{
  "cells": [
    {
      "cell_type": "markdown",
      "id": "ade075df",
      "metadata": {},
      "source": [
        "# Cloud Enhancement Event Detection with REST2\n",
        "\n",
        "REST2 clear-sky modeling, QC (closure, diffuse ratio, tracker-off), and Killinger cloud enhancement event (CEE) detection.\n",
        "\n",
        "Assumes the `bsrn` package is installed via pip."
      ]
    },
    {
      "cell_type": "markdown",
      "id": "99085832",
      "metadata": {},
      "source": [
        "## 1. Setup"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 1,
      "id": "678bb731",
      "metadata": {},
      "outputs": [],
      "source": [
        "import os\n",
        "import sys\n",
        "import pandas as pd\n",
        "import bsrn\n",
        "\n",
        "# Config: input file, output path. REST2 fetches MERRA-2 from Hugging Face (dazhiyang/bsrn-merra2) into RAM.\n",
        "INPUT_FILE = \"/Volumes/Macintosh Research/Data/bsrn-qc/data/QIQ/qiq0124.dat.gz\"\n",
        "OUTPUT_FILE = \"qiq_cee_results.csv\""
      ]
    },
    {
      "cell_type": "markdown",
      "id": "565af1a7",
      "metadata": {},
      "source": [
        "## 2. Read QIQ data"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 2,
      "id": "176d640e",
      "metadata": {},
      "outputs": [
        {
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              "      <th></th>\n",
              "      <th>ghi</th>\n",
              "      <th>bni</th>\n",
              "      <th>dhi</th>\n",
              "      <th>lwd</th>\n",
              "      <th>temp</th>\n",
              "      <th>rh</th>\n",
              "      <th>pressure</th>\n",
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              "      <th>time</th>\n",
              "      <th></th>\n",
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              "      <th>2024-01-01 00:00:00+00:00</th>\n",
              "      <td>33.0</td>\n",
              "      <td>72.0</td>\n",
              "      <td>28.0</td>\n",
              "      <td>189.0</td>\n",
              "      <td>-12.1</td>\n",
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              "      <th>2024-01-01 00:01:00+00:00</th>\n",
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              "      <td>29.0</td>\n",
              "      <td>189.0</td>\n",
              "      <td>-12.2</td>\n",
              "      <td>73.0</td>\n",
              "      <td>995.0</td>\n",
              "    </tr>\n",
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              "      <th>2024-01-01 00:02:00+00:00</th>\n",
              "      <td>39.0</td>\n",
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              "      <td>189.0</td>\n",
              "      <td>-12.2</td>\n",
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              "      <td>995.0</td>\n",
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              "    <tr>\n",
              "      <th>2024-01-01 00:03:00+00:00</th>\n",
              "      <td>40.0</td>\n",
              "      <td>100.0</td>\n",
              "      <td>32.0</td>\n",
              "      <td>189.0</td>\n",
              "      <td>-12.3</td>\n",
              "      <td>73.0</td>\n",
              "      <td>995.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2024-01-01 00:04:00+00:00</th>\n",
              "      <td>40.0</td>\n",
              "      <td>83.0</td>\n",
              "      <td>34.0</td>\n",
              "      <td>189.0</td>\n",
              "      <td>-12.3</td>\n",
              "      <td>74.0</td>\n",
              "      <td>995.0</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "                            ghi    bni   dhi    lwd  temp    rh  pressure\n",
              "time                                                                     \n",
              "2024-01-01 00:00:00+00:00  33.0   72.0  28.0  189.0 -12.1  74.0     995.0\n",
              "2024-01-01 00:01:00+00:00  36.0   83.0  29.0  189.0 -12.2  73.0     995.0\n",
              "2024-01-01 00:02:00+00:00  39.0  102.0  31.0  189.0 -12.2  73.0     995.0\n",
              "2024-01-01 00:03:00+00:00  40.0  100.0  32.0  189.0 -12.3  73.0     995.0\n",
              "2024-01-01 00:04:00+00:00  40.0   83.0  34.0  189.0 -12.3  74.0     995.0"
            ]
          },
          "execution_count": null,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "df = bsrn.io.reader.read_station_to_archive(INPUT_FILE, logical_records=\"lr0100\")\n",
        "if df is None:\n",
        "    raise SystemExit(f\"Failed to read {INPUT_FILE}\")\n",
        "df.head()"
      ]
    },
    {
      "cell_type": "markdown",
      "id": "9d0dbed3",
      "metadata": {},
