def loop(y):
out = cnst.local_data + 'GRIDSAT/MCS18/'
infolder = cnst.local_data + 'GRIDSAT/www.ncei.noaa.gov/data/geostationary-ir-channel-brightness-temperature-gridsat-b1/access/'
filename = 'gridsat_WA_-40_1000km2_15-21UTC' + str(y) + '.nc'
da = None
if os.path.isfile(out + filename):
return
files = glob.glob(infolder + str(y) + '/GRIDSAT-AFRICA_CP*.nc')
files.sort()
for f in files:
print('Doing ' + f)
df = xr.open_dataset(f)
if (df['time.hour']<15) | (df['time.hour']>21):
continue
df.rename({'irwin_cdr': 'tir'}, inplace=True)
df['tir'].values = df['tir'].values-273.15
labels, goodinds = ua.blob_define(df['tir'].values, -40, minmax_area=[16, 25000],
max_area=None) # 7.7x7.7km = 64km2 per pix in gridsat?
df['tir'].values[labels == 0] = 0
df['tir'].values[df['tir'].values < -110] = 0
df['tir'].values = (np.round(df['tir'].values, decimals=2)*100).astype(np.int16)
try:
da = xr.concat([da, df], dim='time')
except TypeError:
da = df.copy()
enc = {'tir': {'complevel': 5, 'shuffle': True, 'zlib': True}}
da.to_netcdf(out + filename, encoding=enc)
da.close()
def file_loop(f):
print('Doing ' + f)
ydic = {}
df = xr.open_dataset(f)
df = df.sel(latitude=slice(-4,13), longitude=slice(-18,20))
if (int(df['time.month'][0]) < 9) & (int(df['time.month'][0]) > 6):
print('return')
return
df = df['lw_out_PBLtop'][df['time.hour'] == 18]
for d in df:
print('Read data')
d.values = u_met.olr_to_bt(d.values)
#ipdb.set_trace()
labels, goodinds = ua.blob_define(d.values, -50, minmax_area=[258, 40000], max_area=None) # 4.4*4.4km: 258 pixel = 5000km2, 40000 pixel = 350000km2
for g in goodinds:
if g==0:
continue
pos = np.where(labels==g)
dic = dictionary()
ts = pd.to_datetime(d['time'].values)
date = ts.strftime('%Y-%m-%d_%H:%M:%S')
dic['date'] = ts
dic['month'] = int(d['time.month'])
dic['year'] = int(d['time.year'])
storm = d.values[pos]
dic['area'] = storm.size
dic['70area'] = np.sum(storm<=-70)
dic['minlon'] = np.min(d.longitude.values[pos[1]])
dic['minlat'] = np.min(d.latitude.values[pos[0]])
dic['maxlon'] = np.max(d.longitude.values[pos[1]])
dic['maxlat'] = np.max(d.latitude.values[pos[0]])
dic['clon'] = dic['minlon'] + (dic['maxlon'] - dic['minlon'])/2
dic['clat'] = dic['minlat'] + (dic['maxlat'] - dic['minlat'])/2
dic['tmin'] = np.min(storm)
dic['tmean'] = np.mean(storm)
dic['t10'] = np.percentile(storm, 10)
dic['t90'] = np.percentile(storm, 10)
ydic[date + '_' + str(g)] = dic
return ydic
def file_loop(f):
print('Doing ' + f)
ydic = {}
df = xr.open_dataset(f)
df = df.sel(lat=slice(-4,25), lon=slice(-18,20))
for d in df['tir']:
labels, goodinds = ua.blob_define(d.values, -50, minmax_area=[83, 25000], max_area=None) # 7.7x7.7km = 64km2 per pix in gridsat? 83 pix is 5000km2
for g in goodinds:
if g==0:
continue
pos = np.where(labels==g)
dic = dictionary()
ts = pd.to_datetime(d['time'].values)
date = ts.strftime('%Y-%m-%d_%H:%M:%S')
dic['date'] = ts
dic['month'] = int(d['time.month'])
dic['year'] = int(d['time.year'])
storm = d.values[pos]
dic['area'] = storm.size
dic['70area'] = np.sum(storm<=-70)
dic['minlon'] = np.min(d.lon.values[pos[1]])
dic['minlat'] = np.min(d.lat.values[pos[0]])
dic['maxlon'] = np.max(d.lon.values[pos[1]])
dic['maxlat'] = np.max(d.lat.values[pos[0]])
dic['clon'] = dic['minlon'] + (dic['maxlon'] - dic['minlon'])/2
dic['clat'] = dic['minlat'] + (dic['maxlat'] - dic['minlat'])/2
dic['tmin'] = np.min(storm)
dic['tmean'] = np.mean(storm)
dic['t10'] = np.percentile(storm, 10)
dic['t90'] = np.percentile(storm, 10)
ydic[date + '_' + str(g)] = dic
return ydic
def saveYearly():
out = '/users/global/cornkle/mymachine/GRIDSAT/MCS18/'
infolder = '/users/global/cornkle/mymachine/GRIDSAT/www.ncei.noaa.gov/data/geostationary-ir-channel-brightness-temperature-gridsat-b1/access/'
years = np.arange(1983, 2018) # list(next(os.walk(msg_folder))[1])
for y in years:
filename = 'gridsat_WA_' + str(y) + '.nc'
da = None
if os.path.isfile(out + filename):
continue
files = glob.glob(infolder + str(y) + '/GRIDSAT-AFRICA_CP*.nc')
files.sort()
for f in files:
print('Doing ' + f)
df = xr.open_dataset(f)
if df['time.hour'] != 18:
continue
df.rename({'irwin_cdr': 'tir'}, inplace=True)
df['tir'].values = df['tir'].values - 273.15
labels, goodinds = ua.blob_define(
df['tir'].values, -70, minmax_area=[83, 25000], max_area=None
) # 7.7x7.7km = 64km2 per pix in gridsat? 83 pix is 5000km2
df['tir'].values[labels == 0] = 0
df['tir'].values[df['tir'].values < -110] = 0
try:
da = xr.concat([da, df], dim='time')
except TypeError:
da = df.copy()
enc = {'tir': {'complevel': 5, 'shuffle': True, 'zlib': True}}
da.to_netcdf(out + filename, encoding=enc)
da.close()
def loop(y):
out = cnst.local_data + 'GRIDSAT/MCS18/'
infolder = cnst.local_data + 'GRIDSAT/www.ncei.noaa.gov/data/geostationary-ir-channel-brightness-temperature-gridsat-b1/access/'
filename = 'gridsat_WA_-70_5000km2_15-21UTC' + str(y) + '.nc'
da = None
if os.path.isfile(out + filename):
return
files = glob.glob(infolder + str(y) + '/GRIDSAT-AFRICA_CP*.nc')
files.sort()
for f in files:
print('Doing ' + f)
df = xr.open_dataset(f)
#ipdb.set_trace()
if (df['time.hour'] < 15) | (df['time.hour'] > 21):
continue
df.rename({'irwin_cdr': 'tir'}, inplace=True)
df['tir'].values = df['tir'].values - 273.15
labels, goodinds = ua.blob_define(
df['tir'].values, -70, minmax_area=[83, 25000],
max_area=None) # 7.7x7.7km = 64km2 per pix in gridsat?
df['tir'].values[labels == 0] = 0
df['tir'].values[df['tir'].values < -110] = 0
df['tir'].values = (np.round(df['tir'].values, decimals=2) *
100).astype(np.int16)
try:
da = xr.concat([da, df], dim='time')
except TypeError:
da = df.copy()
enc = {'tir': {'complevel': 5, 'shuffle': True, 'zlib': True}}
da.to_netcdf(out + filename, encoding=enc)
da.close()
def saveMCS_WA15(year):
trmm_folder = cnst.network_data + 'data/OBS/IMERG_HQ_precip_old'
msg_folder = cnst.network_data + 'data/OBS/MSG_WA30' #meteosat_WA30'
msg_folder2 = cnst.network_data + 'data/OBS/MSG_MAMON'
mJJAS = msg.ReadMsg(msg_folder)
mMAMON = msg.ReadMsg(msg_folder2)
cnt = 0
_y = year
for ids, _m in enumerate(range(3, 12)):
files = glob.glob(
trmm_folder + '/' + str(_y) + '/' + str(_m).zfill(2) + '/*.nc4'
) # area=[-12, 12, 4, 9]) # [-15, 15, 4, 21], [-10, 10, 10, 20]
for tf in files:
t = xr.open_dataset(tf)
_h = t['time.hour'].values[0]
_d = t['time.day'].values[0]
_mi = t['time.minute'].values[0]
# if (_h <15) | (_h>21):
# print('Wrong hour')
# continue
if (_m < 3) | (_m > 11):
print('Wrong month')
continue
da = t['HQprecipitation'].squeeze()
da = da.T
tdic = da.sel(lat=slice(4.3, 9), lon=slice(-14,
14)) #[-12, 15, 5, 25]
if np.sum(tdic.values) <= 0.01:
continue
if _m in [3, 4, 5, 10, 11]:
m = mMAMON
else:
m = mJJAS
date = dt.datetime(_y, _m, _d, _h, _mi)
arr = np.array([15, 30, 45, 60, 0])
# get closest minute
dm = arr - _mi
if (dm < 0).any():
dm = dm[dm < 0]
try:
ind = (np.abs(dm)).argmin()
except ValueError:
continue
# set zero shift time for msg
dt0 = dm[ind]
ndate = date + dt.timedelta(minutes=int(dt0))
m.set_date(ndate.year, ndate.month, ndate.day, ndate.hour,
ndate.minute)
mdic = m.get_data(llbox=[
tdic['lon'].values.min(), tdic['lon'].values.max(),
tdic['lat'].values.min(), tdic['lat'].values.max()
])
# check whether date is completely missing or just 30mins interval exists
# if str(date) == '2004-05-02 13:15:00':
# pdb.set_trace()
if not mdic:
dm = np.delete(dm, np.argmin(np.abs(dm)), axis=0)
try:
dummy = np.min(np.abs(dm)) > 15
except ValueError:
continue
if dummy:
print('Date missing')
continue
ind = (np.abs(dm)).argmin()
dt0 = dm[ind]
ndate = date + dt.timedelta(minutes=int(dt0))
m.set_date(ndate.year, ndate.month, ndate.day, ndate.hour,
ndate.minute)
mdic = m.get_data(llbox=[
tdic['lon'].values.min(), tdic['lon'].values.max(),
tdic['lat'].values.min(), tdic['lat'].values.max()
])
if not mdic:
print('Date missing')
continue
print('TRMM:', date, 'MSG:', ndate.year, ndate.month, ndate.day,
ndate.hour, ndate.minute)
lon1 = mdic['lon'].values
lat1 = mdic['lat'].values
if ids == 0:
inds, weights, shape = u_int.interpolation_weights_grid(
lon1, lat1, t.salem.grid)
orig = mdic['t'].values
try:
outorig = u_int.interpolate_data(orig, inds, weights, shape)
except IndexError:
print('Interpolation problem, continue')
outt = outorig.copy()
labels, goodinds = ua.blob_define(
outt, -40, minmax_area=[556, 40000],
max_area=None) # 7.7x7.7km = 64km2 per pix in gridsat?
