def HRRR_error_wind_speed(DATE):
"""
return the error between the forecast and the analysis
Use this for multiprocessing
"""
# HRRR Analysis
Hu = get_hrrr_variable(DATE, variable='UGRD:'+level, verbose=False)
Hv = get_hrrr_variable(DATE, variable='VGRD:'+level, verbose=False)
#
# HRRR Forecast
Hfu = get_hrrr_variable(DATE-timedelta(hours=fxx), variable='UGRD:'+level, fxx=fxx, verbose=False)
Hfv = get_hrrr_variable(DATE-timedelta(hours=fxx), variable='VGRD:'+level, fxx=fxx, verbose=False)
#
# proof that I grabbed the right data from the archive
#print Hu['msg'], Hu['valid']
#print Hfu['msg'], Hfu['valid']
#
# Calculate the difference between the HRRR analysis and HRRR forecast (fxx-anlys)
# so that the red shows where the forecast is fast and blue shows where forecast
# is slow.
aSPD = wind_uv_to_spd(Hu['value'], Hv['value'])
fSPD = wind_uv_to_spd(Hfu['value'], Hfv['value'])
diff = fSPD-aSPD
#
return diff
def get_HRRR_value(validDATE):
"""
Get HRRR data. Retrun the value, not the latitude and longitude.
"""
runDATE = validDATE - timedelta(hours=fxx)
if variable[:2] == 'UV':
# Then we want to calculate the wind speed from U and V components.
wind_lev = variable.split(':')[-1] # This is the level of interest
Hu = get_hrrr_variable(runDATE, 'UGRD:'+wind_lev,
fxx=fxx, model='hrrr', field='sfc',
value_only=True, verbose=False)
Hv = get_hrrr_variable(runDATE, 'VGRD:'+wind_lev,
fxx=fxx, model='hrrr', field='sfc',
value_only=True, verbose=False)
if np.shape(Hu['value']) == () or np.shape(Hv['value']) == ():
print("!! WARNING !! COULD NOT GET %s %s f%02d" % (variable, runDATE, fxx))
return None
else:
spd = wind_uv_to_spd(Hu['value'], Hv['value'])
return spd
else:
H = get_hrrr_variable(runDATE, variable,
fxx=fxx, model='hrrr', field='sfc',
value_only=True, verbose=False)
if np.shape(H['value']) == ():
# If the data is a nan, then return None. This will be important for filtering.
print("!! WARNING !! COULD NOT GET %s %s f%02d" % (variable, runDATE, fxx))
return None
else:
return H['value']
def get_HRRR_value(validDATE, variable, fxx):
"""
Get HRRR data. Return just the value, not the latitude and longitude.
If data is np.nan, then return None, i.e. the shape of the data is ().
This makes it possible to filter out the None values later.
"""
runDATE = validDATE - timedelta(hours=fxx)
if variable.split(':')[0] == 'UVGRD':
#print("getting U and V components")
Hu = get_hrrr_variable(runDATE, 'UGRD', fxx=fxx,
verbose=False)['value']
Hv = get_hrrr_variable(runDATE, 'VGRD', fxx=fxx,
verbose=False)['value']
H = wind_uv_to_spd(Hu, Hv)
else:
H = get_hrrr_variable(runDATE, variable, fxx=fxx,
verbose=False)['value']
if np.shape(H) == ():
# Then the data is a nan. Return None so it can be filtered out.
print("!! WARNING !! COULD NOT GET %s %s f%02d" %
(variable, runDATE, fxx))
return None
else:
return H
TS_hcon = LocDic_hrrr_time_series(
sDATE,
eDATE,
location,
variable='HGT:level of adiabatic condensation from sfc',
verbose=False,
fxx=fxx)
# Convert the units of each TimeSeries
for loc in location.keys():
# Convert Units for the variables in the Pollywog
TS_temp[loc] = KtoC(TS_temp[loc])
TS_dwpt[loc] = KtoC(TS_dwpt[loc])
# Derive some variables:
TS_wind80[loc] = wind_uv_to_spd(TS_u80[loc], TS_v80[loc])
# Just need one vector of valid dates
TS_dates = np.array(TS_temp['DATETIME'])
# Make a dictionary of map object for each location.
# (This speeds up plotting by creating each map once.)
maps = {}
for loc in location:
l = location[loc]
m = Basemap(resolution='i', projection='cyl',\
llcrnrlon=l['longitude']-.25, llcrnrlat=l['latitude']-.25,\
urcrnrlon=l['longitude']+.25, urcrnrlat=l['latitude']+.25,)
maps[loc] = m
# Create a figure for each location. Add permenant elements to each.
def plot_rose(l):
"""
l is a dictionary with MesoWest and HRRR data at the station for a period of time
"""
MW = l['MesoWest']
HR = l['HRRR']
HR['wind_speed'] = [
wind_uv_to_spd(HR['u'][i], HR['v'][i]) for i in range(len(HR['u']))
]
HR['wind_direction'] = [
wind_uv_to_dir(HR['u'][i], HR['v'][i]) for i in range(len(HR['u']))
]
# Make the wind rose - MesoWest
axMW = new_axes()
axMW.bar(MW['wind_direction'],
MW['wind_speed'],
nsector=16,
normed=True,
bins=range(0, 20, 2))
# Create a legend
set_legend(axMW)
plt.title("MesoWest %s (%s) \n %s - %s" %
(MW['STID'], MW['NAME'], MW['DATETIME'][0].strftime('%d %b %Y'),
MW['DATETIME'][-1].strftime('%d %b %Y')))
plt.grid(True)
# Grid at 5% intervals
plt.yticks(np.arange(5, 105, 5))
axMW.set_yticklabels(
['5%', '10%', '15%', '20%', '25%', '30%', '35%', '40%'])
# Change the plot range
axMW.set_rmax(np.max(np.sum(axMW._info['table'], axis=0)))
plt.savefig(l['SAVE'] + '/' + MW['STID'] + '/rose_MW.png')
print "Saved a windrose - MesoWest", MW['STID']
plt.clf()
plt.cla()
# Make the wind rose - HRRR
axHR = new_axes()
axHR.bar(HR['wind_direction'],
HR['wind_speed'],
nsector=16,
normed=True,
bins=range(0, 20, 2))
# Create a legend
set_legend(axHR)
plt.title("HRRR %s (%s) \n %s - %s" %
(HR['STID'], HR['NAME'], HR['DATETIME'][0].strftime('%d %b %Y'),
MW['DATETIME'][-1].strftime('%d %b %Y')))
plt.grid(True)
# Grid at 5% intervals
plt.yticks(np.arange(5, 105, 5))
axHR.set_yticklabels(
['5%', '10%', '15%', '20%', '25%', '30%', '35%', '40%'])
# Change the plot range
axHR.set_rmax(np.max(np.sum(axHR._info['table'], axis=0)))
plt.savefig(l['SAVE'] + '/' + MW['STID'] + '/rose_HR.png')
print "Saved a windrose - HRRR", HR['STID'], l['SAVE']
plt.clf()
plt.cla()
def make_plots(inputs):
VALIDDATE, fxx = inputs
print 'working on %s f%02d' % (VALIDDATE, fxx)
plt.clf()
plt.cla()
print fxx, VALIDDATE
# === Some housekeeping variables =============================================
# Convert Valid Date to Run Date, adjusted by the forecast
DATE = VALIDDATE - timedelta(hours=fxx)
# Parse Location lat/lon
if ',' in location:
# User put inputted a lat/lon point request
lat, lon = location.split(',')
lat = float(lat)
lon = float(lon)
else:
# User requested a MesoWest station
stninfo = get_station_info([location])
lat = stninfo['LAT']
lon = stninfo['LON']
# Preload the latitude and longitude grid
latlonpath = '/uufs/chpc.utah.edu/common/home/horel-group7/Pando/hrrr/HRRR_latlon.h5'
latlonh5 = h5py.File(latlonpath, 'r')
gridlat = latlonh5['latitude'][:]
gridlon = latlonh5['longitude'][:]
# === Create map of the domain ================================================
if dsize == 'conus' and model != 'hrrrAK':
barb_thin = 70
alpha = 1
t1 = datetime.now()
#m = draw_CONUS_HRRR_map(res=map_res)
m = np.load(
'/uufs/chpc.utah.edu/common/home/u0553130/public_html/Brian_Blaylock/cgi-bin/HRRR_CONUS_map_object_'
+ map_res + '.npy').item()
m.drawcountries(zorder=500)
m.drawstates(zorder=500)
m.drawcoastlines(zorder=500)
m.fillcontinents(color='tan', lake_color='lightblue', zorder=0)
m.drawmapboundary(fill_color='lightblue')
t2 = datetime.now()
else:
# configure some setting based on the requested domain size
if dsize == 'small':
plus_minus_latlon = .27 # +/- latlon box around center point
barb_thin = 1 # Thin out excessive wind barbs
arcgis_res = 1000 # ArcGIS image resolution
bfr = 15 # trim domain buffer
alpha = .75 # Alpha (pcolormesh transparency)
elif dsize == 'medium':
plus_minus_latlon = .75
barb_thin = 2
arcgis_res = 800
bfr = 35
alpha = .75
elif dsize == 'large':
plus_minus_latlon = 2.5
barb_thin = 6
arcgis_res = 800
bfr = 110
alpha = .75
elif dsize == 'xlarge':
plus_minus_latlon = 5
barb_thin = 12
arcgis_res = 700
bfr = 210
alpha = .75
elif dsize == 'xxlarge': # If domain runs into HRRR boundary, then it'll fail
plus_minus_latlon = 10
barb_thin = 25
arcgis_res = 700
bfr = 430
alpha = .75
elif dsize == 'xxxlarge':
plus_minus_latlon = 15
barb_thin = 35
arcgis_res = 1000
bfr = 700
alpha = .75
m = Basemap(resolution=map_res, projection='cyl',\
area_thresh=3000,\
llcrnrlon=lon-plus_minus_latlon, llcrnrlat=lat-plus_minus_latlon,\
urcrnrlon=lon+plus_minus_latlon, urcrnrlat=lat+plus_minus_latlon,)
m.drawstates(zorder=500)
m.drawcountries(zorder=500)
m.drawcoastlines(zorder=500)
#if dsize == 'small' or dsize == 'medium':
# m.drawcounties()
# === Add a Background image ==================================================
# Start the map image
plt.figure(1)
if background == 'arcgis' and dsize != 'conus':
m.arcgisimage(service='World_Shaded_Relief',
xpixels=arcgis_res,
verbose=False)
elif background == 'arcgisSat' and dsize != 'conus':
m.arcgisimage(service='ESRI_Imagery_World_2D',
xpixels=arcgis_res,
verbose=False)
elif background == 'arcgisRoad' and dsize != 'conus':
m.arcgisimage(service='NatGeo_World_Map',
xpixels=arcgis_res,
verbose=False)
elif background == 'terrain':
# Get data
H_ter = get_hrrr_variable(
DATE,
'HGT:surface',
model=model,
fxx=fxx,
outDIR='/uufs/chpc.utah.edu/common/home/u0553130/temp/',
verbose=False,
value_only=True)
H_land = get_hrrr_variable(
DATE,
'LAND:surface',
model=model,
fxx=fxx,
outDIR='/uufs/chpc.utah.edu/common/home/u0553130/temp/',
verbose=False,
value_only=True)
# Plot the terrain
m.contourf(gridlon,
gridlat,
H_ter['value'],
levels=range(0, 4000, 200),
cmap='Greys_r',
zorder=1,
latlon=True)
# Plot Water area
m.contour(gridlon,
gridlat,
H_land['value'],
levels=[0, 1],
colors='b',
zorder=1,
latlon=True)
elif background == 'landuse':
# Get data
from BB_cmap.landuse_colormap import LU_MODIS21
if model == 'hrrr':
VGTYP = 'VGTYP:surface'
else:
VGTYP = 'var discipline=2 center=59 local_table=1 parmcat=0 parm=198'
H_LU = get_hrrr_variable(
DATE,
VGTYP,
model=model,
fxx=fxx,
outDIR='/uufs/chpc.utah.edu/common/home/u0553130/temp/',
verbose=False,
value_only=True)
# Plot the terrain
cm, labels = LU_MODIS21()
m.pcolormesh(gridlon,
gridlat,
H_LU['value'],
cmap=cm,
vmin=1,
vmax=len(labels) + 1,
zorder=1,
latlon=True)
# Add SHAPEFILE
if is_FIRE:
plt.gca().add_collection(
PatchCollection(patches,
facecolor='indianred',
alpha=.65,
edgecolor='k',
linewidths=1,
zorder=1))
# === Figure Title ============================================================
if dsize != 'conus':
m.scatter(lon, lat, marker='+', c='r', s=100, zorder=1000, latlon=True)
plt.title('Center: %s\n%s' % (location, model.upper()),
fontweight='bold')
else:
plt.title('%s' % (model.upper()), fontweight='bold')
plt.title('Run: %s F%02d' % (DATE.strftime('%Y-%m-%d %H:%M UTC'), fxx),
loc='left')
plt.title('Valid: %s' %
(DATE + timedelta(hours=fxx)).strftime('%Y-%m-%d %H:%M UTC'),
loc='right')
# =============================================================================
if '10mWind_Fill' in plotcode or '10mWind_Shade' in plotcode or '10mWind_Barb' in plotcode or '10mWind_Quiver' in plotcode or '10mWind_p95_fill' in plotcode:
# Get data
H_u = get_hrrr_variable(
DATE,
'UGRD:10 m',
model=model,
fxx=fxx,
outDIR='/uufs/chpc.utah.edu/common/home/u0553130/temp/',
verbose=False,
value_only=True)
H_v = get_hrrr_variable(
DATE,
'VGRD:10 m',
model=model,
fxx=fxx,
outDIR='/uufs/chpc.utah.edu/common/home/u0553130/temp/',
verbose=False,
value_only=True)
spd = wind_uv_to_spd(H_u['value'], H_v['value'])
if '10mWind_Fill' in plotcode:
m.pcolormesh(gridlon,
gridlat,
spd,
latlon=True,
cmap=cm_wind(),
vmin=0,
vmax=60,
alpha=alpha)
cb = plt.colorbar(orientation='horizontal', pad=pad, shrink=shrink)
cb.set_label(r'10 m Wind Speed (m s$\mathregular{^{-1}}$)')
if '10mWind_Shade' in plotcode:
m.contourf(gridlon,
gridlat,
spd,
levels=[10, 15, 20, 25],
colors=('yellow', 'orange', 'red'),
alpha=alpha,
extend='max',
zorder=1,
latlon=True)
cb = plt.colorbar(orientation='horizontal', shrink=shrink, pad=pad)
cb.set_label(r'10 m Wind Speed (ms$\mathregular{^{-1}}$)')
if '10mWind_Barb' in plotcode or '10mWind_Quiver' in plotcode:
# For small domain plots, trimming the edges significantly reduces barb plotting time
if barb_thin < 20:
cut_v, cut_h = pluck_point_new(lat, lon, gridlat, gridlon)
Cgridlat = gridlat[cut_v - bfr:cut_v + bfr,
cut_h - bfr:cut_h + bfr]
Cgridlon = gridlon[cut_v - bfr:cut_v + bfr,
cut_h - bfr:cut_h + bfr]
H_u['value'] = H_u['value'][cut_v - bfr:cut_v + bfr,
cut_h - bfr:cut_h + bfr]
H_v['value'] = H_v['value'][cut_v - bfr:cut_v + bfr,
cut_h - bfr:cut_h + bfr]
else:
Cgridlat = gridlat
Cgridlon = gridlon
thin = barb_thin
# Add to plot
if '10mWind_Barb' in plotcode:
m.barbs(Cgridlon[::thin, ::thin],
Cgridlat[::thin, ::thin],
H_u['value'][::thin, ::thin],
H_v['value'][::thin, ::thin],
zorder=200,
length=5.5,
barb_increments={
'half': 2.5,
'full': 5,
'flag': 25
},
latlon=True)
if '10mWind_Quiver' in plotcode:
Q = m.quiver(Cgridlon[::thin, ::thin],
Cgridlat[::thin, ::thin],
H_u['value'][::thin, ::thin],
H_v['value'][::thin, ::thin],
zorder=350,
latlon=True)
qk = plt.quiverkey(Q,
.92,
0.07,
10,
r'10 m s$^{-1}$',
labelpos='S',
coordinates='axes',
color='darkgreen')
qk.text.set_backgroundcolor('w')
if '10mWind_p95_fill' in plotcode:
DIR = '/uufs/chpc.utah.edu/common/home/horel-group8/blaylock/HRRR_OSG/hourly30/UVGRD_10_m/'
FILE = 'OSG_HRRR_%s_m%02d_d%02d_h%02d_f00.h5' % (
('UVGRD_10_m', VALIDDATE.month, VALIDDATE.day, VALIDDATE.hour))
with h5py.File(DIR + FILE, 'r') as f:
spd_p95 = f["p95"][:]
masked = spd - spd_p95
masked = np.ma.array(masked)
masked[masked < 0] = np.ma.masked
m.pcolormesh(gridlon,
gridlat,
masked,
vmax=10,
vmin=0,
latlon=True,
cmap='viridis',
alpha=alpha)
cb = plt.colorbar(orientation='horizontal', pad=pad, shrink=shrink)
cb.set_label(
r'10 m Wind Speed exceeding 95th Percentile (m s$\mathregular{^{-1}}$)'
)
if '80mWind_Fill' in plotcode or '80mWind_Shade' in plotcode or '80mWind_Barb' in plotcode:
# Get data
H_u = get_hrrr_variable(
DATE,
'UGRD:80 m',
model=model,
fxx=fxx,
outDIR='/uufs/chpc.utah.edu/common/home/u0553130/temp/',
verbose=False,
value_only=True)
H_v = get_hrrr_variable(
DATE,
'VGRD:80 m',
model=model,
fxx=fxx,
outDIR='/uufs/chpc.utah.edu/common/home/u0553130/temp/',
verbose=False,
value_only=True)
spd = wind_uv_to_spd(H_u['value'], H_v['value'])
if '80mWind_Fill' in plotcode:
m.pcolormesh(gridlon,
gridlat,
spd,
latlon=True,
cmap=cm_wind(),
vmin=0,
vmax=60,
alpha=alpha)
cb = plt.colorbar(orientation='horizontal', pad=pad, shrink=shrink)
cb.set_label(r'10 m Wind Speed (m s$\mathregular{^{-1}}$)')
if '80mWind_Shade' in plotcode:
m.contourf(gridlon,
gridlat,
spd,
levels=[10, 15, 20, 25],
colors=('yellow', 'orange', 'red'),
alpha=alpha,
extend='max',
zorder=10,
latlon=True)
cb = plt.colorbar(orientation='horizontal', shrink=shrink, pad=pad)
cb.set_label(r'10 m Wind Speed (ms$\mathregular{^{-1}}$)')
if '80mWind_Barb' in plotcode:
# For small domain plots, trimming the edges significantly reduces barb plotting time
if barb_thin < 20:
cut_v, cut_h = pluck_point_new(lat, lon, gridlat, gridlon)
Cgridlat = gridlat[cut_v - bfr:cut_v + bfr,
cut_h - bfr:cut_h + bfr]
Cgridlon = gridlon[cut_v - bfr:cut_v + bfr,
cut_h - bfr:cut_h + bfr]
H_u['value'] = H_u['value'][cut_v - bfr:cut_v + bfr,
cut_h - bfr:cut_h + bfr]
H_v['value'] = H_v['value'][cut_v - bfr:cut_v + bfr,
cut_h - bfr:cut_h + bfr]
else:
Cgridlat = gridlat
Cgridlon = gridlon
# Add to plot
thin = barb_thin
m.barbs(Cgridlon[::thin, ::thin],
Cgridlat[::thin, ::thin],
H_u['value'][::thin, ::thin],
H_v['value'][::thin, ::thin],
zorder=200,
length=5.5,
color='darkred',
barb_increments={
'half': 2.5,
'full': 5,
'flag': 25
},
latlon=True)
if 'Fill80mWind' in plotcode:
m.pcolormesh(gridlon,
gridlat,
spd,
latlon=True,
cmap=cm_wind(),
vmin=0,
vmax=60,
alpha=alpha)
cb = plt.colorbar(orientation='horizontal', pad=pad, shrink=shrink)
cb.set_label(r'80 m Wind Speed (m s$\mathregular{^{-1}}$)')
if 'Gust_Hatch' in plotcode:
H_gust = get_hrrr_variable(
DATE,
'GUST:surface',
model=model,
fxx=fxx,
outDIR='/uufs/chpc.utah.edu/common/home/u0553130/temp/',
verbose=False,
value_only=True)
# Add to plot
m.contourf(gridlon,
gridlat,
H_gust['value'],
levels=[0, 10, 15, 20, 25],
hatches=[None, '.', '\\\\', '*'],
colors='none',
extend='max',
zorder=10,
latlon=True)
cb = plt.colorbar(orientation='horizontal', shrink=shrink, pad=pad)
cb.set_label(r'Surface Wind Gust (ms$\mathregular{^{-1}}$)')
m.contour(gridlon,
gridlat,
H_gust['value'],
levels=[10, 15, 20, 25],
colors='k',
zorder=10,
latlon=True)
if 'dBZ_Fill' in plotcode or 'dBZ_Contour' in plotcode:
from BB_cmap.reflectivity_colormap import reflect_ncdc
# Get Data
if model == 'hrrr':
REFC = 'REFC:entire'
elif model == 'hrrrX' or model == 'hrrrAK':
REFC = 'var discipline=0 center=59 local_table=1 parmcat=16 parm=196'
H_ref = get_hrrr_variable(
DATE,
REFC,
model=model,
fxx=fxx,
outDIR='/uufs/chpc.utah.edu/common/home/u0553130/temp/',
verbose=False,
value_only=True)
# Mask values
dBZ = H_ref['value']
dBZ = np.ma.array(dBZ)
dBZ[dBZ == -10] = np.ma.masked
# Add Contour to plot
if 'dBZ_Contour' in plotcode:
cREF = m.contour(gridlon,
gridlat,
dBZ,
cmap=reflect_ncdc(),
levels=range(10, 80, 10),
latlon=True,
zorder=50)
plt.clabel(cREF,
cREF.levels[::2],
fmt='%2.0f',
colors='k',
fontsize=9)
#cb2 = plt.colorbar(orientation='horizontal', shrink=shrink, pad=pad)
#cb2.set_label('Simulated Composite Reflectivity (dBZ)')
# Add fill to plot
if 'dBZ_Fill' in plotcode:
m.pcolormesh(gridlon,
gridlat,
dBZ,
cmap=reflect_ncdc(),
vmax=80,
vmin=0,
alpha=alpha,
latlon=True)
cb2 = plt.colorbar(orientation='horizontal',
shrink=shrink,
pad=pad)
cb2.set_label('Simulated Composite Reflectivity (dBZ)')
if '2mDPT_p95p05_fill' in plotcode:
H_dpt = get_hrrr_variable(
DATE,
'DPT:2 m',
model=model,
fxx=fxx,
outDIR='/uufs/chpc.utah.edu/common/home/u0553130/temp/',
verbose=False,
value_only=True)
DIR = '/uufs/chpc.utah.edu/common/home/horel-group8/blaylock/HRRR_OSG/hourly30/DPT_2_m/'
FILE = 'OSG_HRRR_%s_m%02d_d%02d_h%02d_f00.h5' % (
('DPT_2_m', VALIDDATE.month, VALIDDATE.day, VALIDDATE.hour))
### Plot Dew Point Depression
with h5py.File(DIR + FILE, 'r') as f:
dpt_p05 = f["p05"][:]
masked = H_dpt[
'value'] - dpt_p05 # both these datasets are in Kelvin, but when we take the difference it is in Celsius
masked = np.ma.array(masked)
masked[masked > 0] = np.ma.masked
mesh_depression = m.pcolormesh(gridlon,
gridlat,
masked,
vmax=10,
vmin=-10,
latlon=True,
cmap='BrBG')
### Plot Dew Point Exceedance
with h5py.File(DIR + FILE, 'r') as f:
dpt_p95 = f["p95"][:]
masked = H_dpt[
'value'] - dpt_p95 # both these datasets are in Kelvin, but when we take the difference it is in Celsius
masked = np.ma.array(masked)
masked[masked < 0] = np.ma.masked
mesh_exceedance = m.pcolormesh(gridlon,
gridlat,
masked,
vmax=10,
vmin=-10,
latlon=True,
cmap='BrBG')
cb = plt.colorbar(orientation='horizontal', pad=pad, shrink=shrink)
cb.set_label(
r'5$\mathregular{^{th}}$/95$\mathregular{^{th}}$ percentile 2 m Dew Point Depression/Exceedance (C)'
)
if '2mTemp_Fill' in plotcode or '2mTemp_Freeze' in plotcode or '2mTemp_p95p05_fill' in plotcode:
# Get Data
H_temp = get_hrrr_variable(
DATE,
'TMP:2 m',
model=model,
fxx=fxx,
outDIR='/uufs/chpc.utah.edu/common/home/u0553130/temp/',
verbose=False,
value_only=True)
TMP = H_temp['value'] - 273.15
# Add fill to plot
if '2mTemp_Fill' in plotcode:
m.pcolormesh(gridlon,
gridlat,
TMP,
cmap=cm_temp(),
vmax=50,
vmin=-50,
alpha=alpha,
zorder=3,
latlon=True)
cbT = plt.colorbar(orientation='horizontal',
shrink=shrink,
pad=pad)
cbT.set_label('2 m Temperature (C)')
# Add freezing contour to plot
if '2mTemp_Freeze' in plotcode:
m.contour(gridlon,
gridlat,
TMP,
colors='b',
levels=[0],
zorder=400,
latlon=True)
if '2mTemp_p95p05_fill' in plotcode:
DIR = '/uufs/chpc.utah.edu/common/home/horel-group8/blaylock/HRRR_OSG/hourly30/TMP_2_m/'
FILE = 'OSG_HRRR_%s_m%02d_d%02d_h%02d_f00.h5' % (
('TMP_2_m', VALIDDATE.month, VALIDDATE.day, VALIDDATE.hour))
### Plot Temperature Depression
with h5py.File(DIR + FILE, 'r') as f:
tmp_p05 = f["p05"][:]
masked = H_temp[
'value'] - tmp_p05 # both these datasets are in Kelvin, but when we take the difference it is in Celsius
masked = np.ma.array(masked)
masked[masked > 0] = np.ma.masked
mesh_depression = m.pcolormesh(gridlon,
gridlat,
masked,
vmax=10,
vmin=-10,
latlon=True,
cmap='bwr')
### Plot Temperature Exceedance
with h5py.File(DIR + FILE, 'r') as f:
tmp_p95 = f["p95"][:]
masked = H_temp[
'value'] - tmp_p95 # both these datasets are in Kelvin, but when we take the difference it is in Celsius
masked = np.ma.array(masked)
masked[masked < 0] = np.ma.masked
mesh_exceedance = m.pcolormesh(gridlon,
gridlat,
masked,
vmax=10,
vmin=-10,
latlon=True,
cmap='bwr')
cb = plt.colorbar(orientation='horizontal', pad=pad, shrink=shrink)
cb.set_label(
r'5$\mathregular{^{th}}$/95$\mathregular{^{th}}$ percentile 2 m Temperature Depression/Exceedance (C)'
)
if '2mRH_Fill' in plotcode:
# Get Data
try:
H_RH = get_hrrr_variable(
DATE,
'RH:2 m',
model=model,
fxx=fxx,
outDIR='/uufs/chpc.utah.edu/common/home/u0553130/temp/',
verbose=False,
value_only=True)
# Add fill to plot
m.pcolormesh(gridlon,
gridlat,
H_RH['value'],
cmap=cm_rh(),
vmin=5,
vmax=90,
zorder=3,
latlon=True)
cbT = plt.colorbar(orientation='horizontal',
pad=pad,
shrink=shrink)
cbT.set_label('2m Relative Humidity (%)')
except:
print "!! Some errors getting the RH value."
