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chryss/wrf39_source_file.ipynb

Created August 17, 2018 16:27
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wrf39_source_file.ipynb
{
"cells": [
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Create source file for volcanic ash driver in WRF-Chem / Volc 3.9.1"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"This is specifically for the Fuego volcano in Guatemala, which started a major eruption in June 2018."
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"----"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"#### Imports go here"
]
},
{
"cell_type": "code",
"execution_count": 200,
"metadata": {},
"outputs": [],
"source": [
"%matplotlib inline\n",
"import csv\n",
"import datetime as dt\n",
"import numpy as np\n",
"from io import StringIO\n",
"from matplotlib import pyplot as plt"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"----"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"#### Basic data goes here"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Fuego is a 3763 m tall volcano of the class M3. Therefore, the distribution of initial ash into bins is:"
]
},
{
"cell_type": "code",
"execution_count": 201,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"'0.13 0.2 0.275 0.225 0.07 0.04 0.03 0.02 0.01 0.0'"
]
},
"execution_count": 201,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"bindist = [0.13, 0.2, 0.275, 0.225, 0.07, 0.04, 0.03, 0.02, 0.01, 0.0]\n",
"bindist_str = \" \".join([str(part) for part in bindist])\n",
"bindist_str"
]
},
{
"cell_type": "code",
"execution_count": 202,
"metadata": {},
"outputs": [],
"source": [
"volcano_vent_height_m = 3763"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"The eruption sequence we have established goes like this:"
]
},
{
"cell_type": "code",
"execution_count": 203,
"metadata": {},
"outputs": [],
"source": [
"eruptionsequence_csv = \"\"\"20180603,1200,6000,100\n",
"20180603,1340,9962,155\n",
"20180603,1615,5791,75\n",
"20180603,1730,15240,150\n",
"20180603,2145,5468,30\n",
"20180604,0640,4663,110\n",
"20180604,1325,4572,20\n",
"20180605,2115,4000,180\n",
"20180606,0015,5182,90\n",
"20180608,1000,5791,840\n",
"20180610,0345,5468,180\n",
"20180610,1300,4572,180\n",
"20180611,0445,5791,660\n",
"20180611,1545,4572,180\n",
"20180612,1345,4420,360\n",
"20180612,1945,4572,180\n",
"\"\"\""
]
},
{
"cell_type": "code",
"execution_count": 204,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"['20180603', '1200', '6000', '100']\n",
"['20180603', '1340', '9962', '155']\n",
"['20180603', '1615', '5791', '75']\n",
"['20180603', '1730', '15240', '150']\n",
"['20180603', '2145', '5468', '30']\n",
"['20180604', '0640', '4663', '110']\n",
"['20180604', '1325', '4572', '20']\n",
"['20180605', '2115', '4000', '180']\n",
"['20180606', '0015', '5182', '90']\n",
"['20180608', '1000', '5791', '840']\n",
"['20180610', '0345', '5468', '180']\n",
"['20180610', '1300', '4572', '180']\n",
"['20180611', '0445', '5791', '660']\n",
"['20180611', '1545', '4572', '180']\n",
"['20180612', '1345', '4420', '360']\n",
"['20180612', '1945', '4572', '180']\n"
]
}
],
"source": [
"f = StringIO(eruptionsequence_csv)\n",
"reader = csv.reader(f, delimiter=',')\n",
"for row in reader:\n",
" print(row)"
]
},
{
"cell_type": "code",
"execution_count": 205,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"array([ -3263., -2763., -2263., -1763., -1263., -763., -263.,\n",
" 237., 737., 1237., 1737., 2237., 2737., 3237.,\n",
" 3737., 4237., 4737., 5237., 5737., 6237., 6737.,\n",
" 7237., 7737., 8237., 8737., 9237., 9737., 10237.,\n",
" 10737., 11237., 11737., 12237., 12737., 13237., 13737.,\n",
" 14237., 14737., 15237., 15737., 16237., 16737., 17237.,\n",
" 17737., 18237., 18737., 19237.])"
