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Fix small discrepancy

FGeo 1 year ago
parent
8e19a8909a
commit
546d540571
5 changed files with 212 additions and 140 deletions
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  1. 99
      0_prepare_data.ipynb
  2. 76
      4_IP_simulations_temporal_images.ipynb
  3. 177
      5_IP_simulations_spatial_images.ipynb
  4. BIN
      data/INMCM/INMCM_NUMDAYS_MON.npy
  5. BIN
      data/WRF/WRF_NUMDAYS_MON.npy

99
0_prepare_data.ipynb
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@ -31,7 +31,7 @@ @@ -31,7 +31,7 @@
},
{
"cell_type": "code",
"execution_count": null,
"execution_count": 3,
"id": "36b9f49e-32e6-4544-a9d3-f6a8ba49d867",
"metadata": {},
"outputs": [],
@ -46,12 +46,12 @@ @@ -46,12 +46,12 @@
"id": "5e16ee8e-f3b0-4251-9691-19d7dfd4aff7",
"metadata": {},
"source": [
"## Preprocessing WRF T2m data"
"### Preprocessing WRF T2m data"
]
},
{
"cell_type": "code",
"execution_count": 42,
"execution_count": 4,
"id": "78a4350c-59fb-479a-b7cd-e2bf9b996d36",
"metadata": {},
"outputs": [],
@ -63,7 +63,7 @@ @@ -63,7 +63,7 @@
},
{
"cell_type": "code",
"execution_count": 43,
"execution_count": 5,
"id": "53cb9cc3-0e56-4da4-920b-2f071a0846fb",
"metadata": {},
"outputs": [],
@ -247,12 +247,12 @@ @@ -247,12 +247,12 @@
},
{
"cell_type": "code",
"execution_count": 98,
"execution_count": null,
"id": "2b6f987e-ba3c-4371-af7b-c9857a7d33d9",
"metadata": {},
"outputs": [],
"source": [
"wrf_raw_ip_data = np.load(f\"WRF-IP-MAP-500-T2-25.npy\")[:wrf_N_days]\n",
"wrf_raw_ip_data = np.load(f\"{src_path}/WRF-IP-MAP-500-T2-25.npy\")[:wrf_N_days]\n",
"wrf_raw_ip_data = wrf_raw_ip_data[:, :24, :, :]\n",
"wrf_raw_ip_data /= (1/240e3) * wrf_raw_ip_data.sum(axis=(-2,-1)).mean()\n",
"\n",
@ -262,22 +262,89 @@ @@ -262,22 +262,89 @@
"np.save(\n",
" f\"./data/WRF/WRF_HOURLY_TOTAL_IP_500_T2_25.npy\",\n",
" wrf_hourly_total_ip,\n",
")\n",
"\n",
")"
]
},
{
"cell_type": "code",
"execution_count": 45,
"id": "17036c19-95f8-40df-a6c9-f8a23cf426f6",
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"4992\n"
]
}
],
"source": [
"wrf_data_LATxMON = np.zeros((180, 12))\n",
"\n",
"for month_idx in range(12):\n",
" monthly_indicies = [\n",
" i for i, date in enumerate(wrf_dt_indicies) if date.month == month_idx + 1\n",
" i for i, date in enumerate(wrf_dt_indicies) \n",
" if date.month == month_idx + 1\n",
" ]\n",
"\n",
" wrf_data_MONxLAT[:, month_idx] = wrf_daily_latitudal_ip[monthly_indicies].mean(\n",
" axis=0\n",
" )\n",
"np.save(\n",
" f\"./data/WRF/WRF_IP_500_T2_25_LATxMON.npy\",\n",
" wrf_data_MONxLAT\n",
")"
" wrf_data_LATxMON[:, month_idx] = \\\n",
" wrf_daily_latitudal_ip[monthly_indicies].mean(axis=0)\n",
"\n",
"np.save(f\"./data/WRF/WRF_IP_500_T2_25_LATxMON.npy\", wrf_data_LATxMON)"
]
},
{
"cell_type": "markdown",
"id": "e24297fc-cf81-4ea7-9a80-cdcaf277474a",
"metadata": {},
"source": [
"### Saving number of days (used for monthly mean) for each month"
]
},
{
"cell_type": "code",
"execution_count": 48,
"id": "6dfdae16-cb07-4580-97ba-414b5b2c1f2b",
"metadata": {},
"outputs": [],
"source": [
"wrf_days = np.array([len([\n",
" i for i, date in enumerate(wrf_dt_indicies) \n",
" if date.month == m + 1\n",
" ]) for m in range(12)])\n",
"\n",
"np.save(f\"./data/WRF/WRF_NUMDAYS_MON.npy\", wrf_days)"
]
},
{
"cell_type": "code",
"execution_count": 53,
"id": "762dd08f-7d1e-4b68-b272-e7aa9b26d9f1",
"metadata": {},
"outputs": [],
"source": [
"inm_days = np.array([len([\n",
" i for i, date in enumerate(inmcm_dt_indicies) \n",
" if date.month == m + 1\n",
" ]) for m in range(12)])\n",
"\n",
"np.save(f\"./data/INMCM/INMCM_NUMDAYS_MON.npy\", inm_days)"
]
},
{
"cell_type": "code",
"execution_count": 52,
"id": "78afb8e1-3975-4e19-993f-1b0c74c3e9bd",
"metadata": {},
"outputs": [],
"source": [
"# for average over months use\n",
"# `(wrf_data_LATxMON[:, :].sum(axis=0)*days).sum()/days.sum()`\n",
"# unstead\n",
"# `wrf_data_LATxMON[:, :].sum(axis=0).mean()`\n",
"# because\n",
"# ((a1+a2+a3)/3 + (b1+b2)/2)/2 != (a1+a2+a3+b1+b2)/5"
]
}
],

76
4_IP_simulations_temporal_images.ipynb
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@ -115,28 +115,14 @@ @@ -115,28 +115,14 @@
"metadata": {},
"outputs": [],
"source": [
"wrf_LATxMON_t2 = np.load(\"./data/WRF/WRF_T2_LATxMON.npy\")\n",
"\n",
"wrf_LATxMON_ip = {\n",
" key: np.load(f\"./data/WRF/WRF_IP_{parameters}_LATxMON.npy\")\n",
" for key, parameters in zip([500, 800, 1000, 1200],\n",
" [\"500_T2_25\", \"800\", \"1000\", \"1200\"])\n",
"}\n",
"\n",
"inm_LATxMON_ip = {\n",
" key: np.load(f\"./data/INMCM/INMCM_IP_{parameters}_LATxMON.npy\")\n",
" for key, parameters in zip([800, 1000, 1200],\n",
" [\"800\", \"1000\", \"1200\"])\n",
"}\n",
"\n",
"wrf_hourly_total_ip = {\n",
" key: np.load(f\"./data/WRF/WRF_HOURLY_TOTAL_IP_{parameters}.npy\")\n",
" key: np.load(f\"./data/WRF/WRF_HOURLY_TOTAL_IP_{parameters}.npy\")[:wrf_N_days]\n",
" for key, parameters in zip([500, 800, 1000, 1200],\n",
" [\"500_T2_25\", \"800\", \"1000\", \"1200\"])\n",
"}\n",
"\n",
"inm_hourly_total_ip = {\n",
" key: np.load(f\"./data/INMCM/INMCM_HOURLY_TOTAL_IP_{parameters}.npy\")\n",
" key: np.load(f\"./data/INMCM/INMCM_HOURLY_TOTAL_IP_{parameters}.npy\")[:inm_N_days]\n",
" for key, parameters in zip([800, 1000, 1200],\n",
" [\"800\", \"1000\", \"1200\"])\n",
"}"
@ -166,9 +152,9 @@ @@ -166,9 +152,9 @@
"\n",
" # fill data for WRF panels\n",
" ax_idx = j * 2\n",
"\n",
" wrf_inds =[i for i, date in enumerate(wrf_dt_indicies) if date.month == m + 1]\n",
" ip = np.load(f\"./data/WRF/WRF_HOURLY_TOTAL_IP_{cape_thres}.npy\")[wrf_inds]\n",