print "!! If you requested an old date, from HRRR version 1, there isn't a RH variable,"
print "!! and this code doesn't get the dwpt and convert it to RH yet."
if '700Temp_Fill' in plotcode or '700Temp_-12c' in plotcode:
# Get Data
H_temp = get_hrrr_variable(
DATE,
'TMP:700 mb',
model=model,
fxx=fxx,
outDIR='/uufs/chpc.utah.edu/common/home/u0553130/temp/',
verbose=False,
value_only=True)
TMP = H_temp['value'] - 273.15
# Add fill to plot
if '700Temp_Fill' in plotcode:
m.pcolormesh(gridlon,
gridlat,
TMP,
cmap=cm_temp(),
vmax=50,
vmin=-50,
alpha=alpha,
zorder=3,
latlon=True)
cbT = plt.colorbar(orientation='horizontal',
shrink=shrink,
pad=pad)
cbT.set_label('700 mb Temperature (C)')
# Add -12 C contour to plot
if '700Temp_-12c' in plotcode:
m.contour(gridlon,
gridlat,
TMP,
colors='b',
levels=[-12],
latlon=True,
zorder=400)
if '500HGT_Contour' in plotcode:
H_500 = get_hrrr_variable(
DATE,
'HGT:500 mb',
model=model,
fxx=fxx,
outDIR='/uufs/chpc.utah.edu/common/home/u0553130/temp/',
verbose=False,
value_only=True)
CS = m.contour(gridlon,
gridlat,
H_500['value'],
levels=range(5040, 6181, 60),
linewidths=1.7,
colors='k',
latlon=True,
zorder=400)
plt.clabel(CS, inline=1, fmt='%2.f')
if '500Wind_Fill' in plotcode or '500Wind_Barb' in plotcode or '500Vort_Fill' in plotcode or '500Conv_Fill' in plotcode:
H_u = get_hrrr_variable(
DATE,
'UGRD:500 mb',
model=model,
fxx=fxx,
outDIR='/uufs/chpc.utah.edu/common/home/u0553130/temp/',
verbose=False,
value_only=True)
H_v = get_hrrr_variable(
DATE,
'VGRD:500 mb',
model=model,
fxx=fxx,
outDIR='/uufs/chpc.utah.edu/common/home/u0553130/temp/',
verbose=False,
value_only=True)
if '500Wind_Fill' in plotcode:
spd = wind_uv_to_spd(H_u['value'], H_v['value'])
m.pcolormesh(gridlon,
gridlat,
spd,
latlon=True,
cmap=cm_wind(),
vmin=0,
vmax=60)
cb = plt.colorbar(orientation='horizontal', pad=pad, shrink=shrink)
cb.set_label(r'500 mb Wind Speed (m s$\mathregular{^{-1}}$)')
if '500Conv_Fill' in plotcode or '500Vort_Fill' in plotcode:
dudx, dudy = np.gradient(H_u['value'], 3, 3)
dvdx, dvdy = np.gradient(H_v['value'], 3, 3)
if '500Vort_Fill' in plotcode:
vorticity = dvdx - dudy
# Mask values
vort = vorticity
vort = np.ma.array(vort)
vort[np.logical_and(vort < .05, vort > -.05)] = np.ma.masked
m.pcolormesh(gridlon,
gridlat,
vort,
latlon=True,
cmap='bwr',
vmax=np.max(vort),
vmin=-np.max(vort))
cb = plt.colorbar(orientation='horizontal',
pad=pad,
shrink=shrink)
cb.set_label(r'500 mb Vorticity (s$\mathregular{^{-1}}$)')
if '500Conv_Fill' in plotcode:
convergence = dudx + dvdy
# Mask values
conv = convergence
conv = np.ma.array(conv)
conv[np.logical_and(conv < .05, conv > -.05)] = np.ma.masked
m.pcolormesh(gridlon,
gridlat,
conv,
latlon=True,
cmap='bwr',
vmax=np.max(conv),
vmin=-np.max(conv))
cb = plt.colorbar(orientation='horizontal',
pad=pad,
shrink=shrink)
cb.set_label(r'500 mb Convergence (s$\mathregular{^{-1}}$)')
if '500Wind_Barb' in plotcode:
# For small domain plots, trimming the edges significantly reduces barb plotting time
if barb_thin < 20:
cut_v, cut_h = pluck_point_new(lat, lon, gridlat, gridlon)
Cgridlat = gridlat[cut_v - bfr:cut_v + bfr,
cut_h - bfr:cut_h + bfr]
Cgridlon = gridlon[cut_v - bfr:cut_v + bfr,
cut_h - bfr:cut_h + bfr]
H_u['value'] = H_u['value'][cut_v - bfr:cut_v + bfr,
cut_h - bfr:cut_h + bfr]
H_v['value'] = H_v['value'][cut_v - bfr:cut_v + bfr,
cut_h - bfr:cut_h + bfr]
else:
Cgridlat = gridlat
Cgridlon = gridlon
thin = barb_thin
m.barbs(Cgridlon[::thin, ::thin],
Cgridlat[::thin, ::thin],
H_u['value'][::thin, ::thin],
H_v['value'][::thin, ::thin],
zorder=200,
length=6,
color='navy',
barb_increments={
'half': 2.5,
'full': 5,
'flag': 25
},
latlon=True)
#plt.ylabel(r'Barbs: half=2.5, full=5, flag=25 (ms$\mathregular{^{-1}}$)')
if 'MSLP_Contour' in plotcode or 'MSLP_Fill' in plotcode:
H = get_hrrr_variable(
DATE,
'MSLMA:mean sea level',
model=model,
fxx=fxx,
outDIR='/uufs/chpc.utah.edu/common/home/u0553130/temp/',
verbose=False,
value_only=True)
if 'MSLP_Contour' in plotcode:
CS = m.contour(gridlon,
gridlat,
H['value'] / 100.,
latlon=True,
levels=range(952, 1200, 4),
colors='k',
zorder=400)
CS.clabel(inline=1, fmt='%2.f', zorder=400)
if 'MSLP_Fill' in plotcode:
m.pcolormesh(gridlon,
gridlat,
H['value'] / 100.,
latlon=True,
cmap='viridis')
cb = plt.colorbar(orientation='horizontal', pad=pad, shrink=shrink)
cb.set_label('Mean Sea Level Pressure (hPa)')
if '2mPOT_Fill' in plotcode:
# Get Data
H_temp = get_hrrr_variable(
DATE,
'POT:2 m',
model=model,
fxx=fxx,
outDIR='/uufs/chpc.utah.edu/common/home/u0553130/temp/',
verbose=False,
value_only=True)
TMP = H_temp['value'] - 273.15
m.pcolormesh(gridlon,
gridlat,
TMP,
cmap="Oranges",
alpha=alpha,
zorder=3,
latlon=True)
cbS = plt.colorbar(orientation='horizontal', shrink=shrink, pad=pad)
cbS.set_label('2 m Potential Temperature (C)')
if 'SkinTemp_Fill' in plotcode:
# Get Data
H_temp = get_hrrr_variable(
DATE,
'TMP:surface',
model=model,
fxx=fxx,
outDIR='/uufs/chpc.utah.edu/common/home/u0553130/temp/',
verbose=False,
value_only=True)
TMP = H_temp['value'] - 273.15
m.pcolormesh(gridlon,
gridlat,
TMP,
cmap="Spectral_r",
alpha=alpha,
zorder=3,
latlon=True)
cbS = plt.colorbar(orientation='horizontal', shrink=shrink, pad=pad)
cbS.set_label('Skin Temperature (C)')
if 'AccumPrecip_Fill' in plotcode or '1hrPrecip_Fill' in plotcode:
if 'AccumPrecip_Fill' in plotcode:
# Get Data
H = get_hrrr_variable(
DATE,
'APCP:surface:0',
model=model,
fxx=fxx,
outDIR='/uufs/chpc.utah.edu/common/home/u0553130/temp/',
verbose=False,
value_only=True)
# Mask values
prec = H['value']
prec = np.ma.array(prec)
prec[prec == 0] = np.ma.masked
m.pcolormesh(gridlon,
gridlat,
prec,
cmap=cm_precip(),
alpha=alpha,
vmin=0,
vmax=762,
zorder=3,
latlon=True)
cbS = plt.colorbar(orientation='horizontal',
shrink=shrink,
pad=pad)
cbS.set_label('Accumulated Precipitation since F00 (mm)')
if '1hrPrecip_Fill' in plotcode:
# Get Data
H = get_hrrr_variable(
DATE,
'APCP:surface',
model=model,
fxx=fxx,
outDIR='/uufs/chpc.utah.edu/common/home/u0553130/temp/',
verbose=False,
value_only=True)
# Mask values
prec = H['value']
prec = np.ma.array(prec)
prec[prec == 0] = np.ma.masked
m.pcolormesh(gridlon,
gridlat,
prec,
cmap=cm_precip(),
alpha=alpha,
vmin=0,
vmax=762,
zorder=3,
latlon=True)
cbS = plt.colorbar(
orientation='horizontal',
shrink=shrink,
pad=pad,
extend="max",
)
cbS.set_label('1 hour Accumulated Precipitation (mm)')
if 'SnowCover_Fill' in plotcode:
# Get Data
H = get_hrrr_variable(
DATE,
'SNOWC',
model=model,
fxx=fxx,
outDIR='/uufs/chpc.utah.edu/common/home/u0553130/temp/',
verbose=False,
value_only=True)
# Mask values
snow = H['value']
snow = np.ma.array(snow)
snow[snow == 0] = np.ma.masked
m.pcolormesh(gridlon,
gridlat,
snow,
cmap="Blues",
alpha=alpha,
zorder=3,
latlon=True)
cbS = plt.colorbar(orientation='horizontal', shrink=shrink, pad=pad)
cbS.set_label('Snow Cover (%)')
if 'PWAT_Fill' in plotcode:
# Get Data
H = get_hrrr_variable(
DATE,
'PWAT:entire',
model=model,
fxx=fxx,
outDIR='/uufs/chpc.utah.edu/common/home/u0553130/temp/',
verbose=False,
value_only=True)
m.pcolormesh(gridlon,
gridlat,
H['value'],
cmap="RdYlGn",
alpha=alpha,
vmin=0,
zorder=3,
latlon=True)
cbS = plt.colorbar(orientation='horizontal', shrink=shrink, pad=pad)
cbS.set_label(
r'Vertically Integrated Liquid Water (kg m$\mathregular{^{-2}}$)')
if 'CAPE_Fill' in plotcode:
# Get Data
H = get_hrrr_variable(
DATE,
'CAPE:surface',
model=model,
fxx=fxx,
outDIR='/uufs/chpc.utah.edu/common/home/u0553130/temp/',
verbose=False,
value_only=True)
# Mask values
cape = H['value']
cape = np.ma.array(cape)
cape[cape == 0] = np.ma.masked
m.pcolormesh(gridlon,
gridlat,
cape,
cmap="Oranges",
alpha=alpha,
zorder=3,
latlon=True)
cbS = plt.colorbar(orientation='horizontal', shrink=shrink, pad=pad)
cbS.set_label(r'Surface CAPE (J kg$\mathregular{^{-1}}$)')
if 'CIN_Fill' in plotcode:
# Get Data
H = get_hrrr_variable(
DATE,
'CIN:surface',
model=model,
fxx=fxx,
outDIR='/uufs/chpc.utah.edu/common/home/u0553130/temp/',
verbose=False,
value_only=True)
# Mask values
cin = H['value']
cin = np.ma.array(cin)
cin[cin == 0] = np.ma.masked
m.pcolormesh(gridlon,
gridlat,
cin,
cmap="BuPu",
alpha=alpha,
zorder=3,
latlon=True)
cbS = plt.colorbar(orientation='horizontal', shrink=shrink, pad=pad)
cbS.set_label(r'Surface CIN (J kg$\mathregular{^{-1}}$)')
if 'RedFlag_Fill' in plotcode or 'RedFlag_Contour' in plotcode or 'RedFlagPot_Fill' in plotcode:
# generalized criteria for red flag warning
# Winds (gusts) greater than 6.7 m/s and RH < 25%
rf_RH = 25
rf_WIND = 6.7
# Get Data
H_gust = get_hrrr_variable(
DATE,
'GUST:surface',
model=model,
fxx=fxx,
outDIR='/uufs/chpc.utah.edu/common/home/u0553130/temp/',
verbose=False,
value_only=True)
H_rh = get_hrrr_variable(
DATE,
'RH:2 m',
model=model,
fxx=fxx,
outDIR='/uufs/chpc.utah.edu/common/home/u0553130/temp/',
verbose=False,
value_only=True)
RedFlag = np.logical_and(H_gust['value'] > rf_WIND,
H_rh['value'] < rf_RH)
if 'RedFlag_Contour' in plotcode:
try:
CS = m.contour(gridlon,
gridlat,
RedFlag,
latlon=True,
colors='maroon',
zorder=400)
except:
# maybe there isn't any contours in this domain
pass
if 'RedFlag_Fill' in plotcode:
RedFlag = np.ma.array(RedFlag)
RedFlag[RedFlag == 0] = np.ma.masked
m.pcolormesh(gridlon,
gridlat,
RedFlag,
cmap="YlOrRd_r",
alpha=alpha,
zorder=4,
latlon=True)
if 'RedFlagPot_Fill' in plotcode:
cdict3 = {
