Weather_colors.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "id": "6313235c",
   "metadata": {},
   "source": [
    "The Icelandic Met Office uses lots of colors for its weather forecast maps. Here are the most common ones as color-ramps created with the [Matplotlib]( https://matplotlib.org/) library."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "866ca125",
   "metadata": {},
   "outputs": [],
   "source": [
    "import matplotlib.pyplot as plt\n",
    "import matplotlib as mpl"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "216b299e",
   "metadata": {},
   "source": [
    "#### Wind in meters per second with 2° bins"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "81425a64",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.colorbar.Colorbar at 0x12a261390>"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1200x120 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig, ax = plt.subplots(figsize=(12, 1.2))\n",
    "fig.subplots_adjust(bottom=0.5)\n",
    "plt.rc('axes', labelsize=14)\n",
    "\n",
    "wind_hex_list = ['#f4fee0','#d6f29d','#b9db75','#9bc44c','#b1fefe',\n",
    "                 '#82d9d9','#53b4b4','#249090','#c3b2fe','#9882e1',\n",
    "                 '#6e54c4','#4326a7','#fea2a2','#e07474','#c24545',\n",
    "                 '#a41717','#f0c02d','#bc9120','#876214','#533307',\n",
    "                 '#f74eff','#c438cc','#902199','#5d0b60','#ffc1d6',\n",
    "                 '#e6a6b7','#c78c9e','#a97383','#8d5a69','#713f4b']\n",
    "\n",
    "wind_cmap = (mpl.colors.ListedColormap(wind_hex_list).with_extremes(over='#552531'))\n",
    "\n",
    "wind_bounds = [0, 2, 4, 6, 8, 10, 12, 14, 16, 18, 20, 22, 24, 26, 28, 30,\n",
    "               32, 34, 36, 38, 40, 42, 44, 46, 48, 50, 52, 54, 56, 58, 60]\n",
    "\n",
    "norm = mpl.colors.BoundaryNorm(wind_bounds, wind_cmap.N)\n",
    "fig.colorbar(\n",
    "    mpl.cm.ScalarMappable(cmap=wind_cmap, norm=norm),\n",
    "    cax=ax,\n",
    "    ticks=wind_bounds,\n",
    "    extend='max',\n",
    "    spacing='proportional',\n",
    "    orientation='horizontal',\n",
    "    label='Wind m/s',\n",
    ")"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "4814dea1",
   "metadata": {},
   "source": [
    "##### Percipitation in millimeters per hour with growing bins"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "b4a3cfe9",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.colorbar.Colorbar at 0x12a3c4670>"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1200x120 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig, ax = plt.subplots(figsize=(12, 1.2))\n",
    "fig.subplots_adjust(bottom=0.5)\n",
    "plt.rc('axes', labelsize=14)\n",
    "\n",
    "precipitation_hex_list = ['#fefc83','#cad300','#aafb78','#00aa08','#025d3e',\n",
    "                          '#06fced','#3366ff','#00017c','#770161','#ff1493']\n",
    "\n",
    "precip_cmap = (mpl.colors.ListedColormap(precipitation_hex_list).with_extremes(over='#ff0000'))\n",
    "\n",
    "precip_bounds = [0.1, 0.3, 0.5, 1, 3, 5, 10, 15, 20, 30, 50]\n",
    "\n",
    "norm = mpl.colors.BoundaryNorm(precip_bounds, precip_cmap.N)\n",
    "fig.colorbar(\n",
    "    mpl.cm.ScalarMappable(cmap=precip_cmap, norm=norm),\n",
    "    cax=ax,\n",
    "    ticks=precip_bounds,\n",
    "    extend='max',\n",
    "    #spacing='proportional',\n",
    "    orientation='horizontal',\n",
    "    label='Precipitation mm/hr'\n",
    ")"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "71ae96b3",
   "metadata": {},
   "source": [