" wrf_inds = [i for i, date in enumerate(wrf_dt_indicies) \n",
" if date.month == m + 1]\n",
" ip = wrf_hourly_total_ip[cape_thres][wrf_inds]\n",
" data[ax_idx, m] = ip.mean()\n",
" data_counter[ax_idx, m] = len(ip)\n",
" data_sqr[ax_idx, m] = np.sum(\n",
@ -177,10 +163,9 @@ @@ -177,10 +163,9 @@
"\n",
" # fill data for INMCM panels\n",
" ax_idx = j * 2 + 1\n",
"\n",
" inmcm_inds =[i for i, date in enumerate(inm_dt_indicies) if date.month == m + 1]\n",
" ip = np.load(f\"./data/INMCM/INMCM_HOURLY_TOTAL_IP_{cape_thres}.npy\")[inmcm_inds]\n",
"\n",
" inmcm_inds =[i for i, date in enumerate(inm_dt_indicies) \n",
" if date.month == m + 1]\n",
" ip = inm_hourly_total_ip[cape_thres][inmcm_inds]\n",
" data[ax_idx, m] = ip.mean()\n",
" data_counter[ax_idx, m] = len(ip)\n",
" data_sqr[ax_idx, m] = np.sum(\n",
@ -316,7 +301,7 @@ @@ -316,7 +301,7 @@
},
{
"cell_type": "code",
"execution_count": 9,
"execution_count": 10,
"id": "5c2a949e-1c6c-409f-9a62-6012fe5900a0",
"metadata": {},
"outputs": [],
@ -325,37 +310,36 @@ @@ -325,37 +310,36 @@
"data_counter = np.zeros((8, 12), dtype=int)\n",
"data_sqr = np.zeros((8, 12))\n",
"\n",
"ranges = {\n",
"wrf_ranges = {\n",
" 0: range(1981, 1990 + 1),\n",
" 1: range(1991, 2000 + 1),\n",
" 2: range(2001, 2010 + 1),\n",
" 3: range(2011, 2020 + 1),\n",
"}\n",
"\n",
"inm_splitted_indicies = {\n",
" 4: np.split(np.arange(len(inm_dt_indicies)), 2)[0],\n",
" 5: np.split(np.arange(len(inm_dt_indicies)), 2)[1]\n",
"inm_ranges = {\n",
" 4: range(0, 5),\n",
" 5: range(5, 10)\n",
"}\n",
"\n",
"for m in range(12):\n",
" for ax_idx in range(6):\n",
" if ax_idx in [0, 1, 2, 3]:\n",
" wrf_inds = [\n",
" i\n",
" for i, date in enumerate(wrf_dt_indicies)\n",
" if date.month == m + 1 and date.year in ranges[ax_idx]\n",
" ]\n",
" ip = np.load(f\"./data/WRF/WRF_HOURLY_TOTAL_IP_{1000}.npy\")[wrf_inds]\n",
" wrf_inds = [i for i, date in enumerate(wrf_dt_indicies)\n",
" if date.month == m + 1\n",
" and date.year in wrf_ranges[ax_idx]\n",
" ]\n",
" ip = wrf_hourly_total_ip[1000][wrf_inds]\n",
" data[ax_idx, m] = ip.mean()\n",
" data_counter[ax_idx, m] = len(ip)\n",
" data_sqr[ax_idx, m] = np.sum(ip.mean(axis=-1) ** 2) / len(ip)\n",
"\n",
" if ax_idx in [4, 5]:\n",
" inmcm_inds = [\n",
" i for i, date in enumerate(inm_dt_indicies) if date.month == m + 1\n",
" ]\n",
" inmcm_inds = np.intersect1d(inmcm_inds, inm_splitted_indicies[ax_idx])\n",
" ip = np.load(f\"./data/INMCM/INMCM_HOURLY_TOTAL_IP_{1000}.npy\")[inmcm_inds]\n",
" inmcm_inds = [i for i, date in enumerate(inm_dt_indicies) \n",
" if date.month == m + 1\n",
" and i//365 in inm_ranges[ax_idx]\n",
" ]\n",
" ip = inm_hourly_total_ip[1000][inmcm_inds]\n",
" data[ax_idx, m] = ip.mean()\n",
" data_counter[ax_idx, m] = len(ip)\n",
" data_sqr[ax_idx, m] = np.sum(ip.mean(axis=-1) ** 2) / len(ip)\n",