'red': ((0.0, 1.0, 1.0), (0.5, 0.5, 0.5), (0.5, 1.0, 1.0),
(1.0, 0.4, 0.4)),
'green': ((0.0, 1.0, 1.0), (0.5, 0.5, 0.5), (0.5, 0.4, 0.4),
(1.0, 0.0, 0.0)),
'blue': ((0.0, 1.0, 1.0), (0.5, 0.5, 0.5), (0.5, 0.0, 0.0),
(1.0, 0.0, 0.0))
}
plt.register_cmap(name='FirePot', data=cdict3)
# Definate Red Flag Area:
RED_FLAG = np.logical_and(H_rh['value'] < rf_RH,
H_gust['value'] > rf_WIND)
# Linear Equation
b = (rf_RH - rf_WIND) * (rf_RH / rf_WIND)
z = -(rf_RH / rf_WIND) * (H_rh['value'] - H_gust['value']) + b
m.pcolormesh(gridlon,
gridlat,
z,
cmap="FirePot",
alpha=alpha,
vmax=200,
vmin=-200,
zorder=3,
latlon=True)
cb = plt.colorbar(orientation='horizontal', pad=pad, shrink=shrink)
cb.set_label(r'Red Flag Potential')
m.contour(gridlon,
gridlat,
z,
colors='k',
levels=[0],
zorder=3,
latlon=True)
m.contour(gridlon,
gridlat,
RED_FLAG,
colors='darkred',
levels=[0],
zorder=3,
latlon=True)
plt.xlabel(
r'Red Flag Criteria: Winds > 6.7 m s$\mathregular{^{-1}}$ and RH < 25%'
)
# =============================================================================
# Hack! Plot an extra HRRR variable not listed on the webpage hrrr_custom.html
# This extra argument will let you attempt to plot a different variable for
# a quicklook.
try:
# Must be a variable from a line in the .idx file
hrrrVAR = form['extraVAR'].value
extraHRRR = get_hrrr_variable(
DATE,
hrrrVAR,
model=model,
fxx=fxx,
outDIR='/uufs/chpc.utah.edu/common/home/u0553130/temp/',
verbose=False,
value_only=True)
m.pcolormesh(gridlon,
gridlat,
extraHRRR['value'],
cmap='viridis',
alpha=alpha,
zorder=3,
latlon=True)
cbS = plt.colorbar(orientation='horizontal', shrink=shrink, pad=pad)
cbS.set_label(hrrrVAR + ' (units)')
except:
pass
# =============================================================================
#plt.ylabel('Section Timer:%s\nFull Timer:%s' % (t2-t1, datetime.now()-firsttimer))
SAVEDIR = '/uufs/chpc.utah.edu/common/home/u0553130/public_html/oper/HRRR_yesterday/%s/%s/' % (
MAP_DIR, DOMAIN)
if not os.path.exists(SAVEDIR):
os.makedirs(SAVEDIR)
SAVEFIG = SAVEDIR + '%s_f%02d' % (VALIDDATE.strftime('h%H'), fxx)
plt.savefig(SAVEFIG) # Plot standard output.
print 'SAVED:', SAVEFIG
def get_hrrr_variable(DATE,
variable,
fxx=0,
model='hrrr',
field='sfc',
removeFile=True,
value_only=False,
verbose=True,
outDIR='./'):
"""
Uses cURL to grab the requested variable from a HRRR grib2 file in the
HRRR archive. Uses the the requested variable string to search the .idx
file and determine the byte range. When the byte range of a variable is
known, cURL is capable of downloading a single variable from a larger GRIB2
file. This function packages the data in a dictionary.
Input:
DATE - The datetime(year, month, day, hour) for the HRRR file you
want. This is the same as the model run time, in UTC.
variable - A string describing the variable you are looking for in the
GRIB2 file. Refer to the .idx files. For example:
https://pando-rgw01.chpc.utah.edu/hrrr/sfc/20180101/hrrr.t00z.wrfsfcf00.grib2.idx
You want to put the variable short name and the level
information. For example, for 2m temperature:
variable='TMP:2 m above ground'
fxx - The forecast hour you desire. Default is the analysis hour,
or f00.
model - The model you want. Options include ['hrrr', 'hrrrX', 'hrrrak']
field - The file output type. Options include ['sfc', 'prs']
removeFile - True: remove the GRIB2 file after it is downloaded
False: do not remove the GRIB2 file after it is downloaded
value_only - True: only return the values, not the lat/lon.
Returns output in 0.2 seconds
False: returns value and lat/lon, grib message, analysis and valid datetime.
Returns output in 0.75-1 seconds
verbose - Prints some diagnostics
outDIR - Specify where the downloaded data should be downloaded.
Default is the current directory.
Tips:
1. The DATE you request represents the model run time. If you want to
retrieve the file based on the model's valid time, you need to
offset the DATE with the forecast lead time. For example:
VALID_DATE = datetime(year, month, day, hour) # We want the model data valid at this time
fxx = 15 # Forecast lead time
RUN_DATE = VALID_DATE-timedelta(hours=fxx) # The model run datetime that produced the data
get_hrrr_variable(RUN_DATE, 'TMP:2 m', fxx=fxx) # The returned data will be a forecast for the requested valid time and lead time
2. You can request both U and V components at a level by using
variable='UVGRD:10 m'
This special request will return the U and V component winds
converted from grid-relative to earth-relative, as well as the
calculated wind speed.
Note: You can still get the grid-relative winds by requesting both
'UGRD:10 m' and 'VGRD:10 m' individually.
"""
## --- Catch Errors -------------------------------------------------------
# Check that you requested the right model name and field name
if model not in ['hrrr', 'hrrrX', 'hrrrak']:
raise ValueError(
"Requested model must be 'hrrr', 'hrrrX', or 'hrrrak'")
if field not in ['prs', 'sfc']:
raise ValueError(
"Requested field must be 'prs' or 'sfc'. We do not store other fields in the archive"
)
# Check that you requested the right forecasts available for the model
if model == 'hrrr' and fxx not in range(37):
raise ValueError(
"HRRR: fxx must be between 0 and 18\nYou requested f%02d" % fxx)
elif model == 'hrrrX' and fxx != 0:
raise ValueError(
"HRRRx: fxx must be 0. We do not store other forecasts in the archive.\nYou requested f%02d"
% fxx)
elif model == 'hrrrak' and fxx not in range(37):
raise ValueError(
"HRRRak: fxx must be between 0 and 37\nYou requested f%02d" % fxx)
# Check that the requested hour exists for the model
if model == 'hrrrak' and DATE.hour not in range(0, 24, 3):
raise ValueError(
"HRRRak: DATE.hour must be 0, 3, 6, 9, 12, 15, 18, or 21\nYou requested %s"
% DATE.hour)
if verbose:
# Check that the request datetime has happened
if DATE > datetime.utcnow():
print "Warning: The datetime you requested hasn't happened yet\nDATE: %s F%02d\n UTC: %s" % (
DATE, fxx, datetime.utcnow())
## ---(Catch Errors)-------------------------------------------------------
## --- Set Temporary File Name --------------------------------------------
# Temporary file name has to be unique, or else when we use multiprocessing
# we might accidentally delete files before we are done with them.
outfile = '%stemp_%s_%s_f%02d_%s.grib2' % (outDIR, model,
DATE.strftime('%Y%m%d%H'), fxx,
variable[:3].replace(":", ''))
if verbose is True:
print ' >> Dowloading tempfile: %s' % outfile
## --- Requested Variable -------------------------------------------------
# A special variable request is 'UVGRD:[level]' which will get both the U
# and V wind components converted to earth-relative direction in a single
# download. Since UGRD always proceeds VGRD, we will set the get_variable
# as UGRD. Else, set get_variable as variable.
if variable.split(':')[0] == 'UVGRD':
# We need both U and V to convert winds from grid-relative to earth-relative
get_variable = 'UGRD:' + variable.split(':')[1]
else:
get_variable = variable
## --- Set Data Source ----------------------------------------------------
"""
Dear User,
Only HRRR files are only downloaded and added to Pando every 3 hours.
That means if you are requesting data for today that hasn't been copied
to Pando yet, you will need to get it from the NOMADS website instead.
But good news! It's an easy fix. All we need to do is redirect you to the
NOMADS server. I'll check that the date you are requesting is not for
today's date. If it is, then I'll send you to NOMADS. Deal? :)
-Sincerely, Brian
"""
# If the datetime requested is less than six hours ago, then the file is
# most likely on Pando. Else, download from NOMADS.
#if DATE+timedelta(hours=fxx) < datetime.utcnow()-timedelta(hours=6):
if DATE < datetime.utcnow() - timedelta(hours=12):
# Get HRRR from Pando
if verbose:
print "Oh, good, you requested a date that should be on Pando."
grib2file = 'https://pando-rgw01.chpc.utah.edu/%s/%s/%s/%s.t%02dz.wrf%sf%02d.grib2' \
% (model, field, DATE.strftime('%Y%m%d'), model, DATE.hour, field, fxx)
fileidx = grib2file + '.idx'
else:
# Get operational HRRR from NOMADS
if model == 'hrrr':
if verbose:
print "/n---------------------------------------------------------------------------"
print "!! Hey! You are requesting a date that is not on the Pando archive yet. !!"
print "!! That's ok, I'll redirect you to the NOMADS server. :) !!"
print "---------------------------------------------------------------------------\n"
#grib2file = 'https://nomads.ncep.noaa.gov/pub/data/nccf/com/hrrr/prod/hrrr.%s/%s.t%02dz.wrf%sf%02d.grib2' \
# % (DATE.strftime('%Y%m%d'), model, DATE.hour, field, fxx)
grib2file = 'https://nomads.ncep.noaa.gov/pub/data/nccf/com/hrrr/prod/hrrr.%s/conus/hrrr.t%02dz.wrf%sf%02d.grib2' \
% (DATE.strftime('%Y%m%d'), DATE.hour, field, fxx)
fileidx = grib2file + '.idx'
elif model == 'hrrrX':
print "\n-------------------------------------------------------------------------"
print "!! Sorry, I haven't download that Experimental HRRR run from ESRL yet !!"
print "!! Try again in a few hours. !!"
print "-------------------------------------------------------------------------\n"
return None
elif model == 'hrrrak':
if verbose:
print "/n---------------------------------------------------------------------------"
print "!! Hey! You are requesting a date that is not on the Pando archive yet. !!"
