def point(datetime, point_name, point_lonlat, number_grid=1):
if point_name:
if "cyclone.hpc.uib.no" in platform.node():
print("detected cyclone")
sites = pd.read_csv(
"../githubclones/islas/weathervis/weathervis/data/sites.csv",
sep=";",
header=0,
index_col=0)
else:
sites = pd.read_csv("../../weathervis/weathervis/data/sites.csv",
sep=";",
header=0,
index_col=0)
point_lonlat = [sites.loc[point_name].lon, sites.loc[point_name].lat]
print(point_lonlat)
#test nearest lonlat
check_all = check_data(date=datetime,
model="AromeArctic",
param=["longitude"],
step=1)
file_all = check_all.file
data_domain = domain_input_handler(dt=datetime,
model="AromeArctic",
domain_name="AromeArctic",
file=file_all,
domain_lonlat=None)
dmap_meps = get_data(model="AromeArctic",
data_domain=data_domain,
param=["longitude"],
file=file_all,
step=1,
date=datetime)
dmap_meps.retrieve()
print("NUMBER GRID")
print(number_grid)
closest_idx = nearest_neighbour_idx(point_lonlat[0],
point_lonlat[1],
dmap_meps.longitude,
dmap_meps.latitude,
nmin=number_grid)
x_llc = np.min(closest_idx[0])
y_llc = np.min(closest_idx[1])
x_urc = np.max(closest_idx[0])
y_urc = np.max(closest_idx[1])
#print(x_llc*2500)
#print(y_llc * 2500)
#print(x_urc * 2500)
#print(y_urc * 2500)
#return [x_llc*2500,y_llc * 2500, x_urc * 2500, y_urc * 2500]
return [y_llc * 2500, x_llc * 2500, y_urc * 2500, x_urc * 2500]
def area(datetime, domain_name, domain_lonlat):
check_all = check_data(date=datetime, model="AromeArctic", step=0)
file_all = check_all.file
data_domain = domain_input_handler(dt=datetime,
model="AromeArctic",
domain_name=domain_name,
domain_lonlat=domain_lonlat,
file=file_all)
iii = data_domain.idx
xllc = min(iii[0])
xurc = np.max(iii[0])
yllc = min(*iii[1])
yurc = max(iii[1])
return [yllc * 2500, xllc * 2500, yurc * 2500, xurc * 2500]
def Z500_VEL(datetime, steps=0, model= "MEPS", domain_name = None, domain_lonlat = None, legend=False, info = False,grid=True):
for dt in datetime: #modelrun at time..
date = dt[0:-2]
hour = int(dt[-2:])
param_sfc = ["air_pressure_at_sea_level", "surface_geopotential"]
param_pl = ["air_temperature_pl", "geopotential_pl"] #"air_temperature_2m",
param_sfx = ["SST","SIC"] #add later
p_levels = [850,1000]
param = param_sfc + param_pl
split = False
print("\n######## Checking if your request is possible ############")
try:
check_all = check_data(date=dt, model=model, param=param, levtype="pl", p_level=p_levels, step=steps)
check_sfx = check_data(date=dt, model=model, param=param_sfx, step=steps)
except ValueError:
split = True
try:
print("--------> Splitting up your request to find match ############")
check_sfc = check_data(date=dt, model=model, param=param_sfc)
check_pl = check_data(date=dt, model=model, param=param_pl, levtype="pl", p_level=p_levels)
check_sfx = check_data(date=dt, model=model, param=param_sfx)
print(check_pl.file)
except ValueError:
print("!!!!! Sorry this plot is not availbale for this date. Try with another datetime !!!!!")
break
print("--------> Found match for your request ############")
if not split:
file_all = check_all.file.loc[0]
data_domain = domain_input_handler(dt, model,domain_name, domain_lonlat, file_all)
#lonlat = np.array(data_domain.lonlat)
dmap_meps = get_data(model=model, data_domain=data_domain, param=param, file=file_all, step=steps,
date=dt, p_level=p_levels)
dmap_mepsdfx = get_data(model=model, data_domain=data_domain, param=param_sfx, file=check_sfx.file.loc[0], step=steps,date=dt)
print("\n######## Retrieving data ############")
print(f"--------> from: {dmap_meps.url} ")
dmap_meps.retrieve()
tmap_meps = dmap_meps # two names for same value, no copying done.
dmap_mepsdfx.retrieve()
else:
# get sfc level data
file_sfc = check_sfc.file.loc[0]
data_domain = domain_input_handler(dt, model,domain_name, domain_lonlat, file_sfc)
#lonlat = np.array(data_domain.lonlat)
dmap_meps = get_data(model=model, param=param_sfc, file=file_sfc, step=steps, date=dt, data_domain=data_domain)
print("\n######## Retrieving data ############")
print(f"--------> from: {dmap_meps.url} ")
dmap_meps.retrieve()
# get pressure level data
file_pl = check_pl.file.loc[0]
tmap_meps = get_data(model=model, data_domain=data_domain, param=param_pl, file=file_pl, step=steps, date=dt, p_level=p_levels)
print("\n######## Retrieving data ############")
print(f"--------> from: {tmap_meps.url} ")
tmap_meps.retrieve()
dmap_mepsdfx = get_data(model=model, data_domain=data_domain, param=param_sfx, file=check_sfx.file.loc[0],
step=steps,
date=dt)
dmap_mepsdfx.retrieve()
#CALCULATE
pt = potential_temperatur(dmap_meps.air_temperature_pl, dmap_meps.pressure*100.)
pt_sst = potential_temperatur(dmap_mepsdfx.SST, dmap_meps.air_pressure_at_sea_level[:,0,:,:])
dpt = pt[:,np.where(dmap_meps.pressure==1000)[0],:,:]-pt[:,np.where(dmap_meps.pressure==850)[0],:,:]
dpt_sst =pt_sst[:,:,:] - pt[:,np.where(dmap_meps.pressure==850)[0],:,:].squeeze()
#dpt_sst =abs(pt_sst[:,:,:] - pt[:,np.where(dmap_meps.pressure==850)[0],:,:].squeeze())
# convert fields
dmap_meps.air_pressure_at_sea_level/=100
tmap_meps.geopotential_pl/=10.0
lon0 = dmap_meps.longitude_of_central_meridian_projection_lambert
lat0 = dmap_meps.latitude_of_projection_origin_projection_lambert
parallels = dmap_meps.standard_parallel_projection_lambert
# setting up projection
# setting up projection
globe = ccrs.Globe(ellipse='sphere', semimajor_axis=6371000., semiminor_axis=6371000.)
crs = ccrs.LambertConformal(central_longitude=lon0, central_latitude=lat0, standard_parallels=parallels,
globe=globe)
for tim in np.arange(np.min(steps), np.max(steps)+1, 1):
fig1, ax1 = plt.subplots(1, 1, figsize=(7, 9),
subplot_kw={'projection': crs})
ttt = tim #+ np.min(steps)
tidx = tim - np.min(steps)
print('Plotting {0} + {1:02d} UTC'.format(dt, ttt))
plev2 = 0
embr = 0
ZS = dmap_meps.surface_geopotential[tidx, 0, :, :]
MSLP = np.where(ZS < 3000, dmap_meps.air_pressure_at_sea_level[tidx, 0, :, :], np.NaN).squeeze()
Z = (tmap_meps.geopotential_pl[tidx, plev2, :, :]).squeeze()
DELTAPT=dpt[tidx, 0, :, :]
DELTAPT = dpt_sst[tidx,:,:]
ICE = dmap_mepsdfx.SIC[tidx, :, :]
DELTAPT = np.where( ICE <= 0.99,DELTAPT,0)
lvl = range(-1,13)
C = [[255,255,255 ], # grey #[255,255,255],#gre
[204,191,189 ], # grey
[155,132,127 ], # grey
[118,86,80], # lillac, 39 64 197 149,53,229
[138,109,81], # blue dark,7,67,194 [218,81,14],
[181,165,102], #
[229,226,124], ##
[213,250,128],
[125,231,111],
[55,212,95],
[25,184,111],
[17,138,234],
[21,82,198],
[37,34,137]]
C = np.array(C)
C = np.divide(C, 255.) # RGB has to be between 0 and 1 in python
CF_prec = plt.contourf(dmap_meps.x, dmap_meps.y, DELTAPT, zorder=0,
antialiased=True,extend = "max", levels=lvl, colors=C, vmin=0, vmax=12)#
CF_ice = plt.contour(dmap_meps.x, dmap_meps.y, ICE, zorder=1, linewidths=2.5, colors="black", levels=[0.1, 0.5]) #
# MSLP with contour labels every 10 hPa
C_P = ax1.contour(dmap_meps.x, dmap_meps.y, MSLP, zorder=1, alpha=1.0,
levels=np.arange(round(np.nanmin(MSLP), -1) - 10, round(np.nanmax(MSLP), -1) + 10, 1),
colors='grey', linewidths=0.5)
C_P = ax1.contour(dmap_meps.x, dmap_meps.y, MSLP, zorder=2, alpha=1.0,
levels=np.arange(round(np.nanmin(MSLP), -1) - 10, round(np.nanmax(MSLP), -1) + 10, 10),
colors='grey', linewidths=1.0)
ax1.clabel(C_P, C_P.levels, inline=True, fmt="%3.0f", fontsize=10)
#CS = ax1.contour(dmap_meps.x, dmap_meps.y, Z, zorder=3, alpha=1.0,
# levels=np.arange(4600, 5800, 20), colors="blue", linewidths=0.7)
#ax1.clabel(CS, CS.levels, inline=True, fmt="%4.0f", fontsize=10)
ax1.add_feature(cfeature.GSHHSFeature(scale='intermediate')) # ‘auto’, ‘coarse’, ‘low’, ‘intermediate’, ‘high, or ‘full’ (default is ‘auto’).
ax1.text(0, 1, "{0}_CAOi_{1}+{2:02d}".format(model, dt, ttt), ha='left', va='bottom', transform=ax1.transAxes, color='black')
##########################################################
legend=True
if legend:
proxy = [plt.axhline(y=0, xmin=1, xmax=1, color="grey"),
plt.axhline(y=0, xmin=1, xmax=1, color="black",linewidth=4)]
try:
ax_cb = adjustable_colorbar_cax(fig1, ax1)
plt.colorbar(CF_prec,cax = ax_cb, fraction=0.046, pad=0.01, aspect=25, label=r"$\theta_{SST}-\theta_{850}$", extend="both")
except:
pass
lg = ax1.legend(proxy, [f"MSLP [hPa]",
f"Sea ice at 10%, 80%, 99%"])
frame = lg.get_frame()
frame.set_facecolor('white')
frame.set_alpha(1)
make_modelrun_folder = setup_directory(OUTPUTPATH, "{0}".format(dt))
if grid:
nicegrid(ax=ax1)
if domain_name != model and data_domain != None: # weird bug.. cuts off when sees no data value
ax1.set_extent(data_domain.lonlat)
print("filename: "+make_modelrun_folder + "/{0}_{1}_CAOi_{2}+{3:02d}.png".format(model, domain_name, dt, ttt))
fig1.savefig(make_modelrun_folder + "/{0}_{1}_CAOi_{2}+{3:02d}.png".format(model, domain_name, dt, ttt), bbox_inches="tight", dpi=200)
ax1.cla()
plt.clf()
plt.close(fig1)
plt.close("all")
def checkget_data_handler(all_param,
date,
model,
step,
p_level=None,
m_level=None,
mbrs=None,
domain_name=None,
domain_lonlat=None,
point_name=None):
fileobj = check_data(model, date=date, step=step).file
print(fileobj)
print(all_param)
print("start finding choices")
all_choices, bad_param = find_best_combinationoffiles(all_param=all_param,
fileobj=fileobj,
m_level=m_level,
p_level=p_level)
bad_param_sfx = []
if bad_param:
new_bad = ["SFX_" + x for x in bad_param]
all_param = all_param + new_bad
all_choices, bad_param_sfx = find_best_combinationoffiles(
all_param=all_param,
fileobj=fileobj,
m_level=m_level,
p_level=p_level)
print("bad_param")
print(bad_param)
print("bad_param_sfx")
print(bad_param_sfx)
#Ass SFX_param to it and try again.
print(all_choices)
print("stopped finding choices")
# RETRIEVE FROM THE BEST COMBINATIONS AND TOWARDS WORSE COMBINATION IF ANY ERROR
for i in range(0, len(all_choices)):
try:
print(
"getting data"
) #our_choice,model,step, date,fileobj,m_level, domain_name=None, domain_lonlat=None
dmet, data_domain, bad_param = retrievenow(
our_choice=all_choices.loc[i],
model=model,
step=step,
date=date,
fileobj=fileobj,
m_level=m_level,
domain_name=domain_name,
domain_lonlat=domain_lonlat,
bad_param=bad_param,
bad_param_sfx=bad_param_sfx,
point_name=point_name)
break
except:
#del (dmet)
print("Oops!", sys.exc_info()[0], "occurred.")
print("Next entry.")
print(" ")
#for i in range(0,bad_param):
return dmet, data_domain, bad_param
param_ml = ["air_temperature_ml", "specific_humidity_ml"]
param_sfc = [
"surface_air_pressure", "air_pressure_at_sea_level",
"air_temperature_0m", "air_temperature_2m", "relative_humidity_2m",
"x_wind_gust_10m", "y_wind_gust_10m", "x_wind_10m", "y_wind_10m",
"specific_humidity_2m", "precipitation_amount_acc",
"convective_cloud_area_fraction", "cloud_area_fraction",
"high_type_cloud_area_fraction", "medium_type_cloud_area_fraction",
"low_type_cloud_area_fraction", "rainfall_amount", "snowfall_amount",
"graupelfall_amount", "land_area_fraction"
]
param_sfc = ["specific_humidity_2m"]
all_param = param_sfc + param_ml + param_pl
fileobj = check_data(args.model,
date=str(args.datetime[0]),
step=args.steps).file
all_choices, bad_param = find_best_combinationoffiles(all_param, fileobj)
#RETRIEVE FROM THE BEST COMBINATIONS AND TOWARDS WORSE COMBINATION IF ANY ERROR
for i in range(0, len(all_choices)):
try:
dmet = retrievenow(all_choices.loc[i], args.model, args.steps,
str(args.datetime[0]))
break
except:
#del(dmet)
print("Oops!", sys.exc_info()[0], "occurred.")
print("Next entry.")
print(" ")
def T850_RH(datetime,
steps=0,
model="MEPS",
domain_name=None,
domain_lonlat=None,
legend=False,
info=False,
save=True):
for dt in datetime: #modelrun at time..
date = dt[0:-2]
hour = int(dt[-2:])
param_sfc = [
"air_pressure_at_sea_level", "air_temperature_2m",
"precipitation_amount_acc", "surface_geopotential"
]
param_pl = ["air_temperature_pl", "relative_humidity_pl"]
param = param_sfc + param_pl
#print(type(steps))
split = False
print("\n######## Checking if your request is possibel ############")
try:
check_all = check_data(date=dt,
model=model,
param=param,
p_level=850)
except ValueError:
split = True
try:
print(
"--------> Splitting up your request to find match ############"
)
check_sfc = check_data(date=dt, model=model, param=param_sfc)
check_pl = check_data(date=dt,
model=model,
param=param_pl,
p_level=850)
except ValueError:
print(
"!!!!! Sorry this plot is not availbale for this date. Try with another datetime !!!!!"