    "#### Percipitation in millimeters per 3 hours with growing bins"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "77d4fe5e",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.colorbar.Colorbar at 0x12a4347f0>"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1200x120 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig, ax = plt.subplots(figsize=(12, 1.2))\n",
    "fig.subplots_adjust(bottom=0.5)\n",
    "plt.rc('axes', labelsize=14)\n",
    "\n",
    "precipitation_3hr_hex_list = ['#d7dc37','#b4fa96','#32b937','#327864',\n",
    "                              '#32fafa','#1e82b9','#003cbe','#783269']\n",
    "\n",
    "precip_3hr_cmap = (mpl.colors.ListedColormap(precipitation_3hr_hex_list).with_extremes(over='#783269'))\n",
    "\n",
    "precip_6hr_bounds = [0.3, 0.7, 1.5, 3, 5, 10, 15, 20, 30]\n",
    "\n",
    "norm = mpl.colors.BoundaryNorm(precip_6hr_bounds, precip_3hr_cmap.N)\n",
    "fig.colorbar(\n",
    "    mpl.cm.ScalarMappable(cmap=precip_3hr_cmap, norm=norm),\n",
    "    cax=ax,\n",
    "    ticks=precip_6hr_bounds,\n",
    "    extend='max',\n",
    "    #spacing='proportional',\n",
    "    orientation='horizontal',\n",
    "    label='Precipitation mm/3hrs.'\n",
    "    )"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "766105c3",
   "metadata": {},
   "source": [
    "#### Percipitation in millimeters per 6 hours with growing bins"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "b0b99e09",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.colorbar.Colorbar at 0x12a48fb80>"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1200x120 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig, ax = plt.subplots(figsize=(12, 1.2))\n",
    "fig.subplots_adjust(bottom=0.5)\n",
    "plt.rc('axes', labelsize=14)\n",
    "\n",
    "precipitation_6hr_hex_list = ['#fefc83','#cad300','#aafb78','#00aa08','#025d3e',\n",
    "                              '#06fced','#3366ff','#00017c','#770161','#ff1493']\n",
    "\n",
    "precip_6hr_cmap = (mpl.colors.ListedColormap(precipitation_6hr_hex_list).with_extremes(over='#ff0000'))\n",
    "\n",
    "precip_6hr_bounds = [0.5, 1, 3, 5, 10, 20, 30, 60, 80, 120, 200]\n",
    "\n",
    "norm = mpl.colors.BoundaryNorm(precip_6hr_bounds, precip_6hr_cmap.N)\n",
    "fig.colorbar(\n",
    "    mpl.cm.ScalarMappable(cmap=precip_6hr_cmap, norm=norm),\n",
    "    cax=ax,\n",
    "    ticks=precip_6hr_bounds,\n",
    "    extend='max',\n",
    "    #spacing='proportional',\n",
    "    orientation='horizontal',\n",
    "    label='Precipitation mm/6hrs.'\n",
    ")"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "02267ea9",
   "metadata": {},
   "source": [
    "#### Accumulated precipitation in millimeters per hour with growing bins"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "e1f05cbd",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.colorbar.Colorbar at 0x12a3c6dd0>"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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",
      "text/plain": [
       "<Figure size 1200x120 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig, ax = plt.subplots(figsize=(12, 1.2))\n",
    "fig.subplots_adjust(bottom=0.5)\n",
    "plt.rc('axes', labelsize=14)\n",
    "\n",
    "precip_accum_hex_list = ['#b7fbfc','#01fffd','#00cdff','#3466fd','#2935c7',\n",
    "                         '#770161','#a6018b','#ff01d1']\n",
    "\n",
    "precip_accum_cmap = (mpl.colors.ListedColormap(precip_accum_hex_list).with_extremes(over='#f8b4ff'))\n",
    "\n",
    "precip_accum_bounds = [1, 10, 25, 50, 75, 100, 150, 200, 400]\n",
