Overshooting Top Area Quantification
Contents
Overshooting Top Area Quantification¶
from skimage.measure import label, regionprops
import numpy as np
import glob
import sys
import xarray as xr
import numpy as np
import cartopy
import scipy
import matplotlib.ticker as mticker
import cartopy.crs as ccrs
import cartopy.feature as cfeature
from cartopy.mpl.gridliner import LONGITUDE_FORMATTER, LATITUDE_FORMATTER
from matplotlib.colors import ListedColormap
from scipy.ndimage import gaussian_filter
import pandas as pd
import glob
from IPython.core.debugger import set_trace
from shapely.geometry import Polygon
import geopandas as gpd
import geopy.distance
import shapely
from cartopy.geodesic import Geodesic
import matplotlib.pyplot as plt
import geopandas as gpd
import pandas as pd
from math import sin, cos, sqrt, atan2, radians
import warnings
warnings.filterwarnings('ignore')
crs = {'init': 'epsg:4326'}
gdf = gpd.GeoDataFrame(crs=crs)
import cProfile
def process_ot_data(file, thresh=.7):
# Set the coordinate reference system
crs = {'init': 'epsg:4326'}
ot_data = gpd.GeoDataFrame()
ds_og = xr.open_dataset(file).squeeze()
time = ds_og.time.values
inx = np.where((ds_og['longitude'] >= llcrnr[1]) & (ds_og['longitude'] <= urcrnr[1]))[0]
iny = np.where((ds_og['latitude'] >= llcrnr[0]) & (ds_og['latitude'] <= urcrnr[0]))[0]
ds = ds_og.sel(npixels=inx, nlines=iny)
lat = ds['latitude'].values
lon = ds['longitude'].values
#time = bedka['time'].isel(time=t).values
lon_2d, lat_2d = np.meshgrid(lon,lat)
lon_2d_corrected = lon_2d + ds['parallax_correction_longitude'].fillna(0).values
lat_2d_corrected = lat_2d + ds['parallax_correction_latitude'].fillna(0).values
#inx = np.where((ds['longitude'] >= llcrnr[1]) & (ds['longitude'] <= urcrnr[1]))[0]
#iny = np.where((ds['latitude'] >= llcrnr[0]) & (ds['latitude'] <= urcrnr[0]))[0]
time_label = pd.to_datetime(ds.time.values).strftime('%Y-%m-%d %H:%M')
ds_whole = ds
ds = ds_og.sel(npixels=inx, nlines=iny)
db = ds.ot_probability
dc = ds.ot_id_number
da = ds.ir_brightness_temperature
dc = ds.ot_id_number
de = ds.ot_anvilmean_brightness_temperature_difference
cloud_hgt = ds.cloud_top_height
trop_hgt = ds.tropopause_height
trop_tmp = ds.tropopause_temperature
trop_pres = ds.tropopause_pressure
smoothed_tb = gaussian_filter(da.values, 1)
plot_num=0
ot_ids = ds_og.where(ds_og.ot_probability > thresh).ot_id_number.values
good_ots = np.unique(ot_ids[np.where(ot_ids > 0)])
for i in good_ots[0:5]:
minpt = np.where(dc == i)
print(minpt)
dbs = db.values[minpt]
ddlat = lat_2d[minpt]
ddlon = lon_2d[minpt]
ddlat_corr = lat_2d_corrected[minpt]
ddlon_corr = lon_2d_corrected[minpt]
das = da.values[minpt]
dde = de.values[minpt]
try:
mintb = np.nanmin(das)
except:
continue
dbs = db.values[minpt]
minloc = np.argmin(das)
prob = dbs[minloc]
ddc = dc.values[minpt]
otid = ddc[minloc]
lat = ddlat[minloc]
lon = ddlon[minloc]
lon_corrected = ddlon_corr[minloc]
lat_corrected = ddlat_corr[minloc]
thgt = trop_hgt.values[minpt][minloc]
ttmp = trop_tmp.values[minpt][minloc]
tprs = trop_pres.values[minpt][minloc]
clhgt = cloud_hgt.values[minpt][minloc]
if prob > thresh:
try:
#print(minpt)
y0, x0 = np.where(ds.ir_brightness_temperature.values == ds.ir_brightness_temperature.values[minpt].min())
x0 = int(x0[0])
y0 = int(y0[0])
lat, lon = ds.sel(nlines=y0, npixels=x0).latitude.values, ds.sel(nlines=y0, npixels=x0).longitude.values
except Exception as e:
print(e)
num = 20
hyp = 20 * np.cos(45, dtype='d')
x1_all = [x0 , x0 + hyp, x0 + num, x0 + hyp, x0 , x0 - hyp, x0 - num, x0 - hyp]
y1_all = [y0 + num, y0 + hyp, y0 , y0 - hyp, y0 - num, y0 - hyp, y0 , y0 + hyp]
pts=np.zeros([8,41])
lats = np.zeros([8,41])
lons = np.zeros([8,41])
x_rads = np.zeros([8,41])
y_rads = np.zeros([8,41])
j=0
for x1, y1 in zip(x1_all,y1_all):
x, y = np.linspace(x0, x1, (num * 2) + 1), np.linspace(y0, y1, (num * 2) + 1)
pts[j,:] = scipy.ndimage.map_coordinates(smoothed_tb, np.vstack((y,x)))
lats[j,:] = scipy.ndimage.map_coordinates(lat_2d, np.vstack((y,x)))
lons[j,:] = scipy.ndimage.map_coordinates(lon_2d, np.vstack((y,x)))
j = j + 1
dels = np.gradient(np.gradient(pts.T,axis=0),axis=0)
tb_rads = pts
# Find inflection points
dels_zero = np.argmax(dels <= 0, axis=0)
# Calculate mean and standard deviation of radials
mean = np.mean(dels_zero)
std = np.std(dels_zero)
# Replace radials with mean radial if greater than 1 standard deviation from mean
dels_zero = np.where(dels_zero > (mean + std), int(np.round(mean)), dels_zero)
ind = np.arange(0,8)
lat_mins = lats[ind, dels_zero]
lon_mins = lons[ind, dels_zero]
# Calcualte the distance for each radial
dists = []
for i in range(len(dels_zero)):
dists.append(geopy.distance.distance((lat, lon), (lat_mins[i], lon_mins[i])).kilometers)
dists = np.array(dists)
# setup a polygon
crs = {'init': 'epsg:4326'}
polygon_geom = Polygon(zip(lon_mins, lat_mins))
gdf = gpd.GeoDataFrame(crs=crs)
gdf = gdf.append({'otid':i,
'geometry':polygon_geom}, ignore_index=True)
gdf['otarea_from_polygon'] = gdf['geometry'].to_crs({'init': 'epsg:32719'})\
.map(lambda p: p.area / 10**6)
otarea_from_polygon = gdf.otarea_from_polygon.values[0]
print(list(tuple(dels[:, 0])))
try:
ot_data = ot_data.append({'time':time,
'lat':lat,
'lon':lon,
'lat_corr':lat_corrected,
'lon_corr':lon_corrected,
'otid':otid,
'mintb':mintb,
'tropopause_height':thgt,
'tropopause_temperature':ttmp,
'tropopause_pressure':tprs,
'cloudtop_height':clhgt,
'prob':prob,
'area_polygon':otarea_from_polygon,
's_radial':dists[0],
's_radial_del2':dels[:, 0],
's_tb':tb_rads[0],
'se_radial':dists[1],
'se_radial_del2':dels[:, 1],
'se_tb':tb_rads[1],
'e_radial':dists[2],
'e_radial_del2':dels[:, 2],
'e_tb':tb_rads[2],
'ne_radial':dists[3],
'ne_radial_del2':dels[:, 3],
'ne_tb':tb_rads[3],
'n_radial':dists[4],
'n_radial_del2':dels[:, 4],
'n_tb':tb_rads[4],
'nw_radial':dists[5],
'nw_radial_del2':dels[:, 5],
'nw_tb':tb_rads[5],
'w_radial':dists[6],
'w_radial_del2':dels[:, 6],
'w_tb':tb_rads[6],
'sw_radial':dists[7],
'sw_radial_del2':dels[:, 7],
'sw_tb':tb_rads[7],
'geometry':polygon_geom}, ignore_index=True)
except Exception as e:
print(e)
try:
ot_data = ot_data[ot_data.area_polygon < 1000]
filename = pd.to_datetime(time).strftime('ot_output/%Y%m%d/%Y%m%d_%H%M.csv')
ot_data.to_csv(filename)
except:
None
ds_og.close()
ds.close()
return ot_data
from dask.distributed import Client
from dask.distributed import progress
client = Client(n_workers=10)
llcrnr=[-33.206342, -65.906586]
urcrnr=[-30.255825, -62.630553]
import glob
datestr='20181110'
ihour='20'
files = sorted(glob.glob(f'/data/relamp/a/mgrover4/ot_data/{datestr}/*{ihour}????.cdf'))
futures = client.map(process_ot_data, files)
progress(futures)
files[7]
'/data/relamp/a/mgrover4/ot_data/20181110/cor1goecnvX1.a1.20181110.200755.cdf'
df_ex.plot()
<AxesSubplot:>
df
| area_polygon | cloudtop_height | e_radial | e_radial_del2 | e_tb | geometry | lat | lat_corr | lon | lon_corr | ... | sw_radial | sw_radial_del2 | sw_tb | time | tropopause_height | tropopause_pressure | tropopause_temperature | w_radial | w_radial_del2 | w_tb | |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 0 | [189.27241165070697] | [17.449001] | [4.212812185367112] | [[0.290618896484375, 0.4165229797363281, 0.477... | [[198.69674682617188, 199.47158813476562, 200.... | POLYGON ((-64.16964 -32.17857, -64.13212 -32.1... | [-32.116074] | [-31.994074] | [-64.16964] | [-64.21364] | ... | [11.6087078557941] | [[0.1139068603515625, 0.16944503784179688, 0.2... | [[198.69674682617188, 198.6677703857422, 198.8... | [2018-11-10T20:01:55.000000512] | [16.613] | [97.78] | [202.53] | [10.111900925920189] | [[0.28107452392578125, 0.4053764343261719, 0.5... | [[198.69674682617188, 198.53431701660156, 198.... |
| 1 | [12.550535250836726] | [16.94] | [1.1013114809103366] | [[0.16606903076171875, -49.933895111083984, -4... | [[201.2448272705078, 200.7417449951172, 200.57... | POLYGON ((-62.65179 -32.87500, -62.63950 -32.8... | [-32.848213] | [-32.725212] | [-62.65179] | [-62.70079] | ... | [2.062344902400382] | [[0.03946685791015625, 0.048191070556640625, 0... | [[201.2448272705078, 201.73597717285156, 202.3... | [2018-11-10T20:01:55.000000512] | [16.303001] | [103.07] | [203.36] | [2.4875926588057156] | [[0.09966278076171875, 0.11639022827148438, 0.... | [[201.2448272705078, 202.0481719970703, 203.05... |
2 rows × 38 columns
df_list = []
for file in files[1:26]:
ds = xr.open_dataset(file).squeeze()
df = process_ot_data(file)
ir_vals = ds.ir_brightness_temperature.values
plt.figure(figsize=(12,8))
lon = ds.longitude.values
lat = ds.latitude.values
ax = plt.subplot(111, projection=ccrs.PlateCarree())
ax.set_extent((-64.5, -63.5, -32.7, -31.7))
cf = plt.contourf(lon, lat, ir_vals, np.arange(190, 270, 2), cmap='gist_rainbow_r')
plt.colorbar(cf, label='Brightness Temperature (K)')
df_ex = df[df.mintb == np.nanmin(df.mintb.values)]
df_ex.plot(ax=ax, facecolor="none", edgecolor='black', lw=3,)
ax.plot(0, 0, color='black', lw=3, label=f'OT Area {np.round(df_ex.area_polygon.values[0], 2)} km2 \n Prob: {np.round(df_ex.prob.values[0], 2)}')
plt.legend(fontsize=16)
gl = ax.gridlines(crs=ccrs.PlateCarree(), draw_labels=True,
linewidth=2, color='gray', alpha=0.5, linestyle='--')
gl.xlabels_top = False
gl.ylabels_right = False
plt.title(pd.to_datetime(df.time.values[0]).strftime('%d %b %y %H:%M:%S'), fontsize=24)
plt.savefig(pd.to_datetime(df.time.values[0]).strftime('compare_plots/Nov10/%b_%d_%y_%H%M.%S.png'), dpi=300)
plt.show()
plt.close()
df_list.append(df)
(array([], dtype=int64), array([], dtype=int64))
(array([100, 100, 100, 101, 101, 101, 101, 101, 102, 102, 102, 102, 102,
102, 103, 103, 103, 103, 103, 103, 104, 104, 104, 104, 104, 104,
105, 105, 105, 105, 105, 105, 106, 106, 106, 106, 106, 107, 107]), array([96, 97, 98, 95, 96, 97, 98, 99, 94, 95, 96, 97, 98, 99, 94, 95, 96,
97, 98, 99, 94, 95, 96, 97, 98, 99, 94, 95, 96, 97, 98, 99, 94, 95,
96, 97, 98, 96, 97]))
[0.12656402587890625, 0.21025848388671875, 0.3373603820800781, 0.416046142578125, 0.4304847717285156, 0.3276824951171875, 0.13058090209960938, -0.10853195190429688, -0.29584503173828125, -0.3458709716796875, -0.24800872802734375, -0.01619720458984375, 0.3123283386230469, 0.6859512329101562, 1.0188484191894531, 1.2207565307617188, 1.2178878784179688, 0.9721183776855469, 0.5715103149414062, 0.13589096069335938, -0.21406936645507812, -0.3926353454589844, -0.4545631408691406, -0.4859619140625, -0.5580825805664062, -0.7099609375, -0.8664016723632812, -0.9381561279296875, -0.8941726684570312, -0.73321533203125, -0.5150375366210938, -0.30658721923828125, -0.14253997802734375, -0.04154205322265625, 0.009613037109375, 0.0297088623046875, 0.0279083251953125, 0.01123809814453125, -0.01207733154296875, -0.02079010009765625, -0.017608642578125]
(array([], dtype=int64), array([], dtype=int64))
(array([142, 143, 143, 144, 144, 144, 145, 145, 145, 146, 146, 146]), array([183, 182, 183, 181, 182, 183, 181, 182, 183, 181, 182, 183]))
[0.04073333740234375, 0.03949737548828125, 0.001239776611328125, -0.06269454956054688, -0.10314178466796875, -0.11721420288085938, -0.11004638671875, -0.08713912963867188, -0.0474395751953125, 0.008617401123046875, 0.06800460815429688, 0.11644744873046875, 0.14737701416015625, 0.15802764892578125, 0.15305328369140625, 0.13581085205078125, 0.09705734252929688, 0.03124237060546875, -0.03972625732421875, -0.08991622924804688, -0.10482025146484375, -0.0802154541015625, -0.041576385498046875, -0.016307830810546875, -0.009830474853515625, -0.020107269287109375, -0.0372772216796875, -0.0509033203125, -0.05612945556640625, -0.0504302978515625, -0.034992218017578125, -0.013088226318359375, 0.007457733154296875, 0.019428253173828125, 0.022548675537109375, 0.019561767578125, 0.018367767333984375, 0.019916534423828125, -0.007862091064453125, -0.039642333984375, -0.04347991943359375]
(array([], dtype=int64), array([], dtype=int64))
area_polygon cloudtop_height e_radial \
0 189.272412 [17.449001] [4.212812185367112]
1 12.550535 [16.94] [1.1013114809103366]
e_radial_del2 \
0 [[0.290618896484375, 0.4165229797363281, 0.477...
