def find_station(*args):
stns = 0
if len(args) == 1:
station_name = args[0]
print("LOOKUP BY STATION NAME: ", station_name)
station_name = station_name.upper()
ghcnd_stations = gp.get_ghcnd_stations()
stns = list(
filter(lambda x: re.search(station_name, x), ghcnd_stations[:, 5]))
print(
"GHCND ID LAT LON ELEV ST STATION NAME")
print(
"###############################################################")
for station_counter in range(len(stns)):
ghcnd_meta = ghcnd_stations[ghcnd_stations[:, 5] ==
stns[station_counter]]
print(ghcnd_meta[0][0], ghcnd_meta[0][1], ghcnd_meta[0][2],
ghcnd_meta[0][3], ghcnd_meta[0][4], ghcnd_meta[0][5])
elif len(args) == 3:
station_lat = args[0]
station_lon = args[1]
distance_limit = args[2]
print("LOOKUP BY STATION LAT: ", station_lat, " LON: ", station_lon,
" DIST LIMIT (mi): ", distance_limit)
target_latlon = (float(station_lat), float(station_lon))
ghcnd_stations = gp.get_ghcnd_stations()
print(
"GHCND ID LAT LON ELEV ST STATION NAME")
print(
"###############################################################")
for ghcnd_counter in range(ghcnd_stations[:, 0].size):
candidate_latlon = (ghcnd_stations[ghcnd_counter][1],
ghcnd_stations[ghcnd_counter][2])
dist = great_circle(target_latlon, candidate_latlon).miles
if dist <= distance_limit:
print(ghcnd_stations[ghcnd_counter][0],
ghcnd_stations[ghcnd_counter][1],
ghcnd_stations[ghcnd_counter][2],
ghcnd_stations[ghcnd_counter][3],
ghcnd_stations[ghcnd_counter][4],
ghcnd_stations[ghcnd_counter][5])
else:
print("USAGE\n NAME or\n LAT LON DIST")
return None
return None
def find_station(*args):
stns=0
if len(args) ==1:
station_name=args[0]
print("LOOKUP BY STATION NAME: ",station_name)
station_name=station_name.upper()
ghcnd_stations=gp.get_ghcnd_stations()
stns=filter(lambda x: re.search(station_name,x), ghcnd_stations[:,5])
print("GHCND ID LAT LON ELEV ST STATION NAME")
print("###############################################################")
for station_counter in xrange(len(stns)):
ghcnd_meta = ghcnd_stations[ghcnd_stations[:,5]== stns[station_counter]]
print(ghcnd_meta[0][0],ghcnd_meta[0][1],ghcnd_meta[0][2],ghcnd_meta[0][3],ghcnd_meta[0][4],ghcnd_meta[0][5])
elif len(args)==3:
station_lat=args[0]
station_lon=args[1]
distance_limit=args[2]
print("LOOKUP BY STATION LAT: ",station_lat," LON: ",station_lon, " DIST LIMIT (mi): ",distance_limit)
target_latlon = (float(station_lat), float(station_lon))
ghcnd_stations=gp.get_ghcnd_stations()
print("GHCND ID LAT LON ELEV ST STATION NAME")
print("###############################################################")
for ghcnd_counter in xrange(ghcnd_stations[:,0].size):
candidate_latlon=(ghcnd_stations[ghcnd_counter][1], ghcnd_stations[ghcnd_counter][2])
dist=great_circle(target_latlon, candidate_latlon).miles
if dist <= distance_limit:
print(ghcnd_stations[ghcnd_counter][0],ghcnd_stations[ghcnd_counter][1],ghcnd_stations[ghcnd_counter][2],ghcnd_stations[ghcnd_counter][3],ghcnd_stations[ghcnd_counter][4],ghcnd_stations[ghcnd_counter][5])
else:
print("USAGE\n NAME or\n LAT LON DIST")
return None
return None
def main():
## get metadata file file (ghcnd-stations.txt)
click.secho('>>> Get metadata file (ghcnd-stations.txt)', fg='green')
gp.get_ghcnd_stations()
## get inventory file (ghcnd-inventory.txt)
click.secho('>>> Get inventory file (ghcnd-inventory.txt)', fg='green')
gp.get_ghcnd_inventory()
## find stations
click.secho('>>> Find stations... Example: "NEW ORLEANS"', fg='green')
gp.find_station("NEW ORLEANS")
## download
click.secho(
'>>> Download data in csv format after finding the target station ID... Example: "NEW ORLEANS LAKEFRONT AP" ',
fg='green')
gp.output_to_csv("USW00053917")
## read data
click.secho('>>> Read data... Example: "NEW ORLEANS LAKEFRONT AP"',
fg='green')
data = pd.read_csv('USW00053917.csv')
print(data.head())
print(data.tail())
# return
return None
def output_to_netcdf(station_id):
print "\nOUTPUTTING TO netCDF: ", station_id, ".nc"
begin_year = 1700
end_year = datetime.datetime.now().year
num_years = (end_year - begin_year) + 1
start_date = date(begin_year, 01, 01)
end_date = date(end_year, 12, 31)
# 5 Elements of GHCN-D
num_elements = 5
tmax = 0
tmin = 1
prcp = 2
snow = 3
snwd = 4
# 3 Flags for each value
num_flags = 3
# Measurement Flag (MFLAG)
mflag = 0
mflag_codes = np.array(
[ord(x) for x in ["B", "D", "H", "K", "L", "O", "P", "T", "W"]],
dtype='b')
mflag_meanings = np.array(["precipitation total formed from two 12 hour totals".replace(" ","_")+" ",\
"precipitation total formed from four six hour totals".replace(" ","_")+" ",\
"represents highest or lowest hourly temperature".replace(" ","_")+" ",\
"converted from knots".replace(" ","_")+" ",\
"temperature appears to be lagged with respect to reported hour of observation".replace(" ","_")+" ",\
"converted from oktas".replace(" ","_")+" ",\
"identified as missing presumed zero in DSI 3200 and 3206".replace(" ","_")+" ",\
"trace of precipitation snowfall or snow depth".replace(" ","_")+" ",\
"converted from 16 point WBAN code for wind direction".replace(" ","_")],\
dtype='str')
# Quality Control Flag (QCFLAG)
qcflag = 1
qcflag_codes = np.array([
ord(x) for x in
["D", "G", "I", "K", "L", "M", "N", "O", "R", "S", "T", "W", "X", "Z"]
],
dtype='b')
qcflag_meanings = np.array(["failed duplicate check".replace(" ","_")+" ",\
"failed gap check".replace(" ","_")+" ",\
"failed internal consistency check".replace(" ","_")+" ",\
"failed streak frequent value check".replace(" ","_")+" ",\
"failed check on length of multiday period".replace(" ","_")+" ",\
"failed megaconsistency check".replace(" ","_")+" ",\
"failed naught check".replace(" ","_")+" ",\
"failed climatological outlier check".replace(" ","_")+" ",\
"failed lagged range check".replace(" ","_")+" ",\
"failed spatial consistency check".replace(" ","_")+" ",\
"failed temporal consistency check".replace(" ","_")+" ",\
"failed bounds check".replace(" ","_")+" ",\
"failed climatological outlier check".replace(" ","_")+" ",\
"flagged as a result of an official Datzilla investigation".replace(" ","_")],\
dtype='str')
# Source Flag (SRCFLAG)
srcflag = 2
srcflag_codes = np.array([ord(x) for x in ["0","6","7","A","a","B","b","C","E","F","G","H","I","K","M","N","Q","R","r","S","s",\
"T","U","u","W","X","Z","z"]],dtype='b')
srcflag_meanings = np.array(["US Cooperative Summary of the Day".replace(" ","_")+" ",\
"CDMP Cooperative Summary of the Day".replace(" ","_")+" ",\
"US Cooperative Summary of the Day Transmitted via WxCoder3".replace(" ","_")+" ",\
"Automated Surface Observing System real time data since January 1 2006".replace(" ","_")+" ",\
"Australian data from the Australian Bureau of Meteorology".replace(" ","_")+" ",\
"US ASOS data for October 2000 to December 2005".replace(" ","_")+" ",\
"Belarus update".replace(" ","_")+" ",\
"Environment Canada".replace(" ","_")+" ",\
"European Climate Assessment and Dataset".replace(" ","_")+" ",\
"US Fort data".replace(" ","_")+" ",\
"Official Global Climate Observing System".replace(" ","_")+" ",\
"High Plains Regional Climate Center real time data".replace(" ","_")+" ",\
"International collection non U.S. data received through personal contacts".replace(" ","_")+" ",\
