cnt += 1
if cnt < 16:
out.write(line)
continue
tokens = line.split()
if len(tokens) == 10:
if ts > 0:
out.write(
"%s\t%s\t%s\t%s\t%3.1f\t%3.1f\t%4.0f\t%4.1f\t%s\t%4.1f\n%s"
% (ts.day, ts.month, ts.year, bps, hic, loc, srad, wvl, drct, dwpc, bpdata)
)
ts = mx.DateTime.DateTime(int(tokens[2]), int(tokens[1]), int(tokens[0]))
bps = int(tokens[3])
hic = mesonet.f2c(float(climate[mgtzone][ts]["high"]))
loc = mesonet.f2c(float(climate[mgtzone][ts]["low"]))
srad = float(climate[mgtzone][ts]["rad"])
wvl = float(climate[mgtzone][ts]["wvl"])
drct = 0
dwpc = mesonet.f2c(float(climate[mgtzone][ts]["dewp"]))
bpdata = ""
elif len(tokens) == 2:
bpdata += line
out.write(
"%s\t%s\t%s\t%s\t%3.1f\t%3.1f\t%4.0f\t%4.1f\t%s\t%4.1f\n%s"
% (ts.day, ts.month, ts.year, bps, hic, loc, srad, wvl, drct, dwpc, bpdata)
)
out.close()
def ob2synop(ob):
"""
Take a given observation {dictionary} and return a sao coded text
http://weather.unisys.com/wxp/Appendices/Formats/SYNOP.html
"""
ar = ["0"] * 50
# character 0 = iR - the precipitation data indicator
#0 -- Precipitation in groups 1 and 3
#1 -- Precipitation reported in group 1 only
#2 -- Precipitation reported in group 3 only
#3 -- Precipitation omitted, no precipitation
#4 -- Precipitation omitted, no observation
if ob.has_key("phour") and ob["phour"] is not None:
ar[0] = "1"
else:
ar[0] = "4"
#character 1 = iX - weather data and station type indicator
#1 -- manned station -- weather group included
#2 -- manned station -- omitted, no significant weather
#3 -- manned station -- omitted, no weather observation
#4 -- automated station -- weather group included (see automated weather codes 4677 and 4561)
#5 -- automated station -- omitted, no significant weather
#6 -- automated station -- omitted, no weather observation
#7 -- automated station -- weather group included (see automated weather codes 4680 and 4531)
ar[1] = "4"
#character 2 = h - height above ground of base of lowest cloud
ar[2] = "/"
if ob.has_key("skyl1") and ob["skyl1"] is not None and ob["skyl1"] > 0:
ar[2] = lookup_skyl( ob["skyl1"] * 0.3048 )
#characters 3-4 = VV - visibility in miles and fractions
ar[3:5] = "//"
if ob.has_key("vsby") and ob["vsby"] is not None and ob["vsby"] >= 0:
ar[3:5] = lookup_vis( ob["vsby"] * 1.609344 )
#character 5 = N - total amount of cloud cover
ar[5] = "/"
if ob.has_key("skyc1") and ob.has_key("skyc2") and ob.has_key("skyc3"):
ar[5] = lookup_skyc( ob['skyc1'], ob['skyc2'], ob['skyc3'] )
#characters 6-7 = dd - direction from which wind is blowing
if ob.has_key("drct") and ob['drct'] is not None:
ar[6:8] = "%02i" % (ob['drct'] / 10.0,)
#characters 8-9 = ff - wind speed in knots
if ob.has_key("sknt") and ob['sknt'] is not None:
ar[8:10] = "%02i" % (ob['sknt'],)
#If character 10 = "1", then
# character 11 = sn - sign of temperature
# characters 12-14 = TTT - current air temperature
if ob.has_key("tmpf") and ob['tmpf'] is not None:
ar[10] = "1"
tmpc = mesonet.f2c( ob["tmpf"] )
if tmpc < 0:
ar[11] = "1"
tmpc = -1. * tmpc
ar[12:15] = "%03i" % (tmpc * 10.0,)
#If character 15 = "2", then
#
# character 16 = sn - sign of temperature
# characters 17-19 = Td - dew point
if ob.has_key("dwpf") and ob['dwpf'] is not None:
ar[15] = "2"
dwpc = mesonet.f2c( ob["dwpf"] )
if dwpc < 0:
ar[16] = "1"
dwpc = -1. * dwpc
ar[17:20] = "%03i" % (dwpc * 10.0,)
#If character 20 = "3", then
#
# characters 21-24 = PO - station pressure (not plotted)
if ob.has_key("alti") and ob["alti"] is not None:
ar[20] = "3"
ar[21:25] = str( "%4i" % (ob["alti"] * 33.8638866667 * 10.0,))
#If character 25 = "4", then
#
# characters 26-29 = PPPP - pressure reduced to sea level
if ob.has_key("pmsl") and ob["pmsl"] is not None:
ar[25] = "4"
ar[26:30] = str( "%4i" % (ob["pmsl"] * 10.0,))
#If character 30 = "5", then
#
# character 31 = a - characteristic of barograph
# characters 32-34 = ppp - pressure change, last 3 hrs.
#If character 35 = "6", then
#
# characters 36-38 = RRR - precipitation
# character 39 = tR - time duration of precipitation
if ob.has_key("phour") and ob["phour"] is not None and ob["phour"] >= 0:
ar[35] = "6"
ar[36:39] = str( "%3i" % ( ob["phour"] * 25.4, ))
ar[39] = "5" # 1 hour total
#If character 40 = "7", then
#
# characters 41-42 = ww - present weather
# character 43 = W1 - most significant past weather
# character 44 = W2 - 2nd most sig. past weather
#ar[40:45] = "7////"
#
#If character 45 = "8", then
#
# character 46 = Nh - Fraction of sky cover
# character 47 = CL - cloud type, low clouds
# character 48 = CM - cloud type, medium clouds
# character 49 = CH - cloud type, high clouds
#ar[45:50] = "8////"
return "".join( ar )
mcursor.execute("""
SELECT id from stations WHERE network in ('IA_ASOS', 'MO_ASOS','KS_ASOS','NE_ASOS','SD_ASOS','ND_ASOS',
'MN_ASOS','WI_ASOS','IL_ASOS','IN_ASOS','MI_ASOS') and archive_begin < '1960-01-01'
""")
for row in mcursor:
stations.append( row[0] )
stations = ['DSM','XXXXX']
for yr in range(1951,2012):
acursor.execute("""
SELECT valid + '10 minutes'::interval, tmpf, dwpf from t%s WHERE station in %s
and extract(month from valid) in (6,7,8) and dwpf > -30 and tmpf > -30
""" % (yr, str(tuple(stations))))
hourly = numpy.zeros( (24,), 'f')
counts = numpy.zeros( (24,), 'f')
for row in acursor:
dwpc = mesonet.f2c( row[2] )
e = 6.112 * math.exp( (17.67 * dwpc) / (dwpc + 243.5))
mixr = 0.62197 * e / (1000.0 - e)
if mixr > 0:
hourly[ row[0].hour - 1] += mixr
counts[ row[0].hour - 1] += 1
climate[:,yr-1951] = hourly / counts * 1000.
avgV = numpy.sum(hourly) / numpy.sum(counts) * 1000.
print yr, avgV
import matplotlib.pyplot as plt
fig = plt.figure()