outt[labels == 0] = 0
outt[outt < -115] = 0
outt = (np.round(outt, decimals=2) * 100).astype(np.int16)
outorig = (np.round(outorig, decimals=2) * 100).astype(np.int16)
df = xr.Dataset(
{
'rain': (['lon', 'lat'], da.values),
'mcs': (['lon', 'lat'], outt),
'tir': (['lon', 'lat'], outorig),
},
coords={
'lon': da.lon,
'lat': da.lat,
'time': date
})
# try:
# blob = xr.DataArray(power_msg[np.newaxis, :],
# coords={'time': date, 'lat': latitudes, 'lon': longitudes},
# dims=['time', 'lat', 'lon']) # [np.newaxis, :])
# except ValueError:
# ipdb.set_trace()
# tir = xr.DataArray(new_savet[np.newaxis, :], coords={'time': date, 'lat': latitudes, 'lon': longitudes},
# dims=['time', 'lat', 'lon'])
try:
ds = xr.concat([ds, df], dim='time')
except TypeError:
ds = df.copy()
savefile = cnst.network_data + 'MCSfiles/TIR_on_GPM/GPM_MCS_' + str(
_y) + '-' + str(_m).zfill(2) + '.nc'
try:
os.remove(savefile)
except OSError:
print('OSError, no dir?')
pass
ds.to_netcdf(path=savefile, mode='w')
print('Saved ' + savefile)
ds.close()
def file_loop(f):
print('Doing file: ' + f)
dic = xr.open_dataset(f)
era = xr.open_dataset(cnst.ERA_DAILY_PL12UTC)
getera = np.where((era['time.day'] == dic['time.day'])
& (era['time.month'] == dic['time.month'])
& (era['time.year'] == dic['time.year']))
try:
era_day = era.isel(time=int(getera[0]))
except TypeError:
print('Era missing')
return
out = dictionary()
res = []
outt = dic['tc_lag0'].values
test70 = outt.copy()
labels, goodinds = ua.blob_define(test70, -70, min_area=200)
if np.sum(goodinds) == 0:
return
outp = dic['p'].values
tminpos = np.where(dic['tc_lag0'].values == np.nanmin(
dic['tc_lag0'].values)) # era position close to min temp
if len(tminpos[0]) > 1:
ptmax = np.nanmax((dic['p'].values)[tminpos])
if ptmax > 0:
prpos = np.where((dic['p'].values)[tminpos] == ptmax)
tminpos = ((tminpos[0])[prpos], (tminpos[1])[prpos])
else:
tminpos = ((tminpos[0])[0], (tminpos[1])[0])
elon = dic['lon'].values[tminpos]
elat = dic['lat'].values[tminpos]
era_day = era_day.sel(latitude=elat, longitude=elon, method='nearest')
e925 = era_day.sel(level=925).mean()
elow = era_day.sel(level=slice(925, 850)).mean('level').mean()
e650 = era_day.sel(level=650).mean()
emid = era_day.sel(level=slice(600, 700)).mean('level').mean()
out['lon'] = dic['lon'].values
out['lat'] = dic['lat'].values
out['hour'] = dic['time.hour'].item()
out['month'] = dic['time.month'].item()
out['year'] = dic['time.year'].item()
out['date'] = dic['time'].values
t_thresh = -50 # -40C ~ 167 W m-2
mask = np.isfinite(outp) & (outt <= t_thresh) & np.isfinite(outt)
mask_area = (outt <= t_thresh) & np.isfinite(outt)
if np.sum(mask) < 3:
return
out['clat'] = np.min(out['lat']) + (
(np.max(out['lat']) - np.min(out['lat'])) * 0.5)
out['clon'] = np.min(out['lon']) + (
(np.max(out['lon']) - np.min(out['lon'])) * 0.5)
isfin = np.sum((np.isfinite(outp)) & ((outt <= t_thresh)))
if isfin < 3:
return
print(
np.nanmax(outt[mask])
) # can be bigger than cutout threshold because of interpolation to 5km grid after cutout
out['area'] = np.sum(mask_area)
out['clat'] = np.min(out['lat']) + (
(np.max(out['lat']) - np.min(out['lat'])) * 0.5)
out['clon'] = np.min(out['lon']) + (
