import ghcnpy
# Provide introduction
ghcnpy.intro()
# Print Latest Version
ghcnpy.get_ghcnd_version()
# Testing Search Capabilities
print("\nTESTING SEARCH CAPABILITIES")
ghcnpy.find_station("Asheville")
# Testing Search Capabilities
print("\nTESTING PULL CAPABILITIES")
outfile = ghcnpy.get_data_station("USW00003812")
print(outfile, " has been downloaded")
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
import ghcnpy
# Provide introduction
ghcnpy.intro()
# Print Latest Version
ghcnpy.get_ghcnd_version()
# Testing Search Capabilities
print("\nTESTING SEARCH CAPABILITIES")
ghcnpy.find_station("Asheville")
# Testing Search Capabilities
print("\nTESTING PULL CAPABILITIES")
outfile=ghcnpy.get_data_station("USW00003812")
print(outfile," has been downloaded")
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