TEMPO_CloudRasterFormat.ipynb

tempo_cloudrasterformat.ipynb

{
  "nbformat": 4,
  "nbformat_minor": 0,
  "metadata": {
    "colab": {
      "provenance": [],
      "authorship_tag": "ABX9TyNxIyPFkJsdSNnV07qk8fFc",
      "include_colab_link": true
    },
    "kernelspec": {
      "name": "python3",
      "display_name": "Python 3"
    },
    "language_info": {
      "name": "python"
    }
  },
  "cells": [
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "view-in-github",
        "colab_type": "text"
      },
      "source": [
        "<a href=\"https://colab.research.google.com/gist/barronh/fa8489fa6183bde20e2b4422fe35cb27/tempo_cloudrasterformat.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "# Rapid Access to TEMPO through gis.earthdata.nasa.gov\n",
        "\n",
        "---\n",
        "    author: Barron H. Henderson\n",
        "    last updated: 2025-03-06\n",
        "---\n",
        "\n",
        "This example shows how to get a single location's NO2 from TEMPO for any amount of time in just a few seconds. The code is adapted from Hazem Mahmoud's code that he shared with me.\n"
      ],
      "metadata": {
        "id": "w_NCE5SdIA0E"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "def getl3(lon, lat, start, end):\n",
        "  \"\"\"\n",
        "  Get Level 3 TEMPO Tropospheric NO2 quickly!\n",
        "\n",
        "  Arguments\n",
        "  ---------\n",
        "  lon : float\n",
        "    Longitude in degrees_east\n",
        "  lat : float\n",
        "    Latitude in degrees_north\n",
        "  start : str or datetime-like\n",
        "    Beginning of time-series. Must be convertable to a time using\n",
        "    pandas.to_datetime with utc=True\n",
        "  end : str or datetime-like\n",
        "    End of time-series. Must be convertable to a time using\n",
        "    pandas.to_datetime with utc=True\n",
        "\n",
        "  Returns\n",
        "  -------\n",
        "  df : pandas.DataFrame\n",
        "    DataFrame with time as an index and NO2 Tropospheric as the only column\n",
        "  \"\"\"\n",
        "  import pandas as pd\n",
        "  import requests\n",
        "  conceptid = 'C2930763263-LARC_CLOUD'  # TEMPO L3 V03\n",
        "  filename = 'TEMPO_NO2_L3_V03_HOURLY_TROPOSPHERIC_VERTICAL_COLUMN'\n",
        "  base_url = 'https://gis.earthdata.nasa.gov/image/rest/services/'\n",
        "  base_url += f'{conceptid}/{filename}/ImageServer/getSamples/'\n",
        "\n",
        "  # time bounds in ms since EPOCH\n",
        "  stime = pd.to_datetime(start, utc=True).timestamp() * 1000\n",
        "  etime = pd.to_datetime(end, utc=True).timestamp() * 1000\n",
        "\n",
        "  # Standard Settings\n",
        "  # variable_name = 'NO2 Troposphere'\n",
        "  params = {\n",
        "      'sampleDistance': '', 'sampleCount': '', 'pixelSize': '', 'outFields': '',\n",
        "      'sliceId': '', 'mosaicRule': '',\n",
        "      # 'mosaicRule': f'{{\"multidimensionalDefinition\"{variable_name}}}',\n",
        "      'returnFirstValueOnly': 'false', 'interpolation': 'RSP_NearestNeighbor',\n",
        "      'geometryType': 'esriGeometryPoint',\n",
        "      'geometry': f'{lon},{lat}',           # required, to be filled in by user\n",
        "      'time': f'{stime:.0f},{etime:.0f}',  # required, to be filled in by user\n",
        "      'f': 'pjson'\n",
        "  }\n",
        "  # Make the request to the API\n",
        "  response = requests.get(base_url, params=params, stream=True)\n",
        "  response.raise_for_status()\n",
        "\n",
        "  # Convert to JSON\n",
        "  data = response.json()\n",
        "  # Convert to DataFrame\n",
        "  varkey = data['samples'][0]['attributes']['Variables']\n",
        "  df = pd.DataFrame.from_records([\n",
        "      {\n",
        "          'time': rec['attributes']['StdTime'],\n",
        "          varkey: rec.get('value', rec['attributes'].get(varkey, ''))\n",
        "      }\n",
        "      for rec in data['samples']\n",
        "  ])\n",
        "  df['time'] = pd.to_datetime(df['time'], unit='ms')\n",
        "  # convert string data to double precision floats\n",
        "  df[varkey] = df[varkey].replace('', 'nan').astype('d')\n",
        "  # Set the index as time\n",
        "  df.set_index('time', inplace=True)\n",
        "  return df"
      ],
      "metadata": {
        "id": "-mxPUoJ8IHqb"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "lon = -84.3885\n",
        "lat = 33.7501\n",
        "start = '2023-09-01 12:00:00+0000'\n",
        "end = '2025-03-01 12:00:00+0000'\n",
        "df = getl3(lon, lat, start, end)"
      ],
      "metadata": {
        "id": "bQeGNnjiJQjZ"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "ax = df.query(f'NO2_Troposphere > -5e15').resample('1h').mean().plot(marker='o', figsize=(24, 3))\n",
        "_ = ax.set(title=f'Hourly Trop NO2 VCD at {lon},{lat} from {start} to {end}', ylabel='NO2 [molec/cm$^3$]', ylim=(None, None))"
      ],
      "metadata": {
        "id": "pFu37NOMJQ1Q"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [],
      "metadata": {
        "id": "rRVEMOgUJl6G"
      },
      "execution_count": null,
      "outputs": []
    }
  ]
}

This makes: