t3.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "id": "2dd01356-77bb-4509-8eb2-4bbda5cd33af",
   "metadata": {},
   "source": [
    "## Energy Balance - Explorador glacier - Patagonia"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "1ddd248f-c20c-4ac1-a7e9-6b69642a4cdd",
   "metadata": {},
   "source": [
    "## import packages"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "70795884-665c-47d8-ae70-59d9ce739b86",
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "import numpy as np"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "4626fcf5-df38-4e0b-88a9-f4d23268e91f",
   "metadata": {},
   "source": [
    "#### read data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "0b3c0e2d-bc83-4102-93c8-96ea54c3b02b",
   "metadata": {},
   "outputs": [
    {
     "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>SW_in(W/m²)</th>\n",
       "      <th>SW_out(W/m²)</th>\n",
       "      <th>LW_in(W/m²)</th>\n",
       "      <th>LW_out(W/m²)</th>\n",
       "      <th>wind_speed(m/s)</th>\n",
       "      <th>wind_direction(degrees)</th>\n",
       "      <th>air_temperature(degrees_Celsius)</th>\n",
       "      <th>atmospheric_pressure(hPa)</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Date_Hour</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2017-01-01 00:00:00</th>\n",
       "      <td>-1.623</td>\n",
       "      <td>1.524</td>\n",
       "      <td>330.5</td>\n",
       "      <td>354.8</td>\n",
       "      <td>7.925</td>\n",
       "      <td>246.8</td>\n",
       "      <td>8.17</td>\n",
       "      <td>989</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2017-01-01 01:00:00</th>\n",
       "      <td>-1.607</td>\n",
       "      <td>0.884</td>\n",
       "      <td>336.2</td>\n",
       "      <td>355.5</td>\n",
       "      <td>6.882</td>\n",
       "      <td>345.6</td>\n",
       "      <td>8.49</td>\n",
       "      <td>990</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2017-01-01 02:00:00</th>\n",
       "      <td>-1.595</td>\n",
       "      <td>0.587</td>\n",
       "      <td>349.5</td>\n",
       "      <td>356.4</td>\n",
       "      <td>6.864</td>\n",
       "      <td>343.7</td>\n",
       "      <td>9.04</td>\n",
       "      <td>990</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2017-01-01 03:00:00</th>\n",
       "      <td>-1.834</td>\n",
       "      <td>0.530</td>\n",
       "      <td>343.1</td>\n",
       "      <td>357.4</td>\n",
       "      <td>7.006</td>\n",
       "      <td>338.3</td>\n",
       "      <td>9.54</td>\n",
       "      <td>990</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2017-01-01 04:00:00</th>\n",
       "      <td>-2.934</td>\n",
       "      <td>0.715</td>\n",
       "      <td>362.3</td>\n",
       "      <td>357.2</td>\n",
       "      <td>5.603</td>\n",
       "      <td>343.3</td>\n",
       "      <td>8.98</td>\n",
       "      <td>990</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2017-03-31 19:00:00</th>\n",
       "      <td>91.100</td>\n",
       "      <td>21.430</td>\n",
       "      <td>358.8</td>\n",
       "      <td>353.3</td>\n",
       "      <td>1.510</td>\n",
       "      <td>230.9</td>\n",
       "      <td>10.35</td>\n",
       "      <td>1000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2017-03-31 20:00:00</th>\n",
       "      <td>16.630</td>\n",
       "      <td>2.588</td>\n",
       "      <td>350.0</td>\n",
       "      <td>351.6</td>\n",
       "      <td>1.357</td>\n",
       "      <td>207.5</td>\n",
       "      <td>10.39</td>\n",
       "      <td>1001</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2017-03-31 21:00:00</th>\n",
       "      <td>0.661</td>\n",
       "      <td>-1.388</td>\n",
       "      <td>370.4</td>\n",
       "      <td>349.6</td>\n",
       "      <td>1.336</td>\n",
       "      <td>176.3</td>\n",
       "      <td>8.54</td>\n",