"US Cooperative Summary of the Day data digitized from paper observer forms".replace(" ","_")+" ",\
"Monthly METAR Extract".replace(" ","_")+" ",\
"Community Collaborative Rain Hail and Snow".replace(" ","_")+" ",\
"Data from several African countries that had been quarantined".replace(" ","_")+" ",\
"Climate Reference Network and Historical Climatology Network Modernized".replace(" ","_")+" ",\
"All Russian Research Institute of Hydrometeorological Information World Data Center".replace(" ","_")+" ",\
"Global Summary of the Day".replace(" ","_")+" ",\
"China Meteorological Administration National Meteorological Information Center".replace(" ","_")+" ",\
"SNOwpack TELemtry data obtained from the Western Regional Climate Center".replace(" ","_")+" ",\
"Remote Automatic Weather Station data obtained from the Western Regional Climate Center".replace(" ","_")+" ",\
"Ukraine update".replace(" ","_")+" ",\
"WBAN ASOS Summary of the Day from Integrated Surface Data ISD".replace(" ","_")+" ",\
"US First Order Summary of the Day".replace(" ","_")+" ",\
"Datzilla official additions or replacements".replace(" ","_")+" ",\
"Uzbekistan update ".replace(" ","_")],\
dtype='str')
# Get Version Number of GHCN-D
ghcnd_version = gp.get_ghcnd_version()
# Grab Data
gp.get_data_station(station_id)
# Read in GHCN-D Data
infile = station_id + ".dly"
file_handle = open(infile, 'r')
ghcnd_contents = file_handle.readlines()
file_handle.close()
# Get Year Start and End of File for time dimensions
station_start_year = int(ghcnd_contents[0][11:15])
station_end_year = int(ghcnd_contents[len(ghcnd_contents) - 1][11:15])
station_start_month = int(ghcnd_contents[0][15:17])
station_end_month = int(ghcnd_contents[len(ghcnd_contents) - 1][15:17])
num_values = 0
results_time = np.zeros((num_years * 12 * 31), dtype='f') - (9999.0)
results_pos = np.zeros((num_years, 12, 31), dtype='f') - (9999.0)
for year in xrange(station_start_year, station_end_year + 1):
year_counter = int(year - station_start_year)
if year == station_start_year:
begin_month = int(station_start_month - 1)
else:
begin_month = 0
if year == station_end_year:
end_month = int(station_end_month)
else:
end_month = 12
for month_counter in xrange(begin_month, end_month):
month = month_counter + 1
for day_counter in xrange(0, 31):
day = day_counter + 1
try:
current_date = date(year, month, day)
num_days = (current_date - start_date).days
results_time[num_values] = num_days
results_pos[year_counter, month_counter,
day_counter] = int(num_values)
num_values += 1
except:
pass
#################################################
# Open netCDF file
outfile = station_id + '.nc'
test = os.path.exists(outfile)
if test == True:
os.remove(outfile)
output = nc.Dataset(outfile, mode='w')
#################################################
# Create global attributes
output.title = "Global Historical Climatology Network - Daily Dataset"
output.summary = "GHCN-Daily is an integrated database of daily climate summaries from land surface stations across the globe. "+\
"GHCN-Daily is comprised of daily climate records from numerous sources that have been integrated and "+\
"subjected to a common suite of quality assurance reviews. This dataset includes the top five core elements: "+\
"PRCP (precipitation), SNOW (snowfall), SNWD (snow depth), TMAX (maximum temperature), TMIN (minimum temperature)"
output.version = ghcnd_version
output.keywords = "daily, station, extremes, temperature, precipitation, snowfall, snow depth"
output.Conventions = "CF-1.6, ACDD-1.3"
output.institution = "Cooperative Institute for Climate and Satellites - North Carolina (CICS-NC) and "+\
"NOAAs National Centers for Environmental Information (NCEI), Asheville, North Carolina, USA"
output.source = "surface observations from multiple sources around the globe"
output.featureType = "timeSeries"
output.history = "File generatred on ", str(datetime.datetime.now())
output.creator_name = "Jared Rennie, CICS-NC"
output.creator_email = "*****@*****.**"
output.creator_url = "http://www.cicsnc.org/people/jared-rennie/"
output.references = "Menne et al 2012: http://dx.doi.org/10.1175/JTECH-D-11-00103.1"
output.comment = "More information can be found here: ftp://ftp.ncdc.noaa.gov/pub/data/ghcn/daily/readme.txt"
#################################################
# Create Dimensions
output.createDimension('element', num_elements)
output.createDimension('time', num_values)
output.createDimension('name_strlen', 30)
output.createDimension('id_strlen', 11)
#################################################
# Create Variables
# LON
lon_var = output.createVariable('lon', datatype='f')
lon_var.standard_name = "longitude"
lon_var.long_name = "station longitude"
lon_var.units = "degrees_east"
# LAT
lat_var = output.createVariable('lat', datatype='f')
lat_var.standard_name = "latitude"
lat_var.long_name = "station latitude"
lat_var.units = "degrees_north"
# ELEV (ALT)
alt_var = output.createVariable('alt', datatype='f')
alt_var.long_name = "vertical distance above the surface"
alt_var.standard_name = "height"
alt_var.units = "m"
alt_var.positive = "up"
alt_var.axis = "Z"
# ID
id_var = output.createVariable('id',
datatype='S1',
dimensions=('id_strlen'))
id_var.long_name = "station identifier"
# NAME
name_var = output.createVariable('station_name',
datatype='S1',
dimensions=('name_strlen'))
name_var.long_name = "station name"
name_var.cf_role = "timeseries_id"
# TIME
time_var = output.createVariable('time', datatype='f8', dimensions='time')
time_var.standard_name = "time"
time_var.long_name = "Time of Measurement"
time_var.units = "days since ", begin_year, "-01-01 00:00:00"
time_var.calendar = "gregorian"
time_var.axis = "T"
time_var[:] = results_time[0:num_values]
time_var.valid_min = np.min(results_time[0:num_values])
# FLAGS
mflag_var = output.createVariable('mflag',
datatype='b',
dimensions=('time', 'element'),
fill_value=32.)
mflag_var.long_name = "measurement flag"
mflag_var.flag_values = mflag_codes
mflag_var.flag_meanings = mflag_meanings
qcflag_var = output.createVariable('qcflag',
datatype='b',
dimensions=('time', 'element'),
fill_value=32.)
qcflag_var.long_name = "quality control flag"
qcflag_var.flag_values = qcflag_codes
qcflag_var.flag_meanings = qcflag_meanings
srcflag_var = output.createVariable('srcflag',
datatype='b',
dimensions=('time', 'element'),
fill_value=32.)
srcflag_var.long_name = "source flag"
srcflag_var.flag_values = srcflag_codes
srcflag_var.flag_meanings = srcflag_meanings
# TMAX
tmax_var = output.createVariable('tmax',
datatype='f',
dimensions=('time'),
fill_value=-9999.)
tmax_var.long_name = "surface maximum temperature"
tmax_var.units = "Celsius"
tmax_var.coordinates = "time lat lon alt station_name"
tmax_var.ancillary_variables = "mflag qcflag srcflag"
# TMIN
tmin_var = output.createVariable('tmin',
datatype='f',
dimensions=('time'),
fill_value=-9999.)
tmin_var.long_name = "surface minimum temperature"
tmin_var.units = "Celsius"
tmin_var.coordinates = "time lat lon alt station_name"
tmin_var.ancillary_variables = "mflag qcflag srcflag"
# PRCP
prcp_var = output.createVariable('prcp',
datatype='f',
dimensions=('time'),
fill_value=-9999.)
