{ "cells": [ { "cell_type": "markdown", "source": [ "# Global Forecasting System - Meteorological forecast" ], "metadata": {} }, { "cell_type": "code", "execution_count": 1, "source": [ "from datetime import datetime, timedelta\n", "import xarray\n", "import numpy as np\n", "import pandas as pd\n", "from mikeio import Dfs2" ], "outputs": [], "metadata": {} }, { "cell_type": "markdown", "source": [ "Let's try to download yesterday's forecast from the OpenDAP server." ], "metadata": {} }, { "cell_type": "code", "execution_count": 2, "source": [ "now = datetime.now()\n", "forecast = datetime(now.year,now.month,now.day) - timedelta(days=1)\n", "dtstr = forecast.strftime(\"%Y%m%d\")\n", "hour = \"12\" # valid options are 00,06,12,18\n", "url = f\"https://nomads.ncep.noaa.gov/dods/gfs_0p25/gfs{dtstr}/gfs_0p25_{hour}z\"" ], "outputs": [], "metadata": {} }, { "cell_type": "code", "execution_count": 3, "source": [ "# code used to generate the example file '../tests/testdata/gfs_wind.nc'\n", "\n", "# ds = xarray.open_dataset(url) # use this instead if you want new data\n", "# ds = ds.sel(lon=slice(10,15), lat=slice(30,40)).isel(time=slice(0,3)) # small subset for illustration\n", "# ds = ds[['msletmsl','ugrd10m','vgrd10m']].load()\n", "# ds.to_netcdf(\"../tests/testdata/gfs_wind.nc\") # save to disk as netcdf" ], "outputs": [], "metadata": {} }, { "cell_type": "code", "execution_count": 4, "source": [ "fn = '../tests/testdata/gfs_wind.nc'\n", "ds = xarray.open_dataset(fn)\n", "#ds = xarray.open_dataset(url) # use this instead if you want new dataa" ], "outputs": [], "metadata": {} }, { "cell_type": "code", "execution_count": 5, "source": [ "ds.time.values[0],ds.time.values[-1]" ], "outputs": [ { "output_type": "execute_result", "data": { "text/plain": [ "(numpy.datetime64('2021-09-02T12:00:00.000000000'),\n", " numpy.datetime64('2021-09-02T18:00:00.000000000'))" ] }, "metadata": {}, "execution_count": 5 } ], "metadata": {} }, { "cell_type": "code", "execution_count": 6, "source": [ "(ds.time.values[-1]- ds.time.values[0]).astype('timedelta64[D]')" ], "outputs": [ { "output_type": "execute_result", "data": { "text/plain": [ "numpy.timedelta64(0,'D')" ] }, "metadata": {}, "execution_count": 6 } ], "metadata": {} }, { "cell_type": "markdown", "source": [ "The forecast contains data for the coming 10 days." ], "metadata": {} }, { "cell_type": "markdown", "source": [ "Running a Mike 21 HD model, needs at least three variables of meteorlogical forcing\n", "* Mean Sea Level Pressure\n", "* U 10m\n", "* V 10m" ], "metadata": {} }, { "cell_type": "code", "execution_count": 7, "source": [ "ds.msletmsl" ], "outputs": [ { "output_type": "execute_result", "data": { "text/html": [ "
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\r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n\r\n", "text/plain": [ "
" ] }, "metadata": { "needs_background": "light" } } ], "metadata": {} }, { "cell_type": "markdown", "source": [ "## Convert to dfs2" ], "metadata": {} }, { "cell_type": "code", "execution_count": 12, "source": [ "lat = ds.lat.values\n", "lon = ds.lon.values\n", "\n", "nx = len(lon)\n", "ny = len(lat)\n", "\n", "x0 = lon[0]\n", "y0 = lat[0]\n", "\n", "dx = np.round((lon[-1] - lon[0]) / nx,2)\n", "dy = np.round((lat[-1] - lat[0]) / ny,2)\n", "\n", "x0, y0, nx, ny, dx, dy" ], "outputs": [ { "output_type": "execute_result", "data": { "text/plain": [ "(10.0, 30.0, 21, 41, 0.24, 0.24)" ] }, "metadata": {}, "execution_count": 12 } ], "metadata": {} }, { "cell_type": "markdown", "source": [ "## Time" ], "metadata": {} }, { "cell_type": "code", "execution_count": 13, "source": [ "t = ds.time.values\n", "print(t[0])\n", "start_time = pd.to_datetime(t).to_pydatetime()[0]" ], "outputs": [ { "output_type": "stream", "name": "stdout", "text": [ "2021-09-02T12:00:00.000000000\n" ] } ], "metadata": {} }, { "cell_type": "markdown", "source": [ "## Variable types" ], "metadata": {} }, { "cell_type": "code", "execution_count": 14, "source": [ "from mikeio.eum import EUMType\n", "EUMType.Air_Pressure" ], "outputs": [ { "output_type": "execute_result", "data": { "text/plain": [ "Air Pressure" ] }, "metadata": {}, "execution_count": 14 } ], "metadata": {} }, { "cell_type": "code", "execution_count": 15, "source": [ "EUMType.Air_Pressure.units" ], "outputs": [ { "output_type": "execute_result", "data": { "text/plain": [ "[hectopascal, millibar]" ] }, "metadata": {}, "execution_count": 15 } ], "metadata": {} }, { "cell_type": "code", "execution_count": 16, "source": [ "EUMType.Wind_Velocity" ], "outputs": [ { "output_type": "execute_result", "data": { "text/plain": [ "Wind Velocity" ] }, "metadata": {}, "execution_count": 16 } ], "metadata": {} }, { "cell_type": "code", "execution_count": 17, "source": [ "EUMType.Wind_Velocity.units" ], "outputs": [ { "output_type": "execute_result", "data": { "text/plain": [ "[meter per sec, feet per sec, miles per hour, km per hour, knot]" ] }, "metadata": {}, "execution_count": 17 } ], "metadata": {} }, { "cell_type": "markdown", "source": [ "This example extracts the first 10 timesteps. Note that in order to download the entire forecast, you have to handle the non-equidistant time axis." ], "metadata": {} }, { "cell_type": "code", "execution_count": 18, "source": [ "ds = ds.isel(time=slice(0,10))\n", "\n", "mslp = ds.msletmsl.values / 100 # conversion from Pa to hPa\n", "u = ds.ugrd10m.values\n", "v = ds.vgrd10m.values" ], "outputs": [], "metadata": {} }, { "cell_type": "markdown", "source": [ "Flip data upside down" ], "metadata": {} }, { "cell_type": "code", "execution_count": 19, "source": [ "mslp = np.flip(mslp, axis=1)\n", "u = np.flip(u, axis=1)\n", "v = np.flip(v, axis=1)" ], "outputs": [], "metadata": {} }, { "cell_type": "code", "execution_count": 20, "source": [ "from mikeio.eum import EUMUnit\n", "\n", "EUMUnit.hectopascal" ], "outputs": [ { "output_type": "execute_result", "data": { "text/plain": [ "hectopascal" ] }, "metadata": {}, "execution_count": 20 } ], "metadata": {} }, { "cell_type": "code", "execution_count": 21, "source": [ "from mikeio.eum import ItemInfo, EUMUnit\n", "\n", "d = [mslp, u, v]\n", "\n", "dfsfilename = f\"gfs_{dtstr}_{hour}.dfs2\"\n", "\n", "coordinate = ['LONG/LAT', x0, y0, 0]\n", "\n", "items = [ItemInfo(\"Mean Sea Level Pressure\", EUMType.Air_Pressure, EUMUnit.hectopascal),\n", " ItemInfo(\"Wind U\", EUMType.Wind_Velocity, EUMUnit.meter_per_sec),\n", " ItemInfo(\"Wind V\", EUMType.Wind_Velocity, EUMUnit.meter_per_sec)]\n", "\n", "dfs = Dfs2()\n", "dfs.write(filename=dfsfilename,\n", " data=d,\n", " start_time = start_time,\n", " dt=3600,\n", " items=items,\n", " coordinate=coordinate, dx=dx, dy=dy\n", ")" ], "outputs": [], "metadata": {} }, { "cell_type": "markdown", "source": [ "![GFS in dfs2 format](../images/gfs.png)" ], "metadata": {} }, { "cell_type": "markdown", "source": [ "## Clean up (don't run this)" ], "metadata": {} }, { "cell_type": "code", "execution_count": 22, "source": [ "import os\n", "os.remove(dfsfilename)" ], "outputs": [], "metadata": {} } ], "metadata": { "kernelspec": { "name": "python3", "display_name": "Python 3.9.6 64-bit" }, "language_info": { "codemirror_mode": { "name": "ipython", "version": 3 }, "file_extension": ".py", "mimetype": "text/x-python", "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", "version": "3.9.6" }, "interpreter": { "hash": "f4041ee05ab07c15354d6207e763f17a216c3f5ccf08906343c2b4fd3fa7a6fb" } }, "nbformat": 4, "nbformat_minor": 4 }