2000_02_25        NaN        NaN  ...      NaN   951436800000   NaN\n",
            "2      2000_02_26      980.0      920.0  ...    915.0   951523200000   NaN\n",
            "3      2000_02_27      978.0      918.0  ...    915.0   951609600000   NaN\n",
            "4      2000_02_28      976.0      915.0  ...    914.0   951696000000   NaN\n",
            "...           ...        ...        ...  ...      ...            ...   ...\n",
            "7607   2020_12_27        NaN        NaN  ...      NaN  1609027200000   NaN\n",
            "7608   2020_12_28        NaN        NaN  ...      NaN  1609113600000   NaN\n",
            "7609   2020_12_29        NaN        NaN  ...      NaN  1609200000000   NaN\n",
            "7610   2020_12_30        NaN        NaN  ...      NaN  1609286400000   NaN\n",
            "7611   2020_12_31        NaN        NaN  ...      NaN  1609372800000   NaN\n",
            "\n",
            "[7612 rows x 23 columns]\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<matplotlib.legend.Legend at 0x7fd1487a6050>"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 133
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 1152x576 with 2 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 443
        },
        "id": "j0fCzU5ehsw0",
        "outputId": "61184623-7b61-4321-a14a-f3988725d5eb"
      },
      "source": [
        "# Load the Sentinel-1 data\n",
        "import pandas\n",
        "S1 = pandas.read_csv('S1_ts.csv')\n",
        "print(S1['time'])\n",
        "dates = pandas.to_datetime(S1['time'],unit='ms')\n",
        "f, axs = plt.subplots(1, 2, figsize=(16, 8))\n",
        "axs[0].scatter(dates,S1['HH'],label='HH')\n",
        "axs[0].scatter(dates,S1['HV'],label='HV')\n",
        "axs[1].scatter(dates,S1['angle'],label='angle')\n",
        "axs[1].legend(loc='best')"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "error",
          "ename": "FileNotFoundError",
          "evalue": "ignored",
          "traceback": [
            "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
            "\u001b[0;31mFileNotFoundError\u001b[0m                         Traceback (most recent call last)",
            "\u001b[0;32m<ipython-input-1-c4a56d4e04b8>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m()\u001b[0m\n\u001b[1;32m      1\u001b[0m \u001b[0;31m# Load the Sentinel-1 data\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m      2\u001b[0m \u001b[0;32mimport\u001b[0m \u001b[0mpandas\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m----> 3\u001b[0;31m \u001b[0mS1\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mpandas\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mread_csv\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m'S1_ts.csv'\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m      4\u001b[0m \u001b[0mprint\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mS1\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;34m'time'\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m      5\u001b[0m \u001b[0mdates\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mpandas\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mto_datetime\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mS1\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;34m'time'\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0munit\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;34m'ms'\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;32m/usr/local/lib/python3.7/dist-packages/pandas/io/parsers.py\u001b[0m in \u001b[0;36mread_csv\u001b[0;34m(filepath_or_buffer, sep, delimiter, header, names, index_col, usecols, squeeze, prefix, mangle_dupe_cols, dtype, engine, converters, true_values, false_values, skipinitialspace, skiprows, skipfooter, nrows, na_values, keep_default_na, na_filter, verbose, skip_blank_lines, parse_dates, infer_datetime_format, keep_date_col, date_parser, dayfirst, cache_dates, iterator, chunksize, compression, thousands, decimal, lineterminator, quotechar, quoting, doublequote, escapechar, comment, encoding, dialect, error_bad_lines, warn_bad_lines, delim_whitespace, low_memory, memory_map, float_precision)\u001b[0m\n\u001b[1;32m    686\u001b[0m     )\n\u001b[1;32m    687\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 688\u001b[0;31m     \u001b[0;32mreturn\u001b[0m \u001b[0m_read\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mfilepath_or_buffer\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mkwds\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    689\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    690\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;32m/usr/local/lib/python3.7/dist-packages/pandas/io/parsers.py\u001b[0m in \u001b[0;36m_read\u001b[0;34m(filepath_or_buffer, kwds)\u001b[0m\n\u001b[1;32m    452\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    453\u001b[0m     \u001b[0;31m# Create the parser.