)
break
print("--------> Found match for your request ############")
if not split:
file_all = check_all.file.loc[0]
data_domain = domain_input_handler(dt, model, domain_name,
domain_lonlat, file_all)
#lonlat = np.array(data_domain.lonlat)
dmap_meps = get_data(model=model,
data_domain=data_domain,
param=param,
file=file_all,
step=steps,
date=dt,
p_level=850)
print("\n######## Retriving data ############")
print(f"--------> from: {dmap_meps.url} ")
dmap_meps.retrieve()
tmap_meps = dmap_meps # two names for same value, no copying done.
else:
# get sfc level data
file_sfc = check_sfc.file.loc[0]
data_domain = domain_input_handler(dt, model, domain_name,
domain_lonlat, file_sfc)
#lonlat = np.array(data_domain.lonlat)
dmap_meps = get_data(model=model,
param=param_sfc,
file=file_sfc,
step=steps,
date=dt,
data_domain=data_domain)
print("\n######## Retriving data ############")
print(f"--------> from: {dmap_meps.url} ")
dmap_meps.retrieve()
# get pressure level data
file_pl = check_pl.file
tmap_meps = get_data(model=model,
data_domain=data_domain,
param=param_pl,
file=file_pl,
step=steps,
date=dt,
p_level=850)
print("\n######## Retriving data ############")
print(f"--------> from: {tmap_meps.url} ")
tmap_meps.retrieve()
# convert fields
dmap_meps.air_pressure_at_sea_level /= 100
dmap_meps.air_temperature_2m -= 273.15
tmap_meps.air_temperature_pl -= 273.15
tmap_meps.relative_humidity_pl *= 100.0
# plot map
fig1, ax1 = plt.subplots(figsize=(7, 9))
lonlat = [
dmap_meps.longitude[0, 0], dmap_meps.longitude[-1, -1],
dmap_meps.latitude[0, 0], dmap_meps.latitude[-1, -1]
]
print(lonlat)
lon0 = dmap_meps.longitude_of_central_meridian_projection_lambert
lat0 = dmap_meps.latitude_of_projection_origin_projection_lambert
map = Basemap(llcrnrlon=lonlat[0],
llcrnrlat=lonlat[2],
urcrnrlon=lonlat[1],
urcrnrlat=lonlat[3],
resolution='i',
projection="lcc",
lon_0=lon0,
lat_0=lat0,
lat_1=lat0,
area_thresh=0.0001)
x, y = map(dmap_meps.longitude,
dmap_meps.latitude) #longitude_of_central_meridian
for tim in np.arange(np.min(steps), np.max(steps) + 1, 1):
tidx = tim - np.min(steps)
print('Plotting {0} + {1:02d} UTC'.format(dt, tim))
# gather, filter and squeeze variables for plotting
plev = 0
#reduces noise over mountains by removing values over a certain height.
Z = dmap_meps.surface_geopotential[tidx, 0, :, :]
TA = np.where(Z < 3000, tmap_meps.air_temperature_pl[tidx,
plev, :, :],
np.NaN).squeeze()
MSLP = np.where(Z < 3000,
dmap_meps.air_pressure_at_sea_level[tidx, 0, :, :],
np.NaN).squeeze()
RH = (tmap_meps.relative_humidity_pl[tidx, plev, :, :]).squeeze()
# clear subplot
plt.cla()
# MSLP with contour labels every 10 hPa
C_P = plt.contour(x,
y,
MSLP,
zorder=1,
alpha=1.0,
levels=np.arange(
round(np.nanmin(MSLP), -1) - 10,
round(np.nanmax(MSLP), -1) + 10, 1),
colors='grey',
linewidths=0.5)
C_P = plt.contour(x,
y,
MSLP,
zorder=2,
alpha=1.0,
levels=np.arange(
round(np.nanmin(MSLP), -1) - 10,
round(np.nanmax(MSLP), -1) + 10, 10),
colors='grey',
linewidths=1.0,
label="MSLP [hPa]")
ax1.clabel(C_P, C_P.levels, inline=True, fmt="%3.0f", fontsize=10)
# air temperature (C)
C_T = plt.contour(x,
y,
TA,
zorder=3,
alpha=1.0,
levels=np.arange(-50, 30, 0.5),
colors="red",
linewidths=0.7,
label="Temp [C]")
# relative humidity above 80%
CF_RH = plt.contourf(x,
y,
RH,
zorder=1,
alpha=0.1,
levels=np.arange(80, 120, 20),
colors="blue",
linewidths=0.7,
label="RH >80% [%]")
map.drawcoastlines(linewidth=0.5, color='black', ax=ax1, zorder=1)
proxy = [
plt.Rectangle(
(0, 0),
1,
1,
fc=pc.get_facecolor()[0],
) for pc in CF_RH.collections
]
proxy1 = [
plt.axhline(y=0, xmin=1, xmax=1, color="red"),
plt.axhline(y=0,
xmin=1,
xmax=1,
color="red",
linestyle="dashed"),
plt.axhline(y=0, xmin=1, xmax=1, color="gray")
]
proxy.extend(proxy1)
if legend:
lg = ax1.legend(proxy, [
f"RH > 80% [%] at {dmap_meps.pressure[plev]:.0f} hPa",
f"T>0 [C] at {dmap_meps.pressure[plev]:.0f} hPa",
f"T<0 [C] at {dmap_meps.pressure[plev]:.0f} hPa",
"MSLP [hPa]", ""
])
frame = lg.get_frame()
frame.set_facecolor('white')
frame.set_alpha(1)
if info:
plt.text(
x=0,
y=-1,
s=
"INFO: Reduced topographic noise by filtering with surface_geopotential bellow 3000",
fontsize=7) #, bbox=dict(facecolor='white', alpha=0.5))
if save:
fig2 = plt.figure(figsize=(2, 1.25))
fig2.legend(proxy, [
f"RH > 80% [%] at {dmap_meps.pressure[plev]:.0f} hPa",
f"T>0 [C] at {dmap_meps.pressure[plev]:.0f} hPa",
f"T<0 [C] at {dmap_meps.pressure[plev]:.0f} hPa",
"MSLP [hPa]", ""
])
fig2.savefig(
"../../../output/{0}_T850_RH_LEGEND.png".format(model),
bbox_inches="tight",
dpi=200)
##########################################################
#plt.show()
fig1.savefig(
"../../../output/{0}_T850_RH_{1}+{2:02d}.png".format(
model, dt, tim),
bbox_inches="tight",
dpi=200)
else:
return fig1
def T850_RH(datetime,
steps=0,
model="MEPS",
domain_name=None,
domain_lonlat=None,
legend=False,
info=False,
save=True,
grid=True):
for dt in datetime: #modelrun at time..
print(dt)
date = dt[0:-2]
hour = int(dt[-2:])
param_sfc = [
"air_pressure_at_sea_level", "air_temperature_2m",
"surface_geopotential"
]
param_pl = ["air_temperature_pl", "relative_humidity_pl"]
param = param_sfc + param_pl
#print(type(steps))
split = False
print("\n######## Checking if your request is possible ############")
try:
check_all = check_data(date=dt,
model=model,
param=param,
p_level=850,
step=steps)
except ValueError:
split = True
try:
print(
"--------> Splitting up your request to find match ############"
)
check_sfc = check_data(date=dt,
model=model,
param=param_sfc,
step=steps)
check_pl = check_data(date=dt,
model=model,
param=param_pl,
p_level=850,
step=steps)
except ValueError:
print(
"!!!!! Sorry this plot is not availbale for this date. Try with another datetime !!!!!"
)
break
print("--------> Found match for your request ############")
if not split:
file_all = check_all.file.loc[0]
data_domain = domain_input_handler(dt, model, domain_name,
domain_lonlat, file_all)
#lonlat = np.array(data_domain.lonlat)
dmap_meps = get_data(model=model,
data_domain=data_domain,
param=param,
file=file_all,
step=steps,
date=dt,
p_level=[850])
print("\n######## Retrieving data ############")
print(f"--------> from: {dmap_meps.url} ")
dmap_meps.retrieve()
tmap_meps = dmap_meps # two names for same value, no copying done.
else:
# get sfc level data
file_sfc = check_sfc.file.loc[0]
data_domain = domain_input_handler(dt, model, domain_name,
domain_lonlat, file_sfc)
#lonlat = np.array(data_domain.lonlat)
dmap_meps = get_data(model=model,
param=param_sfc,
file=file_sfc,
step=steps,
date=dt,
data_domain=data_domain)
print("\n######## Retrieving data ############")
print(f"--------> from: {dmap_meps.url} ")
dmap_meps.retrieve()
# get pressure level data
file_pl = check_pl.file
tmap_meps = get_data(model=model,
data_domain=data_domain,
param=param_pl,
file=file_pl,
step=steps,
date=dt,
p_level=850)
print("\n######## Retrieving data ############")
print(f"--------> from: {tmap_meps.url} ")
tmap_meps.retrieve()
# convert fields
dmap_meps.air_pressure_at_sea_level /= 100
dmap_meps.air_temperature_2m -= 273.15
tmap_meps.air_temperature_pl -= 273.15
tmap_meps.relative_humidity_pl *= 100.0
# plot map
lonlat = [
dmap_meps.longitude[0, 0], dmap_meps.longitude[-1, -1],
dmap_meps.latitude[0, 0], dmap_meps.latitude[-1, -1]
]
print(lonlat)
lon0 = dmap_meps.longitude_of_central_meridian_projection_lambert
lat0 = dmap_meps.latitude_of_projection_origin_projection_lambert
parallels = dmap_meps.standard_parallel_projection_lambert
# setting up projection
globe = ccrs.Globe(ellipse='sphere',
semimajor_axis=6371000.,
semiminor_axis=6371000.)
crs = ccrs.LambertConformal(central_longitude=lon0,
central_latitude=lat0,
standard_parallels=parallels,
globe=globe)
#fig1 = plt.figure(figsize=(7, 9))
for tim in np.arange(np.min(steps), np.max(steps) + 1, 1):
#ax1 = plt.subplot(projection=crs)
fig1, ax1 = plt.subplots(1,
1,
figsize=(7, 9),
subplot_kw={'projection': crs})
ttt = tim
tidx = tim - np.min(steps)
print('Plotting {0} + {1:02d} UTC'.format(dt, tim))
# gather, filter and squeeze variables for plotting
plev = 0
#reduces noise over mountains by removing values over a certain height.
Z = dmap_meps.surface_geopotential[tidx, 0, :, :]
TA = np.where(Z < 3000, tmap_meps.air_temperature_pl[tidx,
plev, :, :],
np.NaN).squeeze()
MSLP = np.where(Z < 3000,
dmap_meps.air_pressure_at_sea_level[tidx, 0, :, :],
np.NaN).squeeze()
RH = (tmap_meps.relative_humidity_pl[tidx, plev, :, :]).squeeze()
# MSLP with contour labels every 10 hPa
C_P = ax1.contour(dmap_meps.x,
dmap_meps.y,
MSLP,
zorder=1,
alpha=1.0,
levels=np.arange(
round(np.nanmin(MSLP), -1) - 10,
round(np.nanmax(MSLP), -1) + 10, 1),
colors='grey',
linewidths=0.5)
C_P = ax1.contour(dmap_meps.x,
dmap_meps.y,
MSLP,
zorder=2,
alpha=1.0,
levels=np.arange(
round(np.nanmin(MSLP), -1) - 10,
round(np.nanmax(MSLP), -1) + 10, 10),
colors='grey',
linewidths=1.0,
label="MSLP [hPa]")
ax1.clabel(C_P, C_P.levels, inline=True, fmt="%3.0f", fontsize=10)
# air temperature (C)
C_T = ax1.contour(dmap_meps.x,
dmap_meps.y,
TA,
zorder=3,
alpha=1.0,
levels=np.arange(-50, 30, 0.5),
colors="red",
linewidths=0.7)
ax1.clabel(C_T,
C_T.levels[::3],
inline=True,
fmt="%3.0f",
fontsize=10)
# relative humidity above 80%
CF_RH = ax1.contourf(dmap_meps.x,
dmap_meps.y,
RH,
zorder=1,
alpha=0.1,
levels=np.arange(80, 120, 20),
colors="blue",
linewidths=0.7,
label="RH >80% [%]")
#lat_p = 60.2
#lon_p = 5.4167
#mainpoint = ax1.scatter(lon_p, lat_p, s=9.0 ** 2, transform=ccrs.PlateCarree(),
# color='lime', zorder=6, linestyle='None', edgecolors="k", linewidths=3)
ax1.add_feature(
cfeature.GSHHSFeature(scale='intermediate')
) #‘auto’, ‘coarse’, ‘low’, ‘intermediate’, ‘high, or ‘full’ (default is ‘auto’).
ax1.text(0,
1,
"{0}_T850_RH_{1}+{2:02d}".format(model, dt, ttt),
ha='left',
va='bottom',
transform=ax1.transAxes,
color='black')
legend = True
if legend:
proxy = [
plt.Rectangle(
(0, 0),
1,
1,
fc=pc.get_facecolor()[0],
) for pc in CF_RH.collections
]
proxy1 = [
plt.axhline(y=0, xmin=1, xmax=1, color="red"),
plt.axhline(y=0,
xmin=1,
xmax=1,
color="red",
linestyle="dashed"),
plt.axhline(y=0, xmin=1, xmax=1, color="gray")
]
proxy.extend(proxy1)
lg = ax1.legend(proxy, [
f"RH > 80% [%] at {dmap_meps.pressure[plev]:.0f} hPa",
f"T>0 [C] at {dmap_meps.pressure[plev]:.0f} hPa",
f"T<0 [C] at {dmap_meps.pressure[plev]:.0f} hPa",
"MSLP [hPa]", ""
])
frame = lg.get_frame()
frame.set_facecolor('white')
frame.set_alpha(1)
#if info:
# plt.text(x=0, y=-1, s="INFO: Reduced topographic noise by filtering with surface_geopotential bellow 3000",
# fontsize=7)#, bbox=dict(facecolor='white', alpha=0.5))
##########################################################
#plt.show()
#lonlat = [dmap_meps.longitude[0, 0], dmap_meps.longitude[-1, -1], dmap_meps.latitude[0, 0],
# dmap_meps.latitude[-1, -1]]
# ax.set_extent((lonlat[0]-5, lonlat[1], lonlat[2], lonlat[3])) # (x0, x1, y0, y1)
# ax.set_extent((dmap_meps.x[0], dmap_meps.x[-1], dmap_meps.y[0], dmap_meps.y[-1])) # (x0, x1, y0, y1)
#ax1.set_extent((lonlat[0], lonlat[1], lonlat[2], lonlat[3]))
#fig1.savefig("../../../../output/{0}_T850_RH_{1}_{2:02d}.png".format(model,dt, tim), bbox_inches="tight", dpi=200)
make_modelrun_folder = setup_directory(OUTPUTPATH,
"{0}".format(dt))
if grid:
nicegrid(ax=ax1)
if domain_name != model and data_domain != None: # weird bug.. cuts off when sees no data value
ax1.set_extent(data_domain.lonlat)
fig1.savefig(make_modelrun_folder +
"/{0}_{1}_T850_RH_{2}+{3:02d}.png".format(
model, domain_name, dt, tim),
bbox_inches="tight",
dpi=200)
ax1.cla()
plt.clf()
plt.close(fig1)
#proxy = [plt.Rectangle((0, 0), 1, 1, fc=pc.get_facecolor()[0], )
# for pc in CF_RH.collections]
#proxy1 = [plt.axhline(y=0, xmin=1, xmax=1, color="red"),eee
# plt.axhline(y=0, xmin=1, xmax=1, color="red", linestyle="dashed"),
# plt.axhline(y=0, xmin=1, xmax=1, color="gray")]
#proxy.extend(proxy1)
#fig2 = plt.figure(figsize=(2, 1.25))
#fig2.legend(proxy, [f"RH > 80% [%] at {dmap_meps.pressure[plev]:.0f} hPa",
# f"T>0 [C] at {dmap_meps.pressure[plev]:.0f} hPa",
# f"T<0 [C] at {dmap_meps.pressure[plev]:.0f} hPa", "MSLP [hPa]", ""])
#fig2.savefig("../../../output/{0}_T850_RH_LEGEND.png".format(model), bbox_inches="tight", dpi=200)
ax1.cla()
plt.clf()
plt.close("all")
def BLH(datetime,
steps=0,
model="MEPS",
domain_name=None,
domain_lonlat=None,
legend=False,
info=False,
save=True,
grid=True):
for dt in datetime: #modelrun at time..
print(dt)
date = dt[0:-2]
hour = int(dt[-2:])
param_sfc = [
"air_pressure_at_sea_level", "surface_geopotential",
"atmosphere_boundary_layer_thickness"
]
param_pl = ["upward_air_velocity_pl"]
param = param_sfc + param_pl
#print(type(steps))
split = False
print("\n######## Checking if your request is possibel ############")
try:
check_all = check_data(date=dt,
model=model,
param=param,
p_level=850,
step=steps)
except ValueError:
split = True
try:
print(
"--------> Splitting up your request to find match ############"
)
check_sfc = check_data(date=dt,
model=model,
param=param_sfc,
step=steps)
check_pl = check_data(date=dt,
model=model,
param=param_pl,
p_level=850,
step=steps)
except ValueError:
print(
"!!!!! Sorry this plot is not availbale for this date. Try with another datetime !!!!!"
)
break
print("--------> Found match for your request ############")
if not split:
file_all = check_all.file.loc[0]
data_domain = domain_input_handler(dt, model, domain_name,
domain_lonlat, file_all)
#lonlat = np.array(data_domain.lonlat)
dmap_meps = get_data(model=model,
data_domain=data_domain,
param=param,
file=file_all,
step=steps,
date=dt,
p_level=[850])
print("\n######## Retriving data ############")
print(f"--------> from: {dmap_meps.url} ")
dmap_meps.retrieve()
tmap_meps = dmap_meps # two names for same value, no copying done.
else:
# get sfc level data
file_sfc = check_sfc.file.loc[0]
data_domain = domain_input_handler(dt, model, domain_name,
domain_lonlat, file_sfc)
#lonlat = np.array(data_domain.lonlat)
dmap_meps = get_data(model=model,
param=param_sfc,
file=file_sfc,
step=steps,
date=dt,
data_domain=data_domain)
print("\n######## Retriving data ############")
print(f"--------> from: {dmap_meps.url} ")
dmap_meps.retrieve()
print("\n2########")
# get pressure level data
print(check_pl)
file_pl = check_pl.file
tmap_meps = get_data(model=model,
data_domain=data_domain,
param=param_pl,
file=file_pl,
step=steps,
date=dt,
p_level=850)
print("\n######## Retriving data ############")
print(f"--------> from: {tmap_meps.url} ")
tmap_meps.retrieve()
print("\n3########")
# convert fields
dmap_meps.air_pressure_at_sea_level /= 100
#dmap_meps.air_temperature_2m -= 273.15
#tmap_meps.air_temperature_pl -= 273.15
#tmap_meps.relative_humidity_pl *= 100.0
lonlat = [
dmap_meps.longitude[0, 0], dmap_meps.longitude[-1, -1],
dmap_meps.latitude[0, 0], dmap_meps.latitude[-1, -1]
]
print(lonlat)
lon0 = dmap_meps.longitude_of_central_meridian_projection_lambert
lat0 = dmap_meps.latitude_of_projection_origin_projection_lambert
parallels = dmap_meps.standard_parallel_projection_lambert
# setting up projection
globe = ccrs.Globe(ellipse='sphere',
semimajor_axis=6371000.,
semiminor_axis=6371000.)
crs = ccrs.LambertConformal(central_longitude=lon0,
central_latitude=lat0,
standard_parallels=parallels,
globe=globe)
# plot map
print("\nplotting")
#fig1 = plt.figure(figsize=(7, 9), projection=crs)
print("\nplotting")
for tim in np.arange(np.min(steps), np.max(steps) + 1, 1):
#ax1 = plt.subplot(projection=crs)
fig1, ax1 = plt.subplots(1,
1,
figsize=(7, 9),
subplot_kw={'projection': crs})
ttt = tim # + np.min(steps)
tidx = tim - np.min(steps)
print('Plotting {0} + {1:02d} UTC'.format(dt, tim))
# gather, filter and squeeze variables for plotting
plev = 0
#reduces noise over mountains by removing values over a certain height.
Z = dmap_meps.surface_geopotential[tidx, 0, :, :]
W = np.where(Z < 3000,
tmap_meps.upward_air_velocity_pl[tidx, plev, :, :],
np.NaN).squeeze()
MSLP = np.where(Z < 3000,
dmap_meps.air_pressure_at_sea_level[tidx, 0, :, :],
np.NaN).squeeze()
BLH = (tmap_meps.atmosphere_boundary_layer_thickness[tidx, :, :]
).squeeze()
# MSLP with contour labels every 10 hPa
C_P = ax1.contour(dmap_meps.x,
dmap_meps.y,
MSLP,
zorder=1,
alpha=1.0,
levels=np.arange(
round(np.nanmin(MSLP), -1) - 10,
round(np.nanmax(MSLP), -1) + 10, 1),
colors='grey',
linewidths=0.5)
C_P = ax1.contour(dmap_meps.x,
dmap_meps.y,
MSLP,
zorder=2,
alpha=1.0,
levels=np.arange(
round(np.nanmin(MSLP), -1) - 10,
round(np.nanmax(MSLP), -1) + 10, 10),
colors='grey',
linewidths=1.0,
label="MSLP [hPa]")
ax1.clabel(C_P, C_P.levels, inline=True, fmt="%3.0f", fontsize=10)
# vertical velocity
C_W = ax1.contour(dmap_meps.x,
dmap_meps.y,
W,
zorder=3,
alpha=1.0,
levels=np.linspace(0.07, 2.0, 4),
colors="red",
linewidths=0.7)
C_W = ax1.contour(dmap_meps.x,
dmap_meps.y,
W,
zorder=3,
alpha=1.0,
levels=np.linspace(-2.0, -0.07, 4),
colors="blue",
linewidths=0.7)
# boundary layer thickness
CF_BLH = ax1.contourf(dmap_meps.x,
dmap_meps.y,
BLH,
zorder=1,
alpha=0.5,
levels=np.arange(500, 5000, 500),
linewidths=0.7,
label="BLH",
cmap="summer",
extend="both")
ax1.add_feature(cfeature.GSHHSFeature(scale='intermediate'))
#ax1.set_extent(data_domain.lonlat)
#divider = make_axes_locatable(ax1) ##__N
#ax_cb = divider.new_horizontal(size="5%", pad=0.1, axes_class=plt.Axes) ##__N
#fig1.add_axes(ax_cb) ##__N
ax_cb = adjustable_colorbar_cax(fig1, ax1)
cb = fig1.colorbar(CF_BLH,
fraction=0.046,
pad=0.01,
aspect=25,
cax=ax_cb,
label="Boundary layer thickness [m]",
extend="both") ##__N
#cb,fig1,ax1 = adjustable_colorbar(fig1,ax1,data= CF_BLH, fraction=0.046, pad=0.01,aspect=25, label="Boundary layer thickness [m]", extend="both")
ax1.text(0, 1, "{0}_BLH_{1}+{2:02d}".format(model, dt, ttt), ha='left', va='bottom', \
transform=ax1.transAxes, color='dimgrey')
legend = True
if legend:
#proxy = [plt.Rectangle((0, 0), 1, 1, fc=pc.get_facecolor()[0], )
# for pc in CF_RH.collections]
proxy = [
plt.axhline(y=0, xmin=1, xmax=1, color="red"),
plt.axhline(y=0,
xmin=1,
xmax=1,
color="blue",
linestyle="dashed"),
plt.axhline(y=0, xmin=1, xmax=1, color="gray")
]
#proxy.extend(proxy1)
lg = ax1.legend(proxy, [
f"W [m s-1]>0.07 m/s at {dmap_meps.pressure[plev]:.0f} hPa",
f"W [m s-1]<0.07 m/s at {dmap_meps.pressure[plev]:.0f} hPa",
"MSLP [hPa]", ""
])
frame = lg.get_frame()
frame.set_facecolor('white')
frame.set_alpha(1)
#if info:
# plt.text(x=0, y=-1, s="INFO: Reduced topographic noise by filtering with surface_geopotential bellow 3000",
# fontsize=7)#, bbox=dict(facecolor='white', alpha=0.5))
##########################################################
#plt.show()
if grid:
nicegrid(ax=ax1)
if domain_name != model and data_domain != None: # weird bug.. cuts off when sees no data value
ax1.set_extent(data_domain.lonlat)
# print(data_domain.lonlat)
make_modelrun_folder = setup_directory(OUTPUTPATH,
"{0}".format(dt))
print("filename: " + make_modelrun_folder +
"/{0}_{1}_{2}_{3}+{4:02d}.png".format(
model, domain_name, "BLH", dt, ttt))
fig1.savefig(make_modelrun_folder +
"/{0}_{1}_{2}_{3}+{4:02d}.png".format(
model, domain_name, "BLH", dt, ttt),
bbox_inches="tight",
dpi=200)
ax1.cla()
plt.clf()
plt.close(fig1)
#proxy = [plt.Rectangle((0, 0), 1, 1, fc=pc.get_facecolor()[0], )
# for pc in CF_RH.collections]
#proxy1 = [plt.axhline(y=0, xmin=1, xmax=1, color="red"),
# plt.axhline(y=0, xmin=1, xmax=1, color="red", linestyle="dashed"),
# plt.axhline(y=0, xmin=1, xmax=1, color="gray")]
#proxy.extend(proxy1)
#fig2 = plt.figure(figsize=(2, 1.25))
#fig2.legend(proxy, [f"RH > 80% [%] at {dmap_meps.pressure[plev]:.0f} hPa",
# f"T>0 [C] at {dmap_meps.pressure[plev]:.0f} hPa",
# f"T<0 [C] at {dmap_meps.pressure[plev]:.0f} hPa", "MSLP [hPa]", ""])
#fig2.savefig("../../../output/{0}_T850_RH_LEGEND.png".format(model), bbox_inches="tight", dpi=200)
ax1.cla()
plt.clf()
plt.close("all")
def surf(datetime, steps=0, model=model, domain_name=None, domain_lonlat=None, legend=False, info=False,grid=True):
for dt in datetime: # modelrun at time..
date = dt[0:-2]
hour = int(dt[-2:])
# param_sfc = ["relative_humidity_2m", "air_temperature_2m", "specific_humidity_2m", "air_pressure_at_sea_level"]
param_sfc = ["air_temperature_2m", "specific_humidity_2m", "air_pressure_at_sea_level", "surface_geopotential"]
param_sfx = ["SST", "SIC"]
param_pl = []
param = param_sfc + param_pl
split = False
print("\n######## Checking if your request is possibel ############")
try:
check_all = check_data(date=dt, model=model, param=param, step=steps)
check_sfx = check_data(date=dt, model=model, param=param_sfx, step=steps)
print(check_all.file)
except ValueError:
print("!!!!! Sorry this plot is not availbale for this date. Try with another datetime !!!!!")
break
print("--------> Found match for your request ############")
if not split:
file_all = check_all.file.loc[0]
file_sfx = check_sfx.file.loc[0]
data_domain = domain_input_handler(dt, model, domain_name, domain_lonlat, file_all)
# lonlat = np.array(data_domain.lonlat)
print(file_all)
dmap_meps = get_data(model=model, data_domain=data_domain, param=param, file=file_all, step=steps,
date=dt)
dmap_meps_sfx = get_data(model=model, data_domain=data_domain, param=param_sfx, file=file_sfx, step=steps,
date=dt)
print("\n######## Retriving data ############")
print(f"--------> from: {dmap_meps.url} ")
dmap_meps.retrieve()
tmap_meps = dmap_meps # two names for same value, no copying done.
dmap_meps_sfx.retrieve()
# convert fields
# dmap_meps.air_pressure_at_sea_level/=100
# u,v = xwind2uwind(tmap_meps.x_wind_10m,tmap_meps.y_wind_10m, tmap_meps.alpha)
# vel = wind_speed(tmap_meps.x_wind_10m,tmap_meps.y_wind_10m)
# plot map
fig1 = plt.figure(figsize=(7, 9))
lon0 = dmap_meps.longitude_of_central_meridian_projection_lambert
lat0 = dmap_meps.latitude_of_projection_origin_projection_lambert
parallels = dmap_meps.standard_parallel_projection_lambert
# setting up projection
globe = ccrs.Globe(ellipse='sphere', semimajor_axis=6371000., semiminor_axis=6371000.)
data = ccrs.LambertConformal(central_longitude=lon0, central_latitude=lat0, standard_parallels=parallels,
globe=globe)
crs = data
crs_lon = ccrs.PlateCarree()
# crs = ccrs.PlateCarree()
for tim in np.arange(np.min(steps), np.max(steps) + 1, 1):
fig1, ax1 = plt.subplots(1, 1, figsize=(7, 9),
subplot_kw={'projection': crs})
ttt = tim # + np.min(steps)
tidx = tim - np.min(steps)
print('Plotting {0} + {1:02d} UTC'.format(dt, ttt))
SI = dmap_meps_sfx.SIC[tidx, :, :].squeeze()
SImask = np.where(SI >= 0.1, dmap_meps_sfx.SIC[tidx, :, :], np.NaN).squeeze()
ZS = dmap_meps.surface_geopotential[tidx, 0, :, :]
MSLP = np.where(ZS < 3000, dmap_meps.air_pressure_at_sea_level[tidx, 0, :, :], np.NaN).squeeze() / 100
# print("SIC: {}, {}".format(min(MSLP[0][:]), max(MSLP[0][:])))
# SST = np.where(SI == 0, dmap_meps_sfx.SST[tidx, :, :], np.NaN).squeeze()
# TP = precip_acc(dmap_meps.precipitation_amount_acc, acc=1)[tidx, 0, :,:].squeeze()
# L = dmap_meps_sfx.LE[tidx,:,:].squeeze()
# L = np.where(ZS < 3000, L, np.NaN).squeeze()
# SH = dmap_meps_sfx.H[tidx,:,:].squeeze()
# SH = np.where(ZS < 3000, SH, np.NaN).squeeze()
SST = dmap_meps_sfx.SST[tidx, :, :].squeeze() - 273.15
es = 6.1094 * np.exp(17.625 * SST / (SST + 243.04))
mslp = dmap_meps.air_pressure_at_sea_level[tidx, :, :].squeeze() / 100
if model == 'AromeArctic':
Q = dmap_meps.specific_humidity_2m[tidx, :, :].squeeze()
else:
Q = 1
# AT = dmap_meps.air_temperature_2m[tidx,:,:].squeeze()
# w =
# Q = w / (w+1)
Qs = 0.622 * es / (mslp - 0.37 * es)
# RH_2m = dmap_meps.relative_humidity_2m[tidx,:,:].squeeze()*100
RH = Q / Qs * 100
# print("RH: {}".format(RH_2m[0][0]))
d = 48.2 - 0.54 * RH
# Ux = dmap_meps.x_wind_10m[tidx, 0, :, :].squeeze()
# Vx = dmap_meps.y_wind_10m[tidx, 0, :, :].squeeze()
# xm,ym = np.meshgrid(dmap_meps.x, dmap_meps.y)
# VELOCITY
# new_x, new_y, new_u, new_v, = vector_scalar_to_grid(src_crs= data, target_proj= crs_lon,regrid_shape = np.shape(Ux), x= dmap_meps.x, y= dmap_meps.y, u= Ux, v= Vx)
# magnitude = (new_u ** 2 + new_v ** 2) ** 0.5
# cmap = plt.get_cmap("viridis") #cividis copper
# wii = Axes.streamplot(ax1, new_x, new_y, new_u, new_v, density=4,zorder=4,transform=crs_lon, linewidth=0.7, color=magnitude, cmap=cmap)
# LATENT
# levelspos=np.arange(80, round(np.nanmax(L), -1) + 10, 40)
# levelsneg = np.arange(-300, -9, 10)
# levels = np.append(levelsneg, levelspos)
# CL = ax1.contour(dmap_meps.x, dmap_meps.y, L, zorder=3, alpha=1.0, colors="red", linewidths=0.7, levels=levels, transform=data)
# ax1.clabel(CL, CL.levels[::2], inline=True, fmt="%3.0f", fontsize=10)
# xx = np.where(L < -10, xm, np.NaN).squeeze()
# yy = np.where(L < -10, ym, np.NaN).squeeze()
# skip = (slice(None, None, 4), slice(None, None, 4))
# ax1.scatter(xx[skip][skip], yy[skip][skip], s=20, zorder=2, marker='x', linewidths=0.9,
# c="white", alpha=0.7, transform=data)
# xx = np.where(L > 80, xm, np.NaN).squeeze()
# yy = np.where(L > 80, ym, np.NaN).squeeze()
# skip = (slice(None, None, 4), slice(None, None, 4))
# ax1.scatter(xx[skip][skip], yy[skip][skip], s=20, zorder=2, marker='.', linewidths=0.9,
# c="black", alpha=0.7, transform=data)
# SENSIBLE
# levelspos = np.arange(80, round(np.nanmax(SH), -1) + 10, 40)
# levelsneg = np.arange(-300, -9, 10)
# levels = np.append(levelsneg, levelspos)
# CSH = ax1.contour(dmap_meps.x, dmap_meps.y, SH, zorder=3, alpha=1.0, colors="blue", linewidths=0.7, levels=levels, transform=data)
# ax1.clabel(CSH, CSH.levels[1::2], inline=True, fmt="%3.0f", fontsize=10)
# xx = np.where(SH < -10, xm, np.NaN).squeeze()
# yy = np.where(SH < -10, ym, np.NaN).squeeze()
# skip = (slice(None, None, 4), slice(None, None, 4))
# ax1.scatter(xx[skip][skip],yy[skip][skip],s=20, zorder=2, marker='x',linewidths=0.9, c= "white", alpha=0.7, transform=data)
# xx = np.where(SH >80, xm, np.NaN).squeeze()
# yy = np.where(SH >80, ym, np.NaN).squeeze()
# skip = (slice(None, None, 4), slice(None, None, 4))
# ax1.scatter(xx[skip][skip], yy[skip][skip], s=20, zorder=2, marker='.', linewidths=0.9, c="black", alpha=0.7,
# transform=data)
# SST
levels = np.arange(-10, 45, 5)
cmap = plt.get_cmap("cividis_r")
Cd = ax1.contourf(dmap_meps.x, dmap_meps.y, d, zorder=1, alpha=0.7, cmap=cmap, levels=levels, extend="both",
transform=data)
Cd10 = ax1.contour(dmap_meps.x, dmap_meps.y, d, zorder=1, alpha=0.7, colors='tab:blue', levels=[10],
transform=data)
SI = ax1.contourf(dmap_meps.x, dmap_meps.y, SImask, zorder=2, alpha=1, colors='azure', transform=data)
ax1.contour(dmap_meps.x, dmap_meps.y, SImask, zorder=2, alpha=1, colors='black', levels=[0.15],
transform=data, linestyles='--')
C_P = ax1.contour(dmap_meps.x, dmap_meps.y, MSLP, zorder=4, alpha=1.0,
levels=np.arange(round(np.nanmin(MSLP), -1) - 10, round(np.nanmax(MSLP), -1) + 10, 2),
colors='dimgrey', linewidths=0.5)
C_P = ax1.contour(dmap_meps.x, dmap_meps.y, MSLP, zorder=4, alpha=1.0,
levels=np.arange(round(np.nanmin(MSLP), -1) - 10, round(np.nanmax(MSLP), -1) + 10, 10),
colors='dimgrey', linewidths=1.0)
ax1.clabel(C_P, C_P.levels, inline=True, fmt="%3.0f", fontsize=10)
ax1.add_feature(cfeature.GSHHSFeature(scale='intermediate'), zorder=3,
facecolor="whitesmoke") # ‘auto’, ‘coarse’, ‘low’, ‘intermediate’, ‘high, or ‘full’ (default is ‘auto’).
ax1.text(0, 1, "{0}_dxs_{1}+{2:02d}".format(model, dt, ttt), ha='left', va='bottom', \
transform=ax1.transAxes, color='black')
##########################################################
# handles, labels = ax1.get_legend_handles_labels()
legend = True
if legend:
proxy = [plt.axhline(y=0, xmin=1, xmax=1, color="tab:blue"), \
plt.axhline(y=0, xmin=1, xmax=1, color="dimgrey", linewidth=0.5), \
plt.axhline(y=0, xmin=1, xmax=1, color="black", linestyle='--'), \
]
lg = plt.legend(proxy, ["d-xs=10", "MSLP [hPa]", "Sea Ice conc. >10%"], loc=1)
# cb = plt.colorbar(CSST, fraction=0.046, pad=0.01, ax=ax1, aspect=25, label ="RH [%]", extend = "both")
ax_cb = adjustable_colorbar_cax(fig1, ax1)
cb = plt.colorbar(Cd, fraction=0.046, pad=0.01, ax=ax1, aspect=25, cax= ax_cb, label="d-excess [$\perthousand$]",
extend="both")
frame = lg.get_frame()
frame.set_facecolor('lightgray')
frame.set_alpha(1)
make_modelrun_folder = setup_directory(OUTPUTPATH, "{0}".format(dt))
print("filename: " + make_modelrun_folder + "/{0}_{1}_dxs_{2}+{3:02d}.png".format(model, domain_name, dt,
ttt))
if grid:
nicegrid(ax=ax1)
if domain_name != model and data_domain != None: # weird bug.. cuts off when sees no data value
ax1.set_extent(data_domain.lonlat)
fig1.savefig(make_modelrun_folder + "/{0}_{1}_dxs_{2}+{3:02d}.png".format(model, domain_name, dt, ttt),
bbox_inches="tight", dpi=200)
ax1.cla()
plt.clf()
plt.close(fig1)
plt.close("all")
def flexpart_EC(datetime,
steps=0,
model="MEPS",
domain_name=None,
domain_lonlat=None,
legend=False,
info=False,
save=True,
grid=True):
for dt in datetime: #modelrun at time..
print(dt)
date = dt[0:-2]
hour = int(dt[-2:])
param_sfc = ["air_pressure_at_sea_level", "surface_geopotential"]
param = param_sfc
#print(type(steps))
split = False
print("\n######## Checking if your request is possible ############")
try:
check_all = check_data(date=dt,
model=model,
param=param,
p_level=850,
step=steps)
except ValueError:
split = True
try:
print(
"--------> Splitting up your request to find match ############"
)
check_sfc = check_data(date=dt,
model=model,
param=param_sfc,
step=steps)
#check_pl = check_data(date=dt, model=model, param=param_pl, p_level=850,step=steps)
except ValueError:
print(
"!!!!! Sorry this plot is not availbale for this date. Try with another datetime !!!!!"
)
break
print("--------> Found match for your request ############")
if not split:
file_all = check_all.file.loc[0]
data_domain = domain_input_handler(dt, model, domain_name,
domain_lonlat, file_all)
#lonlat = np.array(data_domain.lonlat)
dmap_meps = get_data(model=model,
data_domain=data_domain,
param=param,
file=file_all,
step=steps,
date=dt,
p_level=[850])
print("\n######## Retrieving data ############")
print(f"--------> from: {dmap_meps.url} ")
dmap_meps.retrieve()
tmap_meps = dmap_meps # two names for same value, no copying done.
else:
# get sfc level data
file_sfc = check_sfc.file.loc[0]
data_domain = domain_input_handler(dt, model, domain_name,
domain_lonlat, file_sfc)
#lonlat = np.array(data_domain.lonlat)
dmap_meps = get_data(model=model,
param=param_sfc,
file=file_sfc,
step=steps,
date=dt,
data_domain=data_domain)
print("\n######## Retrieving data ############")
print(f"--------> from: {dmap_meps.url} ")
dmap_meps.retrieve()
# get pressure level data
#file_pl = check_pl.file
#tmap_meps = get_data(model=model, data_domain=data_domain, param=param_pl, file=file_pl, step=steps, date=dt, p_level = 850)
#print("\n######## Retrieving data ############")
#print(f"--------> from: {tmap_meps.url} ")
#tmap_meps.retrieve()
# convert fields
dmap_meps.air_pressure_at_sea_level /= 100
# read netcdf files with flexpart output
release_name = 'NYAlesund_S1'
path = "/home/centos/flexpart-arome/{0}/{1}*/flexpart_run_d01_combined.nc".format(
dt, release_name)
findpath = glob.glob(path)
print(findpath)
cdf = nc.Dataset(
findpath[0], "r"
) #"/home/centos/flexpart/{0}/grid_conc_{1}0000.nc".format(release_name,dt), "r")
lats = cdf.variables["XLAT"][:]
lons = cdf.variables["XLONG"][:]
#lons, lats = np.meshgrid(lons, lats)
tim = cdf.variables["time"][:]
levs = cdf.variables["ZTOP"][:]
spec1a = cdf.variables["CONC"][:]
release_name = 'Tromso_S1'
path = "/home/centos/flexpart-arome/{0}/{1}*/flexpart_run_d01_combined.nc".format(
dt, release_name)
findpath = glob.glob(path)
cdf = nc.Dataset(
findpath[0], "r"
) #"/home/centos/flexpart/{0}/grid_conc_{1}0000.nc".format(release_name,dt), "r")
spec1b = cdf.variables["CONC"][:]
# plot map
lonlat = [
dmap_meps.longitude[0, 0], dmap_meps.longitude[-1, -1],
dmap_meps.latitude[0, 0], dmap_meps.latitude[-1, -1]
]
print(lonlat)
lon0 = dmap_meps.longitude_of_central_meridian_projection_lambert
lat0 = dmap_meps.latitude_of_projection_origin_projection_lambert
parallels = dmap_meps.standard_parallel_projection_lambert
# setting up projection
globe = ccrs.Globe(ellipse='sphere',
semimajor_axis=6371000.,
semiminor_axis=6371000.)
crs = ccrs.LambertConformal(central_longitude=lon0,
central_latitude=lat0,
standard_parallels=parallels,
globe=globe)
#fig1 = plt.figure(figsize=(7, 9))
make_modelrun_folder = setup_directory(OUTPUTPATH, "{0}".format(dt))
for tim in np.arange(np.min(steps), np.max(steps) + 1, 1):
#ax1 = plt.subplot(projection=crs)
# determine if image should be created for this time step
stepok = False
if tim < 25:
stepok = True
elif (tim <= 36) and ((tim % 3) == 0):
stepok = True
elif (tim <= 66) and ((tim % 6) == 0):
stepok = True
if stepok == True:
l = 0
for lev in levs[0:8]:
fig1, ax1 = plt.subplots(1,
1,
figsize=(7, 9),
subplot_kw={'projection': crs})
ttt = tim
tidx = tim - np.min(steps)
if lev >= 5000: # TOC for last levels
spec2a = np.sum(spec1a[tim, :, :, :], 0).squeeze()
spec2b = np.sum(spec1b[tim, :, :, :], 0).squeeze()
lev = 0
else:
spec2a = (spec1a[tim, l, :, :]).squeeze()
spec2b = (spec1b[tim, l, :, :]).squeeze()
l = l + 1
print(np.shape(spec2a))
print(np.shape(lons))
print(np.shape(lats))
#print(tidx)
#print(lev)
#print(np.min(spec2))
print(np.max(spec2a))
#spec2[:,:]=0.01
spec2a = np.where(spec2a > 1e-10, spec2a, np.NaN)
spec2b = np.where(spec2b > 1e-10, spec2b, np.NaN)
print('Plotting FLEXPART-EC {0} + {1:02d} UTC, level {2}'.
format(dt, tim, lev))
# gather, filter and squeeze variables for plotting
plev = 0
#reduces noise over mountains by removing values over a certain height.
Z = dmap_meps.surface_geopotential[tidx, 0, :, :]
MSLP = np.where(
Z < 50000,
dmap_meps.air_pressure_at_sea_level[tidx, 0, :, :],
np.NaN).squeeze()
F_P = ax1.pcolormesh(lons,
lats,
spec2a,
norm=colors.LogNorm(vmin=1e-10,
vmax=0.2),
cmap=plt.cm.Reds,
zorder=1,
alpha=0.9,
transform=ccrs.PlateCarree())
F_P = ax1.pcolormesh(lons,
lats,
spec2b,
norm=colors.LogNorm(vmin=1e-10,
vmax=0.2),
cmap=plt.cm.Blues,
zorder=1,
alpha=0.9,
transform=ccrs.PlateCarree())
del spec2a
# MSLP with contour labels every 10 hPa
C_P = ax1.contour(dmap_meps.x,
dmap_meps.y,
MSLP,
zorder=3,
alpha=1.0,
levels=np.arange(960, 1050, 1),
colors='grey',
linewidths=0.5,
transform=crs)
C_P = ax1.contour(dmap_meps.x,
dmap_meps.y,
MSLP,
zorder=4,
alpha=1.0,
levels=np.arange(960, 1050, 10),
colors='grey',
linewidths=1.0,
label="MSLP [hPa]",
transform=crs)
ax1.clabel(C_P,
C_P.levels,
inline=True,
fmt="%3.0f",
fontsize=10)
ax1.add_feature(
cfeature.GSHHSFeature(scale='intermediate'))
ax1.text(0,
1,
"{0}_FP_{1}+{2:02d}".format(model, dt, ttt),
ha='left',
va='bottom',
transform=ax1.transAxes,
color='black')
legend = False
if legend:
proxy = [
plt.Rectangle(
(0, 0),
1,
1,
fc=pc.get_facecolor()[0],
) for pc in CF_T.collections
]
proxy1 = [
plt.axhline(y=0, xmin=1, xmax=1, color="red"),
plt.axhline(y=0,
xmin=1,
xmax=1,
color="red",
linestyle="dashed"),
plt.axhline(y=0, xmin=1, xmax=1, color="gray")
]
proxy.extend(proxy1)
lg = ax1.legend(proxy, [
f"RH > 80% [%] at {dmap_meps.pressure[plev]:.0f} hPa",
f"T>0 [C] at {dmap_meps.pressure[plev]:.0f} hPa",
f"T<0 [C] at {dmap_meps.pressure[plev]:.0f} hPa",
"MSLP [hPa]", ""
])
frame = lg.get_frame()
frame.set_facecolor('white')
frame.set_alpha(1)
#if info:
# plt.text(x=0, y=-1, s="INFO: Reduced topographic noise by filtering with surface_geopotential bellow 3000",
# fontsize=7)#, bbox=dict(facecolor='white', alpha=0.5))
##########################################################
#plt.show()
#lonlat = [dmap_meps.longitude[0, 0], dmap_meps.longitude[-1, -1], dmap_meps.latitude[0, 0],
# dmap_meps.latitude[-1, -1]]
# ax.set_extent((lonlat[0]-5, lonlat[1], lonlat[2], lonlat[3])) # (x0, x1, y0, y1)
# ax.set_extent((dmap_meps.x[0], dmap_meps.x[-1], dmap_meps.y[0], dmap_meps.y[-1])) # (x0, x1, y0, y1)
#ax1.set_extent((lonlat[0], lonlat[1], lonlat[2], lonlat[3]))
#fig1.savefig("../../../../output/{0}_T2M_{1}_{2:02d}.png".format(model,dt, tim), bbox_inches="tight", dpi=200)
if grid:
nicegrid(ax=ax1)
#if domain_name != model and data_domain != None: # weird bug.. cuts off when sees no data value
ax1.set_extent(lonlat)
model = 'FLEXPART_AA'
print(make_modelrun_folder +
"/{0}_{1}_L{2:05.0f}_{3}+{4:02d}.png".format(
model, domain_name, lev, dt, tim))
fig1.savefig(make_modelrun_folder +
"/{0}_{1}_L{2:05.0f}_{3}+{4:02d}.png".format(
model, domain_name, lev, dt, tim),
bbox_inches="tight",
dpi=200)
ax1.cla()
plt.clf()
plt.close(fig1)
plt.close("all")
def IWC_LWC(datetime, steps=0, model= "MEPS", domain_name = None, domain_lonlat = None, legend=False, info = False,grid=True,m_level = [0, 64]):
for dt in datetime: # modelrun at time..