       "      <td>1001</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2017-03-31 22:00:00</th>\n",
       "      <td>2.455</td>\n",
       "      <td>-0.010</td>\n",
       "      <td>373.1</td>\n",
       "      <td>350.2</td>\n",
       "      <td>1.352</td>\n",
       "      <td>233.3</td>\n",
       "      <td>8.96</td>\n",
       "      <td>1001</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2017-03-31 23:00:00</th>\n",
       "      <td>0.260</td>\n",
       "      <td>0.414</td>\n",
       "      <td>342.0</td>\n",
       "      <td>348.5</td>\n",
       "      <td>1.956</td>\n",
       "      <td>159.5</td>\n",
       "      <td>8.78</td>\n",
       "      <td>1001</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>2122 rows × 8 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "                     SW_in(W/m²)  SW_out(W/m²)  LW_in(W/m²)  LW_out(W/m²)  \\\n",
       "Date_Hour                                                                   \n",
       "2017-01-01 00:00:00       -1.623         1.524        330.5         354.8   \n",
       "2017-01-01 01:00:00       -1.607         0.884        336.2         355.5   \n",
       "2017-01-01 02:00:00       -1.595         0.587        349.5         356.4   \n",
       "2017-01-01 03:00:00       -1.834         0.530        343.1         357.4   \n",
       "2017-01-01 04:00:00       -2.934         0.715        362.3         357.2   \n",
       "...                          ...           ...          ...           ...   \n",
       "2017-03-31 19:00:00       91.100        21.430        358.8         353.3   \n",
       "2017-03-31 20:00:00       16.630         2.588        350.0         351.6   \n",
       "2017-03-31 21:00:00        0.661        -1.388        370.4         349.6   \n",
       "2017-03-31 22:00:00        2.455        -0.010        373.1         350.2   \n",
       "2017-03-31 23:00:00        0.260         0.414        342.0         348.5   \n",
       "\n",
       "                     wind_speed(m/s)  wind_direction(degrees)  \\\n",
       "Date_Hour                                                       \n",
       "2017-01-01 00:00:00            7.925                    246.8   \n",
       "2017-01-01 01:00:00            6.882                    345.6   \n",
       "2017-01-01 02:00:00            6.864                    343.7   \n",
       "2017-01-01 03:00:00            7.006                    338.3   \n",
       "2017-01-01 04:00:00            5.603                    343.3   \n",
       "...                              ...                      ...   \n",
       "2017-03-31 19:00:00            1.510                    230.9   \n",
       "2017-03-31 20:00:00            1.357                    207.5   \n",
       "2017-03-31 21:00:00            1.336                    176.3   \n",
       "2017-03-31 22:00:00            1.352                    233.3   \n",
       "2017-03-31 23:00:00            1.956                    159.5   \n",
       "\n",
       "                     air_temperature(degrees_Celsius)  \\\n",
       "Date_Hour                                               \n",
       "2017-01-01 00:00:00                              8.17   \n",
       "2017-01-01 01:00:00                              8.49   \n",
       "2017-01-01 02:00:00                              9.04   \n",
       "2017-01-01 03:00:00                              9.54   \n",
       "2017-01-01 04:00:00                              8.98   \n",
       "...                                               ...   \n",
       "2017-03-31 19:00:00                             10.35   \n",
       "2017-03-31 20:00:00                             10.39   \n",
       "2017-03-31 21:00:00                              8.54   \n",
       "2017-03-31 22:00:00                              8.96   \n",
       "2017-03-31 23:00:00                              8.78   \n",
       "\n",
       "                     atmospheric_pressure(hPa)  \n",