@ -376,7 +360,7 @@ @@ -376,7 +360,7 @@
},
{
"cell_type": "code",
"execution_count": 10,
"execution_count": 11,
"id": "97cc13ff-e014-4626-90ec-1c65a60bdcac",
"metadata": {},
"outputs": [
@ -394,8 +378,7 @@ @@ -394,8 +378,7 @@
"source": [
"fig = plt.figure(figsize=(10, 14), constrained_layout=False)\n",
"ax = [None for _ in range(8)]\n",
"# for n in range(8):\n",
"# ax[n] = fig.add_subplot(4, 2, n + 1)\n",
"\n",
"for n in range(8):\n",
" ax[n] = fig.add_subplot(4, 4, (2*n + 1, 2*n + 2))\n",
"\n",
@ -403,6 +386,7 @@ @@ -403,6 +386,7 @@
"high = [280e3] * 6 + [180] * 2\n",
"step = [20e3] * 6 + [20] * 2\n",
"coeff = [1e3] * 6 + [1] * 2\n",
"\n",
"caption = [\"WRF, 1981–1990, $\\\\varepsilon_0 = 1$ kJ/kg\",\n",
" \"WRF, 1991–2000, $\\\\varepsilon_0 = 1$ kJ/kg\",\n",
" \"WRF, 2001–2010, $\\\\varepsilon_0 = 1$ kJ/kg\",\n",
@ -481,6 +465,14 @@ @@ -481,6 +465,14 @@
"\n",
"fig.savefig(\"./figures_two_parts/ip_pg_partial.eps\", bbox_inches=\"tight\")"
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "7af55182-03fb-4e9f-979b-0df94edbd451",
"metadata": {},
"outputs": [],
"source": []
}
],
"metadata": {

177
5_IP_simulations_spatial_images.ipynb
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@ -45,31 +45,36 @@ @@ -45,31 +45,36 @@
{
"cell_type": "markdown",
"id": "83163834-de47-4f28-8add-768c7b76e1d3",
"metadata": {},
"metadata": {
"jp-MarkdownHeadingCollapsed": true
},
"source": [
"### Helper functions, variables and classes"
]
},
{
"cell_type": "code",
"execution_count": 144,
"execution_count": 411,
"id": "61405aa1-0b4d-4a8a-85a5-3097d37c644e",
"metadata": {},
"outputs": [],
"source": [
"month_name = [\"J\", \"F\", \"M\", \"A\", \"M\", \"J\", \"J\", \"A\", \"S\", \"O\", \"N\", \"D\"]\n",
"month_name_3 = [\"Jan\", \"Feb\", \"Mar\", \"Apr\", \"May\", \"Jun\", \"Jul\", \"Aug\", \"Sep\", \"Oct\", \"Nov\", \"Dec\"]"
"month_name = [\"J\", \"F\", \"M\", \"A\", \"M\", \"J\", \n",
" \"J\", \"A\", \"S\", \"O\", \"N\", \"D\"]\n",
"month_name_3 = [\"Jan\", \"Feb\", \"Mar\", \"Apr\", \"May\", \"Jun\",\n",
" \"Jul\", \"Aug\", \"Sep\", \"Oct\", \"Nov\", \"Dec\"]"
]
},
{
"cell_type": "code",
"execution_count": 3,
"execution_count": 412,
"id": "b929a567-c60d-47e4-a5e3-3ca83b29d07a",
"metadata": {},
"outputs": [],
"source": [
"area_factor = (\n",
" np.cos(np.arange(180) * np.pi / 180) - np.cos(np.arange(1, 181) * np.pi / 180)\n",
" np.cos(np.arange(180) * np.pi / 180) - \n",
" np.cos(np.arange(1, 181) * np.pi / 180)\n",
") / 2"
]
},
@ -97,7 +102,7 @@ @@ -97,7 +102,7 @@
},
{
"cell_type": "code",
"execution_count": 54,
"execution_count": 376,
"id": "4250ebc7-1c87-4ff0-bc58-faaf6732008f",
"metadata": {},
"outputs": [],
@ -113,6 +118,7 @@ @@ -113,6 +118,7 @@
" model (str): model associated with the latitude band\n",