date = dt[0:-2]
hour = int(dt[-2:])
param = ['mass_fraction_of_cloud_condensed_water_in_air_ml',
'mass_fraction_of_cloud_ice_in_air_ml',"air_pressure_at_sea_level","surface_geopotential"]
check_all = check_data(date=dt, model=model, param=param, levtype="ml", m_level=m_level, step=steps)
print(check_all.file)
file_all = check_all.file.loc[0]
data_domain = domain_input_handler(dt, model, domain_name, domain_lonlat, file_all)
# lonlat = np.array(data_domain.lonlat)
print(m_level)
dmap_meps = get_data(model=model, data_domain=data_domain, param=param, file=file_all, step=steps,
date=dt, m_level=m_level)
print("\n######## Retrieving data ############")
print(f"--------> from: {dmap_meps.url} ")
dmap_meps.retrieve()
dmap_meps.air_pressure_at_sea_level /= 100
#CALCULATE
dmap_meps.LWC = np.sum(dmap_meps.mass_fraction_of_cloud_condensed_water_in_air_ml[:,:,:,:],axis=1)
dmap_meps.LWC =dmap_meps.LWC*1000
dmap_meps.units.LWC = "g/kg"
dmap_meps.IWC = np.sum(dmap_meps.mass_fraction_of_cloud_ice_in_air_ml[:,:,:,:],axis=1)
dmap_meps.IWC = dmap_meps.IWC*1000
dmap_meps.units.IWC = "g/kg"
del dmap_meps.mass_fraction_of_cloud_condensed_water_in_air_ml
del dmap_meps.mass_fraction_of_cloud_ice_in_air_ml
# for more normal unit of g/m^2 read
# #https://www.nwpsaf.eu/site/download/documentation/rtm/docs_rttov12/rttov_gas_cloud_aerosol_units.pdf
# https://www.researchgate.net/post/How-to-convert-the-units-of-specific-cloud-liquid-water-from-ERA5-kg-kg-to-kg-m2
dmap_meps.LWC[np.where(dmap_meps.LWC < 0.1)] = np.nan
dmap_meps.IWC[np.where(dmap_meps.IWC < 0.01)] = np.nan
#It is a bug in pcolormesh. supposedly newest is correct, but not older versions. Invalid corner values set to nan
#https://github.com/matplotlib/basemap/issues/470
x,y = np.meshgrid(dmap_meps.x, dmap_meps.y)
#dlon,dlat= np.meshgrid(dmap_meps.longitude, dmap_meps.latitude)
nx, ny = x.shape
mask = (
(x[:-1, :-1] > 1e20) |
(x[1:, :-1] > 1e20) |
(x[:-1, 1:] > 1e20) |
(x[1:, 1:] > 1e20) |
(x[:-1, :-1] > 1e20) |
(x[1:, :-1] > 1e20) |
(x[:-1, 1:] > 1e20) |
(x[1:, 1:] > 1e20)
)
# plot map
lon0 = dmap_meps.longitude_of_central_meridian_projection_lambert
lat0 = dmap_meps.latitude_of_projection_origin_projection_lambert
parallels = dmap_meps.standard_parallel_projection_lambert
globe = ccrs.Globe(ellipse='sphere', semimajor_axis=6371000., semiminor_axis=6371000.)
crs = ccrs.LambertConformal(central_longitude=lon0, central_latitude=lat0, standard_parallels=parallels,
globe=globe)
make_modelrun_folder = setup_directory(OUTPUTPATH, "{0}".format(dt))
for tim in np.arange(np.min(steps), np.max(steps)+1, 1):
#ax1 = plt.subplot(projection=crs)
# determine if image should be created for this time step
stepok=False
if tim<25:
stepok=True
elif (tim<=36) and ((tim % 3) == 0):
stepok=True
elif (tim<=66) and ((tim % 6) == 0):
stepok=True
if stepok==True:
fig1, ax1 = plt.subplots(1, 1, figsize=(7, 9),subplot_kw={'projection': crs})
ttt = tim #+ np.min(steps)
tidx = tim - np.min(steps)
ZS = dmap_meps.surface_geopotential[tidx, 0, :, :]
MSLP = np.where(ZS < 3000, dmap_meps.air_pressure_at_sea_level[tidx, 0, :, :], np.NaN).squeeze()
#pcolor as pcolormesh and this projection is not happy together. If u want faster, try imshow
#dmap_meps.LWC[np.where( dmap_meps.LWC <= 0.09)] = np.nan
data = dmap_meps.LWC[tidx,:nx - 1, :ny - 1].copy()
data[mask] = np.nan
CC=ax1.pcolormesh(x, y, data[:, :], cmap=plt.cm.Reds, vmin=0.1, vmax=4.0,zorder=2)
data = dmap_meps.IWC[tidx,:nx - 1, :ny - 1].copy()
data[mask] = np.nan
CI= ax1.pcolormesh(x, y, data[:, :], cmap=plt.cm.Blues,alpha=0.5, vmin=0.01, vmax=0.1,zorder=3)
# MSLP
# MSLP with contour labels every 10 hPa
C_P = ax1.contour(dmap_meps.x, dmap_meps.y, MSLP, zorder=4, alpha=1.0,
levels=np.arange(960, 1050, 1),
colors='grey', linewidths=0.5)
C_P = ax1.contour(dmap_meps.x, dmap_meps.y, MSLP, zorder=5, alpha=1.0,
levels=np.arange(960, 1050, 10),
colors='grey', linewidths=1.0, label="MSLP [hPa]")
ax1.clabel(C_P, C_P.levels, inline=True, fmt="%3.0f", fontsize=10)
ax1.add_feature(cfeature.GSHHSFeature(scale='intermediate'),zorder=6,facecolor="none",edgecolor="gray") # ‘auto’, ‘coarse’, ‘low’, ‘intermediate’, ‘high, or ‘full’ (default is ‘auto’).
if domain_name != model and data_domain !=None: #weird bug.. cuts off when sees no data value
ax1.set_extent(data_domain.lonlat)
ax1.text(0, 1, "{0}_LWC_IWC_{1}_{2}+{3:02d}".format(model,m_level, dt, ttt), ha='left', va='bottom', \
transform=ax1.transAxes, color='black')
print("filename: "+make_modelrun_folder + "/{0}_{1}_{2}_{3}+{4:02d}.png".format(model, domain_name, "LWP_IWP", dt, ttt))
if grid:
nicegrid(ax=ax1)
legend = True
if legend:
cbar = nice_vprof_colorbar(CF=CI, ax=ax1, extend="max",label='IWC [g/kg]',x0=0.75,y0=0.95,width=0.26,height=0.05,
format='%.3f', ticks=[0.001, np.nanmax(dmap_meps.IWC[tidx, :, :])*0.8])
cbar = nice_vprof_colorbar(CF=CC, ax=ax1, extend="max", label='LWC [g/kg]',x0=0.50,y0=0.95,width=0.26,height=0.05,
format='%.1f',ticks=[0.09, np.nanmax(dmap_meps.LWC[tidx, :, :])*0.8])
proxy = [plt.axhline(y=0, xmin=0, xmax=0, color="gray",zorder=7)]
# proxy.extend(proxy1)
lg = ax1.legend(proxy, ["MSLP [hPa]"])
frame = lg.get_frame()
frame.set_facecolor('white')
frame.set_alpha(0.8)
fig1.savefig(make_modelrun_folder +"/{0}_{1}_{2}_{3}+{4:02d}.png".format(model, domain_name, "clouds", dt, ttt), bbox_inches="tight", dpi=200)
ax1.cla()
plt.clf()
plt.close(fig1)
plt.close("all")
def OLR_sat(datetime,
steps=0,
model="MEPS",
domain_name=None,
domain_lonlat=None,
legend=False,
info=False,
grid=True):
for dt in datetime: #modelrun at time..
param = [
"toa_outgoing_longwave_flux", "air_pressure_at_sea_level",
"surface_geopotential"
]
check_all = check_data(date=dt, model=model, param=param, step=steps)
file_all = check_all.file
data_domain = domain_input_handler(dt, model, domain_name,
domain_lonlat, file_all)
dmap_meps = get_data(model=model,
param=param,
file=file_all,
step=steps,
date=dt,
data_domain=data_domain)
dmap_meps.retrieve()
dmap_meps.air_pressure_at_sea_level /= 100
lon0 = dmap_meps.longitude_of_central_meridian_projection_lambert
lat0 = dmap_meps.latitude_of_projection_origin_projection_lambert
parallels = dmap_meps.standard_parallel_projection_lambert
#fig = plt.figure(figsize=(7, 9))
# setting up projection
globe = ccrs.Globe(ellipse='sphere',
semimajor_axis=6371000.,
semiminor_axis=6371000.)
crs = ccrs.LambertConformal(central_longitude=lon0,
central_latitude=lat0,
standard_parallels=parallels,
globe=globe)
for tim in np.arange(np.min(steps), np.max(steps) + 1, 1):
fig, ax = plt.subplots(1,
1,
figsize=(7, 9),
subplot_kw={'projection': crs})
ttt = tim
tidx = tim - np.min(steps)
ZS = dmap_meps.surface_geopotential[tidx, 0, :, :]
MSLP = np.where(ZS < 3000,
dmap_meps.air_pressure_at_sea_level[tidx, 0, :, :],
np.NaN).squeeze()
#ax = plt.subplot(projection=crs)
print('Plotting {0} + {1:02d} UTC'.format(dt, ttt))
#ax.coastlines('10m')
#ax.pcolormesh(dmap_meps.x, dmap_meps.y, dmap_meps.integral_of_toa_outgoing_longwave_flux_wrt_time[0, 0, :, :], vmin=-230,
# vmax=-110, cmap=plt.cm.Greys_r)
#ax.pcolormesh(dmap_meps.x, dmap_meps.y, dmap_meps.toa_outgoing_longwave_flux[tidx, 0, :, :], vmin=-230,vmax=-110, cmap=plt.cm.Greys_r)
# MSLP
# MSLP with contour labels every 10 hPa
C_P = ax.contour(dmap_meps.x,
dmap_meps.y,
MSLP,
zorder=10,
alpha=0.6,
levels=np.arange(
round(np.nanmin(MSLP), -1) - 10,
round(np.nanmax(MSLP), -1) + 10, 1),
colors='cyan',
linewidths=0.5)
C_P = ax.contour(dmap_meps.x,
dmap_meps.y,
MSLP,
zorder=10,
alpha=0.6,
levels=np.arange(
round(np.nanmin(MSLP), -1) - 10,
round(np.nanmax(MSLP), -1) + 10, 5),
colors='cyan',
linewidths=1.0,
label="MSLP [hPa]")
ax.clabel(C_P, C_P.levels, inline=True, fmt="%3.0f", fontsize=10)
#It is a bug in pcolormesh. supposedly newest is correct, but not older versions. Invalid corner values set to nan
#https://github.com/matplotlib/basemap/issues/470
x, y = np.meshgrid(dmap_meps.x, dmap_meps.y)
#dlon,dlat= np.meshgrid(dmap_meps.longitude, dmap_meps.latitude)
nx, ny = x.shape
mask = ((x[:-1, :-1] > 1e20) | (x[1:, :-1] > 1e20) |
(x[:-1, 1:] > 1e20) | (x[1:, 1:] > 1e20) |
(x[:-1, :-1] > 1e20) | (x[1:, :-1] > 1e20) |
(x[:-1, 1:] > 1e20) | (x[1:, 1:] > 1e20))
data = dmap_meps.toa_outgoing_longwave_flux[tidx, 0, :nx - 1, :ny -
1].copy()
data[mask] = np.nan
#ax.pcolormesh(x, y, data[ :, :])#, cmap=plt.cm.Greys_r)
ax.pcolormesh(x,
y,
data[:, :],
vmin=-230,
vmax=-110,
cmap=plt.cm.Greys_r)
#ax.pcolormesh(dmap_meps.x, dmap_meps.y, dmap_meps.toa_outgoing_longwave_flux[tidx, 0, :, :], cmap=plt.cm.Greys_r)
#lat_p = 78.9243
#lon_p = 11.9312
#mainpoint = ax.scatter(lon_p, lat_p, s=9.0 ** 2, transform=ccrs.PlateCarree(),
# color='lime', zorder=6, linestyle='None', edgecolors="k", linewidths=3)
ax.add_feature(
cfeature.GSHHSFeature(scale='intermediate'),
edgecolor="brown",
linewidth=0.5
) # ‘auto’, ‘coarse’, ‘low’, ‘intermediate’, ‘high, or ‘full’ (default is ‘auto’).
#distancerange="../../data/Table_circle_nm_Andenes.csv"
#dist = pd.read_csv(distancerange)
#lats = dist["lat_300nm"]
#lons = dist["lon_300nm"]
#lons[dmap_meps.longitude]=np.nan
#lons_mask = ma.masked_outside(lons, np.nanmin(dmap_meps.longitude), np.nanmax(dmap_meps.longitude))
#lats_mask = ma.masked_outside(lats, np.nanmin(dmap_meps.latitude), np.nanmax(dmap_meps.latitude))
#C300 = ax.plot(lons_mask,lats_mask, transform = ccrs.PlateCarree())
#lats = dist["lat_400nm"]
#lons = dist["lon_400nm"]
#lons_mask = ma.masked_outside(lons, np.nanmin(dmap_meps.longitude), np.nanmax(dmap_meps.longitude))
#lats_mask = ma.masked_outside(lats, np.nanmin(dmap_meps.latitude), np.nanmax(dmap_meps.latitude))
#C300 = ax.plot(lons_mask, lats_mask, transform=ccrs.PlateCarree())
#lats = dist["lat_500nm"]
#lons = dist["lon_500nm"]
#lons_mask = ma.masked_outside(lons, np.nanmin(dmap_meps.longitude), np.nanmax(dmap_meps.longitude))
#lats_mask = ma.masked_outside(lats, np.nanmin(dmap_meps.latitude), np.nanmax(dmap_meps.latitude))
#C300 = ax.plot(lons, lats, transform=ccrs.PlateCarree())
#lonlat = [dmap_meps.longitude[0, 0], dmap_meps.longitude[0, -1], dmap_meps.latitude[0, 0],
# dmap_meps.latitude[-1, -1]]
#lonlat = [np.nanmin(dmap_meps.longitude), np.nanmax(dmap_meps.longitude), np.nanmin(dmap_meps.latitude),
# np.nanmax(dmap_meps.latitude)]
#print(dmap_meps.longitude[-2, -2])
#print(np.nanmax(dmap_meps.longitude))
#ax.set_extent((lonlat[0], lonlat[1], lonlat[2], lonlat[3])) # (x0, x1, y0, y1)
#ax.set_extent([x[0,0], x[-1,-1], y[0,0], y[-1,-1]], projection=crs) # (x0, x1, y0, y1)
#ax.set_extent((lonlat[0], lonlat[1], lonlat[2], lonlat[3])) # (x0, x1, y0, y1)
make_modelrun_folder = setup_directory(OUTPUTPATH,
"{0}".format(dt))
ax.text(0, 1, "{0}_{1}+{2:02d}".format(model, dt, ttt), ha='left', va='bottom', \
transform=ax.transAxes, color='dimgrey')
#ax.set_extent((lonlat[0], lonlat[1], lonlat[2], lonlat[3])) # (x0, x1, y0, y1)
#ax.set_extent([lonlat[0]+10, lonlat[1], lonlat[2]-2, lonlat[3]]) # (x0, x1, y0, y1)
#ax.set_extent((-18.0,80.0,62.0,88.0)) # (x0, x1, y0, y1)
#ax.set_extent(data_domain.lonlat)
if grid:
nicegrid(ax=ax, color="orange")
fig.savefig(make_modelrun_folder +
"/{0}_{1}_OLR_sat_{2}+{3:02d}.png".format(
model, domain_name, dt, ttt),
bbox_inches="tight",
dpi=200)
ax.cla()
fig.clf()
plt.close(fig)
ax.cla()
plt.clf()
plt.close("all")
def Z500_VEL(datetime,
steps=0,
model="MEPS",
domain_name=None,
domain_lonlat=None,
legend=False,
info=False,
grid=True):
for dt in datetime: #modelrun at time..
date = dt[0:-2]
hour = int(dt[-2:])
param_sfc = [
"air_pressure_at_sea_level", "precipitation_amount_acc",
"surface_geopotential"
]
param_pl = ["x_wind_pl", "y_wind_pl", "geopotential_pl"]
param = param_sfc + param_pl
plevel = [500]
split = False
print("\n######## Checking if your request is possible ############")
try:
check_all = check_data(date=dt,
model=model,
param=param,
levtype="pl",
p_level=plevel,
step=steps)
print(check_all.file)
except ValueError:
split = True
try:
print(
"--------> Splitting up your request to find match ############"
)
check_sfc = check_data(date=dt,
model=model,
param=param_sfc,
step=steps)
check_pl = check_data(date=dt,
model=model,
param=param_pl,
levtype="pl",
p_level=plevel,
step=steps)
print(check_pl.file)
except ValueError:
print(
"!!!!! Sorry this plot is not availbale for this date. Try with another datetime !!!!!"
