{
  "cells": [
    {
      "cell_type": "markdown",
      "metadata": {},
      "source": [
        "# Dfs2 - Meteo data"
      ],
      "id": "f873d419-6c5a-4ba7-af40-22382a0cfff8"
    },
    {
      "cell_type": "code",
      "execution_count": 1,
      "metadata": {},
      "outputs": [],
      "source": [
        "import xarray\n",
        "import pandas as pd\n",
        "import mikeio"
      ],
      "id": "23b246e6"
    },
    {
      "cell_type": "markdown",
      "metadata": {},
      "source": [
        "The file `gfs_wind.nc` contains a small sample of the\n",
        "[GFS](https://nomads.ncep.noaa.gov/) forecast data downloaded via their\n",
        "OpenDAP service"
      ],
      "id": "eea1c9c8-d2f6-4a7b-baff-c10a8814f296"
    },
    {
      "cell_type": "code",
      "execution_count": 3,
      "metadata": {},
      "outputs": [
        {
          "output_type": "display_data",
          "metadata": {},
          "data": {
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              "</style><pre class='xr-text-repr-fallback'>&lt;xarray.Dataset&gt; Size: 32kB\n",
              "Dimensions:   (time: 3, lat: 41, lon: 21)\n",
              "Coordinates: (3)\n",
              "Data variables:\n",
              "    msletmsl  (time, lat, lon) float32 10kB ...\n",
              "    ugrd10m   (time, lat, lon) float32 10kB ...\n",
              "    vgrd10m   (time, lat, lon) float32 10kB ...\n",
              "Attributes: (4)</pre><div class='xr-wrap' style='display:none'><div class='xr-header'><div class='xr-obj-type'>xarray.Dataset</div></div><ul class='xr-sections'><li class='xr-section-item'><input id='section-2622a92a-6bf2-4d2f-b52f-6eafed731656' class='xr-section-summary-in' type='checkbox' disabled ><label for='section-2622a92a-6bf2-4d2f-b52f-6eafed731656' class='xr-section-summary'  title='Expand/collapse section'>Dimensions:</label><div class='xr-section-inline-details'><ul class='xr-dim-list'><li><span class='xr-has-index'>time</span>: 3</li><li><span class='xr-has-index'>lat</span>: 41</li><li><span class='xr-has-index'>lon</span>: 21</li></ul></div><div class='xr-section-details'></div></li><li class='xr-section-item'><input id='section-966e6982-1598-4f31-b7c6-06a69898d849' class='xr-section-summary-in' type='checkbox'  ><label for='section-966e6982-1598-4f31-b7c6-06a69898d849' class='xr-section-summary' >Coordinates: <span>(3)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><ul class='xr-var-list'><li class='xr-var-item'><div class='xr-var-name'><span class='xr-has-index'>time</span></div><div class='xr-var-dims'>(time)</div><div class='xr-var-dtype'>datetime64[ns]</div><div class='xr-var-preview xr-preview'>2021-09-02T12:00:00 ... 2021-09-...</div><input id='attrs-35e7dd3f-829a-44e7-9b14-9b0e41347b8a' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-35e7dd3f-829a-44e7-9b14-9b0e41347b8a' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-d7edb4c1-6f48-45ca-9700-4de956151308' class='xr-var-data-in' type='checkbox'><label for='data-d7edb4c1-6f48-45ca-9700-4de956151308' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>grads_dim :</span></dt><dd>t</dd><dt><span>grads_mapping :</span></dt><dd>linear</dd><dt><span>grads_size :</span></dt><dd>129</dd><dt><span>grads_min :</span></dt><dd>12z02sep2021</dd><dt><span>grads_step :</span></dt><dd>3hr</dd><dt><span>long_name :</span></dt><dd>time</dd><dt><span>minimum :</span></dt><dd>12z02sep2021</dd><dt><span>maximum :</span></dt><dd>12z18sep2021</dd><dt><span>resolution :</span></dt><dd>0.125</dd></dl></div><div class='xr-var-data'><pre>array([&#x27;2021-09-02T12:00:00.000000000&#x27;, &#x27;2021-09-02T15:00:00.000000000&#x27;,\n",