1 [[0.16606903076171875, -49.933895111083984, -4...
e_tb \
0 [[198.69674682617188, 199.47158813476562, 200....
1 [[201.2448272705078, 200.7417449951172, 200.57...
geometry lat \
0 POLYGON ((-64.16964 -32.17857, -64.13212 -32.1... [-32.116074]
1 POLYGON ((-62.65179 -32.87500, -62.63950 -32.8... [-32.848213]
lat_corr lon lon_corr ... sw_radial \
0 [-31.994074] [-64.16964] [-64.21364] ... [11.6087078557941]
1 [-32.725212] [-62.65179] [-62.70079] ... [2.062344902400382]
sw_radial_del2 \
0 [[0.1139068603515625, 0.16944503784179688, 0.2...
1 [[0.03946685791015625, 0.048191070556640625, 0...
sw_tb \
0 [[198.69674682617188, 198.6677703857422, 198.8...
1 [[201.2448272705078, 201.73597717285156, 202.3...
time tropopause_height tropopause_pressure \
0 2018-11-10 20:01:55.000000512 [16.613] [97.78]
1 2018-11-10 20:01:55.000000512 [16.303001] [103.07]
tropopause_temperature w_radial \
0 [202.53] [10.111900925920189]
1 [203.36] [2.4875926588057156]
w_radial_del2 \
0 [[0.28107452392578125, 0.4053764343261719, 0.5...
1 [[0.09966278076171875, 0.11639022827148438, 0....
w_tb
0 [[198.69674682617188, 198.53431701660156, 198....
1 [[201.2448272705078, 202.0481719970703, 203.05...
[2 rows x 38 columns]
(array([], dtype=int64), array([], dtype=int64))
(array([ 99, 99, 100, 100, 100, 100, 101, 101, 101, 101, 101, 102, 102,
102, 102, 102, 103, 103, 103, 103, 103, 104, 104, 104, 104, 104,
105, 105, 105, 105, 105, 106, 106, 106, 106, 106, 107, 107, 107]), array([97, 98, 96, 97, 98, 99, 95, 96, 97, 98, 99, 95, 96, 97, 98, 99, 95,
96, 97, 98, 99, 95, 96, 97, 98, 99, 95, 96, 97, 98, 99, 95, 96, 97,
98, 99, 96, 97, 98]))
[0.06288909912109375, 0.094879150390625, 0.14159774780273438, 0.1853485107421875, 0.2329864501953125, 0.25424957275390625, 0.24602890014648438, 0.21316909790039062, 0.16518783569335938, 0.11025619506835938, 0.046466827392578125, -0.02402496337890625, -0.07268905639648438, -0.06418228149414062, 0.037685394287109375, 0.2624053955078125, 0.6118431091308594, 1.0559425354003906, 1.4646034240722656, 1.6844215393066406, 1.5687828063964844, 1.0328445434570312, 0.23203277587890625, -0.6170082092285156, -1.2929763793945312, -1.621978759765625, -1.6246109008789062, -1.3846817016601562, -1.044219970703125, -0.735626220703125, -0.494537353515625, -0.32617950439453125, -0.2108612060546875, -0.12595367431640625, -0.06191253662109375, -0.01369476318359375, 0.01865386962890625, 0.03394317626953125, 0.0314178466796875, 0.0161895751953125, 0.0067901611328125]
(array([142, 143, 143, 144, 144, 145, 145]), array([183, 182, 183, 182, 183, 182, 183]))
[0.11942291259765625, 0.16849899291992188, 0.1837005615234375, 0.10235595703125, 0.0150909423828125, -0.05118560791015625, -0.0895538330078125, -0.100799560546875, -0.09651947021484375, -0.08716201782226562, -0.06781387329101562, -0.031169891357421875, 0.02423095703125, 0.09526443481445312, 0.166412353515625, 0.21995162963867188, 0.24168014526367188, 0.22012710571289062, 0.15125656127929688, 0.0405120849609375, -0.07615280151367188, -0.15949249267578125, -0.18768692016601562, -0.15228271484375, -0.08076095581054688, -0.00647735595703125, 0.049533843994140625, 0.07269287109375, 0.06192779541015625, 0.02228546142578125, -0.030139923095703125, -0.07707595825195312, -0.10264205932617188, -0.09698867797851562, -0.07193756103515625, -0.042999267578125, -0.018798828125, -0.004150390625, 0.00445556640625, 0.00702667236328125, 0.0058746337890625]
(array([146, 146, 147, 147]), array([182, 183, 182, 183]))
[-0.02791595458984375, -0.052791595458984375, -0.0895538330078125, -0.100799560546875, -0.09651947021484375, -0.08716201782226562, -0.06781387329101562, -0.031169891357421875, 0.02423095703125, 0.09526443481445312, 0.166412353515625, 0.21995162963867188, 0.24168014526367188, 0.22012710571289062, 0.15125656127929688, 0.0405120849609375, -0.07615280151367188, -0.15949249267578125, -0.18768692016601562, -0.15228271484375, -0.08076095581054688, -0.00647735595703125, 0.049533843994140625, 0.07269287109375, 0.06192779541015625, 0.02228546142578125, -0.030139923095703125, -0.07707595825195312, -0.10264205932617188, -0.09698867797851562, -0.07193756103515625, -0.042999267578125, -0.018798828125, -0.004150390625, 0.00445556640625, 0.0103759765625, 0.008087158203125, -52.23549270629883, -52.24013900756836, 52.23005294799805, 104.4657974243164]
(array([], dtype=int64), array([], dtype=int64))
area_polygon cloudtop_height e_radial \
0 192.603001 [17.383001] [5.056214731984114]
e_radial_del2 \
0 [[0.3144989013671875, 0.47414398193359375, 0.6...
e_tb \
0 [[198.81776428222656, 199.01193237304688, 199....
geometry lat \
0 POLYGON ((-64.16964 -32.19643, -64.12743 -32.1... [-32.098213]
lat_corr lon lon_corr ... sw_radial \
0 [-31.976213] [-64.16964] [-64.21364] ... [8.19620124311885]
sw_radial_del2 \
0 [[0.0901947021484375, 0.13414382934570312, 0.1...
sw_tb \
0 [[198.81776428222656, 198.9854736328125, 199.3...
time tropopause_height tropopause_pressure \
0 2018-11-10 20:02:55.000000512 [16.621] [97.67]
tropopause_temperature w_radial \
0 [202.48] [11.79903409512454]
w_radial_del2 \
0 [[0.270599365234375, 0.39258575439453125, 0.48...
w_tb
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e_radial_del2 \
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e_tb \
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geometry lat \
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lat_corr lon lon_corr ... sw_radial \
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sw_radial_del2 \
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sw_tb \
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time tropopause_height tropopause_pressure \
0 2018-11-10 20:03:55.000000512 [16.621] [97.67]
tropopause_temperature w_radial \
0 [202.48] [13.484198645693482]
w_radial_del2 \
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w_tb
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area_polygon cloudtop_height e_radial \
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e_radial_del2 \
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e_tb \
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geometry lat \
0 POLYGON ((-64.13393 -32.15179, -64.11048 -32.1... [-32.098213]
lat_corr lon lon_corr ... sw_radial \
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sw_radial_del2 \
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sw_tb \
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time tropopause_height tropopause_pressure \
0 2018-11-10 20:04:55.000000512 [16.614] [97.78]
tropopause_temperature w_radial \
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w_radial_del2 \
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area_polygon cloudtop_height e_radial \
0 102.383365 [17.480001] [4.214451861565662]
e_radial_del2 \
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e_tb \
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geometry lat \
0 POLYGON ((-64.13393 -32.16964, -64.09171 -32.1... [-32.080357]
lat_corr lon lon_corr ... sw_radial \
0 [-31.957357] [-64.13393] [-64.177925] ... [4.098615676689402]
sw_radial_del2 \
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sw_tb \
0 [[198.8264923095703, 198.63800048828125, 198.7...
time tropopause_height tropopause_pressure \
0 2018-11-10 20:05:55.000000512 [16.62] [97.68]
tropopause_temperature w_radial \
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area_polygon cloudtop_height e_radial \
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e_radial_del2 \
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e_tb \
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geometry lat \
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lat_corr lon lon_corr ... sw_radial \
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sw_radial_del2 \
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sw_tb \
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time tropopause_height tropopause_pressure \
0 2018-11-10 20:06:55.000000512 [16.619001] [97.689995]
tropopause_temperature w_radial \
0 [202.43] [7.5854022343092105]
w_radial_del2 \
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w_tb
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area_polygon cloudtop_height e_radial \
0 76.837805 [17.553001] [4.213632315878133]
1 110.884704 [17.088001] [9.266957650739116]
e_radial_del2 \
0 [[0.31882476806640625, 0.45781707763671875, 0....
1 [[0.21793365478515625, 0.33174896240234375, 0....
e_tb \
0 [[198.30801391601562, 198.9923858642578, 200.3...
1 [[202.23031616210938, 200.74620056152344, 199....
geometry lat \
0 POLYGON ((-64.11607 -32.16072, -64.08793 -32.1... [-32.098213]
1 POLYGON ((-64.18750 -32.21429, -64.14059 -32.1... [-32.13393]
lat_corr lon lon_corr ... sw_radial \
0 [-31.975214] [-64.11607] [-64.16107] ... [2.048975026291856]
1 [-32.01393] [-64.1875] [-64.2305] ... [6.146161992705604]
sw_radial_del2 \
0 [[0.06694793701171875, 0.08073043823242188, 0....
1 [[0.1094207763671875, 0.14652633666992188, 0.1...
sw_tb \
0 [[198.30801391601562, 198.2484588623047, 198.3...
1 [[202.23031616210938, 203.38189697265625, 204....
time tropopause_height tropopause_pressure \
0 2018-11-10 20:07:55.000000512 [16.619001] [97.689995]
1 2018-11-10 20:07:55.000000512 [16.604] [97.9]
tropopause_temperature w_radial \
0 [202.43] [6.74209969265378]
1 [202.51999] [7.58244900587702]
w_radial_del2 \
0 [[0.2472686767578125, 0.3285636901855469, 0.33...
1 [[0.082550048828125, 0.07903289794921875, 0.03...
w_tb
0 [[198.30801391601562, 198.26148986816406, 198....
1 [[202.23031616210938, 204.07794189453125, 206....
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area_polygon cloudtop_height e_radial \
0 108.972626 [17.515001] [2.5277473075272416]
e_radial_del2 \
0 [[0.2411346435546875, 0.30144500732421875, 0.2...
e_tb \
0 [[198.8497314453125, 200.19468688964844, 202.0...
geometry lat \
0 POLYGON ((-64.09821 -32.16072, -64.07008 -32.1... [-32.098213]
lat_corr lon lon_corr ... sw_radial \
0 [-31.975214] [-64.09821] [-64.14321] ... [7.512946010307316]
sw_radial_del2 \
0 [[0.105377197265625, 0.142608642578125, 0.1491...
sw_tb \
0 [[198.8497314453125, 198.35926818847656, 198.0...
time tropopause_height tropopause_pressure \
0 2018-11-10 20:08:55.000000512 [16.623001] [97.63]
tropopause_temperature w_radial \
0 [202.40999] [9.271287121261635]
w_radial_del2 \
0 [[0.3048553466796875, 0.4269447326660156, 0.47...
w_tb
0 [[198.8497314453125, 198.1621856689453, 198.08...
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area_polygon cloudtop_height e_radial \
0 160.428735 [17.594002] [5.896500557904253]
e_radial_del2 \
0 [[0.11626434326171875, 0.18404388427734375, 0....
e_tb \
0 [[197.4491729736328, 197.47320556640625, 197.7...
geometry lat \
0 POLYGON ((-64.13393 -32.17857, -64.09641 -32.1... [-32.13393]
lat_corr lon lon_corr ... sw_radial \
0 [-32.00993] [-64.13393] [-64.178925] ... [8.194097781357573]
sw_radial_del2 \
0 [[0.09295654296875, 0.14492416381835938, 0.202...
sw_tb \
0 [[197.4491729736328, 197.65480041503906, 198.0...
time tropopause_height tropopause_pressure \
0 2018-11-10 20:09:55.000000512 [16.613] [97.759995]
tropopause_temperature w_radial \
0 [202.48999] [10.109931825491815]
w_radial_del2 \
0 [[0.10823822021484375, 0.16721725463867188, 0....
w_tb
0 [[197.4491729736328, 197.6316375732422, 198.03...
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area_polygon cloudtop_height e_radial \
0 180.029248 [17.651001] [7.58244900587702]
e_radial_del2 \
0 [[0.1204376220703125, 0.17798614501953125, 0.2...
e_tb \
0 [[197.20652770996094, 197.0243377685547, 197.0...
geometry lat \
0 POLYGON ((-64.13393 -32.17857, -64.09641 -32.1... [-32.13393]
lat_corr lon lon_corr ... sw_radial \
0 [-32.00993] [-64.13393] [-64.178925] ... [8.194097781357573]
sw_radial_del2 \
0 [[0.08271026611328125, 0.1244354248046875, 0.1...
sw_tb \
0 [[197.20652770996094, 197.58909606933594, 198....
time tropopause_height tropopause_pressure \
0 2018-11-10 20:10:55.000000512 [16.611] [97.78]
tropopause_temperature w_radial \
0 [202.48] [10.109931825491815]
w_radial_del2 \
0 [[0.1290435791015625, 0.19032669067382812, 0.2...
w_tb
0 [[197.20652770996094, 197.64524841308594, 198....
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area_polygon cloudtop_height e_radial \
0 130.289171 [17.191] [10.956451788794539]
1 111.806680 [17.677] [7.583925831089219]
e_radial_del2 \
0 [[0.13604736328125, 0.21047592163085938, 0.284...
1 [[0.08693695068359375, 0.12725448608398438, 0....
e_tb \
0 [[199.85433959960938, 198.78970336914062, 197....
1 [[196.28675842285156, 196.2508544921875, 196.3...
geometry lat \
0 POLYGON ((-64.15179 -32.18750, -64.08144 -32.1... [-32.098213]
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lat_corr lon lon_corr ... sw_radial \
0 [-31.977213] [-64.15179] [-64.195786] ... [6.829820903728827]
1 [-31.992073] [-64.11607] [-64.16107] ... [6.146188551601023]
sw_radial_del2 \
0 [[0.05776214599609375, 0.07970046997070312, 0....
1 [[0.0825653076171875, 0.1217193603515625, 0.15...
sw_tb \
0 [[199.85433959960938, 200.11871337890625, 200....
1 [[196.28675842285156, 196.34254455566406, 196....
time tropopause_height tropopause_pressure \
0 2018-11-10 20:11:55.000000512 [16.624] [97.61]
1 2018-11-10 20:11:55.000000512 [16.621] [97.659996]
tropopause_temperature w_radial \
0 [202.40999] [1.6851648740908816]
1 [202.43] [5.897649012747042]
w_radial_del2 \
0 [[0.0499420166015625, 0.045581817626953125, -0...
1 [[0.123870849609375, 0.1932220458984375, 0.264...
w_tb
0 [[199.85433959960938, 201.13638305664062, 202....
1 [[196.28675842285156, 196.52999877929688, 197....
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area_polygon cloudtop_height e_radial \
0 110.26933 [17.762001] [5.898369028477411]
e_radial_del2 \
0 [[0.13263702392578125, 0.209930419921875, 0.31...
e_tb \
0 [[195.72174072265625, 195.8573760986328, 196.2...
geometry lat \
0 POLYGON ((-64.09821 -32.17857, -64.05131 -32.1... [-32.116074]
lat_corr lon lon_corr ... sw_radial \
0 [-31.991074] [-64.09821] [-64.14321] ... [6.146188551601023]
sw_radial_del2 \
0 [[0.09815216064453125, 0.14402389526367188, 0....
sw_tb \
0 [[195.72174072265625, 195.756591796875, 195.98...
time tropopause_height tropopause_pressure \
0 2018-11-10 20:12:55.000000512 [16.622002] [97.64]
tropopause_temperature w_radial \
0 [202.42] [6.740787427708395]
w_radial_del2 \
0 [[0.10791778564453125, 0.16202545166015625, 0....
w_tb
0 [[195.72174072265625, 195.81478881835938, 196....
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area_polygon cloudtop_height e_radial \
0 121.941273 [17.941] [5.899089044207777]
e_radial_del2 \
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e_tb \
0 [[195.21275329589844, 195.48374938964844, 196....
geometry lat \
0 POLYGON ((-64.08036 -32.17857, -64.03814 -32.1... [-32.116074]
lat_corr lon lon_corr ... sw_radial \
0 [-31.990074] [-64.08036] [-64.12636] ... [6.828484679463366]
sw_radial_del2 \
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sw_tb \
0 [[195.21275329589844, 195.2715606689453, 195.5...
time tropopause_height tropopause_pressure \
0 2018-11-10 20:13:55.000000512 [16.625] [97.6]
tropopause_temperature w_radial \
0 [202.4] [8.425624190043285]
w_radial_del2 \
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w_tb
0 [[195.21275329589844, 195.27247619628906, 195....
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area_polygon cloudtop_height e_radial \
0 108.745111 [18.014] [5.898369028477411]
e_radial_del2 \
0 [[0.2030029296875, 0.3132362365722656, 0.43213...
e_tb \
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geometry lat \
0 POLYGON ((-64.06250 -32.16964, -64.02498 -32.1... [-32.116074]
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time tropopause_height tropopause_pressure \
0 2018-11-10 20:14:55.000000512 [16.628] [97.54]
tropopause_temperature w_radial \
0 [202.37999] [7.5832058153786175]
w_radial_del2 \
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w_tb
0 [[194.73854064941406, 194.5777130126953, 194.7...
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area_polygon cloudtop_height e_radial \
0 95.53254 [17.997002] [6.740787427708395]
e_radial_del2 \
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e_tb \
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geometry lat \
0 POLYGON ((-64.06250 -32.16964, -64.02498 -32.1... [-32.116074]
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sw_tb \
0 [[194.57424926757812, 194.65435791015625, 194....
time tropopause_height tropopause_pressure \
0 2018-11-10 20:15:55.000000512 [16.628] [97.54]
tropopause_temperature w_radial \
0 [202.37999] [6.740787427708395]
w_radial_del2 \
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w_tb
0 [[194.57424926757812, 194.8095703125, 195.2505...
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area_polygon cloudtop_height e_radial \
0 86.517142 [17.884] [5.055230603392055]
e_radial_del2 \
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e_tb \
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time tropopause_height tropopause_pressure \
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tropopause_temperature w_radial \
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area_polygon cloudtop_height e_radial \
0 68.686139 [17.908] [5.898009020612228]
e_radial_del2 \
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e_tb \
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time tropopause_height tropopause_pressure \
0 2018-11-10 20:17:55.000000512 [16.609001] [97.9]
tropopause_temperature w_radial \
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w_radial_del2 \
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area_polygon cloudtop_height e_radial \
0 55.039897 [17.971] [4.213172193239711]
e_radial_del2 \
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e_tb \
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0 [[195.97947692871094, 196.03793334960938, 196....
time tropopause_height tropopause_pressure \
0 2018-11-10 20:18:55.000000512 [16.609001] [97.9]
tropopause_temperature w_radial \
0 [202.34999] [4.213532201112305]
w_radial_del2 \
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area_polygon cloudtop_height e_radial \
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e_radial_del2 \
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e_tb \
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geometry lat \
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1 [[0.0361480712890625, 0.040637969970703125, 0....
sw_tb \
0 [[196.08200073242188, 196.2036590576172, 196.5...
1 [[199.7479705810547, 200.45181274414062, 201.2...
time tropopause_height tropopause_pressure \
0 2018-11-10 20:19:55.000000512 [16.61] [97.89]
1 2018-11-10 20:19:55.000000512 [16.623001] [97.59]
tropopause_temperature w_radial \
0 [202.34] [2.527255313331439]
1 [202.43] [3.3690807925862263]
w_radial_del2 \
0 [[0.17659759521484375, 0.21410751342773438, 0....
1 [[0.15753173828125, 0.21663284301757812, 0.204...
w_tb
0 [[196.08200073242188, 196.30015563964844, 196....
1 [[199.7479705810547, 200.38780212402344, 201.3...
[2 rows x 38 columns]
(array([100, 100, 101, 101, 101, 101, 101, 101, 102, 102, 102, 102, 102,
102, 103, 103, 103, 103, 103, 103, 103, 104, 104, 104, 104, 104,
104, 104, 105, 105, 105, 105, 105, 105, 106, 106, 106, 106, 107]), array([105, 107, 103, 104, 105, 106, 107, 108, 103, 104, 105, 106, 107,
108, 102, 103, 104, 105, 106, 107, 108, 102, 103, 104, 105, 106,
107, 108, 102, 103, 104, 105, 106, 107, 103, 104, 105, 106, 103]))
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(array([103, 104, 105, 105, 106, 106, 106, 107, 107]), array([101, 101, 100, 101, 100, 101, 102, 101, 102]))
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(array([], dtype=int64), array([], dtype=int64))
(array([], dtype=int64), array([], dtype=int64))
(array([], dtype=int64), array([], dtype=int64))
area_polygon cloudtop_height e_radial \
0 39.628697 [18.019001] [3.370393760117005]
1 48.410698 [17.381] [5.053981027543773]
e_radial_del2 \
0 [[0.3508148193359375, 0.4942283630371094, 0.52...
1 [[0.053680419921875, 0.05985260009765625, 0.04...
e_tb \
0 [[196.57545471191406, 197.525390625, 199.17695...
1 [[199.6324920654297, 199.63734436035156, 199.7...
geometry lat \
0 POLYGON ((-64.00893 -32.16072, -63.98548 -32.1... [-32.116074]
1 POLYGON ((-64.08036 -32.17857, -64.04752 -32.1... [-32.151787]
lat_corr lon lon_corr ... sw_radial \
0 [-31.989073] [-64.00893] [-64.054924] ... [3.4148164944284827]
1 [-32.029785] [-64.08036] [-64.12436] ... [2.7312216886114835]
sw_radial_del2 \
0 [[0.13364410400390625, 0.18168258666992188, 0....
1 [[0.0730743408203125, 0.09786605834960938, 0.0...
sw_tb \
0 [[196.57545471191406, 196.55987548828125, 196....
1 [[199.6324920654297, 200.06643676757812, 200.6...
time tropopause_height tropopause_pressure \
0 2018-11-10 20:20:55.000000512 [16.613] [97.829994]
1 2018-11-10 20:20:55.000000512 [16.629002] [97.5]
tropopause_temperature w_radial \
0 [202.31999] [3.370393760117005]
1 [202.40999] [4.211171046058507]
w_radial_del2 \
0 [[0.28490447998046875, 0.3726654052734375, 0.3...
1 [[0.12033843994140625, 0.18297576904296875, 0....
w_tb
0 [[196.57545471191406, 196.361083984375, 196.71...
1 [[199.6324920654297, 199.8274688720703, 200.26...
[2 rows x 38 columns]
(array([101, 101, 101, 101, 102, 102, 102, 102, 103, 103, 103, 103, 103,
103, 104, 104, 104, 104, 104, 104, 104, 105, 105, 105, 105, 105,
105, 105, 106, 106, 106, 106, 107, 107, 107]), array([103, 104, 105, 106, 104, 105, 106, 107, 103, 104, 105, 106, 107,
108, 102, 103, 104, 105, 106, 107, 108, 102, 103, 104, 105, 106,
107, 108, 103, 104, 105, 106, 103, 104, 105]))
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(array([], dtype=int64), array([], dtype=int64))
(array([], dtype=int64), array([], dtype=int64))
(array([], dtype=int64), array([], dtype=int64))
area_polygon cloudtop_height e_radial \
0 42.935395 [17.720001] [4.213172193239711]
1 35.426738 [17.153] [4.211890781314997]
e_radial_del2 \
0 [[0.32773590087890625, 0.49013519287109375, 0....
1 [[0.11627960205078125, 0.18468475341796875, 0....
e_tb \
0 [[197.6013946533203, 197.86659240722656, 198.7...
1 [[200.57473754882812, 200.11427307128906, 199....
geometry lat \
0 POLYGON ((-64.00893 -32.16072, -63.98079 -32.1... [-32.116074]
1 POLYGON ((-64.08036 -32.18750, -64.04283 -32.1... [-32.151787]
lat_corr lon lon_corr ... sw_radial \
0 [-31.991074] [-64.00893] [-64.054924] ... [2.049277453038548]
1 [-32.03079] [-64.08036] [-64.12436] ... [2.0484823874150107]
sw_radial_del2 \
0 [[0.07697296142578125, 0.0860443115234375, 0.0...
1 [[0.0333709716796875, 0.033611297607421875, 0....
sw_tb \
0 [[197.6013946533203, 197.8959197998047, 198.34...
1 [[200.57473754882812, 201.1055145263672, 201.7...
time tropopause_height tropopause_pressure \
0 2018-11-10 20:21:55.000000512 [16.613] [97.829994]
1 2018-11-10 20:21:55.000000512 [16.629002] [97.5]
tropopause_temperature w_radial \
0 [202.31999] [2.5279753290865163]
1 [202.40999] [0.842090267948773]
w_radial_del2 \
0 [[0.1583099365234375, 0.1723175048828125, 0.09...
1 [[0.00386810302734375, -0.024623870849609375, ...
w_tb
0 [[197.6013946533203, 197.9062042236328, 198.52...
1 [[200.57473754882812, 201.18484497070312, 201....
[2 rows x 38 columns]
(array([101, 101, 101, 102, 102, 102, 102, 103, 103, 103, 103, 103, 104,
104, 104, 104, 104, 104, 105, 105, 105, 105, 105, 105, 106, 106,
107]), array([105, 106, 107, 104, 105, 106, 107, 104, 105, 106, 107, 108, 103,
104, 105, 106, 107, 108, 102, 103, 104, 105, 106, 107, 103, 104,
103]))
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(array([106, 107, 107]), array([102, 102, 104]))
[0.142913818359375, 0.18938446044921875, 0.17821121215820312, 0.054538726806640625, -0.0733489990234375, -0.183868408203125, -0.24442672729492188, -0.22888946533203125, -0.14801025390625, -0.027210235595703125, 0.08632278442382812, 0.14858245849609375, 0.15008926391601562, 0.0962982177734375, 0.01793670654296875, -0.04734039306640625, -0.059528350830078125, 0.013782501220703125, 0.17221450805664062, 0.4038887023925781, 0.68377685546875, 0.9845619201660156, 1.2829551696777344, 1.540985107421875, 1.6579551696777344, 1.5344810485839844, 1.1391220092773438, 0.4892463684082031, -0.27054595947265625, -0.9742088317871094, -1.497161865234375, -1.7477035522460938, -1.7240219116210938, -1.4643402099609375, -1.0772933959960938, -0.680633544921875, -0.35819244384765625, -0.1650238037109375, -0.07494354248046875, -0.03391265869140625, -0.0198974609375]
(array([134, 134, 135, 135, 135, 135, 136, 136, 136, 136, 136, 136, 137,
137, 137, 137, 137, 137, 138, 138, 138, 138, 138, 139, 139, 139,
139, 140, 140]), array([153, 154, 152, 153, 154, 155, 152, 153, 154, 155, 156, 157, 152,
153, 154, 155, 156, 157, 152, 153, 154, 155, 156, 153, 154, 155,
156, 153, 154]))
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(array([], dtype=int64), array([], dtype=int64))
(array([], dtype=int64), array([], dtype=int64))
area_polygon cloudtop_height e_radial \
0 36.549424 [17.598001] [4.213172193239711]
1 21.567107 [16.957] [5.053981027543773]
2 54.801224 [17.363] [3.3492287763151327]
e_radial_del2 \
0 [[0.3060302734375, 0.4601173400878906, 0.56620...
1 [[0.1644439697265625, 0.29453277587890625, 0.4...
2 [[0.35837554931640625, 0.466796875, 0.44029617...
e_tb \
0 [[198.419189453125, 198.48056030273438, 199.15...
1 [[201.4723358154297, 200.6999969482422, 200.25...
2 [[201.31373596191406, 201.1080780029297, 201.6...
geometry lat \
0 POLYGON ((-64.00893 -32.15179, -63.98079 -32.1... [-32.116074]
1 POLYGON ((-64.08036 -32.18750, -64.06159 -32.1... [-32.151787]
2 POLYGON ((-63.16964 -32.74107, -63.12743 -32.7... [-32.6875]
lat_corr lon lon_corr ... sw_radial \
0 [-31.992073] [-64.00893] [-64.053925] ... [1.3660919963974485]
1 [-32.032787] [-64.08036] [-64.12336] ... [0.0]
2 [-32.5625] [-63.16964] [-63.21764] ... [4.7684221944552965]
sw_radial_del2 \
0 [[0.0713348388671875, 0.06894683837890625, -0....
1 [[-0.0009307861328125, -0.022663116455078125, ...
2 [[0.27001953125, 0.3944587707519531, 0.4923591...
sw_tb \
0 [[198.419189453125, 198.9285125732422, 199.580...
1 [[201.4723358154297, 202.01995849609375, 202.5...
2 [[201.31373596191406, 201.38848876953125, 202....
time tropopause_height tropopause_pressure \
0 2018-11-10 20:22:55.000000512 [16.618] [97.759995]
1 2018-11-10 20:22:55.000000512 [16.630001] [97.479996]
2 2018-11-10 20:22:55.000000512 [16.394001] [101.53]
tropopause_temperature w_radial \
0 [202.31] [2.5279753290865163]
1 [202.4] [0.0]
2 [203.09999] [3.3495865234572664]
w_radial_del2 \
0 [[0.134521484375, 0.13883209228515625, 0.05400...
1 [[-0.08681488037109375, -0.17217636108398438, ...
2 [[0.4916229248046875, 0.7117385864257812, 0.80...
w_tb
0 [[198.419189453125, 198.8764190673828, 199.602...
1 [[201.4723358154297, 202.31532287597656, 202.9...
2 [[201.31373596191406, 202.47366333007812, 204....
[3 rows x 38 columns]
(array([101, 101, 101, 102, 102, 102, 103, 103, 103, 103, 104, 104, 104,
104, 105, 105, 105, 105, 105, 105, 105, 106, 106, 106, 107]), array([105, 106, 107, 105, 106, 107, 105, 106, 107, 108, 105, 106, 107,
108, 102, 103, 104, 105, 106, 107, 108, 103, 104, 107, 103]))
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(array([106, 107]), array([102, 104]))
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(array([], dtype=int64), array([], dtype=int64))
(array([], dtype=int64), array([], dtype=int64))
(array([], dtype=int64), array([], dtype=int64))
area_polygon cloudtop_height e_radial \
0 35.895178 [17.533] [4.213992393835377]
1 16.283189 [16.904001] [1.6838521170913854]
e_radial_del2 \
0 [[0.30316162109375, 0.42688751220703125, 0.468...
1 [[0.095611572265625, 0.07233428955078125, -0.0...
e_tb \
0 [[198.52304077148438, 198.72459411621094, 199....
1 [[202.87608337402344, 203.99745178222656, 205....
geometry lat \
0 POLYGON ((-64.00893 -32.14286, -63.98548 -32.1... [-32.098213]
1 POLYGON ((-64.04464 -32.18750, -64.03057 -32.1... [-32.169643]
lat_corr lon lon_corr ... sw_radial \
0 [-31.975214] [-64.00893] [-64.053925] ... [2.7326676120165194]
1 [-32.050644] [-64.04464] [-64.08764] ... [2.731468710483498]
sw_radial_del2 \
0 [[0.1533966064453125, 0.21201705932617188, 0.2...
1 [[0.07636260986328125, 0.1056365966796875, 0.1...
sw_tb \
0 [[198.52304077148438, 198.50299072265625, 198....
1 [[202.87608337402344, 202.94618225097656, 203....
time tropopause_height tropopause_pressure \
0 2018-11-10 20:23:55.000000512 [16.621] [97.71]
1 2018-11-10 20:23:55.000000512 [16.599] [98.03]
tropopause_temperature w_radial \
0 [202.26999] [3.3710498925527523]
1 [202.40999] [3.369143412422314]
w_radial_del2 \
0 [[0.248046875, 0.3191184997558594, 0.267414093...
1 [[0.25922393798828125, 0.3778724670410156, 0.4...
w_tb
0 [[198.52304077148438, 198.9658966064453, 199.9...
1 [[202.87608337402344, 202.2110137939453, 202.0...
[2 rows x 38 columns]
(array([102, 102, 103, 103, 103, 104, 104, 104, 105, 105, 105, 105, 106,
106]), array([105, 106, 105, 106, 107, 105, 106, 107, 105, 106, 107, 108, 106,
107]))
[0.2805328369140625, 0.4034767150878906, 0.4349212646484375, 0.18257522583007812, -0.14475631713867188, -0.4529151916503906, -0.6533927917480469, -0.6821136474609375, -0.5670623779296875, -0.3641395568847656, -0.14881515502929688, 0.0111236572265625, 0.09822845458984375, 0.11584091186523438, 0.10009384155273438, 0.09006118774414062, 0.10373687744140625, 0.14702224731445312, 0.19850921630859375, 0.23204421997070312, 0.22979736328125, 0.18269729614257812, 0.10836410522460938, 0.033046722412109375, -0.00911712646484375, 0.010715484619140625, 0.0924072265625, 0.2348480224609375, 0.4619255065917969, 0.7978515625, 1.2430877685546875, 1.7531242370605469, 2.1273193359375, 2.142566680908203, 1.6420974731445312, 0.5787506103515625, -0.7225418090820312, -1.8602333068847656, -2.4985198974609375, -1.9467315673828125, -1.253875732421875]
(array([104, 104, 105, 105, 106, 106, 106, 107, 107, 108, 108, 109]), array([101, 102, 101, 102, 102, 103, 104, 103, 104, 101, 102, 101]))
[0.0620574951171875, 0.06975936889648438, 0.034694671630859375, -0.043979644775390625, -0.104888916015625, -0.14119720458984375, -0.1429595947265625, -0.10337066650390625, -0.03131866455078125, 0.056095123291015625, 0.12457656860351562, 0.1439056396484375, 0.11735916137695312, 0.05895233154296875, -0.007831573486328125, -0.058696746826171875, -0.07553863525390625, -0.044391632080078125, 0.03826141357421875, 0.1756591796875, 0.3804359436035156, 0.6663703918457031, 1.0424270629882812, 1.4836540222167969, 1.8342666625976562, 1.9211883544921875, 1.6328887939453125, 0.933319091796875, 0.0269012451171875, -0.8369789123535156, -1.4694175720214844, -1.740814208984375, -1.698455810546875, -1.4411773681640625, -1.0971527099609375, -0.7813034057617188, -0.526214599609375, -0.3390045166015625, -0.207061767578125, -0.1033477783203125, -0.054718017578125]
(array([], dtype=int64), array([], dtype=int64))
(array([], dtype=int64), array([], dtype=int64))
(array([], dtype=int64), array([], dtype=int64))
area_polygon cloudtop_height e_radial \
0 40.065364 [17.203001] [3.3700973779131975]
1 26.183035 [16.917002] [3.368423817509563]
e_radial_del2 \
0 [[0.19738006591796875, 0.2699546813964844, 0.2...
1 [[0.156097412109375, 0.20935821533203125, 0.18...
e_tb \
0 [[200.98928833007812, 201.6976776123047, 202.8...
1 [[202.45431518554688, 202.84584045410156, 203....
geometry lat \
0 POLYGON ((-63.99107 -32.16964, -63.96762 -32.1... [-32.13393]
1 POLYGON ((-64.06250 -32.19643, -64.03905 -32.1... [-32.169643]
lat_corr lon lon_corr ... sw_radial \
0 [-32.01293] [-63.991074] [-64.03507] ... [2.731440785390396]
1 [-32.050644] [-64.0625] [-64.1055] ... [2.0487842267143392]
sw_radial_del2 \
0 [[0.14391326904296875, 0.197479248046875, 0.20...
1 [[0.02129364013671875, 0.0214691162109375, 0.0...
sw_tb \
0 [[200.98928833007812, 201.21636962890625, 201....
1 [[202.45431518554688, 202.5448760986328, 202.6...
time tropopause_height tropopause_pressure \
0 2018-11-10 20:24:55.000000512 [16.614] [97.79]
1 2018-11-10 20:24:55.000000512 [16.619001] [97.659996]
tropopause_temperature w_radial \
0 [202.31999] [3.3697374401424995]
1 [202.40999] [3.368423817509563]
w_radial_del2 \
0 [[0.20149993896484375, 0.2762413024902344, 0.2...
1 [[0.11313629150390625, 0.134796142578125, 0.10...
w_tb
0 [[200.98928833007812, 200.73773193359375, 200....
1 [[202.45431518554688, 202.41128540039062, 202....
[2 rows x 38 columns]
(array([103, 104, 104, 105, 105, 106, 106, 107, 107, 107, 108, 108, 108,
109, 109]), array([102, 101, 102, 101, 102, 102, 103, 102, 103, 104, 102, 103, 104,
102, 103]))
[0.10384368896484375, 0.14795303344726562, 0.15821075439453125, 0.07250595092773438, -0.011829376220703125, -0.051609039306640625, -0.055118560791015625, -0.042327880859375, -0.0285186767578125, -0.022037506103515625, -0.0081634521484375, 0.025173187255859375, 0.05640411376953125, 0.061397552490234375, 0.039325714111328125, -0.000942230224609375, -0.035022735595703125, -0.0403289794921875, -0.016956329345703125, 0.0340576171875, 0.13063430786132812, 0.2987556457519531, 0.5776329040527344, 0.9828262329101562, 1.4220962524414062, 1.768310546875, 1.8625717163085938, 1.5806007385253906, 0.9683036804199219, 0.14069366455078125, -0.6809043884277344, -1.2825965881347656, -1.5864944458007812, -1.5802078247070312, -1.3774032592773438, -1.1071624755859375, -0.8343582153320312, -0.5993118286132812, -0.4081268310546875, -0.22451019287109375, -0.1259307861328125]
(array([], dtype=int64), array([], dtype=int64))
(array([], dtype=int64), array([], dtype=int64))
(array([], dtype=int64), array([], dtype=int64))
(array([], dtype=int64), array([], dtype=int64))
area_polygon cloudtop_height e_radial \
0 31.901471 [16.972] [2.526497766933553]
e_radial_del2 \
0 [[0.155975341796875, 0.19129180908203125, 0.13...
e_tb \
0 [[202.0133819580078, 202.59620666503906, 203.4...
geometry lat \
0 POLYGON ((-64.06250 -32.20536, -64.03436 -32.1... [-32.169643]
lat_corr lon lon_corr ... sw_radial \
0 [-32.050644] [-64.0625] [-64.1055] ... [3.413994773313793]
sw_radial_del2 \
0 [[0.086639404296875, 0.12660598754882812, 0.15...
sw_tb \
0 [[202.0133819580078, 201.8712921142578, 201.90...
time tropopause_height tropopause_pressure \
0 2018-11-10 20:25:55.000000512 [16.62] [97.64]
tropopause_temperature w_radial \
0 [202.4] [4.211069457198533]
w_radial_del2 \
0 [[0.1910858154296875, 0.2624778747558594, 0.28...
w_tb
0 [[202.0133819580078, 201.8629150390625, 202.09...
[1 rows x 38 columns]
nov_10_df = pd.concat(df_list)
df = nov_10_df
df_list = []
for time in np.unique(df.time):
df_sub = df[df.time == time]
df_list.append(df_sub[df_sub.mintb == np.nanmin(df_sub.mintb.values)])
nov_10_df_filtered = pd.concat(df_list)
df = nov_10_df_filtered.reset_index()
radial_cols = ['n_radial', 'ne_radial', 'e_radial', 'se_radial', 's_radial', 'sw_radial', 'w_radial', 'nw_radial']
radial_del2 = ['n_radial_del2', 'ne_radial_del2', 'e_radial_del2', 'se_radial_del2', 's_radial_del2', 'sw_radial_del2', 'w_radial_del2', 'nw_radial_del2']
radial_tb = ['n_tb', 'ne_tb', 'e_tb', 'se_tb', 's_tb', 'sw_tb', 'w_tb', 'nw_tb']
df_sub = df[df.index == 10]
radials = df_sub[radial_cols].values[0]
del2 = df_sub[radial_del2].values[0]
tb = df_sub[radial_tb].values[0]
mean = np.mean(radials)
std = np.std(radials)
for i in range(len(radial_tb)):
fig = plt.figure(figsize=(20,8))
ax1 = plt.subplot(121)
ax1.plot(tb[i], color='blue', linewidth=2)
plt.axvline(radials[i], color='black', linewidth=3, linestyle='--', label=f'Radial Length: {radials[i]}')
plt.title(radial_tb[i] + df_sub.time[df_sub.index[0]].strftime(' %H%M UTC'))
plt.legend(loc='upper left')
ax2 = plt.subplot(122)
ax2.plot(del2[i], color='blue', linewidth=2)
plt.axvline(radials[i], color='black', linewidth=3, linestyle='--', label=f'Radial Length: {radials[i]}')
plt.axhline(0)
plt.title(radial_tb[i] + df_sub.time[df_sub.index[0]].strftime(' %H%M UTC'))
plt.legend(loc='upper left')
plt.plot()
plt.show()
plt.close()
df.index = df.time
import matplotlib.dates as mdates
months = mdates.MinuteLocator()
from matplotlib.ticker import (MultipleLocator, FormatStrFormatter,
AutoMinorLocator)
import matplotlib.dates as md
df = df[df.prob > .8]
df['datetime'] = pd.to_numeric(df.time.dt.strftime('%H%M'))
ota = df.area_polygon.values
times = df.datetime.values
trop_temp = df.tropopause_temperature.values
mintb = df.mintb.values
trop_hgt = df.tropopause_height.values
ct_height = df.cloudtop_height.values
trop_temp_vals = []
trop_hgt_vals = []
ct_hgt_vals =[]
for i in range(len(trop_temp)):
trop_temp_vals.append(trop_temp[i][0])
trop_hgt_vals.append(trop_hgt[i][0])
ct_hgt_vals.append(ct_height[i][0])
for timestamp in df.datetime.values:
df_sub = df[df.datetime == timestamp]
plt.figure(figsize=(12,8))
ax = plt.subplot(311)
ax.plot(times, ota, linewidth=3, color='black')
ax.scatter(df_sub.datetime, df_sub.area_polygon, s=80, color='black')
plt.ylim(0,150)
plt.yticks(fontsize=12)
plt.xticks(fontsize=12)
plt.ylabel('OTA \n $km^{2}$', fontsize=16)
ax2 = plt.subplot(312)
ax2.plot(times, trop_temp_vals, linewidth=3, color='black', linestyle='--', label='Tropopause Temperature')
ax2.plot(times, mintb, linewidth=3, color='blue', label='OT Minimum Brightness Temperature')
ax2.scatter(df_sub.datetime, df_sub.mintb, s=80, color='black')
plt.ylabel('OT Min \n Brightness Temp (K)', fontsize=16)
plt.legend(loc='upper right')
ax3 = plt.subplot(313)
ax3.plot(times, trop_hgt_vals, linewidth=3, color='black', linestyle='--', label='Tropause Height')
ax3.plot(times, ct_hgt_vals, linewidth=3, color='red', label='OT Cloud Top Height')
ax3.scatter(df_sub.datetime, df_sub.cloudtop_height.values[0], s=80, color='black')
plt.ylabel('OT Cloud Top \n Height (km)', fontsize=16)
plt.xlabel('Time', fontsize=16)
plt.legend(loc='upper right')
plt.suptitle(f'10 November 2018 Supercell \n {df_sub.datetime.values[0]} UTC', fontsize=24)
plt.savefig(f'compare_plots/time_series_{timestamp}.png', dpi=200)
plt.close()
trop_temp = df.tropopause_temperature.mean()
df.prob
time
2018-11-10 20:07:55.000000512 0.82
2018-11-10 20:08:55.000000512 0.81
2018-11-10 20:11:55.000000512 0.89
2018-11-10 20:12:55.000000512 0.88
2018-11-10 20:13:55.000000512 0.89
2018-11-10 20:14:55.000000512 0.90
2018-11-10 20:15:55.000000512 0.90
2018-11-10 20:16:55.000000512 0.90
2018-11-10 20:17:55.000000512 0.90
2018-11-10 20:18:55.000000512 0.92
2018-11-10 20:19:55.000000512 0.91
2018-11-10 20:20:55.000000512 0.92
2018-11-10 20:21:55.000000512 0.88
2018-11-10 20:22:55.000000512 0.87
2018-11-10 20:23:55.000000512 0.86
Name: prob, dtype: float64
plt.figure(figsize=(12,8))
df[df.prob > .8].mintb.plot(linewidth=3, color='blue', label='Minimum Brightness Temperature')
#plt.ylim(0,150)
plt.yticks(fontsize=12)
plt.xticks(fontsize=12)
ax.xaxis.set_minor_locator(AutoMinorLocator())
plt.axhline(trop_temp, label=f'Tropopause Temperature: {np.round(trop_temp,2)} K', linewidth=3, color='black', linestyle='--')
plt.ylabel('Overshooting Top \n Minimum Brightness Temperature (K)', fontsize=16)
plt.xlabel('Time', fontsize=16)
plt.legend(loc='upper right', fontsize=16)
plt.title('10 November 2018 Supercell \n OT Minimum Brightness Temperature', fontsize=24)
Text(0.5, 1.0, '10 November 2018 Supercell \n OT Minimum Brightness Temperature')
df
trop_height = df.tropopause_height.mean()
plt.figure(figsize=(12,8))
df[df.prob > .8].cloudtop_height.plot(linewidth=3, color='red', label='Cloud Top Height')
#plt.ylim(0,150)
plt.yticks(fontsize=12)
plt.xticks(fontsize=12)
ax.xaxis.set_minor_locator(AutoMinorLocator())
plt.axhline(trop_height, label=f'Tropopause Height: {np.round(trop_height,2)} km', linewidth=3, color='black', linestyle='--')
plt.ylabel('Overshooting Top \n Height (km)', fontsize=16)
plt.xlabel('Time', fontsize=16)
plt.legend(loc='lower right', fontsize=16)
plt.title('10 November 2018 Supercell \n OT Cloud Top Height', fontsize=24)
Text(0.5, 1.0, '10 November 2018 Supercell \n OT Cloud Top Height')
df_list = []
for file in files:
df_list.append(process_ot_data(file))
nov_10_df = pd.concat(df_list)
df = nov_10_df
df_list = []
for time in np.unique(df.time):
df_sub = df[df.time == time]
df_list.append(df_sub[df_sub.mintb == np.nanmin(df_sub.mintb.values)])
---------------------------------------------------------------------------
AttributeError Traceback (most recent call last)
<ipython-input-105-8ca377614f7d> in <module>
1 df_list = []
----> 2 for time in np.unique(df.time):
3 df_sub = df[df.time == time]
4
5 df_list.append(df_sub[df_sub.mintb == np.nanmin(df_sub.mintb.values)])
~/a/miniconda3/envs/xesmf_env/lib/python3.7/site-packages/pandas/core/generic.py in __getattr__(self, name)
5272 if self._info_axis._can_hold_identifiers_and_holds_name(name):
5273 return self[name]
-> 5274 return object.__getattribute__(self, name)
5275
5276 def __setattr__(self, name: str, value) -> None:
AttributeError: 'GeoDataFrame' object has no attribute 'time'
nov_10_df_filtered = pd.concat(df_list)
df = nov_10_df_filtered
df.index = pd.to_datetime(df.time)
df = df.drop(columns='time')
df.tropopause_height.values
array([16.61300087, 16.62100029, 16.62100029, 16.61400032, 16.62000084,
16.61900139, 16.61900139, 16.6230011 , 16.61300087, 16.61100006,
16.62100029, 16.62200165, 16.625 , 16.62800026, 16.62800026,
16.60700035, 16.60900116, 16.60900116, 16.61000061, 16.61300087,
16.61300087, 16.61800003, 16.62100029, 16.61400032, 16.62000084,
16.61800003])
df = df[df.prob > .8]
df.to_csv('revised_ot_10_Nov_2007_2024.csv')
import pandas as pd
df = pd.read_csv('revised_ot_10_Nov_2000_2026.csv', index_col='time', parse_dates=True)
df_sub = df[df.time == df.time[11]]
---------------------------------------------------------------------------
AttributeError Traceback (most recent call last)
<ipython-input-7-839ba86931ed> in <module>
----> 1 df_sub = df[df.time == df.time[11]]
~/a/miniconda3/envs/xesmf_env/lib/python3.7/site-packages/pandas/core/generic.py in __getattr__(self, name)
5139 if self._info_axis._can_hold_identifiers_and_holds_name(name):
5140 return self[name]
-> 5141 return object.__getattribute__(self, name)
5142
5143 def __setattr__(self, name: str, value) -> None:
AttributeError: 'DataFrame' object has no attribute 'time'
Plot Vis satellite imagery from this case¶
import metpy
import glob
import xarray as xr
from shapely import wkt
def plot_vis(file, ot_df=df, extent=(-65, -63, -33, -31)):
# Use metpy to read in the file and assign crs
dat = xr.open_dataset(file).metpy.parse_cf('Rad')
# Pull out the crs, and coordinates
proj = dat.metpy.cartopy_crs
x = dat['x']
y = dat['y']
# Deal with matching times
time = pd.to_datetime(file[-18:-3], format='%Y%m%d_%H%M%S')
df_sub = ot_df[(ot_df.minute == time.minute) & (ot_df.mintb == np.min(ot_df.mintb))]
# Setup figure
fig = plt.figure(figsize=(10, 10))
ax = fig.add_subplot(1, 1, 1, projection=proj)
ax.add_feature(cfeature.COASTLINE.with_scale('50m'), linewidth=2)
ax.add_feature(cfeature.STATES.with_scale('50m'), linestyle=':', edgecolor='black')
ax.add_feature(cfeature.BORDERS.with_scale('50m'), linewidth=2, edgecolor='black')
im = ax.imshow(dat, extent=(x.min(), x.max(), y.min(), y.max()), origin='upper', transform=proj, cmap='gist_gray')
plt.colorbar(im, shrink=.7, label='Radiance')
df_sub.plot(ax=ax, transform=ccrs.PlateCarree(), facecolor='none', edgecolor='red', linewidth=3)
plt.title(time, fontsize=24)
ax.plot(0, 0, color='red', lw=3, label=f'OT Area {np.round(df_sub.area_polygon.values[0], 2)} km2')
ax.set_extent((-64.5, -63.5, -32.7, -31.7), crs=ccrs.PlateCarree())
plt.legend(loc='upper right', fontsize=16)
gl = ax.gridlines(crs=ccrs.PlateCarree(), draw_labels=True,
linewidth=2, color='gray', alpha=0.5, linestyle='--')
gl.xlabels_top = False
gl.ylabels_right = False
plt.tight_layout()
plt.savefig(time.strftime('Vis_OT_%Y%m%d_%H%M.png'), dpi=400)
def read_ot_df(file):
df = pd.read_csv(file, index_col='time', parse_dates=True)
#df['minute'] = df.index.minute
df['geometry'] = df['geometry'].apply(wkt.loads)
geo_df = gpd.GeoDataFrame(df, geometry='geometry')
geo_df = geo_df[(geo_df.area_polygon >10) & (geo_df.area_polygon < 500) & (geo_df.prob > .7)]
return geo_df
file = '../overshooting_tops/ot_output/20181110/20181110_2022.csv'
df = read_ot_df(file)
df['time'] = pd.to_datetime(df.index)
df['minute'] = df.time.dt.minute
plot_vis(sat_files[22], ot_df=df)
for file in sat_files[1:26]:
plot_vis(file)
plot_vis(sat_files[11])
sat_files = sorted(glob.glob('/data/keeling/a/mgrover4/meso/relampago_sat_data/clipped_sat_data/*'))
dat = xr.open_dataset(sat_files[12]).metpy.parse_cf('Rad')
proj = dat.metpy.cartopy_crs
x = dat['x']
y = dat['y']
import cartopy.crs as ccrs
import cartopy.feature as cfeature
import matplotlib.pyplot as plt
%matplotlib inline
sat_files[12][-18:-3]
'20181110_201255'
time = pd.to_datetime(sat_files[12][-18:-3], format='%Y%m%d_%H%M%S')
df['minute'] = df.index.minute
df[df.minute == time.minute]
| index | area_circle_mean | area_circle_median | area_circle_polygon | area_cold_pixels | e_radial | e_radial_del2 | e_tb | geometry | lat | ... | sw_radial_del2 | sw_tb | time | tropopause_height | tropopause_pressure | tropopause_temperature | w_radial | w_radial_del2 | w_tb | minute | |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| time | |||||||||||||||||||||