print "!! That's ok, I'll redirect you to the PARALLEL NOMADS server. :) !!"
print "---------------------------------------------------------------------------\n"
grib2file = 'https://nomads.ncep.noaa.gov/pub/data/nccf/com/hrrr/prod/hrrr.%s/alaska/hrrr.t%02dz.wrf%sf%02d.ak.grib2' \
% (DATE.strftime('%Y%m%d'), DATE.hour, field, fxx)
fileidx = grib2file + '.idx'
if verbose:
print 'GRIB2 File: %s' % grib2file
print ' .idx File: %s' % fileidx
print ""
## --- Download Requested Variable ----------------------------------------
try:
## 0) Read the grib2.idx file
try:
# ?? Ignore ssl certificate (else urllib2.openurl wont work).
# Depends on your version of python.
# See here:
# http://stackoverflow.com/questions/19268548/python-ignore-certicate-validation-urllib2
ctx = ssl.create_default_context()
ctx.check_hostname = False
ctx.verify_mode = ssl.CERT_NONE
idxpage = urllib2.urlopen(fileidx, context=ctx)
except:
idxpage = urllib2.urlopen(fileidx)
lines = idxpage.readlines()
## 1) Find the byte range for the requested variable. First find where
# in the .idx file the variable is located. We need the byte number
# The variable begins on. Keep a count (gcnt) of the line number so
# we can also get the beginning byte of the next variable. This is
# our byte range.
gcnt = 0
for g in lines:
expr = re.compile(get_variable)
if expr.search(g):
if verbose is True:
print ' >> Matched a variable: ', g
parts = g.split(':')
rangestart = parts[1]
if variable.split(':')[0] == 'UVGRD':
parts = lines[gcnt + 2].split(
':') # Grab range between U and V variables
else:
parts = lines[gcnt + 1].split(
':') # Grab range for requested variable only
rangeend = int(parts[1]) - 1
if verbose is True:
print ' >> Byte Range:', rangestart, rangeend
byte_range = str(rangestart) + '-' + str(rangeend)
gcnt += 1
## 2) When the byte range is discovered, use cURL to download the file.
os.system('curl -s -o %s --range %s %s' %
(outfile, byte_range, grib2file))
## --- Convert winds to earth-relative --------------------------------
# If the requested variable is 'UVGRD:[level]', then we have to change
# the wind direction from grid-relative to earth-relative.
# You can still get the grid-relative winds by requesting 'UGRD:[level]'
# and # 'VGRD:[level] independently.
# !!! See more information on why/how to do this here:
# https://github.com/blaylockbk/pyBKB_v2/blob/master/demos/HRRR_earthRelative_vs_gridRelative_winds.ipynb
if variable.split(':')[0] == 'UVGRD':
if verbose:
print ' >> Converting winds to earth-relative'
wgrib2 = '/uufs/chpc.utah.edu/sys/installdir/wgrib2/2.0.2/wgrib2/wgrib2'
if model == 'hrrrak':
regrid = 'nps:225.000000:60.000000 185.117126:1299:3000.000000 41.612949:919:3000.000000'
if model == 'hrrr' or model == 'hrrrX':
regrid = 'lambert:262.500000:38.500000:38.500000:38.500000 237.280472:1799:3000.000000 21.138123:1059:3000.000000'
os.system('%s %s -new_grid_winds earth -new_grid %s %s.earth' %
(wgrib2, outfile, regrid, outfile))
os.system('rm -f %s' % outfile) # remove the original file
outfile = outfile + '.earth' # assign the `outfile`` as the regridded file
## 3) Get data from the file, using pygrib and return what we want to use
grbs = pygrib.open(outfile)
# Note: Returning only the variable value is a bit faster than returning
# the variable value with the lat/lon and other details. You can
# specify this when you call the function.
if value_only:
if variable.split(':')[0] == 'UVGRD':
return_this = {
'UGRD': grbs[1].values,
'VGRD': grbs[2].values,
'SPEED': wind_uv_to_spd(grbs[1].values, grbs[2].values)
}
else:
return_this = {'value': grbs[1].values}
if removeFile:
os.system('rm -f %s' % (outfile))
return return_this
else:
if variable.split(':')[0] == 'UVGRD':
value1, lat, lon = grbs[1].data()
if model == 'hrrrak':
lon[lon > 0] -= 360
return_this = {
'UGRD': value1,
'VGRD': grbs[2].values,
'SPEED': wind_uv_to_spd(value1, grbs[2].values),
'lat': lat,
'lon': lon,
'valid': grbs[1].validDate,
'anlys': grbs[1].analDate,
'msg': [str(grbs[1]), str(grbs[2])],
'name': [grbs[1].name, grbs[2].name],
'units': [grbs[1].units, grbs[2].units],
'level': [grbs[1].level, grbs[2].level],
'URL': grib2file
}
else:
value, lat, lon = grbs[1].data()
if model == 'hrrrak':
lon[lon > 0] -= 360
return_this = {
'value': value,
'lat': lat,
'lon': lon,
'valid': grbs[1].validDate,
'anlys': grbs[1].analDate,
'msg': str(grbs[1]),
'name': grbs[1].name,
'units': grbs[1].units,
'level': grbs[1].level,
'URL': grib2file
}
if removeFile:
os.system('rm -f %s' % (outfile))
return return_this
except:
if verbose:
print " _______________________________________________________________"
print " !! Run Date Requested :", DATE, "F%02d" % fxx
print " !! Valid Date Requested :", DATE + timedelta(hours=fxx)
print " !! Current UTC time :", datetime.utcnow()
print " !! ------------------------------------------------------------"
print " !! ERROR downloading GRIB2:", grib2file
print " !! Is the variable right?", variable
print " !! Does the .idx file exist?", fileidx
print " ---------------------------------------------------------------"
return {
'value': np.nan,
'lat': np.nan,
'lon': np.nan,
'valid': np.nan,
'anlys': np.nan,
'msg': np.nan,
'URL': grib2file
}
# Convert the units of each Pollywog
for loc in location.keys():
# Convert Units for the variables in the Pollywog
TS_temp[loc] = KtoF(TS_temp[loc])
TS_dwpt[loc] = KtoF(TS_dwpt[loc])
TS_wind[loc] = mps_to_MPH(TS_wind[loc])
TS_gust[loc] = mps_to_MPH(TS_gust[loc])
TS_u[loc] = mps_to_MPH(TS_u[loc])
TS_v[loc] = mps_to_MPH(TS_v[loc])
TS_u80[loc] = mps_to_MPH(TS_u80[loc])
TS_v80[loc] = mps_to_MPH(TS_v80[loc])
#TS_prec[loc] = mm_to_inches(TS_prec[loc])
# Derive some variables:
TS_wind80[loc] = wind_uv_to_spd(TS_u80[loc], TS_v80[loc])
# Just need one vector of valid dates
TS_dates = np.array(TS_temp['DATETIME'])
# Make a dictionary of map object for each location.
# (This speeds up plotting by creating each map once.)
maps = {}
for loc in location.keys():
l = location[loc]
m = Basemap(resolution='i', projection='cyl',\
llcrnrlon=l['longitude']-.1, llcrnrlat=l['latitude']-.1,\
urcrnrlon=l['longitude']+.1, urcrnrlat=l['latitude']+.1,)
maps[loc] = m
for hh in range(len(TS_dates)):
def make_plots(inputs):
VALID_DATE, fxx = inputs
print 'working on %s f%02d' % (VALID_DATE, fxx)
plt.clf(); plt.cla()
print fxx, VALID_DATE
# === Some housekeeping variables =============================================
# Convert Valid Date to Run Date, adjusted by the forecast
DATE = VALID_DATE - timedelta(hours=fxx)
# Parse Location lat/lon
if ',' in location:
# User put inputted a lat/lon point request
lat, lon = location.split(',')
lat = float(lat)
lon = float(lon)
else:
# User requested a MesoWest station
stninfo = get_station_info([location])
lat = stninfo['LAT']
lon = stninfo['LON']
# Preload the latitude and longitude grid
latlonpath = '/uufs/chpc.utah.edu/common/home/horel-group/archive/HRRR/oper_HRRR_latlon.h5'
latlonh5 = h5py.File(latlonpath, 'r')
gridlat = latlonh5['latitude'][:]
gridlon = latlonh5['longitude'][:]
# === Create map of the domain ================================================
if dsize == 'conus' and model != 'hrrrAK':
barb_thin = 70
alpha = 1
t1= datetime.now()
#m = draw_CONUS_HRRR_map(res=map_res)
m = np.load('/uufs/chpc.utah.edu/common/home/u0553130/public_html/Brian_Blaylock/cgi-bin/HRRR_CONUS_map_object_'+map_res+'.npy').item()
m.drawcountries(zorder=500)
m.drawstates(zorder=500)
m.drawcoastlines(zorder=500)
m.fillcontinents(color='tan',lake_color='lightblue', zorder=0)
m.drawmapboundary(fill_color='lightblue')
t2 = datetime.now()
else:
# configure some setting based on the requested domain size
if dsize == 'small':
plus_minus_latlon = .27 # +/- latlon box around center point
barb_thin = 1 # Thin out excessive wind barbs
arcgis_res = 1000 # ArcGIS image resolution
bfr = 15 # trim domain buffer
alpha = .75 # Alpha (pcolormesh transparency)
elif dsize == 'medium':
plus_minus_latlon = .75
barb_thin = 2
arcgis_res = 800
bfr = 35
alpha = .75
elif dsize == 'large':
plus_minus_latlon = 2.5
barb_thin = 6
arcgis_res = 800
bfr = 110
alpha = .75
elif dsize == 'xlarge':
plus_minus_latlon = 5
barb_thin = 12
arcgis_res = 700
bfr = 210
alpha = .75
elif dsize == 'xxlarge': # If domain runs into HRRR boundary, then it'll fail
plus_minus_latlon = 10
barb_thin = 25
arcgis_res = 700
bfr = 430
alpha = .75
elif dsize == 'xxxlarge':
plus_minus_latlon = 15
barb_thin = 35
arcgis_res = 1000
bfr = 700
alpha = .75
m = Basemap(resolution=map_res, projection='cyl',\
area_thresh=3000,\
llcrnrlon=lon-plus_minus_latlon, llcrnrlat=lat-plus_minus_latlon,\
urcrnrlon=lon+plus_minus_latlon, urcrnrlat=lat+plus_minus_latlon,)
m.drawstates(zorder=500)
m.drawcountries(zorder=500)
m.drawcoastlines(zorder=500)
#if dsize == 'small' or dsize == 'medium':
# m.drawcounties()
# === Add a Background image ==================================================
# Start the map image
plt.figure(1)
if background == 'arcgis' and dsize != 'conus':