]
},
"execution_count": 205,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"heights_m = np.arange(500., 23500., 500.)\n",
"heights_m - volcano_vent_height_m"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Area of grid cell:"
]
},
{
"cell_type": "code",
"execution_count": 206,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"4.444444444444445"
]
},
"execution_count": 206,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"dx = 15000\n",
"area = dx * dx\n",
"1e9 / area"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"----"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"#### Functions for processing steps"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"1st step: Mastin's formula"
]
},
{
"cell_type": "code",
"execution_count": 207,
"metadata": {},
"outputs": [],
"source": [
"def mass_eruption_rate(h_above_vent, correction=False):\n",
" correctionfac = 1.\n",
" if correction:\n",
" correctionfac = 1e9/area\n",
" return 2600. * (h_above_vent * .0005)**4.1494 * correctionfac\n",
"#emiss_ash_mass = eh * 1.e9 / area <-- I'm not sure what this is doing here"
]
},
{
"cell_type": "code",
"execution_count": 208,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"9.94e+06\n"
]
}
],
"source": [
"print(\"{:.2e}\".format(mass_eruption_rate(14600))) "
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"2nd step: get lower, middle, and top height as per height array"
]
},
{
"cell_type": "code",
"execution_count": 209,
"metadata": {},
"outputs": [],
"source": [
"def find_nearest_idx(nparray, a0):\n",
" \"Element in nd array `nparray` closest to the scalar value `a0`\"\n",
" idx = np.abs(nparray - a0).argmin()\n",
" return idx"
]
},
{
"cell_type": "code",
"execution_count": 210,
"metadata": {},
"outputs": [],
"source": [
"def getrefheight_idx(heightarray, plumeelevation, volcanoheight):\n",
" plumeheight = plumeelevation - volcanoheight\n",
" transitionheight = 0.73 * plumeheight + volcanoheight\n",
" minheight_idx = find_nearest_idx(heightarray, volcanoheight)\n",
" midheight_idx = find_nearest_idx(heightarray, transitionheight)\n",
" maxheight_idx = find_nearest_idx(heightarray, plumeelevation)\n",
" return minheight_idx, midheight_idx, maxheight_idx"
]
},
{
"cell_type": "code",
"execution_count": 211,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"22"
]
},
"execution_count": 211,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"find_nearest_idx(heights_m, 15240-volcano_vent_height_m)"
]
},
{
"cell_type": "code",
"execution_count": 212,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"(7, 23, 29)"
]
},
"execution_count": 212,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"getrefheight_idx(heights_m, 15240, volcano_vent_height_m)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"3rd step: get functions for linear and parabolic ash flux"
]
},
{
"cell_type": "code",
"execution_count": 213,
"metadata": {},
"outputs": [],
"source": [
"def linearashflux(h, hB, hM, m):\n",
" return 2 * m * (h - hB) / (hM - hB)**2"
]
},
{
"cell_type": "code",
"execution_count": 214,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"391.34918700062713"
]
},
"execution_count": 214,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"hB = heights_m[7]\n",
"hM = heights_m[23]\n",
"hT = heights_m[29]\n",
"h = heights_m[9] \n",
"m = mass_eruption_rate(14000)\n",
"linearashflux(heights_m[10], hB, hM, m)"
]
},
{
"cell_type": "code",
"execution_count": 215,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"32286307.927551731"
]
},
"execution_count": 215,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"sum([linearashflux(heights_m[ii], hB, hM, m) for ii in range(8, 18)]) * 9 * 500"
]
},
{
"cell_type": "code",
"execution_count": 216,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"8348782.656013378"
]
},
"execution_count": 216,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"mass_eruption_rate(14000)"
]
},
{
"cell_type": "code",
"execution_count": 217,
"metadata": {},
"outputs": [],
"source": [
"def baseparabola(x, xmax, m):\n",
" return (0.75 * m / xmax) * (1 - x**2 / xmax**2)"
]
},
{
"cell_type": "code",
"execution_count": 218,
"metadata": {},
"outputs": [],
"source": [
"def parabolicashflux(h, hM, hT, m):\n",
" xmax = 0.5 * (hT - hM)\n",
" x = h - hM - xmax\n",
" return baseparabola(x, xmax, m)"
]
},
{
"cell_type": "code",
"execution_count": 219,
"metadata": {},
"outputs": [],
"source": [
"def totalashflux(h, hB, hM, hT, mtot):\n",
" m1 = mtot * 0.25\n",
" m2 = mtot * 0.75\n",
" if h < hB:\n",
" return 0.\n",
" elif h <= hM:\n",
" return linearashflux(h, hB, hM, m1)\n",
" elif h <= hT: \n",
" return parabolicashflux(h, hM, hT, m2)\n",
" else:\n",
" return 0."