              "       &#x27;2021-09-02T18:00:00.000000000&#x27;], dtype=&#x27;datetime64[ns]&#x27;)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span class='xr-has-index'>lat</span></div><div class='xr-var-dims'>(lat)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>30.0 30.25 30.5 ... 39.5 39.75 40.0</div><input id='attrs-254f730e-9b87-45b8-af08-5b9555c0f4bc' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-254f730e-9b87-45b8-af08-5b9555c0f4bc' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-d9e35f45-6fd7-488f-8c3d-a691381652f1' class='xr-var-data-in' type='checkbox'><label for='data-d9e35f45-6fd7-488f-8c3d-a691381652f1' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>grads_dim :</span></dt><dd>y</dd><dt><span>grads_mapping :</span></dt><dd>linear</dd><dt><span>grads_size :</span></dt><dd>721</dd><dt><span>units :</span></dt><dd>degrees_north</dd><dt><span>long_name :</span></dt><dd>latitude</dd><dt><span>minimum :</span></dt><dd>-90.0</dd><dt><span>maximum :</span></dt><dd>90.0</dd><dt><span>resolution :</span></dt><dd>0.25</dd></dl></div><div class='xr-var-data'><pre>array([30.  , 30.25, 30.5 , 30.75, 31.  , 31.25, 31.5 , 31.75, 32.  , 32.25,\n",
              "       32.5 , 32.75, 33.  , 33.25, 33.5 , 33.75, 34.  , 34.25, 34.5 , 34.75,\n",
              "       35.  , 35.25, 35.5 , 35.75, 36.  , 36.25, 36.5 , 36.75, 37.  , 37.25,\n",
              "       37.5 , 37.75, 38.  , 38.25, 38.5 , 38.75, 39.  , 39.25, 39.5 , 39.75,\n",
              "       40.  ])</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span class='xr-has-index'>lon</span></div><div class='xr-var-dims'>(lon)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>10.0 10.25 10.5 ... 14.5 14.75 15.0</div><input id='attrs-3df7fdde-f0ac-4020-8885-d6f1ea07599b' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-3df7fdde-f0ac-4020-8885-d6f1ea07599b' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-1f848af8-cb5f-4402-a750-10b2534ed089' class='xr-var-data-in' type='checkbox'><label for='data-1f848af8-cb5f-4402-a750-10b2534ed089' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>grads_dim :</span></dt><dd>x</dd><dt><span>grads_mapping :</span></dt><dd>linear</dd><dt><span>grads_size :</span></dt><dd>1440</dd><dt><span>units :</span></dt><dd>degrees_east</dd><dt><span>long_name :</span></dt><dd>longitude</dd><dt><span>minimum :</span></dt><dd>0.0</dd><dt><span>maximum :</span></dt><dd>359.75</dd><dt><span>resolution :</span></dt><dd>0.25</dd></dl></div><div class='xr-var-data'><pre>array([10.  , 10.25, 10.5 , 10.75, 11.  , 11.25, 11.5 , 11.75, 12.  , 12.25,\n",
              "       12.5 , 12.75, 13.  , 13.25, 13.5 , 13.75, 14.  , 14.25, 14.5 , 14.75,\n",
              "       15.  ])</pre></div></li></ul></div></li><li class='xr-section-item'><input id='section-d57a0145-6ff0-4f8b-8dcd-e3bf7d8be673' class='xr-section-summary-in' type='checkbox'  checked><label for='section-d57a0145-6ff0-4f8b-8dcd-e3bf7d8be673' class='xr-section-summary' >Data variables: <span>(3)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><ul class='xr-var-list'><li class='xr-var-item'><div class='xr-var-name'><span>msletmsl</span></div><div class='xr-var-dims'>(time, lat, lon)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>...</div><input id='attrs-64e2ec71-d1c9-49ce-b924-c216391bf7f5' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-64e2ec71-d1c9-49ce-b924-c216391bf7f5' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-590392f4-27ff-40a6-8f5b-0b9aef3906b6' class='xr-var-data-in' type='checkbox'><label for='data-590392f4-27ff-40a6-8f5b-0b9aef3906b6' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>long_name :</span></dt><dd>** mean sea level mslp (eta model reduction) [pa] </dd></dl></div><div class='xr-var-data'><pre>[2583 values with dtype=float32]</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>ugrd10m</span></div><div class='xr-var-dims'>(time, lat, lon)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>...