" color (str): color associated with the latitude band.\n",
" \"\"\"\n",
"\n",
" def __init__(self, expression, model=None):\n",
" \"\"\"\n",
" Initializes a LatitudeBand object with the given latitude \n",
@ -174,7 +180,10 @@ @@ -174,7 +180,10 @@
" # store latitude bands model and prettify title\n",
" self.model = model\n",
" self.title = f\"{p1}–{p2}\" # like 9° S–18° S\n",
" self.label = self.title.replace(\" \", \"\")"
" self.label = self.title.replace(\" \", \"\")\n",
"\n",
" def __repr__(self):\n",
" return self.label"
]
},
{
@ -187,7 +196,7 @@ @@ -187,7 +196,7 @@
},
{
"cell_type": "code",
"execution_count": 6,
"execution_count": 413,
"id": "2dd01e73-6586-4e1b-b519-4f3e84ae8940",
"metadata": {},
"outputs": [],
@ -198,7 +207,7 @@ @@ -198,7 +207,7 @@
},
{
"cell_type": "code",
"execution_count": 7,
"execution_count": 414,
"id": "9252215e-9586-444e-bb63-b65dcb945dd6",
"metadata": {},
"outputs": [],
@ -214,7 +223,7 @@ @@ -214,7 +223,7 @@
},
{
"cell_type": "code",
"execution_count": 8,
"execution_count": 416,
"id": "69602993-2005-4fed-b425-69575f9fd2cf",
"metadata": {
"editable": true,
@ -240,15 +249,9 @@ @@ -240,15 +249,9 @@
"}\n",
"\n",
"wrf_hourly_total_ip = {\n",
" key: np.load(f\"./data/WRF/WRF_HOURLY_TOTAL_IP_{parameters}.npy\")\n",
" key: np.load(f\"./data/WRF/WRF_HOURLY_TOTAL_IP_{parameters}.npy\")[:wrf_N_days]\n",
" for key, parameters in zip([500, 800, 1000, 1200],\n",
" [\"500_T2_25\", \"800\", \"1000\", \"1200\"])\n",
"}\n",
"\n",
"inm_hourly_total_ip = {\n",
" key: np.load(f\"./data/INMCM/INMCM_HOURLY_TOTAL_IP_{parameters}.npy\")\n",
" for key, parameters in zip([800, 1000, 1200],\n",
" [\"800\", \"1000\", \"1200\"])\n",
"}"
]
},
@ -262,7 +265,25 @@ @@ -262,7 +265,25 @@
},
{
"cell_type": "code",
"execution_count": 265,
"execution_count": 417,
"id": "728962ea-6e40-4827-bbf3-0da19b8e5c5b",
"metadata": {},
"outputs": [],
"source": [
"# for averaging over months we use\n",
"# `(wrf_data_LATxMON[:, :].sum(axis=0)*days).sum()/days.sum()`\n",
"# unstead\n",
"# `wrf_data_LATxMON[:, :].sum(axis=0).mean()`\n",
"# because\n",
"# ((a1+a2+a3)/3 + (b1+b2)/2)/2 != (a1+a2+a3+b1+b2)/5\n",
"\n",
"wrf_numdays = np.load(\"./data/WRF/WRF_NUMDAYS_MON.npy\")\n",
"inm_numdays = np.load(\"./data/INMCM/INMCM_NUMDAYS_MON.npy\")"
]
},
{
"cell_type": "code",
"execution_count": 418,
"id": "90002efb-7421-46cc-8e64-96c6d465c617",
"metadata": {
"editable": true,
@ -278,19 +299,19 @@ @@ -278,19 +299,19 @@
"text": [
"Model | CAPE | T2,°C | Band | % \n",
"-----------------------------------------\n",
"WRF | 500 | 25 | 9°S–9°N | 57.5%\n",
"INMCM | 800 | - | 9°S–9°N | 51.7%\n",
"WRF | 800 | - | 9°S–9°N | 60.2%\n",
"INMCM | 1000 | - | 9°S–9°N | 53.0%\n",
"WRF | 1000 | - | 9°S–9°N | 62.3%\n",
"INMCM | 1200 | - | 9°S–9°N | 53.7%\n",
"WRF | 1200 | - | 9°S–9°N | 63.4%\n",