| 2018-11-10 20:12:55.000000512 | 0 | 121.689502 | 130.434332 | 110.26933 | 0.0 | 7.0 | [0.13263702392578125, 0.209930419921875, 0.310... | [195.72174072265625, 195.8573760986328, 196.25... | POLYGON ((-64.09821 -32.17857, -64.05131 -32.1... | -32.116074 | ... | [0.09815216064453125, 0.14402389526367188, 0.1... | [195.72174072265625, 195.756591796875, 195.987... | 2018-11-10 20:12:55.000000512 | 16.622002 | 97.639999 | 202.419998 | 8.0 | [0.10791778564453125, 0.16202545166015625, 0.2... | [195.72174072265625, 195.81478881835938, 196.1... | 12 |
1 rows × 40 columns
fig = plt.figure(figsize=(10, 10))
ax = fig.add_subplot(1, 1, 1, projection=proj)
ax.add_feature(cfeature.COASTLINE.with_scale('50m'), linewidth=2)
ax.add_feature(cfeature.STATES.with_scale('50m'), linestyle=':', edgecolor='black')
ax.add_feature(cfeature.BORDERS.with_scale('50m'), linewidth=2, edgecolor='black')
im = ax.imshow(dat, extent=(x.min(), x.max(), y.min(), y.max()), origin='upper', transform=proj, cmap='gist_gray')
plt.colorbar(im, shrink=.7, label='Radiance')
df_sub.plot(ax=ax, transform=ccrs.PlateCarree(), facecolor='none', edgecolor='red', linewidth=3)
plt.title(time, fontsize=24)
ax.plot(0, 0, color='red', lw=3, label=f'OT Area {np.round(df_sub.area_circle_polygon.values[0], 2)} km2')
ax.set_extent((-64.5, -63.5, -32.7, -31.7), crs=ccrs.PlateCarree())
plt.legend(loc='upper right', fontsize=16)
<matplotlib.legend.Legend at 0x7ef569aa2048>
12 November 2018¶
llcrnr=[-33.206342, -65.906586]
urcrnr=[-30.255825, -62.630553]
import glob
datestr='20181112'
ihour='02'
files = sorted(glob.glob(f'/data/relamp/a/mgrover4/ot_data/{datestr}/*{ihour}????.cdf'))
files[34]
'/data/relamp/a/mgrover4/ot_data/20181112/cor1goecnvX1.a1.20181112.023454.cdf'
df
df_list = []
filtered_df_list = []
for file in files[30:45]:
ds = xr.open_dataset(file).squeeze()
df = process_ot_data(file, .5)
ir_vals = ds.ir_brightness_temperature.values
plt.figure(figsize=(12,8))
lon = ds.longitude.values
lat = ds.latitude.values
ax = plt.subplot(111, projection=ccrs.PlateCarree())
ax.set_extent((-64.5, -63.25, -31.75, -30.5))
cf = plt.contourf(lon, lat, ir_vals, np.arange(190, 270, 2), cmap='gist_rainbow_r')
cbar = plt.colorbar(cf)
cbar.set_label('Brightness Temperature (K)', fontsize=16)
cbar.ax.tick_params(labelsize=16)
try:
df_ex = df[df.mintb == np.nanmin(df.mintb.values)]
except:
continue
df_ex.plot(ax=ax, facecolor="none", edgecolor='black', lw=3,)
ax.plot(0, 0, color='black', lw=3, label=f'OT Area {np.round(df_ex.area_polygon.values[0], 2)} km2 \n Prob: {np.round(df_ex.prob.values[0], 2)}')
plt.legend(fontsize=16)
#gl = ax.gridlines(crs=ccrs.PlateCarree(), draw_labels=True,
# linewidth=2, color='gray', alpha=0.5, linestyle='--')
#try:
#df_ex = df[df.mintb == np.nanmin(df.mintb.values)]
# df_ex = df[(df.lon > -64.5) & (df.lon < -64.2) & (df.lat > -32.1)]
# If more than one OT, choose the one with the lowest min BT value
# df_ex = df_ex[df_ex.mintb == np.nanmin(df_ex.mintb.values)]
# df_ex.plot(ax=ax, facecolor="none", edgecolor='black', lw=3,)
# ax.plot(0, 0, color='black', lw=3, label=f'OT Area {np.round(df_ex.area_circle_polygon.values[0], 2)} km2 \n Prob: {np.round(df_ex.prob.values[0], 2)}')
# plt.legend(fontsize=16)
# filtered_df_list.append(df_ex)
#except:
# None
gl = ax.gridlines(crs=ccrs.PlateCarree(), draw_labels=True,
linewidth=2, color='gray', alpha=0.5, linestyle='--')
gl.xlabels_top = False
gl.ylabels_right = False
gl.xlabel_style = {'size': 14, 'color': 'k'}
gl.ylabel_style = {'size': 14, 'color': 'k'}
plt.title(pd.to_datetime(df.time.values[0]).strftime('%d %b %y %H:%M:%S'), fontsize=24)
plt.savefig(pd.to_datetime(df.time.values[0]).strftime('compare_plots/Nov12/%b_%d_%y_%H%M.%S.png'), dpi=300)
plt.show()
plt.close()
#df_ex = df[df.mintb == np.nanmin(df.mintb.values)]
#df_ex = df[(df.lon > -64) & (df.lon < -63.75)]
# If more than one OT, choose the one with the lowest min BT value
#df_ex = df_ex[df_ex.mintb == np.nanmin(df_ex.mintb.values)]
#df_ex.plot(ax=ax, facecolor="none", edgecolor='black', lw=3,)
df_list.append(df)
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(array([], dtype=int64), array([], dtype=int64))
(array([], dtype=int64), array([], dtype=int64))
df = pd.concat(filtered_df_list[0:-2])
df.index = df.time
df = pd.read_csv('Revised_OTs_12_November_2018_0232_0243.csv', index_col='time', parse_dates=True)
---------------------------------------------------------------------------
FileNotFoundError Traceback (most recent call last)
<ipython-input-113-459397e5b084> in <module>
----> 1 df = pd.read_csv('Revised_OTs_12_November_2018_0232_0243.csv', index_col='time', parse_dates=True)
~/a/miniconda3/envs/xesmf_env/lib/python3.7/site-packages/pandas/io/parsers.py in read_csv(filepath_or_buffer, sep, delimiter, header, names, index_col, usecols, squeeze, prefix, mangle_dupe_cols, dtype, engine, converters, true_values, false_values, skipinitialspace, skiprows, skipfooter, nrows, na_values, keep_default_na, na_filter, verbose, skip_blank_lines, parse_dates, infer_datetime_format, keep_date_col, date_parser, dayfirst, cache_dates, iterator, chunksize, compression, thousands, decimal, lineterminator, quotechar, quoting, doublequote, escapechar, comment, encoding, dialect, error_bad_lines, warn_bad_lines, delim_whitespace, low_memory, memory_map, float_precision)
684 )
685
--> 686 return _read(filepath_or_buffer, kwds)
687
688
~/a/miniconda3/envs/xesmf_env/lib/python3.7/site-packages/pandas/io/parsers.py in _read(filepath_or_buffer, kwds)
450
451 # Create the parser.
--> 452 parser = TextFileReader(fp_or_buf, **kwds)
453
454 if chunksize or iterator:
~/a/miniconda3/envs/xesmf_env/lib/python3.7/site-packages/pandas/io/parsers.py in __init__(self, f, engine, **kwds)
944 self.options["has_index_names"] = kwds["has_index_names"]
945
--> 946 self._make_engine(self.engine)
947
948 def close(self):
~/a/miniconda3/envs/xesmf_env/lib/python3.7/site-packages/pandas/io/parsers.py in _make_engine(self, engine)
1176 def _make_engine(self, engine="c"):
1177 if engine == "c":
-> 1178 self._engine = CParserWrapper(self.f, **self.options)
1179 else:
1180 if engine == "python":
~/a/miniconda3/envs/xesmf_env/lib/python3.7/site-packages/pandas/io/parsers.py in __init__(self, src, **kwds)
2006 kwds["usecols"] = self.usecols
2007
-> 2008 self._reader = parsers.TextReader(src, **kwds)
2009 self.unnamed_cols = self._reader.unnamed_cols
2010
pandas/_libs/parsers.pyx in pandas._libs.parsers.TextReader.__cinit__()
pandas/_libs/parsers.pyx in pandas._libs.parsers.TextReader._setup_parser_source()
FileNotFoundError: [Errno 2] No such file or directory: 'Revised_OTs_12_November_2018_0232_0243.csv'
df[df.prob > .7]
| area_circle_mean | area_circle_median | area_circle_polygon | cloudtop_height | e_radial | e_radial_del2 | e_tb | geometry | lat | lat_corr | ... | sw_radial | sw_radial_del2 | sw_tb | time | tropopause_height | tropopause_pressure | tropopause_temperature | w_radial | w_radial_del2 | w_tb | |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| time | |||||||||||||||||||||
| 2018-11-12 02:31:54 | 31.228643 | 29.648724 | 28.222460 | 16.671001 | 4.0 | [0.15695953369140625, 0.2196807861328125, 0.23... | [201.8682403564453, 202.02073669433594, 202.48... | POLYGON ((-63.90179 -31.30357, -63.87833 -31.2... | -31.258928 | -33.082355 | ... | 4.0 | [0.0468292236328125, 0.0654754638671875, 0.076... | [201.8682403564453, 201.819580078125, 201.8645... | 2018-11-12 02:31:54 | 16.525002 | 99.339996 | 201.190002 | 3.0 | [0.10903167724609375, 0.13091659545898438, 0.0... | [201.8682403564453, 202.04234313964844, 202.43... |
| 2018-11-12 02:32:54 | 36.964643 | 42.867758 | 34.215735 | 19.121000 | 5.0 | [0.20626068115234375, 0.3034820556640625, 0.35... | [199.60467529296875, 199.59048461914062, 199.9... | POLYGON ((-63.90179 -31.31250, -63.87364 -31.3... | -31.276785 | -31.146786 | ... | 6.0 | [0.08431243896484375, 0.11867523193359375, 0.1... | [199.60467529296875, 199.68209838867188, 199.9... | 2018-11-12 02:32:54 | 19.099001 | 64.379997 | 195.970001 | 3.0 | [0.1099700927734375, 0.12207794189453125, 0.06... | [199.60467529296875, 199.994873046875, 200.605... |
| 2018-11-12 02:33:54 | 37.786199 | 36.914553 | 33.992085 | 19.462002 | 3.0 | [0.20917510986328125, 0.24409866333007812, 0.1... | [197.6213836669922, 198.6660919189453, 200.129... | POLYGON ((-63.88393 -31.33036, -63.86517 -31.3... | -31.294643 | -31.162643 | ... | 5.0 | [0.12989044189453125, 0.18992233276367188, 0.2... | [197.6213836669922, 197.4818115234375, 197.602... | 2018-11-12 02:33:54 | 19.121000 | 64.129997 | 195.979996 | 5.0 | [0.30989837646484375, 0.43511962890625, 0.4718... | [197.6213836669922, 197.2228240966797, 197.444... |
| 2018-11-12 02:34:54 | 39.670733 | 36.908761 | 35.987037 | 19.596001 | 3.0 | [0.29625701904296875, 0.37740325927734375, 0.2... | [197.1431427001953, 198.1394500732422, 199.728... | POLYGON ((-63.88393 -31.33929, -63.86517 -31.3... | -31.3125 | -31.179501 | ... | 5.0 | [0.1294403076171875, 0.1766815185546875, 0.188... | [197.1431427001953, 197.27731323242188, 197.67... | 2018-11-12 02:34:54 | 19.139002 | 63.939999 | 196.000000 | 5.0 | [0.3160858154296875, 0.4221382141113281, 0.419... | [197.1431427001953, 196.90794372558594, 197.30... |
| 2018-11-12 02:39:54 | 61.386926 | 64.241069 | 55.949746 | 19.167002 | 4.0 | [0.186248779296875, 0.25388336181640625, 0.260... | [198.49203491210938, 199.05517578125, 199.9908... | POLYGON ((-63.91964 -31.37500, -63.89619 -31.3... | -31.348215 | -31.217215 | ... | 7.0 | [0.06574249267578125, 0.09463882446289062, 0.1... | [198.49203491210938, 198.52713012695312, 198.6... | 2018-11-12 02:39:54 | 19.154001 | 63.789997 | 195.940002 | 5.0 | [0.19445037841796875, 0.2685394287109375, 0.27... | [198.49203491210938, 198.36566162109375, 198.6... |
| 2018-11-12 02:41:54 | 43.590309 | 49.262605 | 39.091488 | 15.932001 | 4.0 | [0.13306427001953125, 0.17688369750976562, 0.1... | [199.3667755126953, 199.35638427734375, 199.61... | POLYGON ((-63.93750 -31.33036, -63.90936 -31.3... | -31.294643 | -31.186644 | ... | 6.0 | [0.07079315185546875, 0.10318756103515625, 0.1... | [199.3667755126953, 199.43006896972656, 199.63... | 2018-11-12 02:41:54 | 16.014000 | 108.439995 | 195.750000 | 4.0 | [0.1626129150390625, 0.22990798950195312, 0.24... | [199.3667755126953, 199.71197509765625, 200.38... |
| 2018-11-12 02:42:54 | 35.933292 | 32.135175 | 32.424907 | 15.957001 | 5.0 | [0.17418670654296875, 0.249053955078125, 0.281... | [199.37437438964844, 199.26097106933594, 199.4... | POLYGON ((-63.93750 -31.33929, -63.91405 -31.3... | -31.3125 | -31.204500 | ... | 4.0 | [0.04642486572265625, 0.056972503662109375, 0.... | [199.37437438964844, 199.7519073486328, 200.22... | 2018-11-12 02:42:54 | 16.018002 | 108.360001 | 195.759995 | 3.0 | [0.133453369140625, 0.17003631591796875, 0.137... | [199.37437438964844, 199.8429412841797, 200.57... |
7 rows × 40 columns
plt.figure(figsize=(12,8))
#df[df.prob > .7].area_circle_polygon[:4].plot(linewidth=3, color='red', label='1st OT')
df[df.prob > .7].area_circle_polygon[4:].plot(linewidth=3, color='black', label='2nd OT')
plt.ylim(0,150)
plt.yticks(fontsize=12)
plt.xticks(fontsize=12)
ax.xaxis.set_minor_locator(AutoMinorLocator())
plt.ylabel('Overshooting Top Area (km2)', fontsize=16)
plt.xlabel('Time', fontsize=16)