]
},
{
"cell_type": "code",
"execution_count": 220,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"0.0\n",
"0.0\n",
"0.0\n",
"0.0\n",
"0.0\n",
"0.0\n",
"0.0\n",
"0.0\n",
"32.6124322501\n",
"65.2248645001\n",
"97.8372967502\n",
"130.449729\n",
"163.06216125\n",
"195.6745935\n",
"228.28702575\n",
"260.899458\n",
"293.51189025\n",
"326.124322501\n",
"358.736754751\n",
"391.349187001\n",
"423.961619251\n",
"456.574051501\n",
"489.186483751\n",
"521.798916001\n",
"1739.32972\n",
"2782.927552\n",
"3130.79349601\n",
"2782.927552\n",
"1739.32972\n",
"0.0\n",
"0.0\n",
"0.0\n",
"0.0\n",
"0.0\n",
"0.0\n",
"0.0\n",
"0.0\n",
"0.0\n",
"0.0\n",
"0.0\n",
"0.0\n",
"0.0\n",
"0.0\n",
"0.0\n",
"0.0\n",
"0.0\n"
]
}
],
"source": [
"mtot = mass_eruption_rate(14000)\n",
"for ii in range(len(heights_m)):\n",
" print(totalashflux(heights_m[ii], hB, hM, hT, mtot))"
]
},
{
"cell_type": "code",
"execution_count": 221,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"[<matplotlib.lines.Line2D at 0x11674beb8>]"
]
},
"execution_count": 221,
"metadata": {},
"output_type": "execute_result"
},
{
"data": {
"image/png": 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\n",
"text/plain": [
"<Figure size 432x288 with 1 Axes>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"plt.plot(heights_m, [totalashflux(height, 3500, hM, hT, mtot) for height in heights_m])"
]
},
{
"cell_type": "code",
"execution_count": 222,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"8305299.4130133074"
]
},
"execution_count": 222,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"sum([totalashflux(heights_m[ii], hB, hM, hT, mtot)*500 for ii in range(len(heights_m)) ])"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"4th step: putting it together"
]
},
{
"cell_type": "code",
"execution_count": 223,
"metadata": {},
"outputs": [],
"source": [
"def generateheights(row, printzeros=True):\n",
" datestamp = row[0]\n",
" timestamp = row[1]\n",
" datetimestamp = dt.datetime.strptime(datestamp+timestamp, \"%Y%m%d%H%M\")\n",
" plumeelev = int(row[2])\n",
" plumeheight = plumeelev - volcano_vent_height_m\n",
" \n",
" totalmass = mass_eruption_rate(plumeheight)\n",
" idxB, idxM, idxT = getrefheight_idx(heights_m, plumeelev, volcano_vent_height_m)\n",
" hB = 3500\n",
" hM = heights_m[idxM]\n",
" hT = heights_m[idxT]\n",
" for height in heights_m:\n",
" print(\"{} {} {:7.3f} {:.7E} {}\".format(\n",
" datestamp, timestamp+\"00\", height,\n",
" totalashflux(height, hB, hM, hT, totalmass),\n",
" \"0.0000000\"))\n",
" print()\n",
" if printzeros:\n",
" duration = dt.timedelta(seconds=60 * int(row[3]))\n",
" nextdatetimestamp = datetimestamp + duration\n",
" newdatestamp = nextdatetimestamp.strftime(\"%Y%m%d\")\n",
" newtimestamp = nextdatetimestamp.strftime(\"%H%M%S\")\n",
" for height in heights_m:\n",
" print(\"{} {} {:7.3f} {} {}\".format(\n",
" newdatestamp, newtimestamp, height,\n",
" \"0.0000000\", \"0.0000000\"))\n",
" print()\n",
"\n"
]
},
{
"cell_type": "code",
"execution_count": 224,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