(np.max(out['lon']) - np.min(out['lon'])) * 0.5)
out['tmin'] = np.min(outt[mask])
out['tmean'] = np.mean(outt[mask])
out['pmax'] = np.max(outp[mask])
out['pmean'] = np.mean(outp[mask])
try:
out['q925'] = float(e925['q'])
except TypeError:
return
out['q650'] = float(e650['q'])
out['v925'] = float(e925['v'])
out['v650'] = float(e925['v'])
out['u925'] = float(e925['u'])
out['u650'] = float(e650['u'])
out['w925'] = float(e925['w'])
out['w650'] = float(e650['w'])
out['rh925'] = float(e925['r'])
out['rh650'] = float(e650['r'])
out['t925'] = float(e925['t'])
out['t650'] = float(e650['t'])
out['pv925'] = float(e925['pv'])
out['pv650'] = float(e650['pv'])
out['div925'] = float(e925['d'])
out['div650'] = float(e650['d'])
out['q_low'] = float(elow['q'])
out['q_mid'] = float(emid['q'])
out['shear'] = float(e650['u'] - e925['u'])
out['pgt30'] = np.sum(outp[mask] > 30)
out['isvalid'] = np.sum(mask)
out['pgt01'] = np.sum(outp[mask] > 0.1)
#
out['p'] = outp[mask]
out['t'] = outt[mask]
dic.close()
return out
def storm_count_hov():
msg_folder = cnst.GRIDSAT
fname = msg_folder + 'gridsat_WA_-40_1000km2_15-21UTC'
def makedic():
mdic = {}
for m in range(1,13):
mdic[m] = []
return mdic
dic75 = makedic()
dic70 = makedic()
dic60 = makedic()
dic50 = makedic()
dic40 = makedic()
for y in range(1983,2018): #2018
ds = xr.open_dataset(fname + str(y) + '.nc')
for m in range(1,13):
da = ds['tir'][(ds['time.month'] == m) & (ds['time.hour']==18)]#(ds['time.hour']>=15) & (ds['time.hour']<=21)]
da.values = da.values / 100
da = da.sel(lat=slice(5.2, 8), lon=slice(-10, 12))
val = 0
storm = np.array([0]*da.shape[1])
ar = []
pixel = 78 # 78 # 78 = 5000km2 # 15000 = 253
for d in da:
cut = d.sel(lat=slice(5.2, 8), lon=slice(-10, 12))
labels, goodinds = ua.blob_define(cut.values, -40, minmax_area=[pixel, 25000],
max_area=None) # 7.7x7.7km = 64km2 per pix in gridsat?
unarr = []
for ll in labels:
isun = np.unique(ll)
num = isun.size
unarr.append(num)
unarr = np.array(unarr)
# if np.sum(unarr-1) > 0:
# ipdb.set_trace()
storm += unarr-1
val += 1
print('-40 storm', storm)
dic40[m].append(storm/val)
val = 0
storm = np.array([0]*da.shape[1])
ar = []
pixel = 78 #78 # 78 = 5000km2 # 15000 = 253
for d in da:
cut = d.sel(lat=slice(5.2, 8), lon=slice(-10, 12)) # 4.5,8.5
labels, goodinds = ua.blob_define(cut.values, -50, minmax_area=[pixel, 25000],
max_area=None) # 7.7x7.7km = 64km2 per pix in gridsat?
unarr = []
for ll in labels:
isun = np.unique(ll)
num = isun.size
unarr.append(num)
unarr = np.array(unarr)
storm += unarr-1
val += 1
dic50[m].append(storm/val)
val = 0
storm = np.array([0]*da.shape[1])
ar = []
for d in da:
cut = d.sel(lat=slice(5.2, 8), lon=slice(-10, 12))
labels, goodinds = ua.blob_define(cut.values, -60, minmax_area=[pixel, 25000],
max_area=None) # 7.7x7.7km = 64km2 per pix in gridsat?
unarr = []
for ll in labels:
isun = np.unique(ll)
num = isun.size
unarr.append(num)
unarr = np.array(unarr)
storm += unarr-1
val += 1
dic60[m].append(storm/val)
val = 0
storm = np.array([0]*da.shape[1])
ar = []
for d in da:
cut = d.sel(lat=slice(5.2, 8), lon=slice(-10, 12))
labels, goodinds = ua.blob_define(cut.values, -70, minmax_area=[pixel, 25000],
max_area=None) # 7.7x7.7km = 64km2 per pix in gridsat?