       "Date_Hour                                       \n",
       "2017-01-01 00:00:00                        989  \n",
       "2017-01-01 01:00:00                        990  \n",
       "2017-01-01 02:00:00                        990  \n",
       "2017-01-01 03:00:00                        990  \n",
       "2017-01-01 04:00:00                        990  \n",
       "...                                        ...  \n",
       "2017-03-31 19:00:00                       1000  \n",
       "2017-03-31 20:00:00                       1001  \n",
       "2017-03-31 21:00:00                       1001  \n",
       "2017-03-31 22:00:00                       1001  \n",
       "2017-03-31 23:00:00                       1001  \n",
       "\n",
       "[2122 rows x 8 columns]"
      ]
     },
     "execution_count": 2,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "csv_path = r\"C:\\Users\\pauls\\Desktop\\MSc_WaterResources\\2022\\2022-2\\Glaciologia\\Tareas\\T3\\exploradores2017.csv\"\n",
    "raw_data = pd.read_csv(csv_path, parse_dates = [[\"Date\",\"Hour\"]])\n",
    "raw_data = raw_data.set_index(\"Date_Hour\")\n",
    "raw_data"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "20006f58-6621-4118-bd0d-8a60138c40c9",
   "metadata": {
    "tags": []
   },
   "source": [
    "## 1.- Calculo promedio de los cuatro componentes del balance energético superficial"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "a60b4696-6fce-41ad-98be-ed42c40a26fd",
   "metadata": {},
   "source": [
    "### Filtrado de datos\n",
    "\n",
    "Primero que todo es necesario chequear que todas las componentes del balance superficial (a excepción de la temperatura) sean mayores a 0. \n",
    "Arbitrariamente se decidió eliminar la medición completa (todas las variables) si alguno de estos valores es negativo."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "5c3a7950-0a53-4fcd-8e6b-b010e87ef363",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([467, 440,   0,   0,   0,   0,   8], dtype=int64)"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "result = []\n",
    "\n",
    "for i in range(len(raw_data)):\n",
    "    for j in range(len(raw_data.columns)):\n",
    "        if raw_data.iloc[i,j] < 0:\n",
    "            result.append(j)\n",
    "            \n",
    "result\n",
    "count = np.bincount(result)\n",
    "count"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "aa891743-88b5-4592-8dd9-9b3d3c7f0c70",
   "metadata": {},
   "source": [
    "Los resultados anteriores nos muestran que existen valores negativos anómalos en los datos de radiación de onda corta entrante (columna 0) y saliente (columna 1)."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "40108bdb-4f15-47b8-922f-3f2a49aab338",
   "metadata": {},
   "outputs": [],
   "source": [
    "for i in range(len(raw_data)):\n",
    "    if raw_data.iloc[i,0] < 0:\n",
    "        raw_data.iloc[i,:] = np.nan\n",
    "    elif raw_data.iloc[i,1] < 0:\n",
    "        raw_data.iloc[i,:] = np.nan\n",
    "\n",
    "#raw_data"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "f60407aa-2563-4d38-967e-4d4679856b8c",
   "metadata": {},
   "source": [
    "#### Variables"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "ac53eba1-0f3f-4d41-ab85-d91c3c4a4572",
   "metadata": {},
   "outputs": [],
   "source": [
    "raw_data[\"SW_neto(W/m²)\"] = raw_data[\"SW_in(W/m²)\"]-raw_data[\"SW_out(W/m²)\"]\n",
    "raw_data[\"LW_neto(W/m²)\"] = raw_data[\"LW_in(W/m²)\"]-raw_data[\"LW_out(W/m²)\"]\n",
    "#raw_data"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "7408789d-5805-4743-9f2e-cc574c6c5cba",
   "metadata": {},
   "source": [
    "Es necesario volver a chequear en la onda corta si existen valores negativos y filtrarlos"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "a5f0b03b-1465-44bb-a2b2-c36175d782d1",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "58"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "result = []\n",