"WRF | 500 | 25 | 9°S–9°N | 57.53%\n",
"INMCM | 800 | - | 9°S–9°N | 51.72%\n",
"WRF | 800 | - | 9°S–9°N | 60.19%\n",
"INMCM | 1000 | - | 9°S–9°N | 52.99%\n",
"WRF | 1000 | - | 9°S–9°N | 62.30%\n",
"INMCM | 1200 | - | 9°S–9°N | 53.66%\n",
"WRF | 1200 | - | 9°S–9°N | 63.40%\n",
"-----------------------------------------\n",
"INMCM | 1000 | - | 18°S–18°N | 86.1%\n",
"WRF | 1000 | - | 18°S–18°N | 92.1%\n",
"INMCM | 1000 | - | 18°S–18°N | 86.08%\n",
"WRF | 1000 | - | 18°S–18°N | 92.08%\n",
"-----------------------------------------\n",
"INMCM | 1000 | - | 30°S–30°N | 96.5%\n",
"WRF | 1000 | - | 30°S–30°N | 98.8%\n"
"INMCM | 1000 | - | 30°S–30°N | 96.51%\n",
"WRF | 1000 | - | 30°S–30°N | 98.83%\n"
]
}
],
@ -311,19 +332,21 @@ @@ -311,19 +332,21 @@
" capes = [500, 800, 1000, 1200]\n",
"\n",
" for cape in capes:\n",
" wrf_ip = wrf_LATxMON_ip[cape][wrf_band.slice].sum(axis=0).mean() / 240e3 * 100\n",
" wrf_ip_MON = wrf_LATxMON_ip[cape][wrf_band.slice].sum(axis=0)\n",
" wrf_share = (wrf_ip_MON*wrf_numdays).sum() / wrf_numdays.sum() / 240e3 * 100\n",
"\n",
" if cape != 500:\n",
" inm_ip = inm_LATxMON_ip[cape][inm_band.slice].sum(axis=0).mean() / 240e3 * 100\n",
" print(f\"INMCM | {cape:4d} | - |{inm_band.label:>11} | {inm_ip:>4.1f}%\")\n",
" inm_ip_MIN = inm_LATxMON_ip[cape][inm_band.slice].sum(axis=0)\n",
" inm_share = (inm_ip_MIN*inm_numdays).sum() / inm_numdays.sum() / 240e3 * 100\n",
" print(f\"INMCM | {cape:4d} | - |{inm_band.label:>11} | {inm_share:>4.2f}%\")\n",
"\n",
" p = \"25 \" if cape == 500 else \"- \"\n",
" print(f\"WRF | {cape:4d} | {p} |{wrf_band.label:>11} | {wrf_ip:>4.1f}%\") "
" print(f\"WRF | {cape:4d} | {p} |{wrf_band.label:>11} | {wrf_share:>4.2f}%\") "
]
},
{
"cell_type": "code",
"execution_count": 268,
"execution_count": 419,
"id": "7cc0c763-f1ec-4ff4-b4fe-2730be6daec9",
"metadata": {
"editable": true,
@ -339,29 +362,29 @@ @@ -339,29 +362,29 @@
"text": [
"Model | CAPE | T2,°C | Band | Min | Max | Peak-to-peak, %\n",
"---------------------------------------------------------------\n",
"WRF | 500 | 25 | 90°S–9°S | Aug | Feb | 208.8%\n",
"INMCM | 800 | - | 90°S–9°S | Sep | Feb | 165.7%\n",
"WRF | 800 | - | 90°S–9°S | Aug | Feb | 201.8%\n",
"INMCM | 1000 | - | 90°S–9°S | Aug | Feb | 190.5%\n",
"WRF | 1000 | - | 90°S–9°S | Aug | Feb | 215.5%\n",
"INMCM | 1200 | - | 90°S–9°S | Aug | Feb | 212.7%\n",
"WRF | 1200 | - | 90°S–9°S | Aug | Feb | 238.8%\n",
"WRF | 500 | 25 | 90°S–9°S | Aug | Feb | 208.77%\n",
"INMCM | 800 | - | 90°S–9°S | Sep | Feb | 165.65%\n",
"WRF | 800 | - | 90°S–9°S | Aug | Feb | 201.77%\n",
"INMCM | 1000 | - | 90°S–9°S | Aug | Feb | 190.49%\n",
"WRF | 1000 | - | 90°S–9°S | Aug | Feb | 215.48%\n",
"INMCM | 1200 | - | 90°S–9°S | Aug | Feb | 212.74%\n",
"WRF | 1200 | - | 90°S–9°S | Aug | Feb | 238.80%\n",