\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 454\u001b[0;31m     \u001b[0mparser\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mTextFileReader\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mfp_or_buf\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m**\u001b[0m\u001b[0mkwds\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    455\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    456\u001b[0m     \u001b[0;32mif\u001b[0m \u001b[0mchunksize\u001b[0m \u001b[0;32mor\u001b[0m \u001b[0miterator\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;32m/usr/local/lib/python3.7/dist-packages/pandas/io/parsers.py\u001b[0m in \u001b[0;36m__init__\u001b[0;34m(self, f, engine, **kwds)\u001b[0m\n\u001b[1;32m    946\u001b[0m             \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0moptions\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;34m\"has_index_names\"\u001b[0m\u001b[0;34m]\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mkwds\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;34m\"has_index_names\"\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    947\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 948\u001b[0;31m         \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_make_engine\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mengine\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    949\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    950\u001b[0m     \u001b[0;32mdef\u001b[0m \u001b[0mclose\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;32m/usr/local/lib/python3.7/dist-packages/pandas/io/parsers.py\u001b[0m in \u001b[0;36m_make_engine\u001b[0;34m(self, engine)\u001b[0m\n\u001b[1;32m   1178\u001b[0m     \u001b[0;32mdef\u001b[0m \u001b[0m_make_engine\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mengine\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;34m\"c\"\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1179\u001b[0m         \u001b[0;32mif\u001b[0m \u001b[0mengine\u001b[0m \u001b[0;34m==\u001b[0m \u001b[0;34m\"c\"\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 1180\u001b[0;31m             \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_engine\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mCParserWrapper\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mf\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m**\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0moptions\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m   1181\u001b[0m         \u001b[0;32melse\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1182\u001b[0m             \u001b[0;32mif\u001b[0m \u001b[0mengine\u001b[0m \u001b[0;34m==\u001b[0m \u001b[0;34m\"python\"\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;32m/usr/local/lib/python3.7/dist-packages/pandas/io/parsers.py\u001b[0m in \u001b[0;36m__init__\u001b[0;34m(self, src, **kwds)\u001b[0m\n\u001b[1;32m   2008\u001b[0m         \u001b[0mkwds\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;34m\"usecols\"\u001b[0m\u001b[0;34m]\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0musecols\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   2009\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 2010\u001b[0;31m         \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_reader\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mparsers\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mTextReader\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0msrc\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m**\u001b[0m\u001b[0mkwds\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m   2011\u001b[0m         \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0munnamed_cols\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_reader\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0munnamed_cols\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   2012\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;32mpandas/_libs/parsers.pyx\u001b[0m in \u001b[0;36mpandas._libs.parsers.TextReader.__cinit__\u001b[0;34m()\u001b[0m\n",
            "\u001b[0;32mpandas/_libs/parsers.pyx\u001b[0m in \u001b[0;36mpandas._libs.parsers.TextReader._setup_parser_source\u001b[0;34m()\u001b[0m\n",
            "\u001b[0;31mFileNotFoundError\u001b[0m: [Errno 2] No such file or directory: 'S1_ts.csv'"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "iy2mny6YrN30"
      },
      "source": [
        "# Assignment"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "Bml9Y7RdrU_M"
      },
      "source": [
        "## A1: Assess the effect snow properties on albedo\n",
        "\n",
        "Task: use Tartes to assess the effect of snow properties on albedo. You need to do this by means of a sensivity analyses where you plot the spectral albedo over the 350-2500nm range vary the input parameters relative to \n",
        "create a reference single layer snowpack with approximate grain size, density and soot content.\n"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "H4SMh2OuwJcK"
      },
      "source": [
        "\n",
        "### Question 1\n",
        "Perform a sensitivity analysis of the diffuse albedo in function of the grain size (e.g. for grain sizes ranging from 50µm to 2mm). \n",
        "\n",
        "Plot the resulting spectral albedos over the 400-2500nm range. \n",
        "\n",
        "Discuss this figure and explain the differences you see.\n",
        "\n"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "BaIn9xpkwMSw"
      },
      "source": [
        "### Question 2\n",
        "Perform a sensitivity analysis of the diffuse albedo in function of the soot content (e.g. for soot contents ranging from 50ng/g to 1mg/g).\n",
        "\n",
        "Plot the resulting spectral albedos over the 400-2500nm range.\n",
        "\n",
        "Discuss this figure and explain the differences you see."