    "\n",
    "for i in range(len(raw_data)):\n",
    "        if raw_data.iloc[i,8] < 0:\n",
    "            result.append(1)\n",
    "            raw_data.iloc[i,:] = np.nan\n",
    "            \n",
    "sum(result)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "219e059a-7c00-4c45-8335-bbf39f6b4300",
   "metadata": {},
   "source": [
    "#### 1.1.- Short and long waves net"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "6f6070f6-f303-46da-8838-872f531695ae",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "SW:  227.3318655332303 W/m2\n",
      "LW:  4.595749613601235 W/m2\n"
     ]
    }
   ],
   "source": [
    "sw_neto_mean = raw_data[\"SW_neto(W/m²)\"].mean()\n",
    "lw_neto_mean = raw_data[\"LW_neto(W/m²)\"].mean()\n",
    "\n",
    "print(\"SW: \",sw_neto_mean,\"W/m2\")\n",
    "print(\"LW: \",lw_neto_mean,\"W/m2\")"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "7971c533-c30c-4f48-9d86-bd8de89455fa",
   "metadata": {},
   "source": [
    "#### Flujo turbulento de calor sensible"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "83213a4f-5fc9-4a6a-8138-799db8c334de",
   "metadata": {},
   "source": [
    "Calculamos el calor sensible considerando el coeficiente de transferencia C* igual a 0.002 (Hogg et al., 1982) y Ts = 0°C."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "0bfe2034-81ef-4cdd-aeed-ab3994dc25fd",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "SH:  96.2076618857254 W/m2\n"
     ]
    }
   ],
   "source": [
    "raw_data[\"SH(W/m²)\"] = 0.0129*0.002*raw_data[\"atmospheric_pressure(hPa)\"]*100*raw_data[\"wind_speed(m/s)\"]*raw_data[\"air_temperature(degrees_Celsius)\"]\n",
    "sh_neto_mean = raw_data[\"SH(W/m²)\"].mean()\n",
    "print(\"SH: \", sh_neto_mean.mean(), \"W/m2\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "b70fdd1a-79a1-472d-8701-5a56ef8e4ca2",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "raw_data[\"SH(W/m²)\"].plot();"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "9df4b402-b9b0-44c8-ab40-26c273f2d2ae",
   "metadata": {},
   "source": [
    "#### Flujo turbulento de calor latente"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "273ad469-b94c-4b03-918f-c55dfe09d182",
   "metadata": {},
   "source": [
    "El flujo turbulento de calor latente requiere de mayor información, se asumen algunos valores como:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "09e91119-806d-4b61-a399-dd400d146ff0",
   "metadata": {},
   "outputs": [],
   "source": [
    "pa = 1.29\n",
    "Lv = 2.5*10**6\n",
    "#Ls =28.3*10**5\n",
    "C = 0.002\n",
    "es = 611\n",
    "po = 101300"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "fa9d3ec3-b49e-4b66-8482-44904213f2b7",
   "metadata": {},
   "source": [
    "Ahora es necesario calcular vapor pressure at saturation del hielo en función de la temperatura"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "98bfc188-721f-4995-88a9-56a2368e12f7",
   "metadata": {},
   "outputs": [],
   "source": [
    "raw_data[\"e(Pa)\"] = 0.87*2.38*10**11*(2.718**(-5400/(raw_data[\"air_temperature(degrees_Celsius)\"]+273.15)))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "id": "f2758c26-c780-4dd2-9b4c-1027aab4a93e",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "LH:  74.06671328038887 W/m2\n"
     ]
    }
   ],
   "source": [
    "raw_data[\"LH(W/m²)\"] = 0.622*pa*Lv*C*raw_data[\"wind_speed(m/s)\"]*(raw_data[\"e(Pa)\"]-es)/po\n",
    "lh_neto_mean = raw_data[\"LH(W/m²)\"].mean()\n",
    "print(\"LH: \",lh_neto_mean, \"W/m2\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "id": "e1a42240-3068-4a2a-9e1d-4fa4c6cad8ba",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "raw_data[\"LH(W/m²)\"].plot();"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "f42147f7-3948-4653-9ad7-65ceaa332263",
   "metadata": {},
   "source": [
    "## 3.- Albedo"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "5835e367-e03b-42de-b248-4e91b3fe8dbc",