"---------------------------------------------------------------\n",
"WRF | 500 | 25 | 9°S–9°N | Aug | Apr | 57.7%\n",
"INMCM | 800 | - | 9°S–9°N | Jul | May | 29.1%\n",
"WRF | 800 | - | 9°S–9°N | Aug | Apr | 52.1%\n",
"INMCM | 1000 | - | 9°S–9°N | Jul | May | 32.9%\n",
"WRF | 1000 | - | 9°S–9°N | Aug | Apr | 63.0%\n",
"INMCM | 1200 | - | 9°S–9°N | Jul | May | 38.4%\n",
"WRF | 1200 | - | 9°S–9°N | Aug | Apr | 72.8%\n",
"WRF | 500 | 25 | 9°S–9°N | Aug | Apr | 57.68%\n",
"INMCM | 800 | - | 9°S–9°N | Jul | May | 29.09%\n",
"WRF | 800 | - | 9°S–9°N | Aug | Apr | 52.07%\n",
"INMCM | 1000 | - | 9°S–9°N | Jul | May | 32.93%\n",
"WRF | 1000 | - | 9°S–9°N | Aug | Apr | 62.95%\n",
"INMCM | 1200 | - | 9°S–9°N | Jul | May | 38.38%\n",
"WRF | 1200 | - | 9°S–9°N | Aug | Apr | 72.82%\n",
"---------------------------------------------------------------\n",
"WRF | 500 | 25 | 9°N–90°N | Feb | Aug | 226.0%\n",
"INMCM | 800 | - | 9°N–90°N | Feb | Aug | 181.5%\n",
"WRF | 800 | - | 9°N–90°N | Feb | Aug | 220.5%\n",
"INMCM | 1000 | - | 9°N–90°N | Feb | Jul | 190.6%\n",
"WRF | 1000 | - | 9°N–90°N | Feb | Aug | 224.9%\n",
"INMCM | 1200 | - | 9°N–90°N | Feb | Jul | 201.6%\n",
"WRF | 1200 | - | 9°N–90°N | Feb | Aug | 226.7%\n"
"WRF | 500 | 25 | 9°N–90°N | Feb | Aug | 226.01%\n",
"INMCM | 800 | - | 9°N–90°N | Feb | Aug | 181.46%\n",
"WRF | 800 | - | 9°N–90°N | Feb | Aug | 220.47%\n",
"INMCM | 1000 | - | 9°N–90°N | Feb | Jul | 190.60%\n",
"WRF | 1000 | - | 9°N–90°N | Feb | Aug | 224.93%\n",
"INMCM | 1200 | - | 9°N–90°N | Feb | Jul | 201.56%\n",
"WRF | 1200 | - | 9°N–90°N | Feb | Aug | 226.70%\n"
]
}
],
@ -379,7 +402,7 @@ @@ -379,7 +402,7 @@
" month_min = month_name_3[np.argmin(seas_var)]\n",
" month_max = month_name_3[np.argmax(seas_var)]\n",
" pk_pk_ampl = (seas_var.max() - seas_var.min())/seas_var.mean() * 100\n",
" print(f\"INMCM | {cape:4} | - |{inm_band.label:>11} | {month_min} | {month_max} | {pk_pk_ampl:>5.1f}%\")\n",
" print(f\"INMCM | {cape:4} | - |{inm_band.label:>11} | {month_min} | {month_max} | {pk_pk_ampl:>6.2f}%\")\n",
" \n",
" seas_var = wrf_LATxMON_ip[cape][wrf_band.slice].sum(axis=0)\n",
" month_min = month_name_3[np.argmin(seas_var)]\n",
@ -387,12 +410,12 @@ @@ -387,12 +410,12 @@
" pk_pk_ampl = (seas_var.max() - seas_var.min())/seas_var.mean() * 100\n",
" \n",
" p = \"25 \" if cape == 500 else \"- \"\n",
" print(f\"WRF | {cape:4} | {p:<5} |{inm_band.label:>11} | {month_min} | {month_max} | {pk_pk_ampl:>5.1f}%\")"
" print(f\"WRF | {cape:4} | {p:<5} |{inm_band.label:>11} | {month_min} | {month_max} | {pk_pk_ampl:>6.2f}%\")"
]
},
{
"cell_type": "code",
"execution_count": 314,
"execution_count": 420,
"id": "12e57219-ea3e-447b-b858-5240e2f8004b",
"metadata": {
"editable": true,
@ -428,16 +451,6 @@ @@ -428,16 +451,6 @@
"WRF | 18°S–90°S | 17133.10 V\n",