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "tUZ0EUC-w80S"
      },
      "source": [
        "### Question 3\n",
        "Create a single layer snowpack that resembles an ice layer. \n",
        "\n",
        "Plot the diffuse spectral albedo over the 400-2500nm range.  \n",
        "\n",
        "Perform two sensitivity analysis by adding a fresh snow layer of i) different thickness (e.g. ranging from 1mm to 20 cm) and ii) grain size (e.g. ranging from 50µm to 1mm) on top of the ice layer.  \n",
        "\n",
        "Plot the results of the sensitivity analysis over the 400-2500nm range.  \n",
        "\n",
        "Discuss this figure and explain the differences you see.by specifically addressing how the snow layer affects the spectral albedo. "
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "1oenT5l-xn1f"
      },
      "source": [
        "### Question 4a\n",
        "Create a reference snowpack.\n",
        "\n",
        "Perform a sensitivity analysis of the spectral albedo to i) diffuse and direct radiation and ii) varying solar zenith angles (e.g. by varying the SZA between 0 and 85 degrees).\n",
        "\n",
        "Plot the diffuse and spectral spectral albedo for the different SZA's over the 400-2500nm range.\n",
        "\n",
        "Discuss this figure and how SZA and incoming radiation affect the spectral albedo."
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "FJmXyxYczVOq"
      },
      "source": [
        "### Question 4b\n",
        "Convert the diffuse and direct spectral albedos of Q4a into a broadband albedo value. For this exercise you can use the incoming solar radiation derived by [COART](https://cloudsgate2.larc.nasa.gov/jin/coart.html), which is included in the data folder.\n",
        "\n",
        "Plot the broadband albedo as a function of the illumination conditions (X-axis:SZA; Y-axis:broadband albedo). \n",
        "\n",
        "Discuss your result and specifically address how SZA and incoming radiation affect the broadband albedo.\n"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "FP_Ekke83XPf"
      },
      "source": [
        "### Question 5\n",
        "\n",
        "Use the previous figures to discuss the relative importance of grain size, impurities and solar zenith angle on albedo. Thereby, address the magnitude of the changes, the spectral wavelengts where the differences can be seen etc."
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "QsHrDFv1BEoK"
      },
      "source": [
        "## A2: Snow properties from the MODIS spectral albedo over DYE-2\n",
        "\n",
        "Create a lookup table (LUT) that contains the diffuse spectral albedo at the MODIS wavelengths as a function grain size and soot content. Use this LUT to invert the snow properties (soot content and grain size) from the MODIS time series over DYE-2. For this purpose you can focus on the MODIS WSA data only (i.e. for simplicity you can assume that all incoming radiation is diffuse) and you can convert the satellite data to surface properties by maximizing the similarity between your Tartes LUT of spectral albedo and satellite spectral albedo data. This can be done for example by minimizing the cost function of: ![Untitled.png](data:image/png;base64,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) where:\n",
        "-  `i` is the MODIS band corresponding the MODIS [wavelengths](https://modis.gsfc.nasa.gov/about/specifications.php)\n",
        "- `\\alpha_i^o ` is the observed MODIS diffuse spectral albedo for band i\n",
        "- `\\alpha_i^l ` is the LUT diffuse spectral albedo for band `i`\n",
        "- `\\sigma_i` is the standard deviation of LUT spectral albedos in band `i`\n",
        "\n",
        "After this similarity maximization (i.e. cost minimazation), you can select the corresponding input data (soot, grain size) and read out the properties."