      "source": [
        "## 3. REST2 clear-sky modeling\n",
        "\n",
        "MERRA-2 parquet is fetched from Hugging Face into RAM (no disk cache). You will see `Fetching MERRA-2 from Hugging Face: qiq/qiq0124_merra2.parquet` when this cell runs."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 3,
      "id": "258074d3",
      "metadata": {},
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "Fetching MERRA-2 from Hugging Face: qiq/qiq0124_merra2.parquet\n"
          ]
        },
        {
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              "      <th>time</th>\n",
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              "      <th>2024-01-01 00:00:00+00:00</th>\n",
              "      <td>33.0</td>\n",
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              "      <th>2024-01-01 00:03:00+00:00</th>\n",
              "      <td>40.0</td>\n",
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              "      <td>100.0</td>\n",
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              "    <tr>\n",
              "      <th>2024-01-01 00:04:00+00:00</th>\n",
              "      <td>40.0</td>\n",
              "      <td>36.098472</td>\n",
              "      <td>83.0</td>\n",
              "      <td>188.212621</td>\n",
              "      <td>34.0</td>\n",
              "      <td>24.459916</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "                            ghi  ghi_clear    bni   bni_clear   dhi  dhi_clear\n",
              "time                                                                          \n",
              "2024-01-01 00:00:00+00:00  33.0  29.556179   72.0  159.759526  28.0  21.102296\n",
              "2024-01-01 00:01:00+00:00  36.0  31.165183   83.0  166.959123  29.0  21.956312\n",
              "2024-01-01 00:02:00+00:00  39.0  32.792222  102.0  174.103865  31.0  22.800400\n",
              "2024-01-01 00:03:00+00:00  40.0  34.436816  100.0  181.189548  32.0  23.634844\n",
              "2024-01-01 00:04:00+00:00  40.0  36.098472   83.0  188.212621  34.0  24.459916"
            ]
          },
          "execution_count": null,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "df = bsrn.modeling.clear_sky.add_clearsky_columns(\n",
        "    df, station_code=\"QIQ\", model=\"rest2\"\n",
        ")\n",
        "from bsrn.constants import BSRN_STATIONS\n",
        "from bsrn.physics.geometry import get_solar_position\n",
        "\n",
        "meta = BSRN_STATIONS[\"QIQ\"]\n",
        "solpos = get_solar_position(\n",
        "    df.index, lat=meta[\"lat\"], lon=meta[\"lon\"], elev=meta[\"elev\"]\n",
        ")\n",
        "df[\"zenith\"] = solpos[\"zenith\"].to_numpy(dtype=float)\n",
        "df[[\"ghi\", \"ghi_clear\", \"bni\", \"bni_clear\", \"dhi\", \"dhi_clear\"]].head()"
      ]
    },
    {
      "cell_type": "markdown",
      "id": "480778d7",
      "metadata": {},
      "source": [
        "## 4. QC: closure, diffuse ratio, tracker-off\n",
        "\n",
        "**Why PPL and ERL are not used:** Level 1 (PPL) and Level 2 (ERL) QC tests apply physicallt possible and extremely rare bounds that may filter out valid CEE data. CEEs are characterized by GHI exceeding clear-sky or extraterrestrial irradiance—values that PPL/ERL can flag as outliers. This pipeline uses closure, diffuse ratio, and tracker-off checks instead, which are less likely to remove CEE events."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 4,
      "id": "282fe43b",
      "metadata": {},
      "outputs": [
        {
          "data": {
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              "</table>\n",
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            ],
            "text/plain": [
              "                           flag3lowSZA  flag3highSZA  flagKKt  flagKlowSZA  \\\n",
              "time                                                                         \n",
              "2024-01-01 00:00:00+00:00            0             0        0            0   \n",
              "2024-01-01 00:01:00+00:00            0             0        0            0   \n",
              "2024-01-01 00:02:00+00:00            0             0        0            0   \n",
              "2024-01-01 00:03:00+00:00            0             0        0            0   \n",
              "2024-01-01 00:04:00+00:00            0             0        0            0   \n",
              "\n",
              "                           flagKhighSZA  flagTracker  \n",
              "time                                                  \n",
              "2024-01-01 00:00:00+00:00             0            0  \n",
              "2024-01-01 00:01:00+00:00             0            0  \n",