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"20180603 120000 1000.000 0.0000000E+00 0.0000000\n",
"20180603 120000 1500.000 0.0000000E+00 0.0000000\n",
"20180603 120000 2000.000 0.0000000E+00 0.0000000\n",
"20180603 120000 2500.000 0.0000000E+00 0.0000000\n",
"20180603 120000 3000.000 0.0000000E+00 0.0000000\n",
"20180603 120000 3500.000 0.0000000E+00 0.0000000\n",
"20180603 120000 4000.000 2.5862085E-01 0.0000000\n",
"20180603 120000 4500.000 5.1724170E-01 0.0000000\n",
"20180603 120000 5000.000 7.7586255E-01 0.0000000\n",
"20180603 120000 5500.000 1.0344834E+00 0.0000000\n",
"20180603 120000 6000.000 0.0000000E+00 0.0000000\n",
"20180603 120000 6500.000 0.0000000E+00 0.0000000\n",
"20180603 120000 7000.000 0.0000000E+00 0.0000000\n",
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"20180603 120000 8000.000 0.0000000E+00 0.0000000\n",
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"20180603 120000 9000.000 0.0000000E+00 0.0000000\n",
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"20180603 120000 10000.000 0.0000000E+00 0.0000000\n",
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"20180603 120000 20000.000 0.0000000E+00 0.0000000\n",
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"20180603 120000 21000.000 0.0000000E+00 0.0000000\n",
"20180603 120000 21500.000 0.0000000E+00 0.0000000\n",
"20180603 120000 22000.000 0.0000000E+00 0.0000000\n",
"20180603 120000 22500.000 0.0000000E+00 0.0000000\n",
"20180603 120000 23000.000 0.0000000E+00 0.0000000\n",
"\n",
"20180603 134000 500.000 0.0000000 0.0000000\n",
"20180603 134000 1000.000 0.0000000 0.0000000\n",
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"20180603 134000 2000.000 0.0000000 0.0000000\n",
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"20180603 134000 4000.000 0.0000000 0.0000000\n",
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"20180603 134000 22000.000 0.0000000 0.0000000\n",
"20180603 134000 22500.000 0.0000000 0.0000000\n",
"20180603 134000 23000.000 0.0000000 0.0000000\n",
"\n"
]
}
],
"source": [
"examplerow = ['20180603', '1200', '6000', '100']\n",
"generateheights(examplerow, printzeros=True)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"#### Generate output"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"scrolled": true
},
"outputs": [],
"source": [
"print(bindist_str)\n",
"print(\"YYYYMMDD HHMMSS HGTHGTHGT ASH SO2\")\n",
"f = StringIO(eruptionsequence_csv)\n",
"reader = csv.reader(f, delimiter=',')\n",
"for row in reader:\n",
" generateheights(row, printzeros=True)"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": []
}
],
"metadata": {
"kernelspec": {
"display_name": "Python 3",
"language": "python",
"name": "python3"
},
"language_info": {
"codemirror_mode": {
"name": "ipython",
"version": 3
},
"file_extension": ".py",
"mimetype": "text/x-python",
"name": "python",
"nbconvert_exporter": "python",
"pygments_lexer": "ipython3",
"version": "3.6.5"
}
},
"nbformat": 4,
"nbformat_minor": 2
}
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