Update scripts

Add CAPE analysis

Readme.md

@@ -18,7 +18,8 @@ If you want to run the code on a local computer, you need to download all files
 * `rmm.txt`. Source of this text file you can [download directly](http://www.bom.gov.au/climate/mjo/graphics/rmm.74toRealtime.txt) from the Australian Bureau of Meteorology. The columns of the file represent the date (from 1974 onwards), components of the Real-time Multivariate MJO index (RMM), MJO phase and amplitude.
 * `OLR_41year_NOAA.npy` This is a `numpy` array with the shape `(14976, 180, 360)`, containing daily averaged (14976 days) Outgoing Longwave Radiation flux values from NOAA PSL data set [olr.day.mean.nc](https://downloads.psl.noaa.gov/Datasets/olrcdr/olr.day.mean.nc) for every cell of a 1°×1° latitude-longitude grid (180×360) starting with 1980-1-1 and ending with 2021-12-31.
 * `DAILY-ENSO34.npy`. This is a `numpy` array with the shape `(4992,)`, containing daily averaged sea surface temperature in the Niño 3.4 region. Here 4992 is the number of days when every third day in 1980–2020 is taken.
-* `DAILY-IP-MAP-V4.3.npy`. This is a `numpy` array with the shape `(4992, 180, 360)`, containing daily averaged (4992 days) contributions to the ionospheric potential (IP) for every cell of a 1°×1° latitude-longitude grid (180×360) for every third day. The gird cell contributions are calculated with the Weather Research and Forecasting model (WRF) version 4.3.
+* `DAILY-IP-MAP-V4.3.npy`. This is a `numpy` array with the shape `(4992, 180, 360)`, containing daily averaged (4992 days) contributions to the ionospheric potential (IP) for every cell of a 1°×1° latitude-longitude grid (180×360) for every third day. The grid cell contributions are calculated with the Weather Research and Forecasting model (WRF) version 4.3.
+* `DAILY-CAPE-MAP-V4.3.npy`. This is a `numpy` array with the shape `(4992, 180, 360)`, containing daily averaged (4992 days) maximum CAPE values along the vertical axis for every cell of a 1°×1° latitude-longitude grid (180×360) for every third day. CAPE are calculated with the Weather Research and Forecasting model (WRF) version 4.3.
 * `vostok_hourly <...>.txt` text files contain two columns, one of which represents the date and time (column `Datetime`) and the other, hourly averaged potential gradient (PG) values on the basis of the measurements at the Russian Antarctic station Vostok (column `Field`, the units are V/m).
 * `sunspot_number_data.csv` contains information about the total sunspot number for every day of the years 1818--2022. You can download this file [here](https://www.sidc.be/silso/datafiles).
 <!-- You can also download a pure Python version of the code without using the Jupyter notebook ([click here to download a zip-archive](https://git260.ipfran.ru/eee/mjo-gec-2022/archive/pure_python.zip)). With `Git 1.7.10` or later you can also clone a specific branch: -->