              "2024-01-01 00:02:00+00:00             0            0  \n",
              "2024-01-01 00:03:00+00:00             0            0  \n",
              "2024-01-01 00:04:00+00:00             0            0  "
            ]
          },
          "execution_count": null,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "df = bsrn.qc.wrapper.run_qc(\n",
        "    df,\n",
        "    station_code=\"QIQ\",\n",
        "    tests=(\"closure\", \"diff_ratio\", \"tracker\"),\n",
        ")\n",
        "df[[c for c in df.columns if c.startswith(\"flag\")]].head()"
      ]
    },
    {
      "cell_type": "markdown",
      "id": "cd32a357",
      "metadata": {},
      "source": [
        "## 5. Row-wise: mask ghi, dhi, bni to NaN where any QC fails"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 5,
      "id": "c94dfb05",
      "metadata": {},
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "Rows masked: 347\n"
          ]
        }
      ],
      "source": [
        "qc_flags = [\n",
        "    \"flag3lowSZA\", \"flag3highSZA\",\n",
        "    \"flagKKt\", \"flagKlowSZA\", \"flagKhighSZA\",\n",
        "    \"flagTracker\",\n",
        "]\n",
        "fail_mask = pd.Series(False, index=df.index)\n",
        "for col in qc_flags:\n",
        "    if col in df.columns:\n",
        "        fail_mask = fail_mask | (df[col] == 1)\n",
        "\n",
        "for col in (\"ghi\", \"dhi\", \"bni\"):\n",
        "    if col in df.columns:\n",
        "        df.loc[fail_mask, col] = float(\"nan\")\n",
        "\n",
        "print(f\"Rows masked: {fail_mask.sum()}\")"
      ]
    },
    {
      "cell_type": "markdown",
      "id": "af441daa",
      "metadata": {},
      "source": [
        "## 6. Drop unused columns; keep measured + clear-sky"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 6,
      "id": "0e41ada2",
      "metadata": {},
      "outputs": [
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              "      <th>2024-01-01 00:01:00+00:00</th>\n",
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              "      <td>40.0</td>\n",
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              "      <td>36.098472</td>\n",
              "      <td>188.212621</td>\n",
              "      <td>24.459916</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "                            ghi    bni   dhi     zenith  ghi_clear  \\\n",
              "time                                                                 \n",
              "2024-01-01 00:00:00+00:00  33.0   72.0  28.0  86.966703  29.556179   \n",
              "2024-01-01 00:01:00+00:00  36.0   83.0  29.0  86.838164  31.165183   \n",
              "2024-01-01 00:02:00+00:00  39.0  102.0  31.0  86.709987  32.792222   \n",
              "2024-01-01 00:03:00+00:00  40.0  100.0  32.0  86.582173  34.436816   \n",
              "2024-01-01 00:04:00+00:00  40.0   83.0  34.0  86.454723  36.098472   \n",
              "\n",
              "                            bni_clear  dhi_clear  \n",
              "time                                              \n",
              "2024-01-01 00:00:00+00:00  159.759526  21.102296  \n",
              "2024-01-01 00:01:00+00:00  166.959123  21.956312  \n",
              "2024-01-01 00:02:00+00:00  174.103865  22.800400  \n",
              "2024-01-01 00:03:00+00:00  181.189548  23.634844  \n",
              "2024-01-01 00:04:00+00:00  188.212621  24.459916  "
            ]
          },
          "execution_count": null,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "keep = [\"ghi\", \"bni\", \"dhi\", \"zenith\", \"ghi_clear\", \"bni_clear\", \"dhi_clear\"]\n",
        "df = df[[c for c in keep if c in df.columns]].copy()\n",
        "df.head()"
      ]
    },
    {
      "cell_type": "markdown",
      "id": "1f0ae36d",
      "metadata": {},
      "source": [
        "## 7. Killinger CEE detection"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 7,
      "id": "0784a9db",
      "metadata": {},
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "CEE count: 259 1-min points in 54 distinct events\n"
          ]
        },
        {
          "data": {
            "text/html": [
              "<div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
              "    }\n",
              "\n",
              "    .dataframe tbody tr th {\n",
              "        vertical-align: top;\n",
              "    }\n",
              "\n",
              "    .dataframe thead th {\n",
              "        text-align: right;\n",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>is_enhancement</th>\n",
              "      <th>cee_flag</th>\n",
              "      <th>method</th>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>time</th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>2024-01-01 00:00:00+00:00</th>\n",