prcp_var.long_name = "surface precipitation"
prcp_var.units = "mm"
prcp_var.coordinates = "time lat lon alt station_name"
prcp_var.ancillary_variables = "mflag qcflag srcflag"
# SNOW
snow_var = output.createVariable('snow',
datatype='f',
dimensions=('time'),
fill_value=-9999.)
snow_var.long_name = "surface snowfall"
snow_var.units = "mm"
snow_var.coordinates = "time lat lon alt station_name"
snow_var.ancillary_variables = "mflag qcflag srcflag"
# SNWD
snwd_var = output.createVariable('snwd',
datatype='f',
dimensions=('time'),
fill_value=-9999.)
snwd_var.long_name = "surface snow depth"
snwd_var.units = "mm"
snwd_var.coordinates = "time lat lon alt station_name"
snwd_var.ancillary_variables = "mflag qcflag srcflag"
#################################################
# Read in GHCN-D Meta
ghcnd_stations = gp.get_ghcnd_stations()
ghcnd_meta = ghcnd_stations[ghcnd_stations[:, 0] == station_id]
ghcnd_id = ghcnd_meta[0][0]
ghcnd_lat = float(ghcnd_meta[0][1])
ghcnd_lon = float(ghcnd_meta[0][2])
ghcnd_alt = float(ghcnd_meta[0][3])
ghcnd_name = ghcnd_meta[0][5]
ghcnd_name = ghcnd_name.strip()
ghcnd_name = re.sub(' +', ' ', ghcnd_name)
ghcnd_name = ghcnd_name.replace(" ", "_")
for string_counter in xrange(0, len(ghcnd_id)):
id_var[string_counter] = ghcnd_id[string_counter]
lat_var[:] = ghcnd_lat
lon_var[:] = ghcnd_lon
alt_var[:] = ghcnd_alt
for string_counter in xrange(0, len(ghcnd_name)):
name_var[string_counter] = ghcnd_name[string_counter]
#################################################
# Go through GHCN-D Data
ghcnd_data = np.zeros((num_values, num_elements), dtype='f') - (9999.0)
ghcnd_flag = np.zeros(
(num_values, num_elements, num_flags), dtype='i') + 32
for counter in xrange(len(ghcnd_contents)):
element = ghcnd_contents[counter][17:21]
if element == "TMAX" or element == "TMIN" or element == "PRCP" or element == "SNOW" or element == "SNWD":
if element == "TMAX":
element_counter = tmax
divisor = 10.0
if element == "TMIN":
element_counter = tmin
divisor = 10.0
if element == "PRCP":
element_counter = prcp
divisor = 10.0
if element == "SNOW":
element_counter = snow
divisor = 1.0
if element == "SNWD":
element_counter = snwd
divisor = 1.0
year = int(ghcnd_contents[counter][11:15])
year_counter = int(year - station_start_year)
month = int(ghcnd_contents[counter][15:17])
month_counter = int(month - 1)
char = 21
for day_counter in xrange(0, 31):
time_pos = results_pos[year_counter, month_counter,
day_counter]
# Get Values / Flags
if ghcnd_contents[counter][char:char + 5] != "-9999":
ghcnd_data[time_pos, element_counter] = float(
ghcnd_contents[counter][char:char + 5]) / divisor
ghcnd_flag[time_pos, element_counter, mflag] = ord(
ghcnd_contents[counter][char + 5:char + 6])
ghcnd_flag[time_pos, element_counter, qcflag] = ord(
ghcnd_contents[counter][char + 6:char + 7])
ghcnd_flag[time_pos, element_counter, srcflag] = ord(
ghcnd_contents[counter][char + 7:char + 8])
char = char + 8
#################################################
# Assign data to netCDF variables
tmax_var[:] = ghcnd_data[:, tmax]
tmax_var.valid_min = np.min(
np.ma.masked_equal(
np.ma.masked_where(ghcnd_flag[:, tmax, qcflag] != ord(" "),
ghcnd_data[:, tmax]), -9999))
tmax_var.valid_max = np.max(
np.ma.masked_equal(
np.ma.masked_where(ghcnd_flag[:, tmax, qcflag] != ord(" "),
ghcnd_data[:, tmax]), -9999))
tmin_var[:] = ghcnd_data[:, tmin]
tmin_var.valid_min = np.min(
np.ma.masked_equal(
np.ma.masked_where(ghcnd_flag[:, tmin, qcflag] != ord(" "),
ghcnd_data[:, tmin]), -9999))
tmin_var.valid_max = np.max(
np.ma.masked_equal(
np.ma.masked_where(ghcnd_flag[:, tmin, qcflag] != ord(" "),
ghcnd_data[:, tmin]), -9999))
prcp_var[:] = ghcnd_data[:, prcp]
prcp_var.valid_min = np.min(
np.ma.masked_equal(
np.ma.masked_where(ghcnd_flag[:, prcp, qcflag] != ord(" "),
ghcnd_data[:, prcp]), -9999))
prcp_var.valid_max = np.max(
np.ma.masked_equal(
np.ma.masked_where(ghcnd_flag[:, prcp, qcflag] != ord(" "),
ghcnd_data[:, prcp]), -9999))
snow_var[:] = ghcnd_data[:, snow]
snow_var.valid_min = np.min(
np.ma.masked_equal(
np.ma.masked_where(ghcnd_flag[:, snow, qcflag] != ord(" "),
ghcnd_data[:, snow]), -9999))
snow_var.valid_max = np.max(
np.ma.masked_equal(
np.ma.masked_where(ghcnd_flag[:, snow, qcflag] != ord(" "),
ghcnd_data[:, snow]), -9999))
snwd_var[:] = ghcnd_data[:, snwd]
snwd_var.valid_min = np.min(
np.ma.masked_equal(
np.ma.masked_where(ghcnd_flag[:, snwd, qcflag] != ord(" "),
ghcnd_data[:, snwd]), -9999))
snwd_var.valid_max = np.max(
np.ma.masked_equal(
np.ma.masked_where(ghcnd_flag[:, snwd, qcflag] != ord(" "),
ghcnd_data[:, snwd]), -9999))
mflag_var[:, :] = ghcnd_flag[:, :, mflag]
qcflag_var[:, :] = ghcnd_flag[:, :, qcflag]
srcflag_var[:, :] = ghcnd_flag[:, :, srcflag]
#################################################
# Close netCDF file
output.close()
return None
def get_metadata(station_id):
# Get Metadata info from station Inventory file
ghcnd_stations = gp.get_ghcnd_stations()
ghcnd_meta = ghcnd_stations[ghcnd_stations[:, 0] == station_id]
ghcnd_name = "N/A"
ghcnd_lat = "N/A"
ghcnd_lon = "N/A"
ghcnd_alt = "N/A"
ghcnd_id = ghcnd_meta[0][0]
ghcnd_lat = float(ghcnd_meta[0][1])
ghcnd_lon = float(ghcnd_meta[0][2])
ghcnd_alt = float(ghcnd_meta[0][3])
ghcnd_name = ghcnd_meta[0][5]
ghcnd_name = ghcnd_name.strip()
ghcnd_name = re.sub(' +', ' ', ghcnd_name)
ghcnd_name = ghcnd_name.replace(" ", "_")
# Get Metadata info from HOMR
homr_link = 'http://www.ncdc.noaa.gov/homr/services/station/search?qid=GHCND:' + station_id
ghcnd_state = "N/A"
ghcnd_climdiv = "N/A"
ghcnd_county = "N/A"
ghcnd_nwswfo = "N/A"
ghcnd_coopid = "N/A"
ghcnd_wbanid = "N/A"
try:
homr = r.get(homr_link)
homr_json = json.loads(homr.text)
except:
pass
# Get State Station is in (HOMR)
try:
ghcnd_state = json.dumps(
homr_json['stationCollection']['stations'][0]['location']
['nwsInfo']['climateDivisions'][0]['stateProvince'])
except:
pass
# Get Climate Division Station is in (HOMR)
try:
ghcnd_climdiv = json.dumps(
homr_json['stationCollection']['stations'][0]['location']
['nwsInfo']['climateDivisions'][0]['climateDivision'])
except:
pass
# Get County Station is in (HOMR)
try:
ghcnd_county = json.dumps(
homr_json['stationCollection']['stations'][0]['location']
['geoInfo']['counties'][0]['county'])
ghcnd_county = ghcnd_county.replace(" ", "_")
except:
pass
# Get NWS WFO station is in (HOMR)
try:
ghcnd_nwswfo = json.dumps(
homr_json['stationCollection']['stations'][0]['location']
['nwsInfo']['nwsWfos'][0]['nwsWfo'])
except:
pass
# Get COOP ID if exists (HOMR)
has_coop = False
has_wban = False
try:
identifiers = homr_json['stationCollection']['stations'][0][
'identifiers']
for counter in range(0, 10):
for key, value in homr_json['stationCollection']['stations'][0][
'identifiers'][counter].iteritems():
if key == "idType" and homr_json['stationCollection'][
'stations'][0]['identifiers'][counter][key] == "COOP":