eof_analysis.ipynb

@@ -21,7 +21,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 5,
+   "execution_count": 1,
    "id": "2c35172b",
    "metadata": {},
    "outputs": [],
@@ -47,20 +47,30 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 6,
+   "execution_count": 2,
+   "id": "0430e90a-ead2-4744-ad9b-d4d67d6d2784",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "path = './data'"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 3,
    "id": "f35c74b8",
    "metadata": {
     "tags": []
    },
    "outputs": [],
    "source": [
-    "map_contrib = np.load('./data/DAILY-IP-MAP-V4.3.npy')\n",
+    "map_contrib = np.load('%s/DAILY-IP-MAP-V4.3.npy'%path)\n",
     "# original data with the shape (number of days, number of latitudes, number of longitudes)\n",
     "# contains IP values (not normalised) depending on (d, lat, lon)\n",
     "# d (axis 0) is the number of a day starting with 0 and ending with 4991\n",
     "# every third day is taken, 0 corresponds to 1 Jan 1980 and 4991 corresponds to 29 Dec 2020\n",
-    "# lat (axis 1) describes the latitude (an integer in [0, 180])\n",
-    "# lon (axis 2) describes the longitude (an integer in [0, 360])\n",
+    "# lat (axis 1) describes the latitude (an integer in [0, 179])\n",
+    "# lon (axis 2) describes the longitude (an integer in [0, 359])\n",
     "\n",
     "map_contrib /= np.mean(np.sum(map_contrib, axis=(1, 2)))\n",
     "map_contrib *= 240e3\n",
@@ -68,7 +78,7 @@
     "\n",
     "ip = np.sum(map_contrib, axis=(1, 2))  # total IP values for different days\n",
     "\n",
-    "rmm = np.genfromtxt('./data/rmm.txt')\n",
+    "rmm = np.genfromtxt('%s/rmm.txt'%path)\n",
     "rmm = rmm[::3, [3, 4]]  # RMM1 and RMM2\n",
     "# the array should look like the IP data (with every third day taken)\n",
     "angle_rmm = np.arctan2(rmm[:, 1], rmm[:, 0])  # phase angles of RMM values\n",
@@ -95,7 +105,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 7,
+   "execution_count": 4,
    "id": "a39d5c0a",
    "metadata": {},
    "outputs": [
@@ -115,7 +125,7 @@
     "    :return: the same array with ENSO variability removed\n",
     "    \"\"\"\n",
     "\n",
-    "    enso34 = np.load('./data/DAILY-ENSO34.npy')  # mean SST values in the Niño 3.4 region\n",
+    "    enso34 = np.load('%s/DAILY-ENSO34.npy'%path)  # mean SST values in the Niño 3.4 region\n",
     "    assert len(enso34) == a.shape[0]\n",
     "    a_mean_before = np.mean(a, axis=0)  # mean values over days before the procedure\n",
     "\n",
@@ -129,6 +139,7 @@
     "\n",
     "    return a\n",
     "\n",
+    "\n",
     "def remove_seasonal(a):\n",
     "    \"\"\"\n",
     "    Remove the seasonal variability.\n",
@@ -152,6 +163,7 @@
     "\n",
     "    return a\n",
     "\n",
+    "\n",
     "latitude = 15  # the boundary latitude for tropics\n",
     "\n",
     "assert map_contrib.shape[1] == 180  # the next formula assumes this\n",
@@ -179,7 +191,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 8,
+   "execution_count": 5,
    "id": "4f117151",
    "metadata": {},
    "outputs": [],
@@ -206,12 +218,13 @@
     "\n",
     "    return eofs, expvars, pcs\n",
     "\n",
+    "\n",
     "eofs, expvars, pcs = eof_analysis(map_contrib)"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 9,
+   "execution_count": 6,
    "id": "47859ce3-8f1d-4cf2-b3aa-d57946eabb44",
    "metadata": {},
    "outputs": [],
@@ -237,7 +250,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 10,
+   "execution_count": 7,
    "id": "79db66cc",
    "metadata": {},
    "outputs": [],
@@ -291,7 +304,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 11,
+   "execution_count": 8,
    "id": "8b3c241c-d9f4-4502-af62-6ad67a8ccdb6",
    "metadata": {
     "tags": []
@@ -448,7 +461,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 12,
+   "execution_count": 9,
    "id": "8f4ae8f9",
    "metadata": {
     "tags": []
@@ -541,7 +554,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 13,
+   "execution_count": 10,
    "id": "36cb8cd3",
    "metadata": {},
    "outputs": [
@@ -653,6 +666,14 @@
     "fig.savefig('figures/longitudinal_structure.eps', bbox_inches='tight')\n",
     "fig.savefig('figures/longitudinal_structure.png', dpi=300, bbox_inches='tight')"
    ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "4af362e7",
+   "metadata": {},
+   "outputs": [],
+   "source": []
   }
  ],
  "metadata": {

figures/eofs_and_pcs.eps

@@ -1,7 +1,7 @@
 %!PS-Adobe-3.0 EPSF-3.0
 %%Title: eofs_and_pcs.eps
 %%Creator: Matplotlib v3.5.1, https://matplotlib.org/
-%%CreationDate: Fri Sep  2 14:19:13 2022
+%%CreationDate: Mon Nov 14 00:52:07 2022
 %%Orientation: portrait
 %%BoundingBox: -19 33 632 759
 %%HiResBoundingBox: -19.310156 33.199984 631.310156 758.800016

figures/equatorial_variation.eps

@@ -1,7 +1,7 @@
 %!PS-Adobe-3.0 EPSF-3.0
 %%Title: equatorial_variation.eps
 %%Creator: Matplotlib v3.5.1, https://matplotlib.org/
-%%CreationDate: Fri Sep  2 14:19:15 2022
+%%CreationDate: Mon Nov 14 00:52:09 2022
 %%Orientation: portrait
 %%BoundingBox: -17 238 630 554
 %%HiResBoundingBox: -17.400000 238.932563 629.400000 553.067437