              "      <td>&lt;NA&gt;</td>\n",
              "      <td>NaN</td>\n",
              "      <td>killinger</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2024-01-01 00:01:00+00:00</th>\n",
              "      <td>&lt;NA&gt;</td>\n",
              "      <td>NaN</td>\n",
              "      <td>killinger</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2024-01-01 00:02:00+00:00</th>\n",
              "      <td>&lt;NA&gt;</td>\n",
              "      <td>NaN</td>\n",
              "      <td>killinger</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2024-01-01 00:03:00+00:00</th>\n",
              "      <td>&lt;NA&gt;</td>\n",
              "      <td>NaN</td>\n",
              "      <td>killinger</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2024-01-01 00:04:00+00:00</th>\n",
              "      <td>&lt;NA&gt;</td>\n",
              "      <td>NaN</td>\n",
              "      <td>killinger</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "                           is_enhancement  cee_flag     method\n",
              "time                                                          \n",
              "2024-01-01 00:00:00+00:00            <NA>       NaN  killinger\n",
              "2024-01-01 00:01:00+00:00            <NA>       NaN  killinger\n",
              "2024-01-01 00:02:00+00:00            <NA>       NaN  killinger\n",
              "2024-01-01 00:03:00+00:00            <NA>       NaN  killinger\n",
              "2024-01-01 00:04:00+00:00            <NA>       NaN  killinger"
            ]
          },
          "execution_count": null,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "out_cee = bsrn.utils.cee_detection.detect_cee(\n",
        "    \"killinger\",\n",
        "    ghi=df[\"ghi\"],\n",
        "    ghi_clear=df[\"ghi_clear\"],\n",
        "    zenith=df[\"zenith\"],\n",
        "    times=df.index,\n",
        ")\n",
        "df[\"cee_flag\"] = out_cee[\"cee_flag\"].values\n",
        "\n",
        "# Count CEEs: 1-min points flagged, and distinct events (contiguous runs)\n",
        "cee_flag = out_cee[\"cee_flag\"]\n",
        "n_cee_points = (cee_flag == 1).sum()\n",
        "# Count event starts: transition from non-CEE to CEE\n",
        "starts = (cee_flag == 1) & (cee_flag.shift(1, fill_value=0) != 1)\n",
        "n_cee_events = int(starts.sum())\n",
        "print(f\"CEE count: {n_cee_points} 1-min points in {n_cee_events} distinct events\")\n",
        "\n",
        "out_cee.head()"
      ]
    },
    {
      "cell_type": "markdown",
      "id": "a837899b",
      "metadata": {},
      "source": [
        "## 8. Plot CEE results"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 10,
      "id": "c361d252",
      "metadata": {},
      "outputs": [
        {
          "data": {
            "image/png": 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",
            "text/plain": [
              "<Figure size 1200x400 with 1 Axes>"
            ]
          },
          "metadata": {},
          "output_type": "display_data"
        }
      ],
      "source": [
        "import matplotlib.pyplot as plt\n",
        "\n",
        "plot_df = df[[\"ghi\", \"ghi_clear\", \"cee_flag\"]].copy()\n",
        "plot_df = plot_df[plot_df.index.day == 10]\n",
        "if plot_df.empty:\n",
        "    raise ValueError(\"No data for day 10 in current dataframe.\")\n",
        "mask = plot_df[\"cee_flag\"] == 1\n",
        "\n",
        "fig, ax = plt.subplots(figsize=(12, 4))\n",
        "ax.plot(plot_df.index, plot_df[\"ghi\"], label=\"GHI\", linewidth=1.0)\n",
        "ax.plot(plot_df.index, plot_df[\"ghi_clear\"], label=\"GHI clear\", linewidth=1.0)\n",
        "ax.scatter(\n",
        "    plot_df.index[mask],\n",
        "    plot_df.loc[mask, \"ghi\"],\n",
        "    s=8,\n",
        "    color=\"crimson\",\n",
        "    label=\"CEE flag = 1\",\n",
        "    zorder=3,\n",
        ")\n",
        "ax.set_title(\"Cloud enhancement events on day 10 (Killinger)\")\n",
        "ax.set_xlabel(\"Time (UTC)\")\n",
        "ax.set_ylabel(\"Irradiance [W/m$^2$]\")\n",
        "ax.legend(loc=\"upper right\")\n",
        "ax.grid(alpha=0.3)\n",
        "fig.tight_layout()\n",
        "plt.show()"
      ]
    }
  ],
  "metadata": {
    "kernelspec": {
      "display_name": "base",
      "language": "python",
      "name": "python3"
    },
    "language_info": {
      "codemirror_mode": {
        "name": "ipython",
        "version": 3
      },
      "file_extension": ".py",
      "mimetype": "text/x-python",
      "name": "python",
      "nbconvert_exporter": "python",
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