def thetae( altimiter, tpc, tdc ): theta = thermo.theta( altimiter, tpc ) hpa = 33.8639 * altimiter tdk = tdc + 273.15 tpk = tpc + 273.15 func2 = ( -1*5420 )/tdk e = ( 2.53 * pow( 10,11 ) ) * np.exp( func2 ) e_hpa = e / 100 mixr = ( ( 0.622 * e_hpa )/( hpa - e_hpa ) ) thetae = theta*np.exp( ( ( 2.50 * pow( 10, 6 ) ) * mixr )/( 1005 * tpk ) ) return thetae
def thetae(altimiter, tpc, tdc):
theta = thermo.theta(altimiter, tpc)
hpa = 33.8639 * altimiter
tdk = tdc + 273.15
tpk = tpc + 273.15
func2 = (-1 * 5420) / tdk
e = (2.53 * pow(10, 11)) * np.exp(func2)
e_hpa = e / 100
mixr = ((0.622 * e_hpa) / (hpa - e_hpa))
thetae = theta * np.exp(((2.50 * pow(10, 6)) * mixr) / (1005 * tpk))
return thetae
def make_skewT(tmin, tmax, pmax, pmin, skew=30.):
#make a blank skewT diagram
clf()
#get a dense range of p, t0 to contour
yplot = linspace(1050, 100, 100)
xplot = linspace(-50, 50, 100)
xplot, yplot = meshgrid(xplot, yplot)
#lay down a reference grid that labels xplot,yplot points
#in the new (skewT-lnP) coordinate system .
# Each value of the temp matrix holds the actual (data) temperature
# label (in deg C) of the xplot, yplot coordinate pairs
#note that we don't have to transform the y coordinate
#it's still the pressure
#use the real (data) value to get the potential temperature
T = xplot + skew*log(0.001*yplot)
Tk = T + 273.15 #convert from C to K for use in thermo functios
p = yplot*100. #convert from hPa to Pa
th = thermo.theta(p, Tk) #theta labels
#add the mixing ratio
rstar = thermo.r_star(p, Tk) #wsat labels
#saturated adiabat, so Tdew=Tk
thetaeVals = thermo.theta_e(p, Tk, rstar, 0.)
tempLabels = arange(-140., 50., 10.)
con1 = contour(xplot, yplot, T, levels = tempLabels, colors = 'k', linewidths=.5)
ax = gca()
ax.set_yscale('log')
lines = arange(100., 1100., 100.)
yticks(lines, ['100','200','300','400','500','600','700','800','900','1000'])
for line in lines:
axhline(line, ls=':', color='k', linewidth=.5)
thetaLabels = arange(200., 380., 10.)
con2 = contour(xplot, yplot, th, levels = thetaLabels, colors='b', linewidths=.5)
#rsLabels = [.1,.2,.4,.6, 1, 2, 3, 4, 5, 6, 8, 10, 15, 20, 25, 30, 40]
#con3 = contour(xplot, yplot, rstar*1.e3, levels=rsLabels, colors='g', linewidths=.5)
#thetaeLabels = linspace(200,400,21)
#con4 = contour(xplot, yplot, thetaeVals, levels = thetaeLabels, colors='r', linewidths=.5)
axis([tmin, tmax, pmax, pmin])
clabel(con1, inline = False, fmt = '%1.0f')
clabel(con2, inline = False, fmt = '%1.0f')
#clabel(con3, inline = False, fmt = '%1.1f')
#clabel(con4, inline = False, fmt = '%1.0f')
title('skew T - lnp chart')
ylabel('pressure (hPa)')
xlabel('temperature (black, degrees C)')
def make_skewT(tmin, tmax, pmax, pmin, skew=30.):
#make a blank skewT diagram
clf()
#get a dense range of p, t0 to contour
yplot = linspace(1050, 100, 100)
xplot = linspace(-50, 50, 100)
xplot, yplot = meshgrid(xplot, yplot)
#lay down a reference grid that labels xplot,yplot points
#in the new (skewT-lnP) coordinate system .