</div><input id='attrs-e7fc9b7c-eb46-445e-9aea-1aa87f1facb9' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-e7fc9b7c-eb46-445e-9aea-1aa87f1facb9' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-6bf37aca-b2d0-4210-8ce8-c2ce5789376e' class='xr-var-data-in' type='checkbox'><label for='data-6bf37aca-b2d0-4210-8ce8-c2ce5789376e' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>long_name :</span></dt><dd>** 10 m above ground u-component of wind [m/s] </dd></dl></div><div class='xr-var-data'><pre>[2583 values with dtype=float32]</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>vgrd10m</span></div><div class='xr-var-dims'>(time, lat, lon)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>...</div><input id='attrs-b5a1d41b-e290-48bb-bfe4-432344dba2d1' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-b5a1d41b-e290-48bb-bfe4-432344dba2d1' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-02c29fa1-9f61-487d-8cd3-41884f206f00' class='xr-var-data-in' type='checkbox'><label for='data-02c29fa1-9f61-487d-8cd3-41884f206f00' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>long_name :</span></dt><dd>** 10 m above ground v-component of wind [m/s] </dd></dl></div><div class='xr-var-data'><pre>[2583 values with dtype=float32]</pre></div></li></ul></div></li><li class='xr-section-item'><input id='section-8de2a6a0-1651-45f7-9488-7b1bd47756ac' class='xr-section-summary-in' type='checkbox'  ><label for='section-8de2a6a0-1651-45f7-9488-7b1bd47756ac' class='xr-section-summary' >Indexes: <span>(3)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><ul class='xr-var-list'><li class='xr-var-item'><div class='xr-index-name'><div>time</div></div><div class='xr-index-preview'>PandasIndex</div><div></div><input id='index-69ff2455-f9b7-4464-90ca-079af501b985' class='xr-index-data-in' type='checkbox'/><label for='index-69ff2455-f9b7-4464-90ca-079af501b985' title='Show/Hide index repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-index-data'><pre>PandasIndex(DatetimeIndex([&#x27;2021-09-02 12:00:00&#x27;, &#x27;2021-09-02 15:00:00&#x27;,\n",
              "               &#x27;2021-09-02 18:00:00&#x27;],\n",
              "              dtype=&#x27;datetime64[ns]&#x27;, name=&#x27;time&#x27;, freq=None))</pre></div></li><li class='xr-var-item'><div class='xr-index-name'><div>lat</div></div><div class='xr-index-preview'>PandasIndex</div><div></div><input id='index-26c5bbe3-fc11-437a-92c3-27d1a2dbf88d' class='xr-index-data-in' type='checkbox'/><label for='index-26c5bbe3-fc11-437a-92c3-27d1a2dbf88d' title='Show/Hide index repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-index-data'><pre>PandasIndex(Index([ 30.0, 30.25,  30.5, 30.75,  31.0, 31.25,  31.5, 31.75,  32.0, 32.25,\n",
              "        32.5, 32.75,  33.0, 33.25,  33.5, 33.75,  34.0, 34.25,  34.5, 34.75,\n",
              "        35.0, 35.25,  35.5, 35.75,  36.0, 36.25,  36.5, 36.75,  37.0, 37.25,\n",
              "        37.5, 37.75,  38.0, 38.25,  38.5, 38.75,  39.0, 39.25,  39.5, 39.75,\n",
              "        40.0],\n",
              "      dtype=&#x27;float64&#x27;, name=&#x27;lat&#x27;))</pre></div></li><li class='xr-var-item'><div class='xr-index-name'><div>lon</div></div><div class='xr-index-preview'>PandasIndex</div><div></div><input id='index-1afc3f1f-3f52-4c7e-b1d8-8247b903a577' class='xr-index-data-in' type='checkbox'/><label for='index-1afc3f1f-3f52-4c7e-b1d8-8247b903a577' title='Show/Hide index repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-index-data'><pre>PandasIndex(Index([ 10.0, 10.25,  10.5, 10.75,  11.0, 11.25,  11.5, 11.75,  12.0, 12.25,\n",
              "        12.5, 12.75,  13.0, 13.25,  13.5, 13.75,  14.0, 14.25,  14.5, 14.75,\n",
              "        15.0],\n",