"INMCM | 18°S–90°S | 24639.04 V\n"
]
},
{
"data": {
"text/plain": [
"(60, 12)"
]
},
"execution_count": 314,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
@ -458,7 +471,7 @@ @@ -458,7 +471,7 @@
},
{
"cell_type": "code",
"execution_count": 359,
"execution_count": 421,
"id": "df0d50ea-4001-4007-9a4d-bc37b8a1be9f",
"metadata": {
"editable": true,
@ -513,7 +526,7 @@ @@ -513,7 +526,7 @@
},
{
"cell_type": "code",
"execution_count": 273,
"execution_count": 422,
"id": "34e25ddd-4815-4c70-bc3b-982cad6b65b4",
"metadata": {},
"outputs": [],
@ -537,7 +550,7 @@ @@ -537,7 +550,7 @@
},
{
"cell_type": "code",
"execution_count": 274,
"execution_count": 423,
"id": "fc39428e-ea03-494e-97d7-c38ecd8cb044",
"metadata": {},
"outputs": [
@ -647,7 +660,7 @@ @@ -647,7 +660,7 @@
},
{
"cell_type": "code",
"execution_count": 11,
"execution_count": 424,
"id": "57a55242-5fc1-4339-a386-297c20320f3e",
"metadata": {},
"outputs": [],
@ -661,7 +674,7 @@ @@ -661,7 +674,7 @@
},
{
"cell_type": "code",
"execution_count": 12,
"execution_count": 425,
"id": "92d565d9-fd1f-4bff-91e7-1a5185de08a7",
"metadata": {},
"outputs": [],
@ -676,7 +689,7 @@ @@ -676,7 +689,7 @@
},
{
"cell_type": "code",
"execution_count": 13,
"execution_count": 426,
"id": "421b6814-7f62-4325-8f40-08d9dffc720b",
"metadata": {},
"outputs": [],
@ -691,7 +704,7 @@ @@ -691,7 +704,7 @@
},
{
"cell_type": "code",
"execution_count": 14,
"execution_count": 427,
"id": "04ac548c-47d9-4643-b26c-58b3359bd9a5",
"metadata": {},
"outputs": [],
@ -713,7 +726,7 @@ @@ -713,7 +726,7 @@
},
{
"cell_type": "code",
"execution_count": 15,
"execution_count": 428,
"id": "8fc9d1a5-e75b-4dd7-971d-67d900c89207",
"metadata": {},
"outputs": [
@ -1038,7 +1051,7 @@ @@ -1038,7 +1051,7 @@
},
{
"cell_type": "code",
"execution_count": 16,
"execution_count": 429,
"id": "cd3ddeed-7054-46bc-9764-7324b89ad4fe",
"metadata": {},
"outputs": [],
@ -1049,7 +1062,7 @@ @@ -1049,7 +1062,7 @@
},
{
"cell_type": "code",
"execution_count": 17,
"execution_count": 430,
"id": "15f1693c-53bd-448b-9388-6cf4fd53acf6",
"metadata": {},
"outputs": [],
@ -1061,7 +1074,7 @@ @@ -1061,7 +1074,7 @@
},
{
"cell_type": "code",
"execution_count": 18,
"execution_count": 431,
"id": "72c110be-1294-4df7-a7d7-e3197b38a5b9",
"metadata": {},
"outputs": [],
@ -1074,7 +1087,7 @@ @@ -1074,7 +1087,7 @@
},
{
"cell_type": "code",
"execution_count": 19,
"execution_count": 432,
"id": "0dc8e9ad-9444-4880-85b0-405a4f26e6bf",
"metadata": {},
"outputs": [],
@ -1093,7 +1106,7 @@ @@ -1093,7 +1106,7 @@
},
{
"cell_type": "code",
"execution_count": 20,
"execution_count": 433,
"id": "9a85ba47-cb25-4a1f-a036-f1d09f63438b",
"metadata": {},
"outputs": [

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data/INMCM/INMCM_NUMDAYS_MON.npy
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