plt.legend(fontsize=16)
plt.title('12 November 2018 Multicellular OTA', fontsize=24)
Text(0.5, 1.0, '12 November 2018 Multicellular OTA')
df.to_csv('Revised_OTs_12_November_2018_0232_0243.csv')
trop_temp = df.tropopause_temperature.values.mean()
df.tropopause_temperature
time
2018-11-12 02:31:54.000000000 201.190002
2018-11-12 02:32:54.000000000 195.970001
2018-11-12 02:33:54.000000000 195.979996
2018-11-12 02:34:54.000000000 196.000000
2018-11-12 02:37:54.000000000 195.949997
2018-11-12 02:38:54.000000000 195.919998
2018-11-12 02:39:54.000000000 195.940002
2018-11-12 02:40:54.000001024 195.899994
2018-11-12 02:41:54.000000000 195.750000
2018-11-12 02:42:54.000000000 195.759995
Name: tropopause_temperature, dtype: float64
plt.figure(figsize=(12,8))
#df.mintb.plot(linewidth=3, color='blue', label='Minimum Brightness Temperature')
df[df.prob > .7].mintb[:4].plot(linewidth=3, color='blue', label='OT Min Temperature')
df[df.prob > .7][:4].tropopause_temperature.plot(linewidth=3, color='black', label='Tropopause Temperature')
#df[df.prob > .7].mintb[4:].plot(linewidth=3, color='purple', label='OT Min Temperature')
#df[df.prob > .7][4:].tropopause_temperature.plot(linewidth=3, color='black', label='Tropopause Temperature')
#plt.ylim(0,150)
plt.yticks(fontsize=12)
plt.xticks(fontsize=12)
ax.xaxis.set_minor_locator(AutoMinorLocator())
#plt.axhline(trop_temp, label=f'Tropopause Temperature: {np.round(trop_temp,2)} K', linewidth=3, color='black', linestyle='--')
plt.ylabel('Overshooting Top \n Minimum Brightness Temperature (K)', fontsize=16)
plt.xlabel('Time', fontsize=16)
plt.legend(fontsize=16)
plt.title('12 November 2018 Multicellular \n OT Minimum Brightness Temperature', fontsize=24)
Text(0.5, 1.0, '12 November 2018 Multicellular \n OT Minimum Brightness Temperature')
plt.figure(figsize=(12,8))
df[df.prob > .7][0:4].cloudtop_height.plot(linewidth=3, color='red', label='Cloud Top Height')
df[df.prob > .7][0:4].tropopause_height.plot(linewidth=3, color='black', label='Tropopause Height')
#plt.ylim(0,150)
plt.yticks(fontsize=12)
plt.xticks(fontsize=12)
ax.xaxis.set_minor_locator(AutoMinorLocator())
#plt.axhline(trop_height, label=f'Tropopause Height: {np.round(trop_height,2)} km', linewidth=3, color='black', linestyle='--')
plt.ylabel('Overshooting Top \n Height (km)', fontsize=16)
plt.xlabel('Time', fontsize=16)
plt.legend(loc='lower right', fontsize=16)
plt.title('10 November 2018 Supercell \n OT Cloud Top Height', fontsize=24)
Text(0.5, 1.0, '10 November 2018 Supercell \n OT Cloud Top Height')
14 December 2018 Case¶
llcrnr=[-33.206342, -65.906586]
urcrnr=[-30.255825, -62.630553]
import glob
datestr='20181214'
ihour='02'
files = sorted(glob.glob(f'/data/relamp/a/mgrover4/ot_data/{datestr}/*{ihour}????.cdf'))
df_list = []
filtered_df_list = []
for file in files[12:25]:
ds = xr.open_dataset(file).squeeze()
df = process_ot_data(file, .7)
df = df[df.lon > -64.5]
ir_vals = ds.ir_brightness_temperature.values
plt.figure(figsize=(12,8))
lon = ds.longitude.values
lat = ds.latitude.values
ax = plt.subplot(111, projection=ccrs.PlateCarree())
ax.set_extent((-64.7, -63.7, -32.5, -31.5))
cf = plt.contourf(lon, lat, ir_vals, np.arange(190, 270, 2), cmap='gist_rainbow_r')
cbar = plt.colorbar(cf)
cbar.set_label('Brightness Temperature (K)', fontsize=16)
cbar.ax.tick_params(labelsize=16)
try:
df_ex = df[df.mintb == np.nanmin(df.mintb.values)]
except:
continue
df_ex.plot(ax=ax, facecolor="none", edgecolor='black', lw=3,)
ax.plot(0, 0, color='black', lw=3, label=f'OT Area {np.round(df_ex.area_polygon.values[0], 2)} km2 \n Prob: {np.round(df_ex.prob.values[0], 2)}')
plt.legend(fontsize=16)
#gl = ax.gridlines(crs=ccrs.PlateCarree(), draw_labels=True,
# linewidth=2, color='gray', alpha=0.5, linestyle='--')
#try:
#df_ex = df[df.mintb == np.nanmin(df.mintb.values)]
# df_ex = df[(df.lon > -64.5) & (df.lon < -64.2) & (df.lat > -32.1)]
# If more than one OT, choose the one with the lowest min BT value
# df_ex = df_ex[df_ex.mintb == np.nanmin(df_ex.mintb.values)]
# df_ex.plot(ax=ax, facecolor="none", edgecolor='black', lw=3,)
# ax.plot(0, 0, color='black', lw=3, label=f'OT Area {np.round(df_ex.area_circle_polygon.values[0], 2)} km2 \n Prob: {np.round(df_ex.prob.values[0], 2)}')
# plt.legend(fontsize=16)
# filtered_df_list.append(df_ex)
#except:
# None
gl = ax.gridlines(crs=ccrs.PlateCarree(), draw_labels=True,
linewidth=2, color='gray', alpha=0.5, linestyle='--')
gl.xlabels_top = False
gl.ylabels_right = False
gl.xlabel_style = {'size': 14, 'color': 'k'}
gl.ylabel_style = {'size': 14, 'color': 'k'}
plt.title(pd.to_datetime(df.time.values[0]).strftime('%d %b %y %H:%M:%S'), fontsize=24)
plt.savefig(pd.to_datetime(df.time.values[0]).strftime('compare_plots/Dec14/%b_%d_%y_%H%M.%S.png'), dpi=300)
plt.show()
plt.close()
df_list.append(df)
(array([ 95, 95, 95, 95, 95, 95, 96, 96, 96, 96, 96, 96, 96,
97, 97, 97, 97, 97, 97, 97, 98, 98, 98, 98, 98, 99,
99, 99, 99, 99, 100, 100, 101, 101, 102]), array([18, 19, 20, 22, 23, 24, 18, 19, 20, 21, 22, 23, 24, 17, 18, 19, 20,
21, 22, 23, 17, 18, 19, 20, 21, 17, 18, 19, 20, 21, 19, 20, 19, 20,
20]))
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[0.0361480712890625, 0.026714324951171875, -0.028423309326171875, -0.10457992553710938, -0.1451416015625, -0.14679718017578125, -0.1234893798828125, -0.091522216796875, -0.059482574462890625, -0.031436920166015625, -0.00098419189453125, 0.03814697265625, 0.08115386962890625, 0.11807632446289062, 0.12943649291992188, 0.099334716796875, 0.035671234130859375, -0.045436859130859375, -0.11886978149414062, -0.16165542602539062, -0.16832351684570312, -0.139862060546875, -0.08561325073242188, -0.017719268798828125, 0.04883575439453125, 0.09879302978515625, 0.11870574951171875, 0.09964752197265625, 0.048824310302734375, -0.0191497802734375, -0.07604217529296875, -0.09516525268554688, -0.06963348388671875, -0.00447845458984375, 0.06736373901367188, 0.11065292358398438, 0.10879898071289062, 0.058441162109375, -0.00955963134765625, -0.037204742431640625, -0.0330047607421875]
(array([75, 76, 76, 77, 77, 78, 78, 78, 78, 79, 79, 79, 79, 79, 80, 80, 80]), array([63, 62, 63, 62, 63, 61, 62, 63, 64, 60, 61, 62, 63, 64, 62, 63, 64]))
[0.1196746826171875, 0.15272903442382812, 0.14135360717773438, 0.06884384155273438, 0.039638519287109375, 0.06399154663085938, 0.12950897216796875, 0.21693801879882812, 0.30224609375, 0.3599128723144531, 0.3633537292480469, 0.2928657531738281, 0.1572113037109375, -0.020450592041015625, -0.18778610229492188, -0.2945137023925781, -0.32775115966796875, -0.2931060791015625, -0.23276138305664062, -0.18821334838867188, -0.16159820556640625, -0.14406585693359375, -0.12213516235351562, -0.08558273315429688, -0.041973114013671875, -0.002536773681640625, 0.02466583251953125, 0.034320831298828125, 0.02899932861328125, 0.013256072998046875, -0.008380889892578125, -0.030895233154296875, -0.0472869873046875, -0.051239013671875, -0.0411224365234375, -0.018627166748046875, 0.00598907470703125, 0.02236175537109375, 0.02829742431640625, 0.020679473876953125, 0.012725830078125]
(array([88, 89, 89, 89, 90, 90, 90, 90, 91, 91, 91, 92]), array([97, 96, 97, 98, 95, 96, 97, 98, 95, 96, 97, 96]))
[0.2027435302734375, 0.28729248046875, 0.3263130187988281, 0.222503662109375, 0.11342620849609375, 0.0253753662109375, -0.03153228759765625, -0.05692291259765625, -0.07329559326171875, -0.10390853881835938, -0.15213775634765625, -0.20940780639648438, -0.23926925659179688, -0.2059326171875, -0.10415267944335938, 0.051235198974609375, 0.1956329345703125, 0.26447296142578125, 0.24312591552734375, 0.14118576049804688, 0.015193939208984375, -0.07563018798828125, -0.10834884643554688, -0.07820892333984375, -0.016864776611328125, 0.03813934326171875, 0.0621795654296875, 0.042041778564453125, -0.00290679931640625, -0.04659271240234375, -0.0686798095703125, -0.057098388671875, -0.027057647705078125, 0.001613616943359375, 0.017879486083984375, 0.01531982421875, -0.002849578857421875, -0.03101348876953125, -0.0635223388671875, -0.06392669677734375, -0.04793548583984375]
(array([91, 92, 92, 92, 93, 93, 93, 94, 94, 94, 95, 95, 95]), array([89, 88, 89, 90, 88, 89, 90, 88, 89, 90, 88, 89, 90]))
[0.23195648193359375, 0.32250213623046875, 0.348480224609375, 0.20733642578125, 0.07640457153320312, -0.004383087158203125, -0.0407562255859375, -0.052806854248046875, -0.0724029541015625, -0.12208175659179688, -0.17525100708007812, -0.19733047485351562, -0.170928955078125, -0.09360885620117188, -0.005603790283203125, 0.0501251220703125, 0.062351226806640625, 0.034175872802734375, -0.00567626953125, -0.028682708740234375, -0.03270721435546875, -0.0243682861328125, -0.01889801025390625, -0.02862548828125, -0.045711517333984375, -0.060253143310546875, -0.07429885864257812, -0.0941619873046875, -0.1312255859375, -0.19010162353515625, -0.24321365356445312, -0.2563934326171875, -0.201416015625, -0.066619873046875, 0.09944534301757812, 0.23857879638671875, 0.3141975402832031, 0.30704498291015625, 0.2454986572265625, 0.14528656005859375, 0.082733154296875]
(array([ 96, 96, 96, 96, 97, 97, 97, 97, 97, 97, 98, 98, 98,
98, 98, 98, 98, 98, 99, 99, 99, 99, 99, 99, 99, 99,
100, 100, 100, 100, 100, 100, 100, 101, 101, 101, 101, 101, 102]), array([128, 129, 130, 131, 127, 128, 129, 130, 131, 132, 126, 127, 128,
129, 130, 131, 132, 133, 126, 127, 128, 129, 130, 131, 132, 133,
126, 127, 128, 129, 130, 131, 132, 127, 128, 129, 130, 131, 127]))
[0.30635833740234375, 0.4300575256347656, 0.46099853515625, 0.23578643798828125, -0.02477264404296875, -0.2577323913574219, -0.4126548767089844, -0.45536041259765625, -0.40421295166015625, -0.294342041015625, -0.17527008056640625, -0.09086227416992188, -0.048030853271484375, -0.040439605712890625, -0.0457916259765625, -0.043941497802734375, -0.039241790771484375, -0.03849029541015625, -0.033763885498046875, -0.01619720458984375, 0.01216888427734375, 0.044994354248046875, 0.06961441040039062, 0.07682037353515625, 0.0775146484375, 0.08307647705078125, 0.08666610717773438, 0.07820510864257812, 0.05287933349609375, 0.01113128662109375, -0.030731201171875, -0.05515289306640625, -0.055507659912109375, -0.031978607177734375, -0.0010528564453125, 0.018890380859375, 0.01812744140625, -0.005825042724609375, -0.035030364990234375, -0.036861419677734375, -0.02661895751953125]
(array([ 98, 99, 99, 99, 100, 100, 100, 101, 101, 101, 101, 102, 102,
102, 103, 103]), array([28, 28, 29, 30, 27, 28, 29, 25, 26, 27, 28, 26, 27, 28, 27, 28]))
[0.188568115234375, 0.30675506591796875, 0.4585914611816406, 0.5104522705078125, 0.5546150207519531, 0.6119422912597656, 0.647735595703125, 0.6168251037597656, 0.4876670837402344, 0.24959182739257812, -0.038219451904296875, -0.3006095886230469, -0.4683570861816406, -0.49382781982421875, -0.40985107421875, -0.2700843811035156, -0.13100814819335938, -0.03900146484375, 0.002826690673828125, 0.004703521728515625, -0.013034820556640625, -0.031627655029296875, -0.048797607421875, -0.06396865844726562, -0.06685638427734375, -0.048320770263671875, -0.01351165771484375, 0.02752685546875, 0.059391021728515625, 0.07079315185546875, 0.072357177734375, 0.07574462890625, 0.07480239868164062, 0.0609893798828125, 0.03404998779296875, -0.00109100341796875, -0.02707672119140625, -0.028411865234375, -0.009487152099609375, 0.006755828857421875, 0.01003265380859375]