              "      dtype=&#x27;float64&#x27;, name=&#x27;lon&#x27;))</pre></div></li></ul></div></li><li class='xr-section-item'><input id='section-a3bdf087-c937-4851-b2c9-fa80bf3c7b36' class='xr-section-summary-in' type='checkbox'  ><label for='section-a3bdf087-c937-4851-b2c9-fa80bf3c7b36' class='xr-section-summary' >Attributes: <span>(4)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><dl class='xr-attrs'><dt><span>title :</span></dt><dd>GFS 0.25 deg starting from 12Z02sep2021, downloaded Sep 02 17:14 UTC</dd><dt><span>Conventions :</span></dt><dd>COARDS\n",
              "GrADS</dd><dt><span>dataType :</span></dt><dd>Grid</dd><dt><span>history :</span></dt><dd>Thu Sep 02 17:27:02 GMT 2021 : imported by GrADS Data Server 2.0</dd></dl></div></li></ul></div></div>"
            ]
          }
        }
      ],
      "source": [
        "ds = xarray.open_dataset('../../data/gfs_wind.nc')\n",
        "ds"
      ],
      "id": "267f99cd"
    },
    {
      "cell_type": "markdown",
      "metadata": {},
      "source": [
        "Running a Mike 21 HD model, needs at least three variables of\n",
        "meteorological forcing \\* Mean Sea Level Pressure \\* U 10m \\* V 10m\n",
        "\n",
        "Let’s take a look the U 10m"
      ],
      "id": "8c48207a-52b9-47a5-be10-0a635740b6d5"
    },
    {
      "cell_type": "code",
      "execution_count": 4,
      "metadata": {},
      "outputs": [
        {
          "output_type": "display_data",
          "metadata": {},
          "data": {
            "image/png": 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BQERGRgoh5PdQCSGEwWAQsbGxok6dOkKr1YrAwEDRokULERsbm69IZHGJjIwUAApc7u4x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\nIiIihf4Pg5QHUzIdQiEAAAAASUVORK5CYII=\n"
          }
        }
      ],
      "source": [
        "ds.ugrd10m.isel(time=0).plot();"
      ],
      "id": "af6538ff"
    },
    {
      "cell_type": "markdown",
      "metadata": {},
      "source": [
        "## Convert to dfs2\n",
        "\n",
        "### Time"
      ],
      "id": "79e98bb3-fb73-4472-beff-d7e7433b0125"
    },
    {
      "cell_type": "code",
      "execution_count": 5,
      "metadata": {},
      "outputs": [
        {
          "output_type": "display_data",
          "metadata": {},
          "data": {
            "text/plain": [
              "DatetimeIndex(['2021-09-02 12:00:00', '2021-09-02 15:00:00',\n",
              "               '2021-09-02 18:00:00'],\n",
              "              dtype='datetime64[ns]', freq=None)"
            ]
          }
        }
      ],
      "source": [
        "time = pd.DatetimeIndex(ds.time)\n",
        "time"
      ],
      "id": "17781802"
    },
    {
      "cell_type": "markdown",
      "metadata": {},
      "source": [
        "### Variable types"
      ],
      "id": "f1dbfd30-4b87-45ee-9e76-87c836e7b761"
    },
    {
      "cell_type": "code",
      "execution_count": 6,
      "metadata": {},
      "outputs": [
        {
          "output_type": "display_data",
          "metadata": {},
          "data": {
            "text/plain": [
              "Air Pressure"
            ]
          }
        }
      ],
      "source": [
        "mikeio.EUMType.Air_Pressure"
      ],
      "id": "748087c0"
    },
    {
      "cell_type": "code",
      "execution_count": 7,
      "metadata": {},
      "outputs": [
        {
          "output_type": "display_data",
          "metadata": {},
          "data": {
            "text/plain": [
              "[hectopascal, millibar]"
            ]
          }
        }
      ],
      "source": [
        "mikeio.EUMType.Air_Pressure.units"
      ],
      "id": "a6fd42cf"
    },
    {
      "cell_type": "code",
      "execution_count": 8,
      "metadata": {},
      "outputs": [
        {
          "output_type": "display_data",