    "\n",
    "norm = mpl.colors.BoundaryNorm(precip_accum_bounds, precip_accum_cmap.N)\n",
    "fig.colorbar(\n",
    "    mpl.cm.ScalarMappable(cmap=precip_accum_cmap, norm=norm),\n",
    "    cax=ax,\n",
    "    ticks=precip_accum_bounds,\n",
    "    extend='max',\n",
    "    #spacing='proportional',\n",
    "    orientation='horizontal',\n",
    "    label='Precipitation mm/hr'\n",
    ")"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "a4d2b4b5",
   "metadata": {},
   "source": [
    "#### Temperature in celcius with 2° bins"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "9247d32c",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.colorbar.Colorbar at 0x12a57a650>"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1300x120 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig, ax = plt.subplots(figsize=(13, 1.2))\n",
    "fig.subplots_adjust(bottom=0.5)\n",
    "plt.rc('axes', labelsize=14)\n",
    "\n",
    "# '#000000','#ff0000','#000000','#ff0000','#000000',\n",
    "\n",
    "temp2_hex_list = ['#2cb32c','#52cc52','#7ee47e','#b2ffb1','#b3e1b2',\n",
    "                  '#310f99','#4e2cb3','#6f52cc','#977de4','#c5b1ff',\n",
    "                  '#0f5399','#2c6fb3','#528fcc','#7db1e4','#b1d9ff',\n",
    "                  '#ffffb1','#f1e385','#e4c15c','#d99736','#cc6a13',\n",
    "                  '#de7b58','#cc503c','#b92424','#a81124','#950028',\n",
    "                  '#b852cc','#a133b8','#8b1aa2','#79098f']\n",
    "\n",
    "temp2_cmap = (mpl.colors.ListedColormap(temp2_hex_list))\n",
    "\n",
    "temp2_bounds = [-30, -28, -26, -24, -22, -20, -18, -16, -14, -12, -10, -8, -6, -4, -2,\n",
    "               0, 2, 4, 6, 8, 10, 12, 14, 16, 18, 20, 22, 24, 26, 28]\n",
    "\n",
    "norm = mpl.colors.BoundaryNorm(temp2_bounds, temp2_cmap.N)\n",
    "fig.colorbar(\n",
    "    mpl.cm.ScalarMappable(cmap=temp2_cmap, norm=norm),\n",
    "    cax=ax,\n",
    "    ticks=temp2_bounds,\n",
    "    extend='both',\n",
    "    spacing='proportional',\n",
    "    orientation='horizontal',\n",
    "    label='Temperature °C'\n",
    ")"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "b6d6d7b3",
   "metadata": {},
   "source": [
    "#### Temperature in celcius with 1° bins"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "87d250c3",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.colorbar.Colorbar at 0x12a642da0>"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1500x120 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig, ax = plt.subplots(figsize=(15, 1.2))\n",
    "fig.subplots_adjust(bottom=0.5)\n",
    "plt.rc('axes', labelsize=14)\n",
    "\n",
    "temp1_hex_list = ['#300f9c','#401ea9','#4e2dbd','#5e40c4','#7062ca',\n",
    "                  '#8567d7','#997ee9','#af98f3','#c7b2ff','#6b83cb',\n",
    "                  '#0e539e','#1e62a3','#2c70b7','#3f80c4','#528fce',\n",
    "                  '#65a2d9','#81b1e2','#94c7f4','#b3d9fe','#d7ecd9',\n",
    "                  '#ffffaf','#f5f3a0','#f3e381','#ead375','#e3c15d',\n",
    "                  '#deae4a','#db9734','#d48125','#cd6911','#d57237',\n",
    "                  '#dd7c5b','#d4664d','#cb503e','#c43a2f','#b82627',\n",
    "                  '#b11b24','#a81025','#9f0827','#960028','#a7297b',]\n",
    "\n",
    "temp1_cmap = (mpl.colors.ListedColormap(temp1_hex_list))\n",
    "\n",
    "temp1_bounds = [-20, -19, -18, -17, -16, -15, -14, -13, -12, -11, -10, -9, -8, -7, -6, -5, -4, -3, -2, -1,\n",
    "                0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20]\n",
    "\n",
    "norm = mpl.colors.BoundaryNorm(temp1_bounds, temp1_cmap.N)\n",
    "fig.colorbar(\n",
    "    mpl.cm.ScalarMappable(cmap=temp1_cmap, norm=norm),\n",
    "    cax=ax,\n",
    "    boundaries=[0] + temp1_bounds + [13],  # Adding values for extensions.\n",
    "    ticks=temp1_bounds,\n",
    "    extend='both',\n",
    "    spacing='proportional',\n",
    "    orientation='horizontal',\n",
    "    label='Temperature °C'\n",
    ")\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "e8c17944",
   "metadata": {},
   "source": [
    "#### Cloudcover in quartiles (%)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "b943f8e3",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.colorbar.Colorbar at 0x12a7501f0>"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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",
      "text/plain": [
       "<Figure size 1200x120 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig, ax = plt.subplots(figsize=(12, 1.2))\n",
    "fig.subplots_adjust(bottom=0.5)\n",
    "plt.rc('axes', labelsize=14)\n",
    "\n",
    "cloudcover_hex_list = ['#ffffff','#bdc9e1','#74a9cf','#0570b0']\n",
    "\n",
    "cloudcover_cmap = (mpl.colors.ListedColormap(cloudcover_hex_list))\n",
    "\n",
    "cloudcover_bounds = [0, 25, 50, 75, 100]\n",
    "\n",
    "norm = mpl.colors.BoundaryNorm(cloudcover_bounds, cloudcover_cmap.N)\n",
    "fig.colorbar(\n",
    "    mpl.cm.ScalarMappable(cmap=cloudcover_cmap, norm=norm),\n",
    "    cax=ax,\n",
    "    ticks=cloudcover_bounds,\n",
    "    #extend='max',\n",
    "    #spacing='proportional',\n",
    "    orientation='horizontal',\n",
    "    label='Total cloud cover (%)'\n",
    ")"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "32b943df",
   "metadata": {},
   "source": [
    "#### Precipitation types in english/icelandic"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "c7ce57b4",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1200x120 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig, ax = plt.subplots(figsize=(12, 1.2))\n",
    "fig.subplots_adjust(bottom=0.5)\n",
    "plt.rc('axes', labelsize=14)\n",
    "\n",
    "precip_types = ['Drizzle (0)', 'Rain (1)', 'Sleet (2)', 'Snow (3)',\n",
    "                'Freezing drizzle (4)', 'Freezing rain (5)', 'Ice pellets (6)']\n",
    "\n",
    "precip_types_hex_list = ['#88fe86', '#349a68', '#eeaddc', '#c54999',\n",
    "                         '#8fc4fe', '#287abd', '#ff9800']\n",
    "\n",
    "precip_types_cmap = mpl.colors.ListedColormap(precip_types_hex_list)\n",
    "\n",
    "n_types = len(precip_types)\n",
    "\n",
    "type_bounds = [i for i in range(n_types + 1)]\n",
    "\n",
    "norm = mpl.colors.BoundaryNorm(type_bounds, precip_types_cmap.N)\n",
    "colorbar = fig.colorbar(\n",
    "    mpl.cm.ScalarMappable(cmap=precip_types_cmap, norm=norm),\n",
    "    cax = ax,\n",
    "    boundaries = type_bounds,\n",
    "    ticks=[i + 0.5 for i in range(n_types)],\n",
    "    orientation='horizontal'\n",
    ")\n",
    "\n",
    "colorbar.set_ticklabels(precip_types)\n",
    "colorbar.set_label('Precipitation types')\n",
    "\n",
    "for i, label in enumerate(['Súld', 'Rigning', 'Slydda', 'Snjókoma', 'Frostúði', 'Frostregn', 'Snæhagl']):\n",
    "    ax.text((i + 0.5) / n_types, 1.5, label, ha='center', va='center', transform=ax.transAxes, fontsize=12)\n",
    "\n",
    "plt.show()"
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "geo",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.10.13"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 5
}