has_coop = True
if key == "idType" and homr_json['stationCollection'][
'stations'][0]['identifiers'][counter][key] == "WBAN":
has_wban = True
if key == "id" and has_coop:
ghcnd_coopid = homr_json['stationCollection']['stations'][
0]['identifiers'][counter][key]
has_coop = False
if key == "id" and has_wban:
ghcnd_wbanid = homr_json['stationCollection']['stations'][
0]['identifiers'][counter][key]
has_wban = False
except:
pass
# Write everything out
print(station_id)
print(" Station Name: ", ghcnd_name)
print(" Station Lat: ", ghcnd_lat)
print(" Station Lon: ", ghcnd_lon)
print(" Station Elev: ", ghcnd_alt)
print(" Station State: ", ghcnd_state.strip('""'))
print(" Station Climate Division: ", ghcnd_climdiv.strip('""'))
print(" Station County: ", ghcnd_county.strip('""'))
print(" Station NWS Office: ", ghcnd_nwswfo.strip('""'))
print(" Station COOP ID: ", ghcnd_coopid.strip('""'))
print(" Station WBAN ID: ", ghcnd_wbanid.strip('""'))
return None
def get_metadata(station_id):
# Get Metadata info from station Inventory file
ghcnd_stations=gp.get_ghcnd_stations()
ghcnd_meta = ghcnd_stations[ghcnd_stations[:,0] == station_id]
ghcnd_name="N/A"
ghcnd_lat="N/A"
ghcnd_lon="N/A"
ghcnd_alt="N/A"
ghcnd_id=ghcnd_meta[0][0]
ghcnd_lat=float(ghcnd_meta[0][1])
ghcnd_lon=float(ghcnd_meta[0][2])
ghcnd_alt=float(ghcnd_meta[0][3])
ghcnd_name=ghcnd_meta[0][5]
ghcnd_name = ghcnd_name.strip()
ghcnd_name = re.sub(' +',' ',ghcnd_name)
ghcnd_name = ghcnd_name.replace(" ","_")
# Get Metadata info from HOMR
homr_link='http://www.ncdc.noaa.gov/homr/services/station/search?qid=GHCND:'+station_id
ghcnd_state="N/A"
ghcnd_climdiv="N/A"
ghcnd_county="N/A"
ghcnd_nwswfo="N/A"
ghcnd_coopid="N/A"
ghcnd_wbanid="N/A"
try:
homr=r.get(homr_link)
homr_json=json.loads(homr.text)
except:
pass
# Get State Station is in (HOMR)
try:
ghcnd_state=json.dumps(homr_json['stationCollection']['stations'][0]['location']['nwsInfo']['climateDivisions'][0]['stateProvince'])
except:
pass
# Get Climate Division Station is in (HOMR)
try:
ghcnd_climdiv=json.dumps(homr_json['stationCollection']['stations'][0]['location']['nwsInfo']['climateDivisions'][0]['climateDivision'])
except:
pass
# Get County Station is in (HOMR)
try:
ghcnd_county=json.dumps(homr_json['stationCollection']['stations'][0]['location']['geoInfo']['counties'][0]['county'])
ghcnd_county=ghcnd_county.replace(" ","_")
except:
pass
# Get NWS WFO station is in (HOMR)
try:
ghcnd_nwswfo=json.dumps(homr_json['stationCollection']['stations'][0]['location']['nwsInfo']['nwsWfos'][0]['nwsWfo'])
except:
pass
# Get COOP ID if exists (HOMR)
has_coop=False
has_wban=False
try:
identifiers=homr_json['stationCollection']['stations'][0]['identifiers']
for counter in xrange(0,10):
for key, value in homr_json['stationCollection']['stations'][0]['identifiers'][counter].iteritems():
if key == "idType" and homr_json['stationCollection']['stations'][0]['identifiers'][counter][key] == "COOP":
has_coop=True
if key == "idType" and homr_json['stationCollection']['stations'][0]['identifiers'][counter][key] == "WBAN":
has_wban=True
if key == "id" and has_coop:
ghcnd_coopid=homr_json['stationCollection']['stations'][0]['identifiers'][counter][key]
has_coop=False
if key == "id" and has_wban:
ghcnd_wbanid=homr_json['stationCollection']['stations'][0]['identifiers'][counter][key]
has_wban=False
except:
pass
# Write everything out
print(station_id)
print(" Station Name: ",ghcnd_name)
print(" Station Lat: ",ghcnd_lat)
print(" Station Lon: ",ghcnd_lon)
print(" Station Elev: ",ghcnd_alt)
print(" Station State: ",ghcnd_state.strip('""'))
print(" Station Climate Division: ",ghcnd_climdiv.strip('""'))
print(" Station County: ",ghcnd_county.strip('""'))
print(" Station NWS Office: ",ghcnd_nwswfo.strip('""'))
print(" Station COOP ID: ",ghcnd_coopid.strip('""'))
print(" Station WBAN ID: ",ghcnd_wbanid.strip('""'))
return None
def output_to_netcdf(station_id):
print("\nOUTPUTTING TO netCDF: ",station_id,".nc")
begin_year=1700
end_year=datetime.datetime.now().year
num_years=(end_year-begin_year) + 1
start_date=date(begin_year, 1, 1)
end_date=date(end_year,12,31)
# 5 Elements of GHCN-D
num_elements=5
tmax=0
tmin=1
prcp=2
snow=3
snwd=4
# 3 Flags for each value
num_flags=3
# Measurement Flag (MFLAG)
mflag=0
mflag_codes = np.array([ord(x) for x in ["B","D","H","K","L","O","P","T","W"]],dtype='b')
mflag_meanings = np.array(["precipitation total formed from two 12 hour totals".replace(" ","_")+" ",\
"precipitation total formed from four six hour totals".replace(" ","_")+" ",\
"represents highest or lowest hourly temperature".replace(" ","_")+" ",\
"converted from knots".replace(" ","_")+" ",\
"temperature appears to be lagged with respect to reported hour of observation".replace(" ","_")+" ",\
"converted from oktas".replace(" ","_")+" ",\
"identified as missing presumed zero in DSI 3200 and 3206".replace(" ","_")+" ",\
"trace of precipitation snowfall or snow depth".replace(" ","_")+" ",\
"converted from 16 point WBAN code for wind direction".replace(" ","_")],\
dtype='str')
# Quality Control Flag (QCFLAG)
qcflag=1
qcflag_codes = np.array([ord(x) for x in ["D","G","I","K","L","M","N","O","R","S","T","W","X","Z"]],dtype='b')
qcflag_meanings = np.array(["failed duplicate check".replace(" ","_")+" ",\
"failed gap check".replace(" ","_")+" ",\
"failed internal consistency check".replace(" ","_")+" ",\
"failed streak frequent value check".replace(" ","_")+" ",\
"failed check on length of multiday period".replace(" ","_")+" ",\
"failed megaconsistency check".replace(" ","_")+" ",\
"failed naught check".replace(" ","_")+" ",\
"failed climatological outlier check".replace(" ","_")+" ",\
"failed lagged range check".replace(" ","_")+" ",\
"failed spatial consistency check".replace(" ","_")+" ",\
"failed temporal consistency check".replace(" ","_")+" ",\
"failed bounds check".replace(" ","_")+" ",\
"failed climatological outlier check".replace(" ","_")+" ",\
"flagged as a result of an official Datzilla investigation".replace(" ","_")],\
dtype='str')
# Source Flag (SRCFLAG)
srcflag=2
srcflag_codes = np.array([ord(x) for x in ["0","6","7","A","a","B","b","C","E","F","G","H","I","K","M","N","Q","R","r","S","s",\
"T","U","u","W","X","Z","z"]],dtype='b')
srcflag_meanings = np.array(["US Cooperative Summary of the Day".replace(" ","_")+" ",\
"CDMP Cooperative Summary of the Day".replace(" ","_")+" ",\
"US Cooperative Summary of the Day Transmitted via WxCoder3".replace(" ","_")+" ",\
"Automated Surface Observing System real time data since January 1 2006".replace(" ","_")+" ",\
"Australian data from the Australian Bureau of Meteorology".replace(" ","_")+" ",\
"US ASOS data for October 2000 to December 2005".replace(" ","_")+" ",\
"Belarus update".replace(" ","_")+" ",\
"Environment Canada".replace(" ","_")+" ",\