# Each value of the temp matrix holds the actual (data) temperature
# label (in deg C) of the xplot, yplot coordinate pairs
#note that we don't have to transform the y coordinate
#it's still the pressure
#use the real (data) value to get the potential temperature
T = xplot + skew * log(0.001 * yplot)
Tk = T + 273.15 #convert from C to K for use in thermo functios
p = yplot * 100. #convert from hPa to Pa
th = thermo.theta(p, Tk) #theta labels
#add the mixing ratio
rstar = thermo.r_star(p, Tk) #wsat labels
#saturated adiabat, so Tdew=Tk
thetaeVals = thermo.theta_e(p, Tk, rstar, 0.)
tempLabels = arange(-140., 50., 10.)
con1 = contour(xplot,
yplot,
T,
levels=tempLabels,
colors='k',
linewidths=.5)
ax = gca()
ax.set_yscale('log')
lines = arange(100., 1100., 100.)
yticks(lines, [
'100', '200', '300', '400', '500', '600', '700', '800', '900', '1000'
])
for line in lines:
axhline(line, ls=':', color='k', linewidth=.5)
thetaLabels = arange(200., 380., 10.)
con2 = contour(xplot,
yplot,
th,
levels=thetaLabels,
colors='b',
linewidths=.5)
rsLabels = [.1, .2, .4, .6, 1, 2, 3, 4, 5, 6, 8, 10, 15, 20, 25, 30, 40]
con3 = contour(xplot,
yplot,
rstar * 1.e3,
levels=rsLabels,
colors='g',
linewidths=.5)
thetaeLabels = linspace(200, 400, 21)
con4 = contour(xplot,
yplot,
thetaeVals,
levels=thetaeLabels,
colors='r',
linewidths=.5)
axis([tmin, tmax, pmax, pmin])
clabel(con1, inline=False, fmt='%1.0f')
clabel(con2, inline=False, fmt='%1.0f')
clabel(con3, inline=False, fmt='%1.1f')
clabel(con4, inline=False, fmt='%1.0f')
title('skew T - lnp chart')
ylabel('pressure (hPa)')
xlabel('temperature (black, degrees C)')
def Surface(self, **kwargs): ## Here begins the METAR decoder
self.DatDict = {} ## this will serve as the intermediate dictionary for sorting
self.RESULT = {} ## this is the result dictionary
self.count = 0 ## will be used to tell us how many stations were not found
stations = self.StationDict
cycle = kwargs.get("cycle", self.cycle)
file = urllib.urlopen("http://metfs1.agron.iastate.edu/data/text/sao/" + cycle + ".sao")
text = (
file.read()
.replace("\n", "")
.replace("\x03", "")
.replace("\x03\x01", "")
.replace("\r", "")
.strip()
.split("=")
)
## process the text
for line in text:
if line.startswith("METAR"):
line = line.strip("METAR")
if (
line.startswith("K") == False
and line.startswith("C") == False
and line.startswith("M") == False
and line.startswith("P") == False
): ## ignore lines with no metar station
continue
else:
stn_key = line[:4] ## grab the station identifies, i.e. KBNA
try:
## repeat observations are filtered by choosing the longest list of observations
if len(self.DatDict[stn_key].split()) < len(line[4:].split()):
self.DatDict[stn_key] = line[4:]
except:
## if no observation, initialize it in the dictionary
self.DatDict[stn_key] = line[4:]
for stn in self.DatDict.keys():
if not stn in stations:
self.count += 1 ## count the stations missing from the file
continue
stndat = self.DatDict[stn]
if re.search("([0-9][0-9][0-9])([0-9][0-9])(G[0-9][0-9]KT)", stndat):
## These various regular expressions search for patterns
## that correspond with a certain type of data
windat = re.search("([0-9][0-9][0-9])([0-9][0-9])(G[0-9][0-9]KT)", stndat).group()
self.wdir = int(windat[:3])
self.wspd = int(windat[3:-5])
self.gust = int(windat[-4:-2])
elif re.search("([0-9][0-9][0-9])([0-9][0-9]KT)", stndat):
windat = re.search("([0-9][0-9][0-9])([0-9][0-9])KT", stndat).group()
self.wdir = int(windat[:3])
self.wspd = int(windat[3:-2])
self.gust = np.nan
else:
self.wdir = np.nan