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "Snt4M6IUz_Py"
      },
      "source": [
        "### Question 6\n",
        "\n",
        "Plot the obtained time series of obtained grain size and soot content for i) the whole time series and ii) for one year. \n",
        "\n",
        "Plot the temperature, albedo and snowfall data of AWS for the same periods.\n",
        "\n",
        "Discuss the results of your snow property retrieval algorithm by comparing it with the temperature, albedo and snowfall data of AWS. What similarities/differences do you see between the drivers (atmospheric conditions) and the outcome of your retrieval. Can you explain these similarities/differences physically (e.g. as a function of the expected snow conditions and/or satellite setup)? \n",
        "\n",
        "\n",
        "Discuss the role of climate change and/or interannual variability on both the satellite and AWS observations (Check [Dumont et. al.](https://www.nature.com/articles/ngeo2180) for more background)?\n"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "UAh5w7gd0yA0"
      },
      "source": [
        "## A3: Asses the effect of snow properties on MW brightness temperature and backscatter"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "LENH1f4n1Fkl"
      },
      "source": [
        "Task: create a reference single layer snowpack with approximate temperature, sticky hard sphere radius, stickiness and density and calculate i) the [SSMI](https://en.wikipedia.org/wiki/Special_sensor_microwave/imager) brightness temperature (19.35 and 37 GhZ frequencues in both H and V polarisation) and ii) [Sentinel-1](https://earth.esa.int/web/eoportal/satellite-missions/c-missions/copernicus-sentinel-1;jsessionid=1488A2BCD2550E1350E7DD16C3308C20.jvm1) backscatter using SMRT.\n"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "BEqrvu4v1ZZ9"
      },
      "source": [
        "### Question 7\n",
        "Perform a sensitivity analysis of brightness temperature and backscatter for the reference snowpack in function of the temperature (e.g. by varying the temperature between 250K and 290K). \n",
        "\n",
        "Plot the results showing the i) brightness temperature and ii) backscatter (Y-axis) as a function of snowpack temperature (X-axis).\n",
        "\n",
        "Discuss this figure and explain how temperature affects brightness temperature and backscatter in different frequencies and polarisations. Also specifically address the effect of reaching the melting point: is this analysis output realistic or not? What is the effect you see vs. what you expect to see?"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "Qb8SJZdB1cy3"
      },
      "source": [
        "### Question 8a\n",
        "Perform a sensitivity analysis of brightness temperature and backscatter in function of the snow thickness (e.g. by varying the snowpack thickness of the reference snowpack over a soil substrate between 0mm and 10m). \n",
        "\n",
        "Plot the results showing the i) brightness temperature and ii) backscatter (Y-axis) as a function of snowpack thickness (X-axis).\n",
        "\n",
        "Discuss this figure and explain how snowpack thickness affects brightness temperature and backscatter in different frequencies and polarisations. \n"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "sATJqbEJQnD5"
      },
      "source": [
        "### Question 8b\n",
        "Repeat question 8a but now with a ice substrate instead of a soil substrate. \n",
        "\n",
        "Discuss the differences between the results of 8a and 8b and specifically address how the substrate affects the differences in brightness temperature and backscatter. What can you learn from these differences about retrieving snow depth over a soil vs. an ice sheet?\n"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "JdaC28u71cqc"
      },
      "source": [
        "### Question 9\n",
        "Perform a sensitivity analysis of brightness temperature and backscatter in function of the snow grain size  (e.g. by varying the grain size of the reference snowpack from 50µm to 2mm). \n",
        "\n",
        "Plot the results showing the i) brightness temperature and ii) backscatter (Y-axis) as a function of grain size (X-axis).\n",
        "\n",
        "Discuss this figure and explain how snowpack grain size affects brightness temperature and backscatter in different frequencies and polarisations. \n"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "1HF2gJse35x0"
      },
      "source": [
        "### Question 10\n",
        "Perform a sensitivity analysis of brightness temperature and backscatter in function of the liquid water content  (e.g. by varying the LWC of the reference snowpack over a from 0 to 5%). \n",
        "\n",
        "Plot the results showing the i) brightness temperature and ii) backscatter (Y-axis) as a function of LWC (X-axis).\n",
        "\n",