          "metadata": {},
          "data": {
            "text/plain": [
              "Wind Velocity"
            ]
          }
        }
      ],
      "source": [
        "mikeio.EUMType.Wind_Velocity"
      ],
      "id": "6768c6c4"
    },
    {
      "cell_type": "code",
      "execution_count": 9,
      "metadata": {},
      "outputs": [
        {
          "output_type": "display_data",
          "metadata": {},
          "data": {
            "text/plain": [
              "[meter per sec, feet per sec, miles per hour, km per hour, knot]"
            ]
          }
        }
      ],
      "source": [
        "mikeio.EUMType.Wind_Velocity.units"
      ],
      "id": "82cc37c0"
    },
    {
      "cell_type": "code",
      "execution_count": 10,
      "metadata": {},
      "outputs": [],
      "source": [
        "mslp = ds.msletmsl.values / 100 # conversion from Pa to hPa\n",
        "u = ds.ugrd10m.values\n",
        "v = ds.vgrd10m.values"
      ],
      "id": "718e8dcb"
    },
    {
      "cell_type": "code",
      "execution_count": 11,
      "metadata": {},
      "outputs": [
        {
          "output_type": "display_data",
          "metadata": {},
          "data": {
            "text/plain": [
              "<mikeio.Grid2D>\n",
              "x: [10, 10.25, ..., 15] (nx=21, dx=0.25)\n",
              "y: [30, 30.25, ..., 40] (ny=41, dy=0.25)\n",
              "projection: LONG/LAT"
            ]
          }
        }
      ],
      "source": [
        "geometry = mikeio.Grid2D(x=ds.lon.values, y=ds.lat.values, projection=\"LONG/LAT\")\n",
        "geometry"
      ],
      "id": "487bd6d7"
    },
    {
      "cell_type": "code",
      "execution_count": 12,
      "metadata": {},
      "outputs": [],
      "source": [
        "from mikeio import ItemInfo, EUMType, EUMUnit\n",
        "\n",
        "mslp_da = mikeio.DataArray(data=mslp,time=time, geometry=geometry, item=ItemInfo(\"Mean Sea Level Pressure\", EUMType.Air_Pressure, EUMUnit.hectopascal))\n",
        "u_da = mikeio.DataArray(data=u,time=time, geometry=geometry, item=ItemInfo(\"Wind U\", EUMType.Wind_Velocity, EUMUnit.meter_per_sec))\n",
        "v_da = mikeio.DataArray(data=v,time=time, geometry=geometry, item=ItemInfo(\"Wind V\", EUMType.Wind_Velocity, EUMUnit.meter_per_sec))"
      ],
      "id": "b43d09dd"
    },
    {
      "cell_type": "code",
      "execution_count": 13,
      "metadata": {},
      "outputs": [
        {
          "output_type": "display_data",
          "metadata": {},
          "data": {
            "text/plain": [
              "<mikeio.Dataset>\n",
              "dims: (time:3, y:41, x:21)\n",
              "time: 2021-09-02 12:00:00 - 2021-09-02 18:00:00 (3 records)\n",
              "geometry: Grid2D (ny=41, nx=21)\n",
              "items:\n",
              "  0:  Mean Sea Level Pressure <Air Pressure> (hectopascal)\n",
              "  1:  Wind U <Wind Velocity> (meter per sec)\n",
              "  2:  Wind V <Wind Velocity> (meter per sec)"
            ]
          }
        }
      ],
      "source": [
        "mds = mikeio.Dataset([mslp_da, u_da, v_da])\n",
        "mds"
      ],
      "id": "04a7f1fa"
    },
    {
      "cell_type": "code",
      "execution_count": 14,
      "metadata": {},
      "outputs": [],
      "source": [
        "mds.to_dfs(\"gfs.dfs2\")"
      ],
      "id": "e83f612c"
    },
    {
      "cell_type": "markdown",
      "metadata": {},
      "source": [
        "Clean up"
      ],
      "id": "37242aac-b756-4358-b244-da6f6459d104"
    },
    {
      "cell_type": "code",
      "execution_count": 15,
      "metadata": {},
      "outputs": [],
      "source": [
        "import os\n",
        "\n",
        "os.remove(\"gfs.dfs2\")"
      ],
      "id": "76b098b4"
    }
  ],
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