   "metadata": {},
   "source": [
    "#### Cálculo de albedo con todos los datos"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "id": "30d22913-e58b-46f6-8062-e66f28375f6f",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.2161764871821161"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "raw_data[\"albedo\"] = -(raw_data[\"SW_neto(W/m²)\"]/raw_data[\"SW_in(W/m²)\"])+1\n",
    "albedo = raw_data[\"albedo\"].mean()\n",
    "albedo"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "id": "558069ed-e3ab-426f-a4bb-e40662a40b15",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "raw_data[\"albedo\"].plot();"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "3a08e712-3bea-4b90-a5c6-a1470b44a623",
   "metadata": {},
   "source": [
    "#### Cálculo de albedo solo entre 10-15 hrs"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "id": "149b4b32-2631-49d6-914b-a0c8007125dc",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "albedo':  0.2033421129993818\n"
     ]
    }
   ],
   "source": [
    "raw_data[\"albedo'\"] =  raw_data[\"albedo\"]\n",
    "for i in range(len(raw_data)):\n",
    "    if raw_data.index[i].hour >= 10 and raw_data.index[i].hour <= 15:\n",
    "        raw_data[\"albedo'\"][i] = -(raw_data[\"SW_neto(W/m²)\"][i]/raw_data[\"SW_in(W/m²)\"][i])+1\n",
    "    else:\n",
    "        raw_data[\"albedo'\"][i] = np.nan\n",
    "\n",
    "print(\"albedo': \", raw_data[\"albedo'\"].mean())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "id": "0f91dbc1-c8e2-432b-ab3a-64af9caa7a69",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "raw_data[\"albedo'\"].plot();"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "id": "cf142245-8b8e-4ebf-a2e6-6f5c2bac15a2",
   "metadata": {},
   "outputs": [
    {
     "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>SW_in(W/m²)</th>\n",
       "      <th>SW_out(W/m²)</th>\n",
       "      <th>LW_in(W/m²)</th>\n",
       "      <th>LW_out(W/m²)</th>\n",
       "      <th>wind_speed(m/s)</th>\n",
       "      <th>wind_direction(degrees)</th>\n",
       "      <th>air_temperature(degrees_Celsius)</th>\n",
       "      <th>atmospheric_pressure(hPa)</th>\n",
       "      <th>SW_neto(W/m²)</th>\n",
       "      <th>LW_neto(W/m²)</th>\n",
       "      <th>SH(W/m²)</th>\n",
       "      <th>e(Pa)</th>\n",
       "      <th>LH(W/m²)</th>\n",
       "      <th>albedo</th>\n",
       "      <th>albedo'</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>1294.000000</td>\n",
       "      <td>1294.000000</td>\n",
       "      <td>1294.000000</td>\n",
       "      <td>1294.000000</td>\n",
       "      <td>1294.000000</td>\n",
       "      <td>1294.000000</td>\n",
       "      <td>1294.000000</td>\n",
       "      <td>1294.000000</td>\n",
       "      <td>1294.000000</td>\n",
       "      <td>1294.000000</td>\n",
       "      <td>1294.000000</td>\n",
       "      <td>1294.000000</td>\n",
       "      <td>1294.000000</td>\n",
       "      <td>1294.000000</td>\n",
       "      <td>528.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>284.147050</td>\n",
       "      <td>56.815185</td>\n",
       "      <td>359.970943</td>\n",
       "      <td>355.375193</td>\n",
       "      <td>3.713927</td>\n",
       "      <td>237.603563</td>\n",
       "      <td>8.481390</td>\n",
       "      <td>991.036321</td>\n",
       "      <td>227.331866</td>\n",
       "      <td>4.595750</td>\n",
       "      <td>96.207662</td>\n",
       "      <td>1004.546907</td>\n",
       "      <td>74.066713</td>\n",
       "      <td>0.216176</td>\n",
       "      <td>0.203342</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>281.603023</td>\n",
       "      <td>60.084721</td>\n",
       "      <td>24.523512</td>\n",
       "      <td>9.347596</td>\n",
       "      <td>2.584508</td>\n",
       "      <td>72.720509</td>\n",
       "      <td>3.677082</td>\n",