for df_sub in filtered_df_list[:15]:
print(df_sub.time)
3 2018-12-14 02:12:25.000000512
Name: time, dtype: datetime64[ns]
3 2018-12-14 02:13:25.000000512
Name: time, dtype: datetime64[ns]
2 2018-12-14 02:14:25.000000512
Name: time, dtype: datetime64[ns]
2 2018-12-14 02:15:25.000000512
Name: time, dtype: datetime64[ns]
2 2018-12-14 02:16:25.000000512
Name: time, dtype: datetime64[ns]
3 2018-12-14 02:17:25.000000512
Name: time, dtype: datetime64[ns]
3 2018-12-14 02:18:25.000000512
Name: time, dtype: datetime64[ns]
4 2018-12-14 02:19:25.000000512
Name: time, dtype: datetime64[ns]
4 2018-12-14 02:20:25.000000512
Name: time, dtype: datetime64[ns]
4 2018-12-14 02:21:25.000000512
Name: time, dtype: datetime64[ns]
3 2018-12-14 02:22:25.000000512
Name: time, dtype: datetime64[ns]
4 2018-12-14 02:23:25.000000512
Name: time, dtype: datetime64[ns]
6 2018-12-14 02:24:25.000000512
Name: time, dtype: datetime64[ns]
6 2018-12-14 02:25:25.000000512
Name: time, dtype: datetime64[ns]
5 2018-12-14 02:26:25.000000512
Name: time, dtype: datetime64[ns]
len(filtered_df_list)
20
df = pd.concat(filtered_df_list[:15])
df = df[df.time != '2018-12-14 02:22:25.000000000'].reset_index()
df.index = df.time
df.area_circle_polygon.plot()
<AxesSubplot:xlabel='time'>
df.mintb.plot()
<AxesSubplot:xlabel='time'>
plt.figure(figsize=(12,8))
df[df.prob > .7].area_circle_polygon.plot(linewidth=3, color='black')
#df[df.prob > .7].area_circle_polygon[3:].plot(linewidth=3, color='black', label='2nd OT')
plt.ylim(0,150)
plt.yticks(fontsize=12)
plt.xticks(fontsize=12)
ax.xaxis.set_minor_locator(AutoMinorLocator())
plt.ylabel('Overshooting Top Area (km2)', fontsize=16)
plt.xlabel('Time', fontsize=16)
#plt.legend(fontsize=16)
plt.title('14 December 2018 MCS OTA', fontsize=24)
Text(0.5, 1.0, '14 December 2018 MCS OTA')
trop_temp = df.tropopause_temperature.values.mean()
plt.figure(figsize=(12,8))
df[df.prob > .7].tropopause_temperature.plot(linewidth=3, linestyle='--', color='black', label='Tropopause Temperature')
df[df.prob > .7].mintb.plot(linewidth=3, color='blue', label='Minimum Brightness Temperature')
#df[df.prob > .7].mintb[:3].plot(linewidth=3, color='red', label='1st OT')
#df[df.prob > .7].mintb[3:].plot(linewidth=3, color='black', label='2nd OT')
#plt.ylim(0,150)
plt.yticks(fontsize=12)
plt.xticks(fontsize=12)
ax.xaxis.set_minor_locator(AutoMinorLocator())
#plt.axhline(trop_temp, label=f'Tropopause Temperature: {np.round(trop_temp,2)} K', linewidth=3, color='black', linestyle='--')
plt.ylabel('Overshooting Top \n Minimum Brightness Temperature (K)', fontsize=16)
plt.xlabel('Time', fontsize=16)
plt.legend(fontsize=16, loc='lower right')
plt.title('14 December 2018 MCS \n OT Minimum Brightness Temperature', fontsize=24)
Text(0.5, 1.0, '14 December 2018 MCS \n OT Minimum Brightness Temperature')
plt.figure(figsize=(12,8))
df[df.prob > .7].cloudtop_height.plot(linewidth=3, color='red', label='Cloud Top Height')
df[df.prob > .7].tropopause_height.plot(linewidth=3, color='black', label='Tropopause Height')
#plt.ylim(0,150)
plt.yticks(fontsize=12)
plt.xticks(fontsize=12)
ax.xaxis.set_minor_locator(AutoMinorLocator())
#plt.axhline(trop_height, label=f'Tropopause Height: {np.round(trop_height,2)} km', linewidth=3, color='black', linestyle='--')
plt.ylabel('Overshooting Top \n Height (km)', fontsize=16)
plt.xlabel('Time', fontsize=16)
plt.legend(loc='upper right', fontsize=16)
plt.title('14 December 2018 MCS \n OT Cloud Top Height', fontsize=24)
Text(0.5, 1.0, '14 December 2018 MCS \n OT Cloud Top Height')
df[df.prob > .7]
| index | area_circle_mean | area_circle_median | area_circle_polygon | cloudtop_height | e_radial | e_radial_del2 | e_tb | geometry | lat | ... | sw_radial | sw_radial_del2 | sw_tb | time | tropopause_height | tropopause_pressure | tropopause_temperature | w_radial | w_radial_del2 | w_tb | |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| time | |||||||||||||||||||||
| 2018-12-14 02:15:25.000000512 | 2 | 45.161587 | 42.764609 | 40.137743 | 17.133001 | 5.0 | [0.232147216796875, 0.3162193298339844, 0.3344... | [197.88063049316406, 197.86566162109375, 198.3... | POLYGON ((-64.38393 -31.91964, -64.36048 -31.9... | -31.883928 | ... | 4.0 | [0.11138916015625, 0.1387481689453125, 0.11080... | [197.88063049316406, 198.53416442871094, 199.4... | 2018-12-14 02:15:25.000000512 | 16.376001 | 101.699997 | 198.979996 | 3.0 | [0.17822265625, 0.20901870727539062, 0.1239013... | [197.88063049316406, 198.42604064941406, 199.3... |
| 2018-12-14 02:16:25.000000512 | 2 | 52.460898 | 44.429360 | 46.100451 | 17.381001 | 4.0 | [0.2633514404296875, 0.3406410217285156, 0.304... | [196.16455078125, 196.80142211914062, 197.9649... | POLYGON ((-64.36607 -31.92857, -64.34261 -31.9... | -31.883928 | ... | 6.0 | [0.146453857421875, 0.20116043090820312, 0.216... | [196.16455078125, 196.2321014404297, 196.59255... | 2018-12-14 02:16:25.000000512 | 16.324001 | 102.629997 | 198.940002 | 4.0 | [0.29964447021484375, 0.4090423583984375, 0.41... | [196.16455078125, 196.20864868164062, 196.8520... |
| 2018-12-14 02:17:25.000000512 | 3 | 43.147927 | 51.047807 | 39.036624 | 17.379002 | 3.0 | [0.230255126953125, 0.2561492919921875, 0.0994... | [196.31793212890625, 197.85450744628906, 199.8... | POLYGON ((-64.34821 -31.93750, -64.33414 -31.9... | -31.901785 | ... | 6.0 | [0.15830230712890625, 0.22165679931640625, 0.2... | [196.31793212890625, 196.1490478515625, 196.29... | 2018-12-14 02:17:25.000000512 | 16.321001 | 102.680000 | 198.940002 | 5.0 | [0.357269287109375, 0.5048332214355469, 0.5647... | [196.31793212890625, 195.51943969726562, 195.4... |
| 2018-12-14 02:18:25.000000512 | 3 | 75.347240 | 62.038170 | 69.240941 | 17.264000 | 4.0 | [0.32587432861328125, 0.4842567443847656, 0.55... | [195.41659545898438, 196.1536407470703, 197.54... | POLYGON ((-64.34821 -31.95536, -64.32007 -31.9... | -31.901785 | ... | 7.0 | [0.1096038818359375, 0.16022872924804688, 0.20... | [195.41659545898438, 195.2171173095703, 195.23... | 2018-12-14 02:18:25.000000512 | 16.313002 | 102.809998 | 198.940002 | 8.0 | [0.19608306884765625, 0.25012969970703125, 0.2... | [195.41659545898438, 195.24977111816406, 195.4... |
| 2018-12-14 02:19:25.000000512 | 4 | 83.139718 | 71.943845 | 74.079337 | 17.222000 | 7.0 | [0.187957763671875, 0.2776336669921875, 0.3737... | [195.19509887695312, 195.1221923828125, 195.42... | POLYGON ((-64.36607 -31.96429, -64.33324 -31.9... | -31.919643 | ... | 7.0 | [0.17630767822265625, 0.264617919921875, 0.344... | [195.19509887695312, 195.6378936767578, 196.43... | 2018-12-14 02:19:25.000000512 | 16.297001 | 103.070000 | 198.979996 | 5.0 | [0.2090606689453125, 0.30672454833984375, 0.35... | [195.19509887695312, 195.67250061035156, 196.5... |
| 2018-12-14 02:20:25.000000512 | 4 | 73.823728 | 55.992223 | 67.684649 | 17.255001 | 5.0 | [0.294921875, 0.42510223388671875, 0.496726989... | [195.96913146972656, 196.3324737548828, 197.28... | POLYGON ((-64.34821 -31.97322, -64.32476 -31.9... | -31.9375 | ... | 6.0 | [0.21070098876953125, 0.3087921142578125, 0.38... | [195.96913146972656, 196.5013427734375, 197.45... | 2018-12-14 02:20:25.000000512 | 16.297001 | 103.070000 | 198.970001 | 5.0 | [0.29141998291015625, 0.4183769226074219, 0.48... | [195.96913146972656, 196.2245635986328, 197.06... |
| 2018-12-14 02:21:25.000000512 | 4 | 72.997399 | 71.928863 | 64.809910 | 16.992001 | 4.0 | [0.29632568359375, 0.42275238037109375, 0.4506... | [196.79432678222656, 197.6276092529297, 199.05... | POLYGON ((-64.33035 -31.98215, -64.30691 -31.9... | -31.9375 | ... | 7.0 | [0.18886566162109375, 0.28931427001953125, 0.3... | [196.79432678222656, 196.8221893310547, 197.22... | 2018-12-14 02:21:25.000000512 | 16.302000 | 103.000000 | 198.940002 | 7.0 | [0.27332305908203125, 0.39104461669921875, 0.4... | [196.79432678222656, 196.5672607421875, 196.88... |
| 2018-12-14 02:22:25.000000512 | 3 | 58.082477 | 52.920342 | 51.882368 | 17.131001 | 3.0 | [0.2072296142578125, 0.24999618530273438, 0.17... | [197.58828735351562, 198.59449768066406, 200.0... | POLYGON ((-64.49107 -31.82143, -64.46761 -31.8... | -31.776787 | ... | 6.0 | [0.08758544921875, 0.1188201904296875, 0.12540... | [197.58828735351562, 197.18954467773438, 196.9... | 2018-12-14 02:22:25.000000512 | 16.294001 | 103.149994 | 198.929993 | 7.0 | [0.2810211181640625, 0.3928070068359375, 0.433... | [197.58828735351562, 197.1873016357422, 197.34... |
| 2018-12-14 02:23:25.000000512 | 4 | 62.704236 | 52.840929 | 56.217753 | 17.098001 | 6.0 | [0.28281402587890625, 0.3996734619140625, 0.46... | [197.28517150878906, 197.1569366455078, 197.59... | POLYGON ((-64.33035 -31.98215, -64.30222 -31.9... | -31.919643 | ... | 7.0 | [0.13660430908203125, 0.19193649291992188, 0.2... | [197.28517150878906, 197.67198181152344, 198.3... | 2018-12-14 02:23:25.000000512 | 16.309000 | 102.889999 | 198.899994 | 4.0 | [0.27106475830078125, 0.37026214599609375, 0.3... | [197.28517150878906, 198.03897094726562, 199.3... |
| 2018-12-14 02:24:25.000000512 | 6 | 42.713085 | 36.695613 | 37.921218 | 17.098001 | 4.0 | [0.2642974853515625, 0.344482421875, 0.3178443... | [197.22984313964844, 197.70590209960938, 198.7... | POLYGON ((-64.31250 -31.96429, -64.28905 -31.9... | -31.919643 | ... | 5.0 | [0.1492919921875, 0.21879196166992188, 0.27700... | [197.22984313964844, 197.2263641357422, 197.52... | 2018-12-14 02:24:25.000000512 | 16.314001 | 102.790001 | 198.860001 | 5.0 | [0.34625244140625, 0.4963417053222656, 0.54927... | [197.22984313964844, 197.44638061523438, 198.3... |
| 2018-12-14 02:25:25.000000512 | 6 | 43.132270 | 42.752282 | 38.579593 | 16.927000 | 5.0 | [0.3408050537109375, 0.4969329833984375, 0.579... | [198.26483154296875, 197.85511779785156, 198.1... | POLYGON ((-64.31250 -31.97322, -64.28905 -31.9... | -31.919643 | ... | 5.0 | [0.1100311279296875, 0.14833831787109375, 0.15... | [198.26483154296875, 198.66009521484375, 199.2... | 2018-12-14 02:25:25.000000512 | 16.310001 | 102.869995 | 198.860001 | 3.0 | [0.17779541015625, 0.18876266479492188, 0.0699... | [198.26483154296875, 199.30963134765625, 200.7... |
| 2018-12-14 02:26:25.000000512 | 5 | 54.653763 | 44.410219 | 48.283925 | 16.632000 | 4.0 | [0.2240753173828125, 0.3194236755371094, 0.347... | [199.13343811035156, 199.2487335205078, 199.81... | POLYGON ((-64.29464 -31.97322, -64.26181 -31.9... | -31.919643 | ... | 5.0 | [0.0725860595703125, 0.09778213500976562, 0.09... | [199.13343811035156, 199.3341827392578, 199.68... | 2018-12-14 02:26:25.000000512 | 16.320002 | 102.689995 | 198.819992 | 4.0 | [0.1581878662109375, 0.19797134399414062, 0.16... | [199.13343811035156, 199.46617126464844, 200.1... |
12 rows × 41 columns
df[df.prob > .7].to_csv('Revised_OTs_14_Dec_2018_0214_0225.csv')
df = pd.read_csv('Revised_OTs_14_Dec_2018_0212_0225.csv')