"European Climate Assessment and Dataset".replace(" ","_")+" ",\
"US Fort data".replace(" ","_")+" ",\
"Official Global Climate Observing System".replace(" ","_")+" ",\
"High Plains Regional Climate Center real time data".replace(" ","_")+" ",\
"International collection non U.S. data received through personal contacts".replace(" ","_")+" ",\
"US Cooperative Summary of the Day data digitized from paper observer forms".replace(" ","_")+" ",\
"Monthly METAR Extract".replace(" ","_")+" ",\
"Community Collaborative Rain Hail and Snow".replace(" ","_")+" ",\
"Data from several African countries that had been quarantined".replace(" ","_")+" ",\
"Climate Reference Network and Historical Climatology Network Modernized".replace(" ","_")+" ",\
"All Russian Research Institute of Hydrometeorological Information World Data Center".replace(" ","_")+" ",\
"Global Summary of the Day".replace(" ","_")+" ",\
"China Meteorological Administration National Meteorological Information Center".replace(" ","_")+" ",\
"SNOwpack TELemtry data obtained from the Western Regional Climate Center".replace(" ","_")+" ",\
"Remote Automatic Weather Station data obtained from the Western Regional Climate Center".replace(" ","_")+" ",\
"Ukraine update".replace(" ","_")+" ",\
"WBAN ASOS Summary of the Day from Integrated Surface Data ISD".replace(" ","_")+" ",\
"US First Order Summary of the Day".replace(" ","_")+" ",\
"Datzilla official additions or replacements".replace(" ","_")+" ",\
"Uzbekistan update ".replace(" ","_")],\
dtype='str')
# Get Version Number of GHCN-D
ghcnd_version=gp.get_ghcnd_version()
# Grab Data
gp.get_data_station(station_id)
# Read in GHCN-D Data
infile = station_id+".dly"
file_handle = open(infile, 'r')
ghcnd_contents = file_handle.readlines()
file_handle.close()
# Get Year Start and End of File for time dimensions
station_start_year = int(ghcnd_contents[0][11:15])
station_end_year = int(ghcnd_contents[len(ghcnd_contents)-1][11:15])
station_start_month = int(ghcnd_contents[0][15:17])
station_end_month = int(ghcnd_contents[len(ghcnd_contents)-1][15:17])
num_values=0
results_time = np.zeros((num_years*12*31),dtype='f')-(9999.0)
results_pos = np.zeros((num_years,12,31),dtype='f')-(9999.0)
for year in xrange(station_start_year,station_end_year+1):
year_counter = int(year - station_start_year)
if year == station_start_year:
begin_month = int(station_start_month-1)
else:
begin_month = 0
if year == station_end_year:
end_month = int(station_end_month)
else:
end_month = 12
for month_counter in xrange(begin_month,end_month):
month = month_counter + 1
for day_counter in xrange(0,31):
day = day_counter + 1
try:
current_date=date(year,month,day)
num_days = (current_date-start_date).days
results_time[num_values] = num_days
results_pos[year_counter,month_counter,day_counter] = int(num_values)
num_values+=1
except:
pass
#################################################
# Open netCDF file
outfile=station_id+'.nc'
test=os.path.exists(outfile)
if test == True:
os.remove(outfile)
output = nc.Dataset(outfile, mode='w')
#################################################
# Create global attributes
output.title = "Global Historical Climatology Network - Daily Dataset"
output.summary = "GHCN-Daily is an integrated database of daily climate summaries from land surface stations across the globe. "+\
"GHCN-Daily is comprised of daily climate records from numerous sources that have been integrated and "+\
"subjected to a common suite of quality assurance reviews. This dataset includes the top five core elements: "+\
"PRCP (precipitation), SNOW (snowfall), SNWD (snow depth), TMAX (maximum temperature), TMIN (minimum temperature)"
output.version=ghcnd_version
output.keywords = "daily, station, extremes, temperature, precipitation, snowfall, snow depth"
output.Conventions = "CF-1.6, ACDD-1.3"
output.institution = "Cooperative Institute for Climate and Satellites - North Carolina (CICS-NC) and "+\
"NOAAs National Centers for Environmental Information (NCEI), Asheville, North Carolina, USA"
output.source = "surface observations from multiple sources around the globe"
output.featureType = "timeSeries"
output.history = "File generatred on ",str(datetime.datetime.now())
output.creator_name = "Jared Rennie, CICS-NC"
output.creator_email = "*****@*****.**"
output.creator_url = "http://www.cicsnc.org/people/jared-rennie/"
output.references= "Menne et al 2012: http://dx.doi.org/10.1175/JTECH-D-11-00103.1"
output.comment="More information can be found here: ftp://ftp.ncdc.noaa.gov/pub/data/ghcn/daily/readme.txt"
#################################################
# Create Dimensions
output.createDimension('element',num_elements)
output.createDimension('time',num_values)
output.createDimension('name_strlen',30)
output.createDimension('id_strlen',11)
#################################################
# Create Variables
# LON
lon_var = output.createVariable('lon', datatype='f')
lon_var.standard_name = "longitude"
lon_var.long_name = "station longitude"
lon_var.units = "degrees_east"
# LAT
lat_var = output.createVariable('lat', datatype='f')
lat_var.standard_name = "latitude"
lat_var.long_name = "station latitude"
lat_var.units = "degrees_north"
# ELEV (ALT)
alt_var = output.createVariable('alt', datatype='f')
alt_var.long_name = "vertical distance above the surface"
alt_var.standard_name = "height"
alt_var.units = "m"
alt_var.positive = "up"
alt_var.axis = "Z"
# ID
id_var = output.createVariable('id', datatype='S1', dimensions=('id_strlen'))
id_var.long_name = "station identifier"
# NAME
name_var = output.createVariable('station_name', datatype='S1', dimensions=('name_strlen'))
name_var.long_name = "station name"
name_var.cf_role = "timeseries_id"
# TIME
time_var = output.createVariable('time', datatype='f8', dimensions='time')
time_var.standard_name = "time"
time_var.long_name = "Time of Measurement"
time_var.units = "days since ",begin_year,"-01-01 00:00:00"
time_var.calendar = "gregorian"
time_var.axis = "T"
time_var[:] = results_time[0:num_values]
time_var.valid_min = np.min(results_time[0:num_values])
# FLAGS
mflag_var = output.createVariable('mflag', datatype='b', dimensions=('time','element'), fill_value=32.)
mflag_var.long_name = "measurement flag"
mflag_var.flag_values=mflag_codes
mflag_var.flag_meanings=mflag_meanings
qcflag_var = output.createVariable('qcflag', datatype='b', dimensions=('time','element'), fill_value=32.)
qcflag_var.long_name = "quality control flag"
qcflag_var.flag_values=qcflag_codes
qcflag_var.flag_meanings=qcflag_meanings
srcflag_var = output.createVariable('srcflag', datatype='b', dimensions=('time','element'), fill_value=32.)
srcflag_var.long_name = "source flag"
srcflag_var.flag_values=srcflag_codes
srcflag_var.flag_meanings=srcflag_meanings
# TMAX
tmax_var = output.createVariable('tmax', datatype='f', dimensions=('time'), fill_value=-9999.)