)
break
print("--------> Found match for your request ############")
if not split:
file_all = check_all.file.loc[0]
data_domain = domain_input_handler(dt, model, domain_name,
domain_lonlat, file_all)
#lonlat = np.array(data_domain.lonlat)
print(file_all)
dmap_meps = get_data(model=model,
data_domain=data_domain,
param=param,
file=file_all,
step=steps,
date=dt,
p_level=plevel)
print("\n######## Retrieving data ############")
print(f"--------> from: {dmap_meps.url} ")
dmap_meps.retrieve()
tmap_meps = dmap_meps # two names for same value, no copying done.
else:
# get sfc level data
file_sfc = check_sfc.file.loc[0]
data_domain = domain_input_handler(dt, model, domain_name,
domain_lonlat, file_sfc)
#lonlat = np.array(data_domain.lonlat)
dmap_meps = get_data(model=model,
param=param_sfc,
file=file_sfc,
step=steps,
date=dt,
data_domain=data_domain)
print("\n######## Retrieving data ############")
print(f"--------> from: {dmap_meps.url} ")
dmap_meps.retrieve()
# get pressure level data
file_pl = check_pl.file.loc[0]
tmap_meps = get_data(model=model,
data_domain=data_domain,
param=param_pl,
file=file_pl,
step=steps,
date=dt,
p_level=plevel)
print("\n######## Retrieving data ############")
print(f"--------> from: {tmap_meps.url} ")
tmap_meps.retrieve()
# convert fields
dmap_meps.air_pressure_at_sea_level /= 100
#dmap_meps.precipitation_amount_acc*=1000.0
print(dmap_meps.units.precipitation_amount_acc)
tmap_meps.geopotential_pl /= 10.0
tmap_meps.units.geopotential_pl = "m"
u, v = xwind2uwind(tmap_meps.x_wind_pl, tmap_meps.y_wind_pl,
tmap_meps.alpha)
vel = wind_speed(tmap_meps.x_wind_pl, tmap_meps.y_wind_pl)
# plot map
lonlat = [
dmap_meps.longitude[0, 0], dmap_meps.longitude[-1, -1],
dmap_meps.latitude[0, 0], dmap_meps.latitude[-1, -1]
]
lon0 = dmap_meps.longitude_of_central_meridian_projection_lambert
lat0 = dmap_meps.latitude_of_projection_origin_projection_lambert
parallels = dmap_meps.standard_parallel_projection_lambert
# setting up projection
globe = ccrs.Globe(ellipse='sphere',
semimajor_axis=6371000.,
semiminor_axis=6371000.)
crs = ccrs.LambertConformal(central_longitude=lon0,
central_latitude=lat0,
standard_parallels=parallels,
globe=globe)
for tim in np.arange(np.min(steps), np.max(steps) + 1, 1):
fig1, ax1 = plt.subplots(1,
1,
figsize=(7, 9),
subplot_kw={'projection': crs})
ttt = tim #+ np.min(steps)
tidx = tim - np.min(steps)
print('Plotting {0} + {1:02d} UTC'.format(dt, ttt))
plev2 = 0
embr = 0
ZS = dmap_meps.surface_geopotential[tidx, 0, :, :]
MSLP = np.where(ZS < 3000,
dmap_meps.air_pressure_at_sea_level[tidx, 0, :, :],
np.NaN).squeeze()
acc = 1
TP = precip_acc(dmap_meps.precipitation_amount_acc,
acc=acc)[tidx, 0, :, :].squeeze()
VEL = (vel[tidx, plev2, :, :]).squeeze()
Z = (tmap_meps.geopotential_pl[tidx, plev2, :, :]).squeeze()
Ux = u[tidx, 0, :, :].squeeze()
Vx = v[tidx, 0, :, :].squeeze()
uxx = tmap_meps.x_wind_pl[tidx, 0, :, :].squeeze()
vxx = tmap_meps.y_wind_pl[tidx, 0, :, :].squeeze()
cmap = plt.get_cmap("tab20c")
lvl = [0.01, 0.1, 0.2, 0.5, 1, 2, 5, 10, 15, 20, 25, 30]
norm = mcolors.BoundaryNorm(lvl, cmap.N)
try: #workaround for a stupid matplotlib error not handling when all values are outside of range in lvl or all just nans..
#https://github.com/SciTools/cartopy/issues/1290
#cmap = mcolors.ListedColormap('hsv', 'hsv') #plt.get_cmap("hsv")PuBu
#TP.filled(np.nan) #fill mask with nan to avoid: UserWarning: Warning: converting a masked element to nan.
CF_prec = plt.contourf(dmap_meps.x,
dmap_meps.y,
TP,
zorder=1,
cmap=cmap,
norm=norm,
alpha=0.4,
antialiased=True,
levels=lvl,
extend="max") #
except:
pass
# MSLP with contour labels every 10 hPa
C_P = ax1.contour(dmap_meps.x,
dmap_meps.y,
MSLP,
zorder=1,
alpha=1.0,
levels=np.arange(
round(np.nanmin(MSLP), -1) - 10,
round(np.nanmax(MSLP), -1) + 10, 1),
colors='grey',
linewidths=0.5)
C_P = ax1.contour(dmap_meps.x,
dmap_meps.y,
MSLP,
zorder=2,
alpha=1.0,
levels=np.arange(
round(np.nanmin(MSLP), -1) - 10,
round(np.nanmax(MSLP), -1) + 10, 10),
colors='grey',
linewidths=1.0)
ax1.clabel(C_P, C_P.levels, inline=True, fmt="%3.0f", fontsize=10)
####REMOVE LATER 60.2;5.4166666666667;60;None;N
skip = 20
skip = (slice(40, -40, 50), slice(40, -40, 50)) #70
xm, ym = np.meshgrid(dmap_meps.x, dmap_meps.y)
CVV = ax1.barbs(xm[skip],
ym[skip],
uxx[skip] * 1.94384,
vxx[skip] * 1.94384,
zorder=5)
#CS = ax1.contour(dmap_meps.x, dmap_meps.y, VEL, zorder=3, alpha=1.0,
# levels=np.arange(-80, 80, 5), colors="green", linewidths=0.7)
# geopotential
CS = ax1.contour(dmap_meps.x,
dmap_meps.y,
Z,
zorder=3,
alpha=1.0,
levels=np.arange(4600, 5800, 20),
colors="blue",
linewidths=0.7)
ax1.clabel(CS, CS.levels, inline=True, fmt="%4.0f", fontsize=10)
ax1.add_feature(
cfeature.GSHHSFeature(scale='intermediate')
) # ‘auto’, ‘coarse’, ‘low’, ‘intermediate’, ‘high, or ‘full’ (default is ‘auto’).
ax1.text(0,
1,
"{0}_Z500_{1}+{2:02d}".format(model, dt, ttt),
ha='left',
va='bottom',
transform=ax1.transAxes,
color='black')
if grid:
nicegrid(ax=ax1)
##########################################################
legend = True
if legend:
proxy = [
plt.axhline(y=0, xmin=1, xmax=1, color="gray"),
plt.axhline(y=0, xmin=1, xmax=1, color="blue")
]
try:
ax_cb = adjustable_colorbar_cax(fig1, ax1)
cb = plt.colorbar(CF_prec,
cax=ax_cb,
fraction=0.046,
pad=0.01,
aspect=25,
label=f"{acc}h acc. prec. [mm/{acc}h]",
extend="both")
except:
pass
lg = ax1.legend(proxy, [
f"MSLP [hPa]",
f"Geopotential height[{tmap_meps.units.geopotential_pl}] at {dmap_meps.pressure[plev2]:.0f} hPa"
],
loc="upper right")
frame = lg.get_frame()
frame.set_facecolor('white')
frame.set_alpha(1)
#if info:
# plt.text(x=0, y=-1, s="INFO: Reduced topographic noise by filtering with surface_geopotential bellow 3000",
# fontsize=7) # , bbox=dict(facecolor='white', alpha=0.5))
#plt.show()
if domain_name != model and data_domain != None: # weird bug.. cuts off when sees no data value
ax1.set_extent(data_domain.lonlat)
make_modelrun_folder = setup_directory(OUTPUTPATH,
"{0}".format(dt))
fig1.savefig(make_modelrun_folder +
"/{0}_{1}_Z500_VEL_P_{2}+{3:02d}.png".format(
model, domain_name, dt, ttt),
bbox_inches="tight",
dpi=200)
ax1.cla()
plt.clf()
plt.close(fig1)
#proxy = [plt.axhline(y=0, xmin=1, xmax=1, color="green"),
# plt.axhline(y=0, xmin=1, xmax=1, color="blue")]
#fig2 = plt.figure(figsize=(2, 1.25))
#fig2.legend(proxy, [f"Wind strength [m/s] at {tmap_meps.pressure[plev2]:.0f} hPa",
# f"Geopotential [{tmap_meps.units.geopotential_pl}]{tmap_meps.pressure[plev2]:.0f} hPa"])
#fig2.savefig(make_modelrun_folder+"/{0}_Z500_VEL_P_LEGEND.png".format(model), bbox_inches="tight", dpi=200)
#plt.close(fig2)
#try:
# fig3, ax3 = plt.subplots()
# fig3.colorbar(CF_prec, fraction=0.046, pad=0.04)
# ax3.remove()
# fig3.savefig(make_modelrun_folder+"/{0}_{1}_Z500_VEL_P_COLORBAR.png".format(model, domain_name), bbox_inches="tight", dpi=200)
# plt.close(fig3)
#except:
# pass
ax1.cla()
plt.clf()
plt.close("all")
def surf(datetime,
steps=0,
model="AromeArctic",
domain_name=None,
domain_lonlat=None,
legend=False,
info=False,
grid=True):
for dt in datetime: #modelrun at time..
date = dt[0:-2]
hour = int(dt[-2:])
param_sfc = [
"surface_geopotential", "air_pressure_at_sea_level", "x_wind_10m",
"y_wind_10m", "precipitation_amount_acc", "wind_speed"
]
param_sfx = ["LE", "H", "SST"]
param_pl = []
param = param_sfc + param_pl
split = False
sfx = False
print("\n######## Checking if your request is possible ############")
try:
check_all = check_data(date=dt,
model=model,
param=param,
step=steps)
check_sfx = check_data(date=dt,
model=model,
param=param_sfx,
step=steps)
print(check_all.file)
except ValueError:
try:
sfx = True
param_sfx = ["SFX_LE", "SFX_H", "SFX_SST"]
check_all = check_data(date=dt,
model=model,
param=param,
step=steps)
check_sfx = check_data(date=dt,
model=model,
param=param_sfx,
step=steps)
except ValueError:
print(
"!!!!! Sorry this plot is not availbale for this date. Try with another datetime !!!!!"
)
break
print("--------> Found match for your request ############")
if not split:
file_all = check_all.file.loc[0]
file_sfx = check_sfx.file.loc[0]
data_domain = domain_input_handler(dt, model, domain_name,
domain_lonlat, file_all)
#lonlat = np.array(data_domain.lonlat)
print(file_all)
dmap_meps = get_data(model=model,
data_domain=data_domain,
param=param,
file=file_all,
step=steps,
date=dt)
dmap_meps_sfx = get_data(model=model,
data_domain=data_domain,
param=param_sfx,
file=file_sfx,
step=steps,
date=dt)
print("\n######## Retrieving data ############")
print(f"--------> from: {dmap_meps.url} ")
dmap_meps.retrieve()
tmap_meps = dmap_meps # two names for same value, no copying done.
dmap_meps_sfx.retrieve()
if sfx: #["SFX_LE", "SFX_H", "SFX_SST"]
dmap_meps_sfx.LE = dmap_meps_sfx.SFX_LE
dmap_meps_sfx.H = dmap_meps_sfx.SFX_H
dmap_meps_sfx.SST = dmap_meps_sfx.SFX_SST
dmap_meps_sfx.units.LE = dmap_meps_sfx.units.SFX_LE
dmap_meps_sfx.units.H = dmap_meps_sfx.units.SFX_H
dmap_meps_sfx.units.SST = dmap_meps_sfx.units.SFX_SST
# convert fields
dmap_meps.air_pressure_at_sea_level /= 100
u, v = xwind2uwind(tmap_meps.x_wind_10m, tmap_meps.y_wind_10m,
tmap_meps.alpha)
vel = wind_speed(tmap_meps.x_wind_10m, tmap_meps.y_wind_10m)
lon0 = dmap_meps.longitude_of_central_meridian_projection_lambert
lat0 = dmap_meps.latitude_of_projection_origin_projection_lambert
parallels = dmap_meps.standard_parallel_projection_lambert
# setting up projection
globe = ccrs.Globe(ellipse='sphere',
semimajor_axis=6371000.,
semiminor_axis=6371000.)