m.arcgisimage(service='World_Shaded_Relief', xpixels=arcgis_res, verbose=False)
elif background == 'arcgisSat' and dsize != 'conus':
m.arcgisimage(service='ESRI_Imagery_World_2D', xpixels=arcgis_res, verbose=False)
elif background == 'arcgisRoad' and dsize != 'conus':
m.arcgisimage(service='NatGeo_World_Map', xpixels=arcgis_res, verbose=False)
elif background == 'terrain':
# Get data
H_ter = get_hrrr_variable(DATE, 'HGT:surface',
model=model, fxx=fxx,
outDIR='/uufs/chpc.utah.edu/common/home/u0553130/temp/',
verbose=False, value_only=True)
H_land = get_hrrr_variable(DATE, 'LAND:surface',
model=model, fxx=fxx,
outDIR='/uufs/chpc.utah.edu/common/home/u0553130/temp/',
verbose=False, value_only=True)
# Plot the terrain
m.contourf(gridlon, gridlat, H_ter['value'],
levels=range(0, 4000, 200),
cmap='Greys_r',
zorder=1,
latlon=True)
# Plot Water area
m.contour(gridlon, gridlat, H_land['value'],
levels=[0, 1],
colors='b',
zorder=1,
latlon=True)
elif background == 'landuse':
# Get data
from BB_cmap.landuse_colormap import LU_MODIS21
if model=='hrrr':
VGTYP = 'VGTYP:surface'
else:
VGTYP = 'var discipline=2 center=59 local_table=1 parmcat=0 parm=198'
H_LU = get_hrrr_variable(DATE, VGTYP,
model=model, fxx=fxx,
outDIR='/uufs/chpc.utah.edu/common/home/u0553130/temp/',
verbose=False,
value_only=True)
# Plot the terrain
cm, labels = LU_MODIS21()
m.pcolormesh(gridlon, gridlat, H_LU['value'],
cmap=cm, vmin=1, vmax=len(labels) + 1,
zorder=1,
latlon=True)
# Add SHAPEFILE
if is_FIRE:
plt.gca().add_collection(PatchCollection(patches,
facecolor='indianred',
alpha=.65,
edgecolor='k',
linewidths=1,
zorder=1))
# === Figure Title ============================================================
if dsize != 'conus':
m.scatter(lon, lat, marker='+', c='r', s=100, zorder=1000, latlon=True)
plt.title('Center: %s\n%s' % (location, model.upper()), fontweight='bold')
else:
plt.title('%s' % (model.upper()), fontweight='bold')
plt.title('Run: %s F%02d' % (DATE.strftime('%Y-%m-%d %H:%M UTC'), fxx), loc='left')
plt.title('Valid: %s' % (DATE+timedelta(hours=fxx)).strftime('%Y-%m-%d %H:%M UTC') , loc='right')
# =============================================================================
if '10mWind_Fill' in plotcode or '10mWind_Shade' in plotcode or '10mWind_Barb' in plotcode:
# Get data
H_u = get_hrrr_variable(DATE, 'UGRD:10 m',
model=model, fxx=fxx,
outDIR='/uufs/chpc.utah.edu/common/home/u0553130/temp/',
verbose=False, value_only=True)
H_v = get_hrrr_variable(DATE, 'VGRD:10 m',
model=model, fxx=fxx,
outDIR='/uufs/chpc.utah.edu/common/home/u0553130/temp/',
verbose=False, value_only=True)
spd = wind_uv_to_spd(H_u['value'], H_v['value'])
if '10mWind_Fill' in plotcode:
m.pcolormesh(gridlon, gridlat, spd,
latlon=True,
cmap='magma_r',
vmin=0, alpha=alpha)
cb = plt.colorbar(orientation='horizontal', pad=pad, shrink=shrink)
cb.set_label(r'10 m Wind Speed (m s$\mathregular{^{-1}}$)')
if '10mWind_Shade' in plotcode:
m.contourf(gridlon, gridlat, spd,
levels=[10, 15, 20, 25],
colors=('yellow', 'orange', 'red'),
alpha=alpha,
extend='max',
zorder=1,
latlon=True)
cb = plt.colorbar(orientation='horizontal', shrink=shrink, pad=pad)
cb.set_label(r'10 m Wind Speed (ms$\mathregular{^{-1}}$)')
if '10mWind_Barb' in plotcode:
# For small domain plots, trimming the edges significantly reduces barb plotting time
if barb_thin < 20:
cut_v, cut_h = pluck_point_new(lat, lon, gridlat, gridlon)
Cgridlat = gridlat[cut_v-bfr:cut_v+bfr, cut_h-bfr:cut_h+bfr]
Cgridlon = gridlon[cut_v-bfr:cut_v+bfr, cut_h-bfr:cut_h+bfr]
H_u['value'] = H_u['value'][cut_v-bfr:cut_v+bfr, cut_h-bfr:cut_h+bfr]
H_v['value'] = H_v['value'][cut_v-bfr:cut_v+bfr, cut_h-bfr:cut_h+bfr]
else:
Cgridlat = gridlat
Cgridlon = gridlon
# Add to plot
thin = barb_thin
m.barbs(Cgridlon[::thin,::thin], Cgridlat[::thin,::thin],
H_u['value'][::thin,::thin], H_v['value'][::thin,::thin],
zorder=200, length=5.5,
barb_increments={'half':2.5, 'full':5,'flag':25},
latlon=True)
if '80mWind_Fill' in plotcode or '80mWind_Shade' in plotcode or '80mWind_Barb' in plotcode:
# Get data
H_u = get_hrrr_variable(DATE, 'UGRD:80 m',
model=model, fxx=fxx,
outDIR='/uufs/chpc.utah.edu/common/home/u0553130/temp/',
verbose=False, value_only=True)
H_v = get_hrrr_variable(DATE, 'VGRD:80 m',
model=model, fxx=fxx,
outDIR='/uufs/chpc.utah.edu/common/home/u0553130/temp/',
verbose=False, value_only=True)
spd = wind_uv_to_spd(H_u['value'], H_v['value'])
if '80mWind_Fill' in plotcode:
m.pcolormesh(gridlon, gridlat, spd,
latlon=True,
cmap='magma_r',
vmin=0, alpha=alpha)
cb = plt.colorbar(orientation='horizontal', pad=pad, shrink=shrink)
cb.set_label(r'10 m Wind Speed (m s$\mathregular{^{-1}}$)')
if '80mWind_Shade' in plotcode:
m.contourf(gridlon, gridlat, spd,
levels=[10, 15, 20, 25],
colors=('yellow', 'orange', 'red'),
alpha=alpha,
extend='max',
zorder=10,
latlon=True)
cb = plt.colorbar(orientation='horizontal', shrink=shrink, pad=pad)
cb.set_label(r'10 m Wind Speed (ms$\mathregular{^{-1}}$)')
if '80mWind_Barb' in plotcode:
# For small domain plots, trimming the edges significantly reduces barb plotting time
if barb_thin < 20:
cut_v, cut_h = pluck_point_new(lat, lon, gridlat, gridlon)
Cgridlat = gridlat[cut_v-bfr:cut_v+bfr, cut_h-bfr:cut_h+bfr]
Cgridlon = gridlon[cut_v-bfr:cut_v+bfr, cut_h-bfr:cut_h+bfr]
H_u['value'] = H_u['value'][cut_v-bfr:cut_v+bfr, cut_h-bfr:cut_h+bfr]
H_v['value'] = H_v['value'][cut_v-bfr:cut_v+bfr, cut_h-bfr:cut_h+bfr]
else:
Cgridlat = gridlat
Cgridlon = gridlon
# Add to plot
thin = barb_thin
m.barbs(Cgridlon[::thin,::thin], Cgridlat[::thin,::thin],
H_u['value'][::thin,::thin], H_v['value'][::thin,::thin],
zorder=200, length=5.5, color='darkred',
barb_increments={'half':2.5, 'full':5,'flag':25},
latlon=True)
if 'Fill80mWind' in plotcode:
m.pcolormesh(gridlon, gridlat, spd,
latlon=True,
cmap='plasma_r',
vmin=0,
alpha=alpha)
cb = plt.colorbar(orientation='horizontal', pad=pad, shrink=shrink)
cb.set_label(r'80 m Wind Speed (m s$\mathregular{^{-1}}$)')
if 'Gust_Hatch' in plotcode:
H_gust = get_hrrr_variable(DATE, 'GUST:surface',
model=model, fxx=fxx,
outDIR='/uufs/chpc.utah.edu/common/home/u0553130/temp/',
verbose=False, value_only=True)
# Add to plot
m.contourf(gridlon, gridlat, H_gust['value'],
levels=[0, 10, 15, 20, 25],
hatches=[None, '.', '\\\\', '*'],
colors='none',
extend='max',
zorder=10,
latlon=True)
cb = plt.colorbar(orientation='horizontal', shrink=shrink, pad=pad)
cb.set_label(r'Surface Wind Gust (ms$\mathregular{^{-1}}$)')
m.contour(gridlon, gridlat, H_gust['value'],
levels=[10, 15, 20, 25],
colors='k',
zorder=10,
latlon=True)
if 'dBZ_Fill' in plotcode or 'dBZ_Contour' in plotcode:
from BB_cmap.reflectivity_colormap import reflect_ncdc
# Get Data
if model == 'hrrr':
REFC = 'REFC:entire'
elif model == 'hrrrX' or model == 'hrrrAK':
REFC = 'var discipline=0 center=59 local_table=1 parmcat=16 parm=196'
H_ref = get_hrrr_variable(DATE, REFC,
model=model, fxx=fxx,
outDIR='/uufs/chpc.utah.edu/common/home/u0553130/temp/',
verbose=False, value_only=True)
# Mask values
dBZ = H_ref['value']
dBZ = np.ma.array(dBZ)
dBZ[dBZ == -10] = np.ma.masked
# Add Contour to plot
if 'dBZ_Contour' in plotcode:
cREF = m.contour(gridlon, gridlat, dBZ,
cmap=reflect_ncdc(),
levels=range(10, 80, 10),
latlon=True,
zorder=50)
plt.clabel(cREF, cREF.levels[::2], fmt='%2.0f', colors='k', fontsize=9)
#cb2 = plt.colorbar(orientation='horizontal', shrink=shrink, pad=pad)
#cb2.set_label('Simulated Composite Reflectivity (dBZ)')
# Add fill to plot
if 'dBZ_Fill' in plotcode:
m.pcolormesh(gridlon, gridlat, dBZ,
cmap=reflect_ncdc(),
vmax=80, vmin=0,
alpha=alpha,
latlon=True)
cb2 = plt.colorbar(orientation='horizontal', shrink=shrink, pad=pad)
cb2.set_label('Simulated Composite Reflectivity (dBZ)')
if '2mTemp_Fill' in plotcode or '2mTemp_Freeze' in plotcode:
# Get Data
H_temp = get_hrrr_variable(DATE, 'TMP:2 m',
model=model, fxx=fxx,
outDIR='/uufs/chpc.utah.edu/common/home/u0553130/temp/',
verbose=False, value_only=True)
TMP = H_temp['value']-273.15
# Add fill to plot
if '2mTemp_Fill' in plotcode:
m.pcolormesh(gridlon, gridlat, TMP,
cmap="Spectral_r",
alpha=alpha,
zorder=3, latlon=True)
cbT = plt.colorbar(orientation='horizontal', shrink=shrink, pad=pad)
cbT.set_label('2m Temperature (C)')
# Add freezing contour to plot
if '2mTemp_Freeze' in plotcode:
m.contour(gridlon, gridlat, TMP,
colors='b',
levels=[0],
zorder=400,
latlon=True)
if '2mRH_Fill' in plotcode:
# Get Data
try:
H_RH = get_hrrr_variable(DATE, 'RH:2 m',
model=model, fxx=fxx,
outDIR='/uufs/chpc.utah.edu/common/home/u0553130/temp/',
verbose=False, value_only=True)
# Add fill to plot
m.pcolormesh(gridlon, gridlat, H_RH['value'], cmap="BrBG",
vmin=0, vmax=100,
zorder=3,
latlon=True)
cbT = plt.colorbar(orientation='horizontal', pad=pad, shrink=shrink)
cbT.set_label('2m Relative Humidity (%)')
except:
print "!! Some errors getting the RH value."
print "!! If you requested an old date, from HRRR version 1, there isn't a RH variable,"
print "!! and this code doesn't get the dwpt and convert it to RH yet."