        "Discuss this figure and explain how LWC affects brightness temperature and backscatter in different frequencies and polarisations. \n",
        "\n",
        "Compare your results with Q7 and discuss the shortcomings of the temperature-only analysis on Q7.\n"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "TV81-ri336BA"
      },
      "source": [
        "### Question 11\n",
        "Perform a sensitivity analysis of brightness temperature and backscatter in function of the satellite zenith angle  (e.g. by varying the SZA from 0 to 80 degrees). \n",
        "\n",
        "Plot the results showing the i) brightness temperature and ii) backscatter (Y-axis) as a function of SZA (X-axis).\n",
        "\n",
        "Discuss this figure and explain how SZA affects brightness temperature and backscatter in different frequencies and polarisations. What are the implications of this change on potential satellite data retrievals?\n"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "NSUxRCkK4w65"
      },
      "source": [
        "### Question 12\n",
        "Use the previous figures to discuss the relative importance of temperature, grain size, liquid water content and satellite zenith angle on the satellite observations. Thereby, discuss how these affect the satellite signal and how that can be used to retrieve snow properties?\n"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "WoRsCDvQVux9"
      },
      "source": [
        "## A4: Snow properties from active and passive MW over DYE-2"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "UxbjtKau5Fwz"
      },
      "source": [
        "### Question 13\n",
        "Create a time series of modelled SMRT brightness temperature and Sentinel-1 backscatter for the reference snowpack where you may assume that the snowpack has the same temperature as the air temperature. For computational efficiency you may use a subset of the time series data (e.g. one-year only).\n",
        "\n",
        "Plot the modelled time series of different brightness temperatures and backscatter (frequencies and polarisations."
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "eFyY2NnrYcEE"
      },
      "source": [
        "### Question 14\n",
        "Plot the observed time series of brightness temperature and backscatter (from downloaded data) over DYE-2. \n",
        "\n",
        "Compare the modelled time series of Question 13 with the corresponding observed satellite time series. \n",
        "\n",
        "Discuss how the modelled snowpack would need to change in order to correspond to the observations. Illustrate this by some examples (e.g. by allowing the snowpack to melt in summer, by adding a fresh snow layer in winter etc)."
      ]
    },
    {
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      "source": [
        "### Question 15\n",
        "Apply a melt detection algorithm (e.g. winter threshold method) on the observed SSMI and Sentinel-1 time series.\n",
        "\n",
        "Compare the observed melt with the AWS T2m temperatures. \n",
        "\n",
        "Discuss your result in terms of accuracy, interannual variability and potential trends.\n"
      ]
    },
    {
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      "metadata": {
        "id": "Gl9Q6GhHZjaz"
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      "source": [
        "# Background literature\n",
        "\n",
        "*   Verjans, V., Leeson, A. A., Stevens, C. M., MacFerrin, M., Noël, B., and van den Broeke, M. R.: Development of physically based liquid water schemes for Greenland firn-densification models, The Cryosphere, 13, 1819–1842, https://doi.org/10.5194/tc-13-1819-2019, 2019.\n",
        "*   Vandecrux, B., MacFerrin, M., Machguth, H., Colgan, W. T., van As, D., Heilig, A., Stevens, C. M., Charalampidis, C., Fausto, R. S., Morris, E. M., Mosley-Thompson, E., Koenig, L., Montgomery, L. N., Miège, C., Simonsen, S. B., Ingeman-Nielsen, T., and Box, J. E.: Firn data compilation reveals widespread decrease of firn air content in western Greenland, The Cryosphere, 13, 845–859, https://doi.org/10.5194/tc-13-845-2019, 2019.\n",
        "* Dumont, M., Brun, E., Picard, G. et al. Contribution of light-absorbing impurities in snow to Greenland’s darkening since 2009. Nature Geosci 7, 509–512 (2014). https://doi.org/10.1038/ngeo2180\n",
        "* Picard, G., Sandells, M., Löwe, H.. SMRT: an active–passive microwave radiative transfer model for snow with multiple microstructure and scattering formulations (v1.0), Geosci. Model Dev., 11, 2763–2788, 2018\n",
        "https://doi.org/10.5194/gmd-11-2763-2018.\n",
        "* Q. Libois, G. Picard, J. France, L. Arnaud, M. Dumont , C. Carmagnola , and M. D. King, Influence of grain shape on light penetration in snow, The Cryosphere, 7, 1803-1818, 2013, https://doi.org/10.5194/tc-7-1803-2013  \n",
        "\n",
        "\n",
        "\n"
      ]
    },
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        ""
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