unarr = []
for ll in labels:
isun = np.unique(ll)
num = isun.size
unarr.append(num)
unarr = np.array(unarr)
storm += unarr-1
val += 1
dic70[m].append(storm/val)
val = 0
storm = np.array([0]*da.shape[1])
ar = []
for d in da:
cut = d.sel(lat=slice(5.2, 8), lon=slice(-10, 12))
labels, goodinds = ua.blob_define(cut.values, -75, minmax_area=[pixel, 25000],
max_area=None) # 7.7x7.7km = 64km2 per pix in gridsat?
unarr = []
for ll in labels:
isun = np.unique(ll)
num = isun.size
unarr.append(num)
unarr = np.array(unarr)
storm += unarr-1
val += 1
dic75[m].append(storm/val)
print(40, dic40[3])
print(50, dic50[3])
print(60, dic60[3])
print(70, dic70[3])
print(75, dic75[3])
pkl.dump(dic40, open(cnst.network_data + 'data/CLOVER/saves/storm_HOVcount_10W-12E_5-8N_-40C_5000km2_18.p', #4f5-8f5N
'wb'))
pkl.dump(dic50, open(cnst.network_data + 'data/CLOVER/saves/storm_HOVcount_10W-12E_5-8N_-50C_5000km2_18.p', #4f5-8f5N
'wb'))
pkl.dump(dic60, open(cnst.network_data + 'data/CLOVER/saves/storm_HOVcount_10W-12E_5-8N_-60C_5000km2_18.p',
'wb'))
pkl.dump(dic70, open(cnst.network_data + 'data/CLOVER/saves/storm_HOVcount_10W-12E_5-8N_-70C_5000km2_18.p',
'wb'))
pkl.dump(dic75, open(cnst.network_data + 'data/CLOVER/saves/storm_HOVcount_10W-12E_5-8N_-75C_5000km2_18.p',
'wb'))
def storm_count(area=False):
msg_folder = cnst.GRIDSAT
fname = msg_folder + 'gridsat_WA_-40_1000km2_15-21UTC'
def makedic():
mdic = {}
for m in range(1,13):
mdic[m] = []
return mdic
dic75 = makedic()
dic70 = makedic()
dic60 = makedic()
dic50 = makedic()
dic40 = makedic()
area75 = makedic()
area70 = makedic()
area60 = makedic()
area50 = makedic()
area40 = makedic()
for y in range(1983,2018): #2018
ds = xr.open_dataset(fname + str(y) + '.nc')
for m in range(1,13):
da = ds['tir'][(ds['time.month'] == m) & (ds['time.hour']==18)]#(ds['time.hour']>=15) & (ds['time.hour']<=21)]
da.values = da.values / 100
val = 0
storm = 0
ar = []
pixel = 78 # 78 # 78 = 5000km2 # 15000 = 253
for d in da:
cut = d.sel(lat=slice(5.2, 8), lon=slice(-10, 12)) # 4.5,8.5
labels, goodinds = ua.blob_define(cut.values, -40, minmax_area=[pixel, 25000],
max_area=None) # 7.7x7.7km = 64km2 per pix in gridsat?
if area:
for gi in goodinds:
ar.append(np.sum(labels == gi))
storm += np.float(goodinds.size)
val += 1
dic40[m].append(storm/val)
if area:
try:
area40[m].append(np.percentile(ar, 90))
except IndexError:
area40[m].append(np.nan)
val = 0
storm = 0
ar = []
pixel = 78 #78 # 78 = 5000km2 # 15000 = 253
for d in da:
cut = d.sel(lat=slice(5.2, 8), lon=slice(-10, 12)) # 4.5,8.5
labels, goodinds = ua.blob_define(cut.values, -50, minmax_area=[pixel, 25000],
max_area=None) # 7.7x7.7km = 64km2 per pix in gridsat?