       "      <td>6.445518</td>\n",
       "      <td>228.554193</td>\n",
       "      <td>22.179049</td>\n",
       "      <td>84.625905</td>\n",
       "      <td>254.508704</td>\n",
       "      <td>74.221650</td>\n",
       "      <td>0.112903</td>\n",
       "      <td>0.061719</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>0.191000</td>\n",
       "      <td>0.010000</td>\n",
       "      <td>281.900000</td>\n",
       "      <td>332.500000</td>\n",
       "      <td>0.099000</td>\n",
       "      <td>27.270000</td>\n",
       "      <td>-0.835000</td>\n",
       "      <td>968.000000</td>\n",
       "      <td>0.003000</td>\n",
       "      <td>-63.200000</td>\n",
       "      <td>-0.841857</td>\n",
       "      <td>506.920500</td>\n",
       "      <td>-1.857032</td>\n",
       "      <td>0.008977</td>\n",
       "      <td>0.077007</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>50.755000</td>\n",
       "      <td>9.885000</td>\n",
       "      <td>346.000000</td>\n",
       "      <td>348.700000</td>\n",
       "      <td>1.287500</td>\n",
       "      <td>175.100000</td>\n",
       "      <td>5.667000</td>\n",
       "      <td>988.000000</td>\n",
       "      <td>40.782500</td>\n",
       "      <td>-10.350000</td>\n",
       "      <td>18.935742</td>\n",
       "      <td>804.918015</td>\n",
       "      <td>10.977783</td>\n",
       "      <td>0.133560</td>\n",
       "      <td>0.133358</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>192.200000</td>\n",
       "      <td>36.115000</td>\n",
       "      <td>363.100000</td>\n",
       "      <td>354.700000</td>\n",
       "      <td>3.087500</td>\n",
       "      <td>226.450000</td>\n",
       "      <td>8.465000</td>\n",
       "      <td>992.000000</td>\n",
       "      <td>154.080000</td>\n",
       "      <td>10.100000</td>\n",
       "      <td>71.661636</td>\n",
       "      <td>975.692711</td>\n",
       "      <td>47.768307</td>\n",
       "      <td>0.218806</td>\n",
       "      <td>0.222300</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>442.750000</td>\n",
       "      <td>85.975000</td>\n",
       "      <td>375.925000</td>\n",
       "      <td>361.575000</td>\n",
       "      <td>5.949500</td>\n",
       "      <td>316.675000</td>\n",
       "      <td>11.167500</td>\n",
       "      <td>995.000000</td>\n",
       "      <td>354.050000</td>\n",
       "      <td>20.175000</td>\n",
       "      <td>161.056350</td>\n",
       "      <td>1170.741326</td>\n",
       "      <td>121.440066</td>\n",
       "      <td>0.255258</td>\n",
       "      <td>0.249355</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>1214.000000</td>\n",
       "      <td>293.700000</td>\n",
       "      <td>421.000000</td>\n",
       "      <td>394.900000</td>\n",
       "      <td>11.080000</td>\n",
       "      <td>347.300000</td>\n",
       "      <td>19.150000</td>\n",
       "      <td>1005.000000</td>\n",
       "      <td>1057.500000</td>\n",
       "      <td>53.300000</td>\n",
       "      <td>386.350860</td>\n",
       "      <td>1966.517619</td>\n",
       "      <td>418.585288</td>\n",
       "      <td>0.997425</td>\n",
       "      <td>0.391232</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "       SW_in(W/m²)  SW_out(W/m²)  LW_in(W/m²)  LW_out(W/m²)  wind_speed(m/s)  \\\n",
       "count  1294.000000   1294.000000  1294.000000   1294.000000      1294.000000   \n",
       "mean    284.147050     56.815185   359.970943    355.375193         3.713927   \n",
       "std     281.603023     60.084721    24.523512      9.347596         2.584508   \n",
       "min       0.191000      0.010000   281.900000    332.500000         0.099000   \n",
       "25%      50.755000      9.885000   346.000000    348.700000         1.287500   \n",
       "50%     192.200000     36.115000   363.100000    354.700000         3.087500   \n",