tmax_var.long_name = "surface maximum temperature"
tmax_var.units = "Celsius"
tmax_var.coordinates = "time lat lon alt station_name"
tmax_var.ancillary_variables = "mflag qcflag srcflag"
# TMIN
tmin_var = output.createVariable('tmin', datatype='f', dimensions=('time'), fill_value=-9999.)
tmin_var.long_name = "surface minimum temperature"
tmin_var.units = "Celsius"
tmin_var.coordinates = "time lat lon alt station_name"
tmin_var.ancillary_variables = "mflag qcflag srcflag"
# PRCP
prcp_var = output.createVariable('prcp', datatype='f', dimensions=('time'), fill_value=-9999.)
prcp_var.long_name = "surface precipitation"
prcp_var.units = "mm"
prcp_var.coordinates = "time lat lon alt station_name"
prcp_var.ancillary_variables = "mflag qcflag srcflag"
# SNOW
snow_var = output.createVariable('snow', datatype='f', dimensions=('time'), fill_value=-9999.)
snow_var.long_name = "surface snowfall"
snow_var.units = "mm"
snow_var.coordinates = "time lat lon alt station_name"
snow_var.ancillary_variables = "mflag qcflag srcflag"
# SNWD
snwd_var = output.createVariable('snwd', datatype='f', dimensions=('time'), fill_value=-9999.)
snwd_var.long_name = "surface snow depth"
snwd_var.units = "mm"
snwd_var.coordinates = "time lat lon alt station_name"
snwd_var.ancillary_variables = "mflag qcflag srcflag"
#################################################
# Read in GHCN-D Meta
ghcnd_stations=gp.get_ghcnd_stations()
ghcnd_meta = ghcnd_stations[ghcnd_stations[:,0] == station_id]
ghcnd_id=ghcnd_meta[0][0]
ghcnd_lat=float(ghcnd_meta[0][1])
ghcnd_lon=float(ghcnd_meta[0][2])
ghcnd_alt=float(ghcnd_meta[0][3])
ghcnd_name=ghcnd_meta[0][5]
ghcnd_name = ghcnd_name.strip()
ghcnd_name = re.sub(' +',' ',ghcnd_name)
ghcnd_name = ghcnd_name.replace(" ","_")
for string_counter in xrange(0,len(ghcnd_id)):
id_var[string_counter] = ghcnd_id[string_counter]
lat_var[:] = ghcnd_lat
lon_var[:] = ghcnd_lon
alt_var[:] = ghcnd_alt
for string_counter in xrange(0,len(ghcnd_name)):
name_var[string_counter] = ghcnd_name[string_counter]
#################################################
# Go through GHCN-D Data
ghcnd_data= np.zeros((num_values,num_elements),dtype='f')-(9999.0)
ghcnd_flag= np.zeros((num_values,num_elements,num_flags),dtype='i')+32
for counter in xrange(len(ghcnd_contents)):
element = ghcnd_contents[counter][17:21]
if element == "TMAX" or element == "TMIN" or element == "PRCP" or element == "SNOW" or element == "SNWD":
if element == "TMAX":
element_counter=tmax
divisor = 10.0
if element == "TMIN":
element_counter=tmin
divisor = 10.0
if element == "PRCP":
element_counter=prcp
divisor = 10.0
if element == "SNOW":
element_counter=snow
divisor = 1.0
if element == "SNWD":
element_counter=snwd
divisor = 1.0
year = int(ghcnd_contents[counter][11:15])
year_counter = int(year - station_start_year)
month = int(ghcnd_contents[counter][15:17])
month_counter = int(month - 1)
char=21
for day_counter in xrange(0,31):
time_pos=results_pos[year_counter,month_counter,day_counter]
# Get Values / Flags
if ghcnd_contents[counter][char:char+5] != "-9999":
ghcnd_data[time_pos,element_counter] = float(ghcnd_contents[counter][char:char+5]) / divisor
ghcnd_flag[time_pos,element_counter,mflag] = ord(ghcnd_contents[counter][char+5:char+6])
ghcnd_flag[time_pos,element_counter,qcflag] = ord(ghcnd_contents[counter][char+6:char+7])
ghcnd_flag[time_pos,element_counter,srcflag] = ord(ghcnd_contents[counter][char+7:char+8])
char = char + 8
#################################################
# Assign data to netCDF variables
tmax_var[:] = ghcnd_data[:,tmax]
tmax_var.valid_min = np.min(np.ma.masked_equal(np.ma.masked_where(ghcnd_flag[:,tmax,qcflag]!=ord(" "),ghcnd_data[:,tmax]),-9999))
tmax_var.valid_max = np.max(np.ma.masked_equal(np.ma.masked_where(ghcnd_flag[:,tmax,qcflag]!=ord(" "),ghcnd_data[:,tmax]),-9999))
tmin_var[:] = ghcnd_data[:,tmin]
tmin_var.valid_min = np.min(np.ma.masked_equal(np.ma.masked_where(ghcnd_flag[:,tmin,qcflag]!=ord(" "),ghcnd_data[:,tmin]),-9999))
tmin_var.valid_max = np.max(np.ma.masked_equal(np.ma.masked_where(ghcnd_flag[:,tmin,qcflag]!=ord(" "),ghcnd_data[:,tmin]),-9999))
prcp_var[:] = ghcnd_data[:,prcp]
prcp_var.valid_min = np.min(np.ma.masked_equal(np.ma.masked_where(ghcnd_flag[:,prcp,qcflag]!=ord(" "),ghcnd_data[:,prcp]),-9999))
prcp_var.valid_max = np.max(np.ma.masked_equal(np.ma.masked_where(ghcnd_flag[:,prcp,qcflag]!=ord(" "),ghcnd_data[:,prcp]),-9999))
snow_var[:] = ghcnd_data[:,snow]
snow_var.valid_min = np.min(np.ma.masked_equal(np.ma.masked_where(ghcnd_flag[:,snow,qcflag]!=ord(" "),ghcnd_data[:,snow]),-9999))
snow_var.valid_max = np.max(np.ma.masked_equal(np.ma.masked_where(ghcnd_flag[:,snow,qcflag]!=ord(" "),ghcnd_data[:,snow]),-9999))
snwd_var[:] = ghcnd_data[:,snwd]
snwd_var.valid_min = np.min(np.ma.masked_equal(np.ma.masked_where(ghcnd_flag[:,snwd,qcflag]!=ord(" "),ghcnd_data[:,snwd]),-9999))
snwd_var.valid_max = np.max(np.ma.masked_equal(np.ma.masked_where(ghcnd_flag[:,snwd,qcflag]!=ord(" "),ghcnd_data[:,snwd]),-9999))
mflag_var[:,:] = ghcnd_flag[:,:,mflag]
qcflag_var[:,:] = ghcnd_flag[:,:,qcflag]
srcflag_var[:,:] = ghcnd_flag[:,:,srcflag]
#################################################
# Close netCDF file
output.close()
return None
def plot_snowfall(station_id):
print("\nPLOTTING SNOWFALL DATA FOR STATION: ",station_id)
# Declare Other Variables
begin_year=1895
num_elements=1 # SNOW
snow=0
num_days=366
end_year=datetime.now().year
num_years=(end_year-begin_year) + 1
# Get station metadatafile
ghcnd_stations=gp.get_ghcnd_stations()
ghcnd_meta = ghcnd_stations[ghcnd_stations[:,0] == station_id]
ghcnd_id=ghcnd_meta[0][0]
ghcnd_lat=float(ghcnd_meta[0][1])
ghcnd_lon=float(ghcnd_meta[0][2])
ghcnd_alt=float(ghcnd_meta[0][3])
ghcnd_name=ghcnd_meta[0][5]
ghcnd_name = ghcnd_name.strip()
ghcnd_name = re.sub(' +',' ',ghcnd_name)
ghcnd_name = ghcnd_name.replace(" ","_")
# Grab Data
gp.get_data_station(station_id)
#################################################
# Read in GHCN-D Data (Original, QC'd data removed)
infile = station_id+".dly"
ghcnd_value = np.zeros((num_years,12,31,num_elements),dtype='f')
file_handle = open(infile, 'r')
contents = file_handle.readlines()
file_handle.close()
valid_end=-9999
valid_begin=9999
for counter in xrange(len(contents)):
element = contents[counter][17:21]
if element == "SNOW":
element_counter=snow
year = int(contents[counter][11:15])
year_counter = year-begin_year
valid_begin=min(valid_begin,year)
valid_end=max(valid_end,year)
month = int(contents[counter][15:17])
month_counter = month-1
char=21
for day_counter in xrange(0,31):
if contents[counter][char:char+5] != "-9999" and contents[counter][char+6:char+7] == " ":
ghcnd_value[year_counter][month_counter][day_counter][element_counter] = float(contents[counter][char:char+5])
last_day=day_counter+1
char = char + 8
# Get day of year for last day with valid data
last_day=datetime(year, month, last_day).timetuple().tm_yday