if area:
for gi in goodinds:
ar.append(np.sum(labels==gi))
storm += np.float(goodinds.size)
val += 1
dic50[m].append(storm/val)
if area:
try:
area50[m].append(np.percentile(ar, 90))
except IndexError:
area50[m].append(np.nan)
val = 0
storm = 0
ar = []
for d in da:
cut = d.sel(lat=slice(5.2, 8), lon=slice(-10, 12))
labels, goodinds = ua.blob_define(cut.values, -60, minmax_area=[pixel, 25000],
max_area=None) # 7.7x7.7km = 64km2 per pix in gridsat?
if area:
for gi in goodinds:
ar.append(np.sum(labels==gi))
storm += np.float(goodinds.size)
print(m, goodinds, val)
val += 1
dic60[m].append(storm/val)
if area:
try:
area60[m].append(np.percentile(ar, 90))
except IndexError:
area60[m].append(np.nan)
val = 0
storm = 0
ar = []
for d in da:
cut = d.sel(lat=slice(5.2, 8), lon=slice(-10, 12))
labels, goodinds = ua.blob_define(cut.values, -70, minmax_area=[pixel, 25000],
max_area=None) # 7.7x7.7km = 64km2 per pix in gridsat?
if area:
for gi in goodinds:
ar.append(np.sum(labels==gi))
storm += np.float(goodinds.size)
val += 1
dic70[m].append(storm/val)
if area:
try:
area70[m].append(np.percentile(ar, 90))
except IndexError:
area70[m].append(np.nan)
val = 0
storm = 0
ar = []
for d in da:
cut = d.sel(lat=slice(5.2, 8), lon=slice(-10, 12))
labels, goodinds = ua.blob_define(cut.values, -75, minmax_area=[pixel, 25000],
max_area=None) # 7.7x7.7km = 64km2 per pix in gridsat?
if area:
for gi in goodinds:
ar.append(np.sum(labels==gi))
storm += np.float(goodinds.size)
val += 1
dic75[m].append(storm/val)
if area:
try:
area75[m].append(np.percentile(ar, 90))
except IndexError:
area75[m].append(np.nan)
print(40, dic40[3])
print(50, dic50[3])
print(60, dic60[3])
print(70, dic70[3])
print(75, dic75[3])
pkl.dump(dic40, open(cnst.network_data + 'data/CLOVER/saves/storm_count_10W-12E_5-8N_-40C_5000km2_18.p', #4f5-8f5N
'wb'))
pkl.dump(dic50, open(cnst.network_data + 'data/CLOVER/saves/storm_count_10W-12E_5-8N_-50C_5000km2_18.p', #4f5-8f5N
'wb'))
pkl.dump(dic60, open(cnst.network_data + 'data/CLOVER/saves/storm_count_10W-12E_5-8N_-60C_5000km2_18.p',
'wb'))
pkl.dump(dic70, open(cnst.network_data + 'data/CLOVER/saves/storm_count_10W-12E_5-8N_-70C_5000km2_18.p',
'wb'))
pkl.dump(dic75, open(cnst.network_data + 'data/CLOVER/saves/storm_count_10W-12E_5-8N_-75C_5000km2_18.p',
'wb'))
if area:
pkl.dump(area40, open(cnst.network_data + 'data/CLOVER/saves/storm_90centArea_12W-10E_5-8N_-40C_5000km2_1800.p', #4f5-8f5N
'wb'))
pkl.dump(area50, open(cnst.network_data + 'data/CLOVER/saves/storm_90centArea_12W-10E_5-8N_-50C_5000km2_1800.p', #4f5-8f5N
'wb'))
pkl.dump(area60, open(cnst.network_data + 'data/CLOVER/saves/storm_90centArea_12W-10E_5-8N_-60C_5000km2_1800.p',
'wb'))
pkl.dump(area70, open(cnst.network_data + 'data/CLOVER/saves/storm_90centArea_12W-10E_5-8N_-70C_5000km2_1800.p',
'wb'))
pkl.dump(area75, open(cnst.network_data + 'data/CLOVER/saves/storm_90centArea_12W-10E_5-8N_-75C_5000km2_1800.p',
'wb'))