if '700Temp_Fill' in plotcode or '700Temp_-12c' in plotcode:
# Get Data
H_temp = get_hrrr_variable(DATE, 'TMP:700 mb',
model=model, fxx=fxx,
outDIR='/uufs/chpc.utah.edu/common/home/u0553130/temp/',
verbose=False, value_only=True)
TMP = H_temp['value']-273.15
# Add fill to plot
if '700Temp_Fill' in plotcode:
m.pcolormesh(gridlon, gridlat, TMP,
cmap="Spectral_r",
alpha=alpha,
zorder=3, latlon=True)
cbT = plt.colorbar(orientation='horizontal', shrink=shrink, pad=pad)
cbT.set_label('700 mb Temperature (C)')
# Add -12 C contour to plot
if '700Temp_-12c' in plotcode:
m.contour(gridlon, gridlat, TMP,
colors='b',
levels=[-12],
latlon=True,
zorder=400)
if '500HGT_Contour' in plotcode:
H_500 = get_hrrr_variable(DATE, 'HGT:500 mb',
model=model, fxx=fxx,
outDIR='/uufs/chpc.utah.edu/common/home/u0553130/temp/',
verbose=False, value_only=True)
CS = m.contour(gridlon, gridlat, H_500['value'],
levels=range(5040, 6181, 60),
linewidths=1.7,
colors='k',
latlon=True,
zorder=400)
plt.clabel(CS, inline=1, fmt='%2.f')
if '500Wind_Fill' in plotcode or '500Wind_Barb' in plotcode or '500Vort_Fill' in plotcode or '500Conv_Fill' in plotcode:
H_u = get_hrrr_variable(DATE, 'UGRD:500 mb',
model=model, fxx=fxx,
outDIR='/uufs/chpc.utah.edu/common/home/u0553130/temp/',
verbose=False, value_only=True)
H_v = get_hrrr_variable(DATE, 'VGRD:500 mb',
model=model, fxx=fxx,
outDIR='/uufs/chpc.utah.edu/common/home/u0553130/temp/',
verbose=False, value_only=True)
if '500Wind_Fill' in plotcode:
spd = wind_uv_to_spd(H_u['value'], H_v['value'])
m.pcolormesh(gridlon, gridlat, spd,
latlon=True, cmap='BuPu', vmin=0)
cb = plt.colorbar(orientation='horizontal', pad=pad, shrink=shrink)
cb.set_label(r'500 mb Wind Speed (m s$\mathregular{^{-1}}$)')
if '500Conv_Fill' in plotcode or '500Vort_Fill' in plotcode:
dudx, dudy = np.gradient(H_u['value'], 3, 3)
dvdx, dvdy = np.gradient(H_v['value'], 3, 3)
if '500Vort_Fill' in plotcode:
vorticity = dvdx - dudy
# Mask values
vort = vorticity
vort = np.ma.array(vort)
vort[np.logical_and(vort < .05, vort > -.05) ] = np.ma.masked
m.pcolormesh(gridlon, gridlat, vort,
latlon=True, cmap='bwr',
vmax=np.max(vort),
vmin=-np.max(vort))
cb = plt.colorbar(orientation='horizontal', pad=pad, shrink=shrink)
cb.set_label(r'500 mb Vorticity (s$\mathregular{^{-1}}$)')
if '500Conv_Fill' in plotcode:
convergence = dudx + dvdy
# Mask values
conv = convergence
conv = np.ma.array(conv)
conv[np.logical_and(conv < .05, conv > -.05) ] = np.ma.masked
m.pcolormesh(gridlon, gridlat, conv,
latlon=True, cmap='bwr',
vmax=np.max(conv),
vmin=-np.max(conv))
cb = plt.colorbar(orientation='horizontal', pad=pad, shrink=shrink)
cb.set_label(r'500 mb Convergence (s$\mathregular{^{-1}}$)')
if '500Wind_Barb' in plotcode:
# For small domain plots, trimming the edges significantly reduces barb plotting time
if barb_thin < 20:
cut_v, cut_h = pluck_point_new(lat, lon, gridlat, gridlon)
Cgridlat = gridlat[cut_v-bfr:cut_v+bfr, cut_h-bfr:cut_h+bfr]
Cgridlon = gridlon[cut_v-bfr:cut_v+bfr, cut_h-bfr:cut_h+bfr]
H_u['value'] = H_u['value'][cut_v-bfr:cut_v+bfr, cut_h-bfr:cut_h+bfr]
H_v['value'] = H_v['value'][cut_v-bfr:cut_v+bfr, cut_h-bfr:cut_h+bfr]
else:
Cgridlat = gridlat
Cgridlon = gridlon
thin = barb_thin
m.barbs(Cgridlon[::thin, ::thin], Cgridlat[::thin, ::thin], H_u['value'][::thin, ::thin], H_v['value'][::thin, ::thin],
zorder=200, length=6, color='navy',
barb_increments={'half':2.5, 'full':5,'flag':25},
latlon=True)
#plt.ylabel(r'Barbs: half=2.5, full=5, flag=25 (ms$\mathregular{^{-1}}$)')
if 'MSLP_Contour' in plotcode or 'MSLP_Fill' in plotcode:
H = get_hrrr_variable(DATE, 'MSLMA:mean sea level',
model=model, fxx=fxx,
outDIR='/uufs/chpc.utah.edu/common/home/u0553130/temp/',
verbose=False, value_only=True)
if 'MSLP_Contour' in plotcode:
CS = m.contour(gridlon, gridlat, H['value']/100.,
latlon=True,
levels=range(952, 1200, 4),
colors='k',
zorder=400)
CS.clabel(inline=1, fmt='%2.f',
zorder=400)
if 'MSLP_Fill' in plotcode:
m.pcolormesh(gridlon, gridlat, H['value']/100.,
latlon=True,
cmap='viridis')
cb = plt.colorbar(orientation='horizontal', pad=pad, shrink=shrink)
cb.set_label('Mean Sea Level Pressure (hPa)')
if '2mPOT_Fill' in plotcode:
# Get Data
H_temp = get_hrrr_variable(DATE, 'POT:2 m',
model=model, fxx=fxx,
outDIR='/uufs/chpc.utah.edu/common/home/u0553130/temp/',
verbose=False, value_only=True)
TMP = H_temp['value']-273.15
m.pcolormesh(gridlon, gridlat, TMP,
cmap="Oranges",
alpha=alpha,
zorder=3, latlon=True)
cbS = plt.colorbar(orientation='horizontal', shrink=shrink, pad=pad)
cbS.set_label('2 m Potential Temperature (C)')
if 'SkinTemp_Fill' in plotcode:
# Get Data
H_temp = get_hrrr_variable(DATE, 'TMP:surface',
model=model, fxx=fxx,
outDIR='/uufs/chpc.utah.edu/common/home/u0553130/temp/',
verbose=False, value_only=True)
TMP = H_temp['value']-273.15
m.pcolormesh(gridlon, gridlat, TMP,
cmap="Spectral_r",
alpha=alpha,
zorder=3, latlon=True)
cbS = plt.colorbar(orientation='horizontal', shrink=shrink, pad=pad)
cbS.set_label('Skin Temperature (C)')
if 'AccumPrecip_Fill' in plotcode or '1hrPrecip_Fill' in plotcode:
#import matplotlib.colors
#cmap = matplotlib.colors.LinearSegmentedColormap.from_list("Precip", ["#00db16", "blue", "#d10000", 'black'])
cdict3 = {'red': ((0.0, 0.0, 0.0),
(0.25, 1.0, 1.0),
(0.5, 0.0, 0.0),
(1.0, 1.0, 1.0)),
'green': ((0.0, 0.7, 0.7),
(0.25, 1.0, 1.0),
(0.5, 0.0, 0.0),
(1.0, 0.0, 0.0)),
'blue': ((0.0, 0.18, 0.18),
(0.25, 1.0, 1.0),
(0.5, 1.0, 1.0),
(1.0, 0.0, 0.0))
}
plt.register_cmap(name='BlueRed3', data=cdict3)
cmap = 'BlueRed3'
if 'AccumPrecip_Fill' in plotcode:
# Get Data
H = get_hrrr_variable(DATE, 'APCP:surface:0',
model=model, fxx=fxx,
outDIR='/uufs/chpc.utah.edu/common/home/u0553130/temp/',
verbose=False, value_only=True)
# Mask values
prec = H['value']
prec = np.ma.array(prec)
prec[prec == 0] = np.ma.masked
m.pcolormesh(gridlon, gridlat, prec,
cmap='BlueRed3',
alpha=alpha,
vmin=.25,
zorder=3, latlon=True)
cbS = plt.colorbar(orientation='horizontal', shrink=shrink, pad=pad)
cbS.set_label('Accumulated Precipitation since F00 (mm)')
if '1hrPrecip_Fill' in plotcode:
# Get Data
H = get_hrrr_variable(DATE, 'APCP:surface',
model=model, fxx=fxx,
outDIR='/uufs/chpc.utah.edu/common/home/u0553130/temp/',
verbose=False, value_only=True)
# Mask values
prec = H['value']
prec = np.ma.array(prec)
prec[prec == 0] = np.ma.masked
m.pcolormesh(gridlon, gridlat, prec,
cmap='BlueRed3',
alpha=alpha,
vmin=.25, vmax=20,
zorder=3, latlon=True)
cbS = plt.colorbar(orientation='horizontal', shrink=shrink, pad=pad, extend="max",)
cbS.set_label('1 hour Accumulated Precipitation (mm)')
if 'SnowCover_Fill' in plotcode:
# Get Data
H = get_hrrr_variable(DATE, 'SNOWC',
model=model, fxx=fxx,
outDIR='/uufs/chpc.utah.edu/common/home/u0553130/temp/',
verbose=False, value_only=True)
# Mask values
snow = H['value']
snow = np.ma.array(snow)
snow[snow == 0] = np.ma.masked
m.pcolormesh(gridlon, gridlat, snow,
cmap="Blues",
alpha=alpha,
zorder=3, latlon=True)
cbS = plt.colorbar(orientation='horizontal', shrink=shrink, pad=pad)
cbS.set_label('Snow Cover (%)')
# =============================================================================
# Hack! Plot an extra HRRR variable not listed on the webpage hrrr_custom.html
# This extra argument will let you attempt to plot a different variable for
# a quicklook.
try:
# Must be a variable from a line in the .idx file
hrrrVAR = form['extraVAR'].value
extraHRRR = get_hrrr_variable(DATE, hrrrVAR,
model=model, fxx=fxx,
outDIR='/uufs/chpc.utah.edu/common/home/u0553130/temp/',
verbose=False, value_only=True)
m.pcolormesh(gridlon, gridlat, extraHRRR['value'],
cmap='viridis',
alpha=alpha,
zorder=3, latlon=True)
cbS = plt.colorbar(orientation='horizontal', shrink=shrink, pad=pad)
cbS.set_label(hrrrVAR+' (units)')
except:
pass
# =============================================================================
#plt.ylabel('Section Timer:%s\nFull Timer:%s' % (t2-t1, datetime.now()-firsttimer))
SAVEDIR = '/uufs/chpc.utah.edu/common/home/u0553130/public_html/PhD/HRRR/Events_Day/%s/' % EVENT
if not os.path.exists(SAVEDIR):
os.makedirs(SAVEDIR)
SAVEFIG = SAVEDIR + '%s_f%02d' % (VALID_DATE.strftime('%Y-%m-%d_h%H'), fxx)
plt.savefig(SAVEFIG) # Plot standard output.
print 'SAVED:', SAVEFIG
def plot_for_each_fxx(f):
## HRRR Model Run date
RUN = D-timedelta(hours=f)
## Get HRRR data for this run
if var.split(':')[0] == 'UVGRD':
# Calculate wind speed from U and V components
level = var.split(':')[1]
HU = get_hrrr_variable(RUN, 'UGRD:'+level, fxx=f, value_only=True, verbose=False)
HV = get_hrrr_variable(RUN, 'VGRD:'+level, fxx=f, value_only=True, verbose=False)
speed = wind_uv_to_spd(HU['value'], HV['value'])
H = HU
H['value'] = speed
else:
H = get_hrrr_variable(RUN, var, fxx=f, value_only=True, verbose=False)
## Convert units and get values in bounding box area
if var == 'TMP:2 m' or var == 'DPT:2 m':
Hpoint = H['value'][x,y]-273.15
Harea = H['value'][x-box_radius:x+box_radius+1,y-box_radius:y+box_radius+1]-273.15
else:
Hpoint = H['value'][x,y]
Harea = H['value'][x-box_radius:x+box_radius+1,y-box_radius:y+box_radius+1]
## Calculate the HRRR percentiles in the boxed area for this run
HP = np.percentile(Harea, [0,25,50,75,100])
## --- Plot OSG percentiles ---------------------------------------
plt.fill_between(percentiles, area_P[0][0], area_P[0][4],
color='lightgrey',
label='p100-p00',
zorder=1)
plt.fill_between(percentiles, area_P[0][1], area_P[0][3],
color='grey',
label='p25-p75',
zorder=1)
plt.plot(percentiles, area_P[0][2],
color='lightgrey',
label="p50",
zorder=1)
## Plot all percentiles for each grid box
#for i in range(box_radius*2+1):
# for j in range(box_radius*2+1):
# plt.plot(percentiles, PP[i,j,:])
## Plot OSG percentiles for the single grid box nearest the station of interest
plt.plot(percentiles, PP[box_radius,box_radius,:],
linestyle='--',
color='k',
label='%s OSG Percentiles' % stn,
zorder=1)
## --- Plot RUN percentiles -------------------------------------
# Linear interpolation to find the percentile for the value
for i in range(5):
p100Hpoint = HP[i]
p100PP = area_P[0][i]
if p100Hpoint >= np.max(p100PP):
p100Y = p100Hpoint
p100X = 100
print "Hpoint > PP, exceeds p100", D, f
elif p100Hpoint <= np.min(p100PP):
p100Y = p100Hpoint
p100X = 0
print "Hpoint < PP exceeds p00", D, f,
else:
p100y1 = np.min(filter(lambda x: x>=p100Hpoint, p100PP))
p100y1_idx = np.where(p100PP==p100y1)[0][0]
p100x1 = percentiles[p100y1_idx]