       "75%     442.750000     85.975000   375.925000    361.575000         5.949500   \n",
       "max    1214.000000    293.700000   421.000000    394.900000        11.080000   \n",
       "\n",
       "       wind_direction(degrees)  air_temperature(degrees_Celsius)  \\\n",
       "count              1294.000000                       1294.000000   \n",
       "mean                237.603563                          8.481390   \n",
       "std                  72.720509                          3.677082   \n",
       "min                  27.270000                         -0.835000   \n",
       "25%                 175.100000                          5.667000   \n",
       "50%                 226.450000                          8.465000   \n",
       "75%                 316.675000                         11.167500   \n",
       "max                 347.300000                         19.150000   \n",
       "\n",
       "       atmospheric_pressure(hPa)  SW_neto(W/m²)  LW_neto(W/m²)     SH(W/m²)  \\\n",
       "count                1294.000000    1294.000000    1294.000000  1294.000000   \n",
       "mean                  991.036321     227.331866       4.595750    96.207662   \n",
       "std                     6.445518     228.554193      22.179049    84.625905   \n",
       "min                   968.000000       0.003000     -63.200000    -0.841857   \n",
       "25%                   988.000000      40.782500     -10.350000    18.935742   \n",
       "50%                   992.000000     154.080000      10.100000    71.661636   \n",
       "75%                   995.000000     354.050000      20.175000   161.056350   \n",
       "max                  1005.000000    1057.500000      53.300000   386.350860   \n",
       "\n",
       "             e(Pa)     LH(W/m²)       albedo     albedo'  \n",
       "count  1294.000000  1294.000000  1294.000000  528.000000  \n",
       "mean   1004.546907    74.066713     0.216176    0.203342  \n",
       "std     254.508704    74.221650     0.112903    0.061719  \n",
       "min     506.920500    -1.857032     0.008977    0.077007  \n",
       "25%     804.918015    10.977783     0.133560    0.133358  \n",
       "50%     975.692711    47.768307     0.218806    0.222300  \n",
       "75%    1170.741326   121.440066     0.255258    0.249355  \n",
       "max    1966.517619   418.585288     0.997425    0.391232  "
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "raw_data.describe()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "c0555fcc-f822-4eab-9781-436269d1cf3f",
   "metadata": {},
   "source": [
    "## 3.- Estimación derretimiento"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "c2f1fdaa-b425-448b-97b1-ca6981541b8c",
   "metadata": {},
   "source": [
    "Primero que todo se asume que la temperatura superficial del glaciar está en el punto de fusión."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "id": "58b3e4f4-cf0d-4748-b39f-2bf815de7282",
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "9.363840349321755 m/s\n"
     ]
    }
   ],
   "source": [
    "dw = 1000\n",
    "lf = 3.34*10**5\n",
    "melt = (sw_neto_mean+lw_neto_mean+sh_neto_mean+lh_neto_mean)/(dw*lf)\n",
    "print(melt*86400*(31+28+31),\"m/s\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "id": "6b412c23-725a-4e89-9c69-adb9763a38ce",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "402.2019903129458"
      ]
     },
     "execution_count": 20,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "sw_neto_mean+lw_neto_mean+sh_neto_mean+lh_neto_mean"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "id": "ad40e554-5524-4602-b49e-9da405302b98",
   "metadata": {},
   "outputs": [],
   "source": [
    "raw_data.to_excel(\"exploradores_full.xlsx\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "42c0a81b-27b1-4350-ad7c-e0c538a68639",
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
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