data = ccrs.LambertConformal(central_longitude=lon0,
central_latitude=lat0,
standard_parallels=parallels,
globe=globe)
crs = data
crs_lon = ccrs.PlateCarree()
#crs = ccrs.PlateCarree()
for tim in np.arange(np.min(steps), np.max(steps) + 1, 1):
fig1, ax1 = plt.subplots(1,
1,
figsize=(7, 9),
subplot_kw={'projection': crs})
ttt = tim #+ np.min(steps)
tidx = tim - np.min(steps)
print('Plotting {0} + {1:02d} UTC'.format(dt, ttt))
ZS = dmap_meps.surface_geopotential[tidx, 0, :, :]
MSLP = np.where(ZS < 3000,
dmap_meps.air_pressure_at_sea_level[tidx, 0, :, :],
np.NaN).squeeze()
#TP = precip_acc(dmap_meps.precipitation_amount_acc, acc=1)[tidx, 0, :,:].squeeze()
L = dmap_meps_sfx.LE[tidx, :, :].squeeze()
L = np.where(ZS < 3000, L, np.NaN).squeeze()
SH = dmap_meps_sfx.H[tidx, :, :].squeeze()
SH = np.where(ZS < 3000, SH, np.NaN).squeeze()
SST = dmap_meps_sfx.SST[tidx, :, :].squeeze()
Ux = dmap_meps.x_wind_10m[tidx, 0, :, :].squeeze()
Vx = dmap_meps.y_wind_10m[tidx, 0, :, :].squeeze()
xm, ym = np.meshgrid(dmap_meps.x, dmap_meps.y)
uxx = dmap_meps.x_wind_10m[tidx, 0, :, :].squeeze()
vxx = dmap_meps.y_wind_10m[tidx, 0, :, :].squeeze()
#VELOCITY
#new_x, new_y, new_u, new_v, = vector_scalar_to_grid(src_crs= data, target_proj= crs_lon,regrid_shape = np.shape(Ux), x= dmap_meps.x, y= dmap_meps.y, u= Ux, v= Vx)
#magnitude = (new_u ** 2 + new_v ** 2) ** 0.5
#cmap = plt.get_cmap("viridis") #cividis copper
#wii = plt.quiver(new_x, new_y, new_u, new_v)
#wii = ax1.quiver(xm, ym, Ux, Vx)
#wii = Axes.streamplot(ax1, new_x, new_y, new_u, new_v, density=4,zorder=4,transform=crs_lon, linewidth=0.7, color=magnitude, cmap=cmap)
#LATENT
#levelspos=np.arange(80, round(np.nanmax(L), -1) + 10, 40)
#levelsneg = np.arange(-300, -9, 10)
#levels = np.append(levelsneg, levelspos)
#CL = ax1.contour(dmap_meps.x, dmap_meps.y, L, zorder=3, alpha=1.0, colors="red", linewidths=0.7, levels=levels, transform=data)
#ax1.clabel(CL, CL.levels[::2], inline=True, fmt="%3.0f", fontsize=10)
#xx = np.where(L < -10, xm, np.NaN).squeeze()
#yy = np.where(L < -10, ym, np.NaN).squeeze()
#skip = (slice(None, None, 4), slice(None, None, 4))
#ax1.scatter(xx[skip][skip], yy[skip][skip], s=20, zorder=2, marker='x', linewidths=0.9,
# c="white", alpha=0.7, transform=data)
#xx = np.where(L > 80, xm, np.NaN).squeeze()
#yy = np.where(L > 80, ym, np.NaN).squeeze()
#skip = (slice(None, None, 4), slice(None, None, 4))
#ax1.scatter(xx[skip][skip], yy[skip][skip], s=20, zorder=2, marker='.', linewidths=0.9,
# c="black", alpha=0.7, transform=data)
#SENSIBLE
levelspos = np.arange(20, round(np.nanmax(SH), -10) + 10, 40)
levelsneg = np.arange(-300, -19, 40)
levels = np.append(levelsneg, levelspos)
levels = np.linspace(-300, 300, 15)
CSH = plt.contour(dmap_meps.x,
dmap_meps.y,
SH,
alpha=1.0,
colors="blue",
linewidths=0.7,
levels=levels)
#ax1.clabel(CSH, CSH.levels[1::2], inline=True, fmt="%3.0f", fontsize=10)
#xx = np.where(SH < -10, xm, np.NaN).squeeze()
#yy = np.where(SH < -10, ym, np.NaN).squeeze()
#skip = (slice(None, None, 4), slice(None, None, 4))
#ax1.scatter(xx[skip][skip],yy[skip][skip],s=20, zorder=2, marker='x',linewidths=0.9, c= "white", alpha=0.7, transform=data)
#xx = np.where(SH >80, xm, np.NaN).squeeze()
#yy = np.where(SH >80, ym, np.NaN).squeeze()
#skip = (slice(None, None, 4), slice(None, None, 4))
#ax1.scatter(xx[skip][skip], yy[skip][skip], s=20, zorder=2, marker='.', linewidths=0.9, c="black", alpha=0.7,
# transform=data)
#SST_new
#levels=np.arange(270,294,2)
SST = SST - 273.15
levels = [np.min(SST), np.max(SST), 3]
levels = [-2, 0, 2, 4, 6, 8, 10, 12, 14, 16, 18, 20, 22, 24]
C_SS = ax1.contour(dmap_meps.x,
dmap_meps.y,
SST,
colors="k",
linewidths=2,
levels=levels,
zorder=9)
ax1.clabel(C_SS,
C_SS.levels,
inline=True,
fmt="%3.0f",
fontsize=10)
#MSLP
#MSLP with contour labels every 10 hPa
C_P = ax1.contour(dmap_meps.x,
dmap_meps.y,
MSLP,
zorder=10,
alpha=1.0,
levels=np.arange(
round(np.nanmin(MSLP), -1) - 10,
round(np.nanmax(MSLP), -1) + 10, 1),
colors='grey',
linewidths=0.5)
C_P = ax1.contour(dmap_meps.x,
dmap_meps.y,
MSLP,
zorder=10,
alpha=1.0,
levels=np.arange(
round(np.nanmin(MSLP), -1) - 10,
round(np.nanmax(MSLP), -1) + 10, 10),
colors='grey',
linewidths=1.0,
label="MSLP [hPa]")
ax1.clabel(C_P, C_P.levels, inline=True, fmt="%3.0f", fontsize=10)
#wind#
#skip = (slice(50, -50, 50), slice(50, -50, 50))
skip = (slice(40, -40, 50), slice(40, -40, 50)) #70
CVV = ax1.barbs(xm[skip],
ym[skip],
uxx[skip] * 1.94384,
vxx[skip] * 1.94384,
zorder=10)
#lat_p = 60.2
#lon_p = 5.4167
#mainpoint = ax1.scatter(lon_p, lat_p, s=9.0 ** 2, transform=ccrs.PlateCarree(),
# color='lime', zorder=6, linestyle='None', edgecolors="k", linewidths=3)
#LATENT_new
#levels=np.arange(270,294,2)
cmap = plt.get_cmap("coolwarm")
levels = np.linspace(-100, 300, 7)
CSST = ax1.contourf(dmap_meps.x,
dmap_meps.y,
L,
zorder=1,
levels=levels,
alpha=0.7,
cmap=cmap,
extend="both",
transform=data)
ax1.add_feature(
cfeature.GSHHSFeature(scale='intermediate'),
zorder=3,
facecolor="whitesmoke",
edgecolor="gray"
) # ‘auto’, ‘coarse’, ‘low’, ‘intermediate’, ‘high, or ‘full’ (default is ‘auto’).
ax1.text(0,
1,
"{0}_surf_{1}+{2:02d}".format(model, dt, ttt),
ha='left',
va='bottom',
transform=ax1.transAxes,
color='black')
##########################################################
#handles, labels = ax1.get_legend_handles_labels()
legend = True
if legend:
proxy = [
plt.axhline(y=0, xmin=1, xmax=1, color="blue"),
plt.axhline(y=0, xmin=1, xmax=1, color="black")
]
lg = plt.legend(
proxy,
[f"Sensible heat [{dmap_meps_sfx.units.H}] ", f"SST [C]"],
loc=1)
ax_cb = adjustable_colorbar_cax(fig1, ax1)
cb = plt.colorbar(
CSST,
cax=ax_cb,
fraction=0.046,
pad=0.01,
ax=ax1,
aspect=25,
label=f"Latent heat [{dmap_meps_sfx.units.LE}]",
extend="both")
frame = lg.get_frame()
lg.set_zorder(102)
frame.set_facecolor('lightgray')
frame.set_alpha(1)
#plt.title("{0}_surf_{1}+{2:02d}.png".format(model, dt, ttt))
#print("test")
#print(OUTPUTPATH)
#print("{0}".format(dt))
make_modelrun_folder = setup_directory(OUTPUTPATH,
"{0}".format(dt))
if grid:
nicegrid(ax=ax1)
if domain_name != model and data_domain != None: # weird bug.. cuts off when sees no data value
ax1.set_extent(data_domain.lonlat)
fig1.savefig(make_modelrun_folder +
"/{0}_{1}_surf_{2}+{3:02d}.png".format(
model, domain_name, dt, ttt),
bbox_inches="tight",
dpi=200)
ax1.cla()
#cb.remove()
#lg.remove()
#plt.draw()
plt.clf()
plt.close(fig1)
plt.close("all")
def set_variable2d(modelruntime, steps, lvl, xres, yres, model):
variable2d_arome = {}
variable3d_arome = {}
variable2d_sfx = {}
resol = 7 #?
variable2d_arome['surface_air_pressure'] = {}
variable2d_arome['surface_air_pressure']['name'] = 'SP'
variable2d_arome['surface_air_pressure']['units'] = 'Pa'
variable2d_arome['surface_air_pressure'][
'description'] = 'log of surface pressure'
variable2d_arome['surface_air_pressure']['precision'] = resol
variable2d_arome['air_temperature_2m'] = {}
variable2d_arome['air_temperature_2m']['name'] = 'T2m'
variable2d_arome['air_temperature_2m']['units'] = 'K'
variable2d_arome['air_temperature_2m']['description'] = 'Temperature at 2m'
variable2d_arome['air_temperature_2m']['precision'] = resol
variable2d_arome['surface_geopotential'] = {}
variable2d_arome['surface_geopotential']['name'] = 'Zg'
variable2d_arome['surface_geopotential']['units'] = 'm^2/s^2'
variable2d_arome['surface_geopotential'][
'description'] = 'surface geopotential'
variable2d_arome['surface_geopotential']['precision'] = resol
variable2d_arome['land_area_fraction'] = {}
variable2d_arome['land_area_fraction']['name'] = 'LS'
variable2d_arome['land_area_fraction']['units'] = 'none'
variable2d_arome['land_area_fraction']['description'] = 'land sea mask'
variable2d_arome['land_area_fraction']['precision'] = resol
variable2d_arome['x_wind_10m'] = {}
variable2d_arome['x_wind_10m']['name'] = 'U_lon_10m'
variable2d_arome['x_wind_10m']['units'] = 'm/s'
variable2d_arome['x_wind_10m']['description'] = 'zonal wind at 10m'
variable2d_arome['x_wind_10m']['precision'] = resol
variable2d_arome['y_wind_10m'] = {}
variable2d_arome['y_wind_10m']['name'] = 'V_lat_10m'
variable2d_arome['y_wind_10m']['units'] = 'm/s'
variable2d_arome['y_wind_10m']['description'] = 'meriodional wind at 10m'
variable2d_arome['y_wind_10m']['precision'] = resol
variable2d_arome['specific_humidity_2m'] = {}
variable2d_arome['specific_humidity_2m']['name'] = 'Q2m'
variable2d_arome['specific_humidity_2m']['units'] = 'kg/kg'
variable2d_arome['specific_humidity_2m'][
'description'] = 'specific humidity at 2m'
variable2d_arome['specific_humidity_2m']['precision'] = resol
variable2d_arome[
'integral_of_surface_downward_sensible_heat_flux_wrt_time'] = {}
variable2d_arome[
'integral_of_surface_downward_sensible_heat_flux_wrt_time'][
'name'] = 'SSHF_CUM'
variable2d_arome[
'integral_of_surface_downward_sensible_heat_flux_wrt_time'][
'units'] = 'J.m-2'
variable2d_arome[
'integral_of_surface_downward_sensible_heat_flux_wrt_time'][
'description'] = 'Cum.Sensible heat flux'
variable2d_arome[
'integral_of_surface_downward_sensible_heat_flux_wrt_time'][
'precision'] = resol
variable2d_sfx['FMU'] = {}
variable2d_sfx['FMU']['name'] = 'USTRESS'
variable2d_sfx['FMU']['units'] = 'Kg.m-1.s-1'
variable2d_sfx['FMU']['description'] = 'Surface wind stress (u)'
variable2d_sfx['FMU']['precision'] = resol
variable2d_sfx['FMV'] = {}
variable2d_sfx['FMV']['name'] = 'VSTRESS'
variable2d_sfx['FMV']['units'] = 'Kg.m-1.s-1'
variable2d_sfx['FMV']['description'] = 'Surface wind stress (v)'
variable2d_sfx['FMV']['precision'] = resol
variable3d_arome['air_temperature_ml'] = {}
variable3d_arome['air_temperature_ml']['name'] = 'T'
variable3d_arome['air_temperature_ml']['units'] = 'K'
variable3d_arome['air_temperature_ml'][
'description'] = 'temperature on pressure sigmal levels'
variable3d_arome['air_temperature_ml'][
'precision'] = resol # digit precision
variable3d_arome['divergence_vertical'] = {}
variable3d_arome['divergence_vertical']['name'] = 'NH_dW'
variable3d_arome['divergence_vertical']['units'] = 'm/s * g'
variable3d_arome['divergence_vertical'][
'description'] = 'Non Hydrostatic divergence of vertical velocity: D = -g(w(i) -w(i-1))'
variable3d_arome['divergence_vertical']['precision'] = resol
variable3d_arome['x_wind_ml'] = {}
variable3d_arome['x_wind_ml']['name'] = 'U_X'
variable3d_arome['x_wind_ml']['units'] = 'm/s'
variable3d_arome['x_wind_ml'][
'description'] = 'U wind along x axis on pressure sigmal levels'
variable3d_arome['x_wind_ml']['precision'] = resol
variable3d_arome['y_wind_ml'] = {}
variable3d_arome['y_wind_ml']['name'] = 'V_Y'
variable3d_arome['y_wind_ml']['units'] = 'm/s'
variable3d_arome['y_wind_ml'][
'description'] = 'V wind along y axis on pressure sigmal levels'
variable3d_arome['y_wind_ml']['precision'] = resol
# variable3d_arome['PRESS.DEPART']={}
# variable3d_arome['PRESS.DEPART']['name'] = 'NH_dP'
# variable3d_arome['PRESS.DEPART']['units'] = 'Pa'
# variable3d_arome['PRESS.DEPART']['description'] = 'NH departure from pressure'
# variable3d_arome['PRESS.DEPART']['precision'] = 1
variable3d_arome['specific_humidity_ml'] = {}
variable3d_arome['specific_humidity_ml']['name'] = 'Q'
variable3d_arome['specific_humidity_ml']['units'] = 'kg/kg'
variable3d_arome['specific_humidity_ml'][
'description'] = 'specific humidity on pressure sigmal levels'
variable3d_arome['specific_humidity_ml']['precision'] = resol
variable3d_arome['turbulent_kinetic_energy_ml'] = {}
variable3d_arome['turbulent_kinetic_energy_ml']['name'] = 'TKE'
variable3d_arome['turbulent_kinetic_energy_ml']['units'] = 'm^2/s^2'
variable3d_arome['turbulent_kinetic_energy_ml'][
'description'] = 'Turbulent kinetic energy on pressure sigmal levels'
variable3d_arome['turbulent_kinetic_energy_ml']['precision'] = resol
variable3d_arome['cloud_area_fraction_ml'] = {}
variable3d_arome['cloud_area_fraction_ml']['name'] = 'CLDFRA'
variable3d_arome['cloud_area_fraction_ml']['units'] = 'none'
variable3d_arome['cloud_area_fraction_ml'][
'description'] = 'cloud fraction'
variable3d_arome['cloud_area_fraction_ml']['precision'] = 1
#2dArome
print("retrive 2darome")
param2d_arome = [*variable2d_arome.keys()]
print(param2d_arome)
print("check 2d arome")
arome2d = check_data(date=modelruntime, model=model, param=param2d_arome)
#
print(arome2d)
file_arome2d = arome2d.file
print(arome2d.file)
dmap_arome2d = get_data(model=model,
file=file_arome2d,
param=param2d_arome,
step=steps,
date=modelruntime)
print(dmap_arome2d.url)
dmap_arome2d.retrieve()
print(dmap_arome2d.__dir__)
print("retrive 3darome")
# 3dArome This can be included in 2darome for timeefficency
param3d_arome = [*variable3d_arome.keys()]
arome3d = check_data(date=modelruntime,