p100y2 = p100PP[p100y1_idx-1]
p100x2 = percentiles[p100y1_idx-1]
p100m = (p100y2-p100y1)/(p100x2-p100x1)
p100b = 0-p100m*p100x1+p100y1
p100Y = p100Hpoint
p100X = (p100Y-p100b)/p100m
plt.axhline(p100Hpoint, xmax=p100X/100, color='r', linewidth=1, zorder=50)
plt.axvline(p100X, ymax=(p100Hpoint-ymin)/(ymax-ymin), color='r', linewidth=1, zorder=50)
plt.scatter(p100X, p100Hpoint, s=25, color='r', zorder=50)
## Plot value from current HRRR run for every grid box
#for i in range(box_radius*2+1):
# for j in range(box_radius*2+1):
# plt.axhline(Harea[i,j], linewidth=.1)
## Plot HRRR value for stn grid box for urrent HRRR run
# Linear interpolation to find the percentile for the value
stnPP = PP[box_radius,box_radius,:]
if Hpoint >= np.max(stnPP):
Y = Hpoint
X = 100
print "station Hpoint > PP, exceeds p100", D, f
elif Hpoint <= np.min(stnPP):
Y = Hpoint
X = 0
print "station Hpoint < PP, exceeds p00", D, f
else:
y1 = np.min(filter(lambda x: x>=Hpoint, stnPP))
y1_idx = np.where(stnPP==y1)[0][0]
x1 = percentiles[y1_idx]
y2 = stnPP[y1_idx-1]
x2 = percentiles[y1_idx-1]
m = (y2-y1)/(x2-x1)
b = 0-m*x1+y1
Y = Hpoint
X = (Y-b)/m
plt.axhline(Hpoint, xmax=X/100, color='k', lw=6, zorder=100)
plt.axvline(X, ymax=(Hpoint-ymin)/(ymax-ymin), color='k', lw=6, zorder=100)
plt.scatter(X, Hpoint, s=150, color='k', zorder=100)
## --- Other Plot Elements --------------------------------------
plt.grid()
plt.legend(loc='upper left')
plt.xlabel('Percentiles\n%sx%s km box centered at %s' % (np.shape(PP)[0]*3, np.shape(PP)[0]*3, stn))
xtick_labels = [0,'','','','',5,10,25,33,50,66,75,90,95,'','','','',100]
plt.xticks(percentiles, xtick_labels)
plt.xlim([0,100])
plt.ylabel(var)
plt.ylim([ymin, ymax])
plt.title('Valid: %s' % D.strftime('%Y-%b-%d %H:%M'), loc='Left')
plt.title('%s\n%s' % (stn, var), loc='Center', fontweight='bold')
plt.title('Run: %s F%02d' % (RUN.strftime("%Y-%b-%d %H:%M"), f), loc='right')
plt.savefig(SAVEDIR+'%s_f%02d.png' % (D.strftime('%Y-%m-%d_%H'), f))
plt.close()
return None
def plot_for_each_fxx_with_Map(f):
fig, (ax1, ax2) = plt.subplots(1, 2, figsize=[15,6])
plt.sca(ax1)
## HRRR Model Run date
RUN = D-timedelta(hours=f)
## Get HRRR data for this run
if var.split(':')[0] == 'UVGRD':
# Calculate wind speed from U and V components
level = var.split(':')[1]
HU = get_hrrr_variable(RUN, 'UGRD:'+level, fxx=f, value_only=True, verbose=False)
HV = get_hrrr_variable(RUN, 'VGRD:'+level, fxx=f, value_only=True, verbose=False)
speed = wind_uv_to_spd(HU['value'], HV['value'])
H = {'value': speed}
Hpoint = speed[x,y]
Harea = speed[x-box_radius:x+box_radius+1,y-box_radius:y+box_radius+1]
HUarea = HU['value'][x-box_radius:x+box_radius+1,y-box_radius:y+box_radius+1]
HVarea = HV['value'][x-box_radius:x+box_radius+1,y-box_radius:y+box_radius+1]
else:
H = get_hrrr_variable(RUN, var, fxx=f, value_only=True, verbose=False)
## Convert units and get values in bounding box area
if var == 'TMP:2 m' or var == 'DPT:2 m':
H['value'] = H['value']-273.15
Hpoint = H['value'][x,y]
Harea = H['value'][x-box_radius:x+box_radius+1,y-box_radius:y+box_radius+1]
else:
Hpoint = H['value'][x,y]
Harea = H['value'][x-box_radius:x+box_radius+1,y-box_radius:y+box_radius+1]
## Calculate the HRRR percentiles in the boxed area for this run
HP = np.percentile(Harea, [0,25,50,75,100])
## --- Plot OSG percentiles ---------------------------------------
plt.fill_between(percentiles, area_P[0][0], area_P[0][4],
color='lightgrey',
label='p100-p00',
zorder=1)
plt.fill_between(percentiles, area_P[0][1], area_P[0][3],
color='grey',
label='p25-p75',
zorder=1)
plt.plot(percentiles, area_P[0][2],
color='lightgrey',
label="p50",
zorder=1)
## Plot all percentiles for each grid box
#for i in range(box_radius*2+1):
# for j in range(box_radius*2+1):
# plt.plot(percentiles, PP[i,j,:])
## Plot OSG percentiles for the single grid box nearest the station of interest
plt.plot(percentiles, PP[box_radius,box_radius,:],
linestyle='--',
color='k',
label='%s OSG Percentiles' % stn,
zorder=1)
## --- Plot RUN percentiles -------------------------------------
# Linear interpolation to find the percentile for the value
for i in range(5):
p100Hpoint = HP[i]
p100PP = area_P[0][i]
if p100Hpoint >= np.max(p100PP):
p100Y = p100Hpoint
p100X = 100
print "Hpoint > PP, exceeds p100", D, f
elif p100Hpoint <= np.min(p100PP):
p100Y = p100Hpoint
p100X = 0
print "Hpoint < PP exceeds p00", D, f,
else:
p100y1 = np.min(filter(lambda x: x>=p100Hpoint, p100PP))
p100y1_idx = np.where(p100PP==p100y1)[0][0]
p100x1 = percentiles[p100y1_idx]
p100y2 = p100PP[p100y1_idx-1]
p100x2 = percentiles[p100y1_idx-1]
p100m = (p100y2-p100y1)/(p100x2-p100x1)
p100b = 0-p100m*p100x1+p100y1
p100Y = p100Hpoint
p100X = (p100Y-p100b)/p100m
plt.axhline(p100Hpoint, xmax=p100X/100, color='r', linewidth=1, zorder=50)
plt.axvline(p100X, ymax=(p100Hpoint-ymin)/(ymax-ymin), color='r', linewidth=1, zorder=50)
plt.scatter(p100X, p100Hpoint, s=25, color='r', zorder=50)
## Plot value from current HRRR run for every grid box
#for i in range(box_radius*2+1):
# for j in range(box_radius*2+1):
# plt.axhline(Harea[i,j], linewidth=.1)
## Plot HRRR value for stn grid box for urrent HRRR run
# Linear interpolation to find the percentile for the value
stnPP = PP[box_radius,box_radius,:]
if Hpoint >= np.max(stnPP):
Y = Hpoint
X = 100
print "station Hpoint > PP, exceeds p100", D, f
elif Hpoint <= np.min(stnPP):
Y = Hpoint
X = 0
print "station Hpoint < PP, exceeds p00", D, f
else:
y1 = np.min(filter(lambda x: x>=Hpoint, stnPP))
y1_idx = np.where(stnPP==y1)[0][0]
x1 = percentiles[y1_idx]
y2 = stnPP[y1_idx-1]
x2 = percentiles[y1_idx-1]
m = (y2-y1)/(x2-x1)
b = 0-m*x1+y1
Y = Hpoint
X = (Y-b)/m
plt.axhline(Hpoint, xmax=X/100, color='k', lw=6, zorder=100)
plt.axvline(X, ymax=(Hpoint-ymin)/(ymax-ymin), color='k', lw=6, zorder=100)
plt.scatter(X, Hpoint, s=150, color='k', zorder=100)
## --- Other Plot Elements --------------------------------------
plt.grid()
plt.legend(loc='upper left')
plt.xlabel('Percentiles\n%sx%s km box centered at %s' % (np.shape(PP)[0]*3, np.shape(PP)[0]*3, stn))
xtick_labels = [0,'','','','',5,10,25,33,50,66,75,90,95,'','','','',100]
plt.xticks(percentiles, xtick_labels)
plt.xlim([0,100])
plt.ylabel(label_units)
plt.ylim([ymin, ymax])
plt.title('Run: %s F%02d' % (RUN.strftime("%Y-%b-%d %H:%M"), f), loc='left')
#plt.savefig(SAVEDIR+'%s_f%02d.png' % (D.strftime('%Y-%m-%d_%H'), f))
#plt.close()
#####----- Side Map -------
plt.sca(ax2)
am.drawcounties()
am.arcgisimage(service='World_Shaded_Relief')
if var == 'REFC:entire':
dBZ = H['value']
dBZ = np.ma.array(dBZ)
dBZ[dBZ == -10] = np.ma.masked
vmax = 80
vmin = 0
H['value'] = dBZ
elif var in ['UVGRD:10 m', 'UVGRD:80 m', 'GUST:surface']:
vmax = np.max(Harea)
vmin = 0
else:
vmax = np.max(Harea)
vmin = np.min(Harea)
am.pcolormesh(lon, lat, H['value'], alpha=.3, cmap=cmap, vmax=vmax, vmin=vmin)
cb = plt.colorbar(orientation='vertical', pad=0.01, shrink=0.85)
cb.set_label(label_units)
# Station Point
am.scatter(MWlon, MWlat)
# Bottom Boundary
am.drawgreatcircle(LONS_BOX[0,-1], LATS_BOX[0,-1],
LONS_BOX[0,0], LATS_BOX[0,0],
color='k')
# Left Boundary
am.drawgreatcircle(LONS_BOX[0,0], LATS_BOX[0,0],
LONS_BOX[-1,0], LATS_BOX[-1,0],
color='k')
# Top Boundary
am.drawgreatcircle(LONS_BOX[-1,-1], LATS_BOX[-1,-1],
LONS_BOX[-1,0], LATS_BOX[-1,0],
color='k')
# Right Boundary
am.drawgreatcircle(LONS_BOX[-1,-1], LATS_BOX[-1,-1],
LONS_BOX[0,-1], LATS_BOX[0,-1],
color='k')
# Barbs
if var == 'UVGRD:10 m' or var == 'UVGRD:80 m':
am.barbs(LONS_BOX, LATS_BOX, HUarea, HVarea,
zorder=200, length=5, color='k',
barb_increments={'half':2.5, 'full':5,'flag':25},
latlon=True)
plt.title('Valid: %s' % D.strftime('%Y-%b-%d %H:%M'), loc='right')
plt.suptitle('%s %s' % (stn, var))
plt.savefig(SAVEDIR+'h%02d_f%02d.png' % (D.hour, f))
plt.close()
return None
ax3.scatter(P_wind['DATETIME'][fxx],
P_wind[loc][fxx],
c='darkorange',
s=60)
if l['is MesoWest'] is True:
# we alreaded loaded mesowest data into a
if a != 'ERROR':
ax3.plot(a['DATETIME'],
mps_to_MPH(a['wind_speed']),
c='k',
ls='--')
# plt.barbs can not take a datetime object, so find the date indexes:
idx = mpl.dates.date2num(P_u['DATETIME'])
ax3.barbs(idx,
wind_uv_to_spd(P_u[loc], P_v[loc]),
P_u[loc],
P_v[loc],
length=6,
barb_increments=dict(half=5, full=10, flag=50))
leg3 = ax3.legend()
leg3.get_frame().set_linewidth(0)
ax3.grid()
#ax3.set_ylabel(r'Wind Speed (ms$\mathregular{^{-1}}$)')
ax3.set_ylabel('Wind Speed (mph)')
ax3.set_ylim([0, np.nanmax(P_gust[loc]) + 3])
ax3.set_yticks([0, np.nanmax(P_gust[loc]) + 3], 2.5)
ax3.set_xlim([P_gust['DATETIME'][0], P_gust['DATETIME'][-1]])
ax3.xaxis.set_major_locator(mdates.HourLocator(range(0, 24, 3)))
ax3.xaxis.set_minor_locator(mdates.HourLocator(range(0, 24, 1)))
uH = get_hrrr_variable(attime, variable='UGRD:10 m')
vH = get_hrrr_variable(attime, variable='VGRD:10 m')
uHX = get_hrrr_variable(attime,
variable='UGRD:10 m',
model='hrrrX',
removeFile=True,
value_only=True)
vHX = get_hrrr_variable(attime,
variable='VGRD:10 m',
model='hrrrX',
removeFile=True,
value_only=True)
H = {
'lat': uH['lat'],
'lon': uH['lon'],
'value': wind_uv_to_spd(uH['value'], vH['value'])
}
HX = {'value': wind_uv_to_spd(uHX['value'], vHX['value'])}
else:
H = get_hrrr_variable(attime, variable=variable)
HX = get_hrrr_variable(attime,
variable=variable,
model='hrrrX',
removeFile=True,
value_only=True)
Hx, Hy = m(H['lon'], H['lat'])
HRRR_plot = ax1.pcolormesh(Hx,
Hy,
H['value'] - offset,
vmin=vmin,
vmax=vmax,
cpu_count = multiprocessing.cpu_count() # variable = 'UGRD:10 m' p = multiprocessing.Pool(cpu_count) U = p.map(get_HRRR_value, requestDATES) U = np.array([x for x in U if x is not None]) p.close() # variable = 'VGRD:10 m' p = multiprocessing.Pool(cpu_count) V = p.map(get_HRRR_value, requestDATES) V = np.array([x for x in V if x is not None]) p.close() # Convert U and V to Speed and Direction WSPD = wind_uv_to_spd(U, V) WDIR = wind_uv_to_dir(U, V) # get a single H dictionary (need the lat/lon arrays) H = get_hrrr_variable(requestDATES[0], 'UGRD:10 m') # Histogram bins num_dir_sectors = 8 # Number of directional sectors (sectors calculated in histogram function) speed_bins = np.arange(0, 10, 2) # Speed bins num_spd_bins = len(speed_bins) # Number of speed bins rose_dim = num_spd_bins * num_dir_sectors x_dim, y_dim = H['value'].shape # make rose table for each point in HRRR indexes = [[i, j] for i in range(x_dim) for j in range(y_dim)]