self.wspd = np.nan
self.gust = np.nan
if re.search(
"\s(M[0-9][0-9])/(M[0-9][0-9])\s|\s([0-9][0-9])/(M[0-9][0-9])\s|\s(M[0-9][0-9])/(NIL)\s|\s([0-9][0-9])/(NIL)\s|\s([0-9][0-9])/([0-9][0-9])\s",
stndat,
):
self.tempdat = (
re.search(
"\s(M[0-9][0-9])/(M[0-9][0-9])\s|\s([0-9][0-9])/(M[0-9][0-9])\s|\s(M[0-9][0-9])/(NIL)\s|\s([0-9][0-9])/(NIL)\s|\s([0-9][0-9])/([0-9][0-9])\s",
stndat,
)
.group()
.replace("M", "-")
.split("/")
)
self.tempdat[0] = int(self.tempdat[0])
self.tempdat[1] = int(self.tempdat[1])
else:
self.tempdat = [np.nan, np.nan]
if re.search("SLP[0-9][0-9][0-9]", stndat):
self.slpdat = re.search("SLP[0-9][0-9][0-9]", stndat).group().replace("SLP", "")
if int(self.slpdat) <= 500:
self.slpdat = 1000 + int(self.slpdat[:2]) + int(self.slpdat[2]) / 10
elif int(self.slpdat) > 500:
self.slpdat = 900 + int(self.slpdat[:2]) + int(self.slpdat[2]) / 10
else:
self.slpdat = np.nan
if re.search("A[0-9][0-9][0-9][0-9]", stndat):
self.altdat = re.search("A[0-9][0-9][0-9][0-9]", stndat).group().strip("A")
self.altdat = int(self.altdat[:2]) + float(self.altdat[2:]) / 100
else:
self.altdat = np.nan
if re.search("[0-9][0-9]SM|[0-9]SM", stndat):
self.vis = re.search("[0-9][0-9]SM|[0-9]SM", stndat).group().replace("SM", "")
else:
self.vis = np.nan
if re.search("\s(P[0-9][0-9][0-9][0-9])", stndat):
self.precip = re.search("\s(P[0-9][0-9][0-9][0-9])", stndat).group().replace("P", "")
self.precip = float(str(self.precip)[:2] + "." + str(self.precip)[3:])
else:
self.precip = np.nan
self.RESULT[stn] = {}
self.RESULT[stn]["TMPC"] = self.tempdat[0]
self.RESULT[stn]["TMPF"] = self.tempdat[0] * 1.8 + 32
self.RESULT[stn]["DWPC"] = self.tempdat[1]
self.RESULT[stn]["DWPF"] = self.tempdat[1] * 1.8 + 32
self.RESULT[stn]["THTA"] = therm.theta(self.altdat, self.tempdat[0])
self.RESULT[stn]["MIXR"] = therm.mixingratio(self.altdat, self.tempdat[0], self.tempdat[1])
self.RESULT[stn]["THTE"] = therm.thetae(self.altdat, self.tempdat[0], self.tempdat[1])
self.RESULT[stn]["RELH"] = therm.relhumid(self.tempdat[0], self.tempdat[1])
self.RESULT[stn]["WDIR"] = self.wdir
self.RESULT[stn]["WSPD"] = self.wspd
self.RESULT[stn]["GUST"] = self.gust
self.RESULT[stn]["VWIN"] = vect.VWIN(self.wdir, self.wspd)
self.RESULT[stn]["UWIN"] = vect.UWIN(self.wdir, self.wspd)
self.RESULT[stn]["UMET"] = vect.UMET(self.wdir, self.wspd)
self.RESULT[stn]["VMET"] = vect.VMET(self.wdir, self.wspd)
self.RESULT[stn]["PRES"] = self.slpdat
self.RESULT[stn]["ALTI"] = self.altdat
self.RESULT[stn]["VISI"] = self.vis
self.RESULT[stn]["PCPN"] = self.precip
file.close()
return self.RESULT
def Surface(self, **kwargs): ## Here begins the METAR decoder
self.DatDict = {
} ## this will serve as the intermediate dictionary for sorting
self.RESULT = {} ## this is the result dictionary
self.count = 0 ## will be used to tell us how many stations were not found
stations = self.StationDict
cycle = kwargs.get('cycle', self.cycle)
file = urllib.urlopen(
'http://metfs1.agron.iastate.edu/data/text/sao/' + cycle + '.sao')
text = file.read().replace('\n', '').replace('\x03', '').replace(
'\x03\x01', '').replace('\r', '').strip().split('=')
## process the text
for line in text:
if line.startswith('METAR'):
line = line.strip('METAR')
if line.startswith('K') == False and line.startswith(
'C'
) == False and line.startswith('M') == False and line.startswith(
'P') == False: ## ignore lines with no metar station
continue
else:
stn_key = line[:4] ## grab the station identifies, i.e. KBNA
try:
## repeat observations are filtered by choosing the longest list of observations
if len(self.DatDict[stn_key].split()) < len(
line[4:].split()):
self.DatDict[stn_key] = line[4:]
except:
## if no observation, initialize it in the dictionary
self.DatDict[stn_key] = line[4:]
for stn in self.DatDict.keys():
if not stn in stations:
self.count += 1 ## count the stations missing from the file
continue
stndat = self.DatDict[stn]
if re.search('([0-9][0-9][0-9])([0-9][0-9])(G[0-9][0-9]KT)',
stndat):
## These various regular expressions search for patterns
## that correspond with a certain type of data
windat = re.search(
'([0-9][0-9][0-9])([0-9][0-9])(G[0-9][0-9]KT)',
stndat).group()
self.wdir = int(windat[:3])
self.wspd = int(windat[3:-5])
self.gust = int(windat[-4:-2])
elif re.search('([0-9][0-9][0-9])([0-9][0-9]KT)', stndat):
windat = re.search('([0-9][0-9][0-9])([0-9][0-9])KT',
stndat).group()
self.wdir = int(windat[:3])
self.wspd = int(windat[3:-2])
self.gust = np.nan
else:
self.wdir = np.nan
self.wspd = np.nan
self.gust = np.nan
if re.search(
'\s(M[0-9][0-9])/(M[0-9][0-9])\s|\s([0-9][0-9])/(M[0-9][0-9])\s|\s(M[0-9][0-9])/(NIL)\s|\s([0-9][0-9])/(NIL)\s|\s([0-9][0-9])/([0-9][0-9])\s',
stndat):
self.tempdat = re.search(
'\s(M[0-9][0-9])/(M[0-9][0-9])\s|\s([0-9][0-9])/(M[0-9][0-9])\s|\s(M[0-9][0-9])/(NIL)\s|\s([0-9][0-9])/(NIL)\s|\s([0-9][0-9])/([0-9][0-9])\s',
stndat).group().replace('M', '-').split('/')
self.tempdat[0] = int(self.tempdat[0])
self.tempdat[1] = int(self.tempdat[1])
else:
self.tempdat = [np.nan, np.nan]
if re.search('SLP[0-9][0-9][0-9]', stndat):
self.slpdat = re.search('SLP[0-9][0-9][0-9]',
stndat).group().replace('SLP', '')
if int(self.slpdat) <= 500:
self.slpdat = 1000 + int(
self.slpdat[:2]) + int(self.slpdat[2]) / 10
elif int(self.slpdat) > 500:
self.slpdat = 900 + int(
self.slpdat[:2]) + int(self.slpdat[2]) / 10
else:
self.slpdat = np.nan
if re.search('A[0-9][0-9][0-9][0-9]', stndat):
self.altdat = re.search('A[0-9][0-9][0-9][0-9]',
stndat).group().strip('A')
self.altdat = int(
self.altdat[:2]) + float(self.altdat[2:]) / 100
else:
self.altdat = np.nan
if re.search('[0-9][0-9]SM|[0-9]SM', stndat):
self.vis = re.search('[0-9][0-9]SM|[0-9]SM',
stndat).group().replace('SM', '')
else:
self.vis = np.nan
if re.search('\s(P[0-9][0-9][0-9][0-9])', stndat):
self.precip = re.search('\s(P[0-9][0-9][0-9][0-9])',
stndat).group().replace('P', '')
self.precip = float(
str(self.precip)[:2] + '.' + str(self.precip)[3:])
else:
self.precip = np.nan
self.RESULT[stn] = {}
self.RESULT[stn]['TMPC'] = self.tempdat[0]
self.RESULT[stn]['TMPF'] = self.tempdat[0] * 1.8 + 32
self.RESULT[stn]['DWPC'] = self.tempdat[1]
self.RESULT[stn]['DWPF'] = self.tempdat[1] * 1.8 + 32
self.RESULT[stn]['THTA'] = therm.theta(self.altdat,
self.tempdat[0])
self.RESULT[stn]['MIXR'] = therm.mixingratio(
self.altdat, self.tempdat[0], self.tempdat[1])
self.RESULT[stn]['THTE'] = therm.thetae(self.altdat,
self.tempdat[0],
self.tempdat[1])
self.RESULT[stn]['RELH'] = therm.relhumid(self.tempdat[0],
self.tempdat[1])
self.RESULT[stn]['WDIR'] = self.wdir
self.RESULT[stn]['WSPD'] = self.wspd
self.RESULT[stn]['GUST'] = self.gust
self.RESULT[stn]['VWIN'] = vect.VWIN(self.wdir, self.wspd)
self.RESULT[stn]['UWIN'] = vect.UWIN(self.wdir, self.wspd)
self.RESULT[stn]['UMET'] = vect.UMET(self.wdir, self.wspd)
self.RESULT[stn]['VMET'] = vect.VMET(self.wdir, self.wspd)
self.RESULT[stn]['PRES'] = self.slpdat
self.RESULT[stn]['ALTI'] = self.altdat
self.RESULT[stn]['VISI'] = self.vis
self.RESULT[stn]['PCPN'] = self.precip
file.close()
return self.RESULT