model=model,
param=param3d_arome,
m_level=lvl)
file_arome3d = arome3d.file
print(file_arome3d)
dmap_arome3d = get_data(model=model,
file=file_arome3d,
param=param3d_arome,
step=steps,
date=modelruntime,
m_level=lvl)
print(dmap_arome3d.url)
dmap_arome3d.retrieve()
print(dmap_arome3d.__dir__)
print("retrive sfxarome")
#2dsfx
param2d_sfx = [*variable2d_sfx.keys()]
sfx2d = check_data(date=modelruntime, model=model, param=param2d_sfx)
file_sfx2d = sfx2d.file
dmap_sfx2d = get_data(model=model,
file=file_sfx2d,
param=param2d_sfx,
step=steps,
date=modelruntime)
dmap_sfx2d.retrieve()
#attr
url = f"https://thredds.met.no/thredds/dodsC/aromearcticarchive/2020/07/01/arome_arctic_full_2_5km_20200701T18Z.nc?projection_lambert,x,y"
dataset = Dataset(url)
attr = {}
proj = dataset.variables["projection_lambert"]
#proj=dmap_arome2d.projection_lambert
#for t in range( 0, len(dmap_arome2d.time )):
for t in np.arange(np.min(steps), np.max(steps) + 1, 1):
tidx = t - np.min(steps)
print("Inside for loop")
output = outputpath + modelruntime + "/"
print(output)
if not os.path.exists(output):
os.makedirs(output)
print("Directory ", output, " Created ")
else:
print("Directory ", output, " exist ")
print(
"####################################################################################"
)
print(t)
print(
"####################################################################################"
)
validdate = datetime.datetime(
int(modelruntime[0:4]), int(modelruntime[4:6]),
int(modelruntime[6:8]), int(
modelruntime[8:10])) + datetime.timedelta(hours=int(t))
date_time = validdate.strftime("%Y%m%d_%H")
print(
"####################################################################################"
)
print(date_time)
print(
"####################################################################################"
)
#flexpart dont like 00, want 24
if validdate.hour == 0:
dateminus1d = validdate - datetime.timedelta(days=1)
date_time = dateminus1d.strftime("%Y%m%d") + "_24"
#d = datetime.today() - timedelta(days=days_to_subtract)
print(date_time)
ncid = Dataset(output + 'AR' + date_time + '.nc', 'w')
attr['reference_lon'] = proj.getncattr("longitude_of_central_meridian")
attr['ydim'] = np.long(
len(dmap_arome2d.y[::yres])
) #np.long(dataset.variables["y"].getncattr("_ChunkSizes")) # Use: None
attr['forecast'] = validdate.strftime("%H") # 23
attr['x_resolution'] = np.double(
"2500.0") #np.double("2500.0")*xres # Use: None
attr['center_lon'] = proj.getncattr("longitude_of_central_meridian")
attr['rotation_radian'] = 0.0
attr['xdim'] = np.long(
len(dmap_arome2d.x[::xres])
) #np.long(dataset.variables["x"].getncattr("_ChunkSizes")) # Use: None
attr['input_lat'] = proj.getncattr(
"latitude_of_projection_origin") # Use: None
attr['reference_lat'] = proj.getncattr("latitude_of_projection_origin")
attr['y_resolution'] = np.double(
"2500.0") #np.double("2500.0")*yres # Use: None
attr['date'] = validdate.strftime("%Y%m%d") # "20180331"
attr['input_lon'] = proj.getncattr(
"longitude_of_central_meridian") # Use: None
attr['input_position'] = (794.0, 444.0) # ?? # Use: None
attr['geoid'] = proj.getncattr("earth_radius") #6370000#6371229.0 #
attr['center_lat'] = proj.getncattr("latitude_of_projection_origin")
print("Create netcdf")
ncid.setncatts(attr)
x = ncid.createDimension('X', len(dmap_arome2d.x[::xres]))
y = ncid.createDimension('Y', len(dmap_arome2d.y[::yres]))
level = ncid.createDimension('level', len(dmap_arome3d.hybrid))
xs = ncid.createVariable('X', 'i4', ('X', ))
xs.units = 'none'
xs[:] = range(1, len(dmap_arome2d.x[::xres]) + 1)
ys = ncid.createVariable('Y', 'i4', ('Y', ))
ys.units = 'none'
ys[:] = range(1, len(dmap_arome2d.y[::yres]) + 1)
levels = ncid.createVariable('level', 'i4', ('level', ))
levels.units = 'none'
levels[:] = range(1, len(dmap_arome3d.hybrid) + 1)
nc_ak = ncid.createVariable('Ak', 'f4', ('level', ))
nc_ak.units = 'none'
nc_ak[:] = dmap_arome3d.ap
nc_bk = ncid.createVariable('Bk', 'f4', ('level', ))
nc_bk.units = 'none'
nc_bk[:] = dmap_arome3d.b
vid = ncid.createVariable('LON', 'f4', ('Y', 'X'), zlib=True)
vid.description = 'longitude of the center grid'
vid[:] = dmap_arome2d.longitude[::xres, ::yres]
vid = ncid.createVariable('LAT', 'f4', ('Y', 'X'), zlib=True)
vid.description = 'latitude of the center grid'
vid[:] = dmap_arome2d.latitude[::xres, ::yres]
print(param3d_arome)
for param in param3d_arome:
vid = ncid.createVariable(variable3d_arome[param]['name'],
'f4', ('level', 'Y', 'X'),
zlib=True)
vid.units = variable3d_arome[param]['units']
vid.description = variable3d_arome[param]['description']
expressiondata = f"dmap_arome3d.{param}[{tidx},:,::{xres},::{yres}]"
data = eval(expressiondata)
vid[:] = data
print(param2d_arome)
for param in param2d_arome:
vid = ncid.createVariable(variable2d_arome[param]['name'],
'f4', ('Y', 'X'),
zlib=True)
vid.units = variable2d_arome[param]['units']
vid.description = variable2d_arome[param]['description']
expressiondata = f"dmap_arome2d.{param}[{tidx},0,::{xres},::{yres}]"
data = eval(expressiondata)
if param == "surface_air_pressure":
print(param)
data = np.log(data)
vid[:] = data
for param in param2d_sfx:
vid = ncid.createVariable(variable2d_sfx[param]['name'],
'f4', ('Y', 'X'),
zlib=True)
vid.units = variable2d_sfx[param]['units']
vid.description = variable2d_sfx[param]['description']
expressiondata = f"dmap_sfx2d.{param}[{tidx},::{xres},::{yres}]"
data = eval(expressiondata)
vid[:] = data
ncid.close()
def Z500_VEL(datetime,
steps=0,
model="MEPS",
domain_name=None,
domain_lonlat=None,
legend=False,
info=False):
for dt in datetime: #modelrun at time..
date = dt[0:-2]
hour = int(dt[-2:])
param_sfc = [
"air_pressure_at_sea_level", "precipitation_amount_acc",
"surface_geopotential"
]
param_pl = ["x_wind_pl", "y_wind_pl", "geopotential_pl"]
param = param_sfc + param_pl
split = False
print("\n######## Checking if your request is possibel ############")
try:
check_all = check_data(date=dt,
model=model,
param=param,
p_level=500)
except ValueError:
split = True
try:
print(
"--------> Splitting up your request to find match ############"
)
check_sfc = check_data(date=dt, model=model, param=param_sfc)
check_pl = check_data(date=dt,
model=model,
param=param_pl,
p_level=500)
except ValueError:
print(
"!!!!! Sorry this plot is not availbale for this date. Try with another datetime !!!!!"
)
break
print("--------> Found match for your request ############")
if not split:
file_all = check_all.file.loc[0]
data_domain = domain_input_handler(dt, model, domain_name,
domain_lonlat, file_all)
#lonlat = np.array(data_domain.lonlat)
dmap_meps = get_data(model=model,
data_domain=data_domain,
param=param,
file=file_all,
step=steps,
date=dt,
p_level=500)
print("\n######## Retriving data ############")
print(f"--------> from: {dmap_meps.url} ")
dmap_meps.retrieve()
tmap_meps = dmap_meps # two names for same value, no copying done.
else:
# get sfc level data
file_sfc = check_sfc.file.loc[0]
data_domain = domain_input_handler(dt, model, domain_name,
domain_lonlat, file_sfc)
#lonlat = np.array(data_domain.lonlat)
dmap_meps = get_data(model=model,
param=param_sfc,
file=file_sfc,
step=steps,
date=dt,
data_domain=data_domain)
print("\n######## Retriving data ############")
print(f"--------> from: {dmap_meps.url} ")
dmap_meps.retrieve()
# get pressure level data
file_pl = check_pl.file
tmap_meps = get_data(model=model,
data_domain=data_domain,
param=param_pl,
file=file_pl,
step=steps,
date=dt,
p_level=500)
print("\n######## Retriving data ############")
print(f"--------> from: {tmap_meps.url} ")
tmap_meps.retrieve()
print("DONE RETRIEVE")
# convert fields
print("cond airpressure")
dmap_meps.air_pressure_at_sea_level /= 100
print("cond geopotential_pl")
tmap_meps.geopotential_pl /= 10.0
print("cond uv")
u, v = xwind2uwind(tmap_meps.x_wind_pl, tmap_meps.y_wind_pl,
tmap_meps.alpha)
print("cond vel")
vel = wind_speed(tmap_meps.x_wind_pl, tmap_meps.y_wind_pl)
print("map setup")
# plot map
fig1, ax1 = plt.subplots(figsize=(7, 9))
lonlat = [
dmap_meps.longitude[0, 0], dmap_meps.longitude[-1, -1],
dmap_meps.latitude[0, 0], dmap_meps.latitude[-1, -1]
]
lon0 = dmap_meps.longitude_of_central_meridian_projection_lambert
lat0 = dmap_meps.latitude_of_projection_origin_projection_lambert
print("map =map")
map = Basemap(llcrnrlon=lonlat[0],
llcrnrlat=lonlat[2],
urcrnrlon=lonlat[1],
urcrnrlat=lonlat[3],
resolution='i',
projection="lcc",
lon_0=lon0,
lat_0=lat0,
lat_1=lat0,
area_thresh=0.0001)
print("map x y")
x, y = map(dmap_meps.longitude, dmap_meps.latitude)
print("start for loop")
for tim in np.arange(np.min(steps), np.max(steps) + 1, 1):
tidx = tim - np.min(steps)
print('Plotting {0} + {1:02d} UTC'.format(dt, tim))
# gather, filter and squeeze variables for plotting
#plev1 = 3
plev2 = 0
ZS = dmap_meps.surface_geopotential[tidx, 0, :, :]
MSLP = np.where(ZS < 3000,
dmap_meps.air_pressure_at_sea_level[tidx, 0, :, :],
np.NaN).squeeze()
TP = precip_acc(dmap_meps.precipitation_amount_acc,
acc=1)[tidx, 0, :, :].squeeze()
VEL = (vel[tidx, plev2, :, :]).squeeze()
Z = (tmap_meps.geopotential_pl[tidx, plev2, :, :]).squeeze()
# velocity bars
#U = (u[tim, plev2, :, :]).squeeze()
#V = (v[tim, plev2, :, :]).squeeze()
# Rotation in basemap: I thought it was not needed when plotting in same proj as model.
# https://psysmon.mertl-research.at/sourcedoc/autogen/psysmon.packages.geometry.editGeometry.Basemap.rotate_vector.html
# https://www-k12.atmos.washington.edu/~ovens/wrfwinds.html
# Ue, Ve = map.rotate_vector(U, V, tmap_meps.longitude, tmap_meps.latitude) #so that shapes in plot is relative correct to shape in wind?
# clear subplot
#plt.cla()
cmap = plt.get_cmap(
"viridis"
) # PuBuGn PuBuGn, nipy_spectral twilight , plasma, gist_ncar viridis inferno ,,,rainbow
# lvl = [1, 2, 5, 10, 20, 50, 100, 200, 500, 1000, 2000, 5000]
lvl = [0.01, 0.2, 0.5, 1, 3, 5, 10, 15, 20, 25, 30]
norm = mcolors.BoundaryNorm(lvl, cmap.N)
try: # workaround for a stupid matplotlib error not handling when all values are outside of range in lvl or all just nans..
# https://github.com/SciTools/cartopy/issues/1290
CF_prec = ax1.contourf(x,
y,
TP,
zorder=10,
cmap=cmap,
norm=norm,
alpha=0.4,
antialiased=True,
levels=lvl,
extend="max") #
except:
pass
# MSLP with contour labels every 10 hPa
C_P = ax1.contour(x,
y,
MSLP,
zorder=1,
alpha=1.0,
levels=np.arange(
round(np.nanmin(MSLP), -1) - 10,
round(np.nanmax(MSLP), -1) + 10, 1),
colors='grey',
linewidths=0.5)
C_P = ax1.contour(x,
y,
MSLP,
zorder=2,
alpha=1.0,
levels=np.arange(
round(np.nanmin(MSLP), -1) - 10,
round(np.nanmax(MSLP), -1) + 10, 10),
colors='grey',
linewidths=1.0,
label="MSLP [hPa]")
ax1.clabel(C_P, C_P.levels, inline=True, fmt="%3.0f", fontsize=10)
#skip = (slice(None, None, 7), slice(None, None, 7))
#Cq = plt.barbs(x[skip][skip],y[skip][skip],Ue[skip][skip],Ve[skip][skip], zorder=1000, color = "r")
CS = ax1.contour(x,
y,
VEL,
zorder=3,
alpha=1.0,
levels=np.arange(-80, 80, 5),
colors="green",
linewidths=0.7)
# geopotential
CS = ax1.contour(x,
y,
Z,
zorder=3,
alpha=1.0,
levels=np.arange(4600, 5800, 20),
colors="blue",
linewidths=0.7)
ax1.clabel(CS, CS.levels, inline=True, fmt="%4.0f", fontsize=10)
map.drawcoastlines(linewidth=0.5, color='black', ax=ax1, zorder=5)
##########################################################
if legend:
proxy = [
plt.axhline(y=0, xmin=1, xmax=1, color="green"),
plt.axhline(y=0, xmin=1, xmax=1, color="blue")
]
plt.colorbar(CF_prec, fraction=0.046, pad=0.04)
lg = ax1.legend(proxy, [
f"Wind strength [m/s] at {dmap_meps.pressure[plev2]:.0f} hPa",
f"Geopotential [{tmap_meps.units_geopotential_pl}] at {dmap_meps.pressure[plev2]:.0f} hPa"
])
frame = lg.get_frame()
frame.set_facecolor('white')
frame.set_alpha(1)
if info:
plt.text(
x=0,
y=-1,
s=
"INFO: Reduced topographic noise by filtering with surface_geopotential bellow 3000",
fontsize=7) # , bbox=dict(facecolor='white', alpha=0.5))
#plt.show()
fig1.savefig(
"../../../output/{0}_Z500_VEL_P_{1}+{2:02d}.png".format(
model, dt, tim),
bbox_inches="tight",
dpi=200)
ax1.cla()
proxy = [
plt.axhline(y=0, xmin=1, xmax=1, color="green"),
plt.axhline(y=0, xmin=1, xmax=1, color="blue")
]
fig2 = plt.figure(figsize=(2, 1.25))
fig2, ax2 = plt.subplots()
fig2.legend(proxy, [
f"Wind strength [m/s] at {dmap_meps.pressure[plev2]:.0f} hPa",
f"Geopotential [{tmap_meps.units_geopotential_pl}]{dmap_meps.pressure[plev2]:.0f} hPa"
])
fig2.savefig("../../../output/{0}_Z500_VEL_P_LEGEND.png".format(model),
bbox_inches="tight",
dpi=200)
fig3, ax3 = plt.subplots()
fig3.colorbar(CF_prec, fraction=0.046, pad=0.04)
ax3.remove()
fig3.savefig(
"../../../output/{0}_Z500_VEL_P_COLORBAR.png".format(model),
bbox_inches="tight",
dpi=200)
plt.clf()