last_day=last_day+92 # Shift three months
if last_day >=365:
last_day=last_day-365
# Convert from mm to inch
ghcnd_value=(ghcnd_value*0.0393701)
# Get Record / Average Values for every day in year
average_snow = np.zeros((num_days),dtype='f')-(9999.0)
day_of_year=0
day_before=0
for month_counter in [9, 10, 11, 12, 1, 2, 3, 4, 5, 6, 7, 8, 9]:
for day_counter in xrange(0,31):
try:
# Check if leap-year date is valid
datetime(year=2012,month=month_counter+1,day=day_counter+1)
average_snow[day_of_year] = day_before + ma.average(ghcnd_value[(valid_begin-begin_year):(valid_end-begin_year),month_counter,day_counter,snow])
day_before=average_snow[day_of_year]
day_of_year=day_of_year+1
except:
pass
#################################################
# Create Accumulations
new_year_counter=0
snow_accum = np.zeros((num_years+1,num_days),dtype='f')
total_accum = np.zeros((num_years+1),dtype='f')
for year_counter in xrange(0,num_years):
for month_counter in xrange(0,12):
if month_counter==9: # Month Begins in Oct
new_year_counter=year_counter+1
day_of_year=0
day_before=0
for day_counter in xrange(0,31):
try:
# Check if date is valid
datetime(year=year_counter+begin_year,month=month_counter+1,day=day_counter+1)
snow_accum[new_year_counter][day_of_year] = day_before + ghcnd_value[year_counter,month_counter,day_counter,snow]
total_accum[new_year_counter]=snow_accum[new_year_counter][day_of_year]
day_before=snow_accum[new_year_counter][day_of_year]
day_of_year=day_of_year+1
except:
pass
if month_counter+1==12 and snow_accum[year_counter][365]==0:
snow_accum[year_counter][365]=snow_accum[year_counter][364]
#################################################
# PLOT
# Mask Zero Data before plotting
#snow_accum = ma.masked_values(snow_accum, 0.)
total_accum = ma.masked_values(total_accum, 0.)
#Get Some Stats Needed For Plotting
x_axis=range(num_days)
x_axis_end=range(last_day)
current_loc = num_years-1
current_snow = "%6.2f" % total_accum[current_loc]
current_year = current_loc + begin_year
current_data=snow_accum[current_loc,0:last_day]
current_last=snow_accum[current_loc,last_day]
max_snow = "%6.2f" % np.max(total_accum)
max_loc = np.argmax(total_accum)
max_year = max_loc+begin_year
min_snow = "%6.2f" % np.min(total_accum[np.where(total_accum != 0)])
min_loc = np.nanargmin(total_accum)
min_year = min_loc+begin_year
avg_snow = "%6.2f" % average_snow[365]
# Create Figure
fig, ax1 = plt.subplots(figsize=(15, 8), edgecolor='white', facecolor='white', dpi=300)
# Add grid lines
plt.grid(color='black', linestyle='--', linewidth=0.5, alpha=0.3)
# Plot Accumulated SNOW (Sort by end of year accumulation and plot by range of color)
order=np.argsort(snow_accum[:,364])
color_pos=np.linspace(0.5,1,num_years)
order_counter=0
color_counter=0
for year_counter in xrange(0,num_years):
pos=order[order_counter]
if pos != (num_years-1):
plt.plot(x_axis, snow_accum[pos,:], linewidth=0.5, color=colors.rgb2hex(pylab.cm.GnBu(color_pos[color_counter])[0:3]))
color_counter=color_counter+1
order_counter=order_counter+1
# Overlay Record Max Snow Year
if max_loc==current_loc:
plt.plot(x_axis_end, snow_accum[max_loc,0:last_day], color='#084081', linewidth=3, label='Max ('+str(max_year-1)+'-'+str(max_year)+': '+str(max_snow)+'")')
else:
plt.plot(x_axis, snow_accum[max_loc,:], color='#084081', linewidth=3, label='Max ('+str(max_year-1)+'-'+str(max_year)+': '+str(max_snow)+'")')
# Overlay Record Min Snow Year
if min_loc==current_loc:
plt.plot(x_axis_end, snow_accum[min_loc,0:last_day], color='#66ff99', linewidth=3, label='Min ('+str(min_year-1)+'-'+str(min_year)+': '+str(min_snow)+'")')
else:
plt.plot(x_axis, snow_accum[min_loc,:], color='#66ff99', linewidth=3, label='Min ('+str(min_year-1)+'-'+str(min_year)+': '+str(min_snow)+'")')
# Overlay Average SNOW
plt.plot(x_axis, average_snow[:], color='#e6b800', linewidth=3, markeredgecolor='white', label='Avg ('+str(avg_snow)+'")')
# Overlay Current Snow Year
plt.plot(x_axis_end, current_data, color='black', linewidth=3, label='Current ('+str(current_year-1)+'-'+str(current_year)+': '+str(current_snow)+'")')
plt.plot(x_axis_end[last_day-1],current_last, marker='o', color='black', markersize=10)
# Plot Legend
plt.legend(bbox_to_anchor=(0., -.102, 1., -1.02), loc=3, ncol=4, mode="expand", borderaxespad=0., fontsize=12)
# Plot X/Y Limits
ymin=0
ymax=int(5 * round(float((np.max(snow_accum) + 10))/5))
plt.ylim(ymin,ymax)
plt.xlim(-5, num_days)
# Plot Y-Axis Label
plt.yticks(range(ymin, ymax, 10), [r'{}"'.format(x) for x in range(ymin, ymax, 10)], fontsize=10)
plt.ylabel(r'Accumulated Snowfall (inches)', fontsize=12)
# Plot X-Axis Label
month_pos=[0,31,60,91,121,152,182,213,244,274,305,335]
month_names=["Oct","Nov","Dec","Jan","Feb","Mar","Apr","May","Jun","Jul","Aug","Sep"]
plt.xticks(month_pos, month_names, fontsize=10)
# Plot 2nd Y Axis Labels
ax3 = ax1.twinx()
plt.yticks(range(ymin, ymax, 10), [r'{}"'.format(x) for x in range(ymin, ymax, 10)], fontsize=10)
plt.ylim(ymin, ymax)
# Plot Title/Subtitle
plt.suptitle(station_id+': '+ghcnd_name, fontsize=20)
plt.title('LAT= '+str(ghcnd_lat)+' | LON= '+str(ghcnd_lon)+' | ELEV= '+str(int(ghcnd_alt*3.2808399))+'\'', fontsize=15)
# Save Figure
plt.savefig(station_id+'_snowfall.png', dpi=300)
plt.clf()
return None
def plot_temperature(station_id,begin_date,end_date):
print("\nPLOTTING TEMPERATURE DATA FOR STATION: ",station_id)
# Declare Other Variables
begin_year=1895
num_elements=2 # TMAX/TMIN
tmax=0
tmin=1
end_year=datetime.now().year
num_years=(end_year-begin_year) + 1
# Get station metadatafile
ghcnd_stations=gp.get_ghcnd_stations()
ghcnd_meta = ghcnd_stations[ghcnd_stations[:,0] == station_id]
ghcnd_id=ghcnd_meta[0][0]
ghcnd_lat=float(ghcnd_meta[0][1])
ghcnd_lon=float(ghcnd_meta[0][2])
ghcnd_alt=float(ghcnd_meta[0][3])
ghcnd_name=ghcnd_meta[0][5]
ghcnd_name = ghcnd_name.strip()
ghcnd_name = re.sub(' +',' ',ghcnd_name)
ghcnd_name = ghcnd_name.replace(" ","_")
# Grab Data
gp.get_data_station(station_id)
#################################################
# Read in GHCN-D Data (Original, QC'd data removed)
infile = station_id+".dly"
ghcnd_value = np.zeros((num_years,12,31,num_elements),dtype='f')-(9999.0)
file_handle = open(infile, 'r')
contents = file_handle.readlines()
file_handle.close()
for counter in xrange(len(contents)):
element = contents[counter][17:21]
if element == "TMAX" or element == "TMIN":
if element == "TMAX":
element_counter=tmax
if element == "TMIN":
element_counter=tmin
year = int(contents[counter][11:15])
year_counter = year-begin_year
month = int(contents[counter][15:17])
month_counter = month-1
char=21
for day_counter in xrange(0,31):
if contents[counter][char:char+5] != "-9999" and contents[counter][char+6:char+7] == " ":
ghcnd_value[year_counter][month_counter][day_counter][element_counter] = float(contents[counter][char:char+5]) / 10.0
char = char + 8
# Mask Missing, convert from C to F
ghcnd_nonmiss = ma.masked_values(ghcnd_value, -9999.)