figures/ip_pg_variation_partial.eps

@@ -1,7 +1,7 @@
 %!PS-Adobe-3.0 EPSF-3.0
 %%Title: ip_pg_variation_partial.eps
 %%Creator: Matplotlib v3.5.1, https://matplotlib.org/
-%%CreationDate: Fri Sep  2 14:21:20 2022
+%%CreationDate: Mon Nov 14 01:38:19 2022
 %%Orientation: portrait
 %%BoundingBox: -11 74 624 718
 %%HiResBoundingBox: -11.200000 74.184563 623.200000 717.815437

figures/longitudinal_structure.eps

@@ -1,7 +1,7 @@
 %!PS-Adobe-3.0 EPSF-3.0
 %%Title: longitudinal_structure.eps
 %%Creator: Matplotlib v3.5.1, https://matplotlib.org/
-%%CreationDate: Fri Sep  2 14:19:16 2022
+%%CreationDate: Mon Nov 14 00:52:11 2022
 %%Orientation: portrait
 %%BoundingBox: -15 26 628 766
 %%HiResBoundingBox: -15.621875 26.357913 627.621875 765.642087

figures/rmm_diagram.eps

@@ -1,7 +1,7 @@
 %!PS-Adobe-3.0 EPSF-3.0
 %%Title: rmm_diagram.eps
 %%Creator: Matplotlib v3.5.1, https://matplotlib.org/
-%%CreationDate: Fri Sep  2 14:21:16 2022
+%%CreationDate: Mon Nov 14 01:38:16 2022
 %%Orientation: portrait
 %%BoundingBox: 141 233 471 559
 %%HiResBoundingBox: 141.380469 233.603125 470.619531 558.396875

map_of_contributions.ipynb

@@ -54,17 +54,27 @@
   {
    "cell_type": "code",
    "execution_count": 2,
+   "id": "02837325-92a2-471e-a2c4-20a0e78b38d8",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "path = './data'"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 3,
    "id": "56b259ed",
    "metadata": {},
    "outputs": [],
    "source": [
-    "map_contrib = np.load('./data/DAILY-IP-MAP-V4.3.npy')\n",
+    "map_contrib = np.load('%s/DAILY-IP-MAP-V4.3.npy'%path)\n",
     "# original data with the shape (number of days, number of latitudes, number of longitudes)\n",
     "# contains IP values (not normalised) depending on (d, lat, lon)\n",
     "# d (axis 0) is the number of a day starting with 0 and ending with 4991\n",
     "# every third day is taken, 0 corresponds to 1 Jan 1980 and 4991 corresponds to 29 Dec 2020\n",
-    "# lat (axis 1) describes the latitude (an integer in [0, 180])\n",
-    "# lon (axis 2) describes the longitude (an integer in [0, 360])\n",
+    "# lat (axis 1) describes the latitude (an integer in [0, 179])\n",
+    "# lon (axis 2) describes the longitude (an integer in [0, 359])\n",
     "\n",
     "map_contrib /= np.mean(np.sum(map_contrib, axis=(1, 2)))\n",
     "map_contrib *= 240e3\n",
@@ -72,7 +82,7 @@
     "\n",
     "avg_map_contrib = np.mean(map_contrib, axis=0)  # long-term means of contributions\n",
     "\n",
-    "rmm = np.genfromtxt('./data/rmm.txt')\n",
+    "rmm = np.genfromtxt('%s/rmm.txt'%path)\n",
     "rmm = rmm[::3, [3, 4]]  # RMM1 and RMM2\n",
     "# the array should look like the IP data (with every third day taken)\n",
     "angle_rmm = np.arctan2(rmm[:, 1], rmm[:, 0])  # phase angles of RMM values\n",
@@ -105,7 +115,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 3,
+   "execution_count": 4,
    "id": "b2a2b2a9",
    "metadata": {},
    "outputs": [
@@ -132,7 +142,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 4,
+   "execution_count": 5,
    "id": "87d48691",
    "metadata": {
     "tags": []
@@ -218,6 +228,14 @@
     "fig.savefig('figures/map_of_contributions.png', dpi=300, bbox_inches='tight')\n",
     "fig.savefig('figures/map_of_contributions.eps', bbox_inches='tight')"
    ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "560b26b1-7abd-4145-9126-f49bfb33b456",
+   "metadata": {},
+   "outputs": [],
+   "source": []
   }
  ],
  "metadata": {