ghcnd_nonmiss=(ghcnd_nonmiss*1.8) + 32
# Get Record / Average Values for every day in year
# For averages, use 1981-2010
record_max_ghcnd = np.zeros((12,31),dtype='f')-(9999.0)
record_min_ghcnd = np.zeros((12,31),dtype='f')-(9999.0)
average_max_ghcnd = np.zeros((12,31),dtype='f')-(9999.0)
average_min_ghcnd = np.zeros((12,31),dtype='f')-(9999.0)
for month_counter in xrange(0,12):
for day_counter in xrange(0,31):
record_max_ghcnd[month_counter,day_counter] = ma.max(ghcnd_nonmiss[:,month_counter,day_counter,tmax])
record_min_ghcnd[month_counter,day_counter] = ma.min(ghcnd_nonmiss[:,month_counter,day_counter,tmin])
average_max_ghcnd[month_counter,day_counter] = ma.average(ghcnd_nonmiss[(1980-begin_year):(2010-begin_year),month_counter,day_counter,tmax])
average_min_ghcnd[month_counter,day_counter] = ma.average(ghcnd_nonmiss[(1980-begin_year):(2010-begin_year),month_counter,day_counter,tmin])
#################################################
# Gather Data based Upon Date Requested
begin_yy = int(begin_date[0:4])
begin_mm = int(begin_date[4:6])
begin_dd = int(begin_date[6:8])
end_yy = int(end_date[0:4])
end_mm = int(end_date[4:6])
end_dd = int(end_date[6:8])
num_days = (date(end_yy, end_mm, end_dd) - date(begin_yy, begin_mm, begin_dd)).days
num_days=num_days+1
num_months = ((date(end_yy, end_mm, end_dd).year - date(begin_yy, begin_mm, begin_dd).year)*12 +\
date(end_yy, end_mm, end_dd).month - date(begin_yy, begin_mm, begin_dd).month) + 1
record_max = np.zeros((num_days),dtype='f')-(9999.0)
record_min = np.zeros((num_days),dtype='f')-(9999.0)
average_max = np.zeros((num_days),dtype='f')-(9999.0)
average_min = np.zeros((num_days),dtype='f')-(9999.0)
raw_max = np.zeros((num_days),dtype='f')-(9999.0)
raw_min = np.zeros((num_days),dtype='f')-(9999.0)
month_pos = np.zeros((num_months),dtype='i')-(9999.0)
month_names = np.empty((num_months),dtype='S7')
num_days=0
num_months=0
for year_counter in xrange(begin_yy,end_yy+1):
if year_counter == begin_yy:
start_month=begin_mm
end_month=12
elif year_counter == end_yy:
start_month=1
end_month=end_mm
else:
start_month=1
end_month=12
for month_counter in xrange(start_month,end_month+1):
month_pos[num_months] = num_days
month_names[num_months] = calendar.month_name[month_counter][0:3]+" '"+str(year_counter)[2:4]
for day_counter in xrange(begin_dd,end_dd+1):
try:
# Check if date is valid
datetime(year=year_counter,month=month_counter,day=day_counter)
record_max[num_days] = record_max_ghcnd[month_counter-1,day_counter-1]
record_min[num_days] = record_min_ghcnd[month_counter-1,day_counter-1]
average_max[num_days] = average_max_ghcnd[month_counter-1,day_counter-1]
average_min[num_days] = average_min_ghcnd[month_counter-1,day_counter-1]
raw_max[num_days] = ghcnd_nonmiss[year_counter-begin_year,month_counter-1,day_counter-1,tmax]
raw_min[num_days] = ghcnd_nonmiss[year_counter-begin_year,month_counter-1,day_counter-1,tmin]
num_days=num_days+1
except:
pass
num_months=num_months+1
x_axis=range(num_days)
#################################################
# PLOT
fig, ax1 = plt.subplots(figsize=(15, 8), edgecolor='white', facecolor='white', dpi=300)
# Add grid lines
plt.grid(color='black', linestyle='--', linewidth=0.5, alpha=0.3)
# Plot Record TMAX/TMIN
plt.bar(x_axis, record_max - record_min, bottom=record_min, edgecolor='none', color='#c3bba4', width=1, label="Record Max/Min")
# Plot Average TMAX/TMIN
plt.bar(x_axis, average_max - average_min, bottom=average_min, edgecolor='none', color='#9a9180', width=1, label="Average Max/Min")
# Plot Raw TMAX/TMIN
plt.bar(x_axis, raw_max - raw_min, bottom=raw_min, edgecolor='black', linewidth=0.5, color='#5a3b49', width=1, label="Actual Max/Min")
# Find New Max/Min Records
new_max_records = raw_max[raw_max >= record_max]
new_min_records = raw_min[raw_min <= record_min]
# Plot New Max/Min Records
plt.scatter(np.where(raw_max >= record_max)[0] + 0.5, new_max_records + 1.25, s=15, zorder=10, color='#d62728', alpha=0.75, linewidth=0, label="New Max Record")
plt.scatter(np.where(raw_min <= record_min)[0] + 0.5, new_min_records - 1.25, s=15, zorder=10, color='#1f77b4', alpha=0.75, linewidth=0, label="New Min Record")
# Plot Legend
plt.legend(bbox_to_anchor=(0., -.102, 1., -1.02), loc=3, ncol=5, mode="expand", borderaxespad=0., fontsize=12)
# Plot X/Y Limits
ymin=int(5 * round(float((min(record_min) - 10))/5))
ymax=int(5 * round(float((max(record_max) + 10))/5))
plt.ylim(ymin, ymax)
plt.xlim(-5, (num_days))
# Plot Y-Axis Label
plt.yticks(range(ymin, ymax, 10), [r'{}$^\circ$'.format(x) for x in range(ymin, ymax, 10)], fontsize=10)
plt.ylabel(r'Temperature ($^\circ$F)', fontsize=12)
# Plot X-Axis Label
plt.xticks(month_pos, month_names, fontsize=10)
# Plot 2nd Y Axis Labels
ax3 = ax1.twinx()
plt.yticks(range(ymin, ymax, 10), [r'{}$^\circ$'.format(x) for x in range(ymin, ymax, 10)], fontsize=10)
plt.ylim(ymin, ymax)
# Plot Title/Subtitle
plt.suptitle(station_id+': '+ghcnd_name, fontsize=20)
plt.title('LAT= '+str(ghcnd_lat)+' | LON= '+str(ghcnd_lon)+' | ELEV= '+str(int(ghcnd_alt*3.2808399))+'\'', fontsize=15)
# Save Figure
plt.savefig(station_id+'_temperature.png', dpi=300)
plt.clf()
return None