distance = abs(tpvmod.rEarth * tpvmod.distance_on_unit_sphere(
event_latnow, event_lonnow, lats_tpvs_now[iii],
lons_tpvs_now[iii]))
distnow = distance / 1000.
if iii == 0:
distsave = distnow
dist_prev = distnow
else:
if distnow < dist_prev:
distsave = distnow
distarr.append(distsave)
#print(distarr)
distarr = np.array(distarr)
mstats(distarr)
del indnow
[indnow] = np.where(distarr < 100.)
N100 = len(indnow)
del indnow
[indnow] = np.where(distarr < 250.)
N250 = len(indnow)
del indnow
[indnow] = np.where(distarr < 500.)
N500 = len(indnow)
del indnow
[indnow] = np.where(distarr < 750.)
cint_trth = 1
cflevs_trth = np.arange(cbar_min_trth, cbar_max_trth, cint_trth)
#cflevs_trth_cntrs = cflevs_trth[0:12]
cflevs_trth_cntrs = cflevs_trth
cflevs_trth_ticks = np.arange(cbar_min_trth,cbar_max_trth,4*cint_trth)
cmap_opt = plt.cm.jet
#cmap_opt = plt.cm.Blues_r
trth = ndimage.gaussian_filter(trth,0.75)
trth = um.filter_numeric_nans(trth,trth_thresh+cint_trth,trth_thresh+cint_trth,'high')
plotvar = trth
mstats(plotvar)
#plotvar, lons = um.addcyclic(trth[tt,:,:].squeeze(), lonin)
#plotvar = ndimage.gaussian_filter(plotvar,0.75)
titletext1 = 'Tropopause potential temperature gradient %s at %s UTC' % (dt.strftime('%d %b %Y'), dt.strftime('%H00'))
if plot_field == 'trpr' :
cbarlabel = 'Pressure (hPa)'
cint_trth = 20
cbar_max_trth = 800.
cbar_min_trth = 300.
cflevs_trth = np.arange(cbar_min_trth, cbar_max_trth, cint_trth)
#cflevs_trth_cntrs = cflevs_trth[0:12]
cflevs_trth_cntrs = cflevs_trth
cbar_min_slp = -30 #1004-(20*cint_slp)
cbar_max_slp = 30 #1004+(20*cint_slp)
cflevs_slp = np.arange(cbar_min_slp, cbar_max_slp + (cint_slp / 2),
cint_slp)
#cflevs_slp = [-10,-9,-8,-7,-6,-5,-4,-3,-2,-1,1,2,3,4,5,6,7,8,9,10]
if (subtract_mean_standardize == True):
subtract_mean = False
if (diff == False):
infile = np.load(fLoad)
den_arr = infile['den_arr']
count_arr = infile['count_arr']
hit_arr = infile['hit_arr']
mstats(den_arr)
tpvs_peryear = hit_arr / (nyears_tpv[0])
if (plot_tpvsperyear == True):
den_arr = tpvs_peryear
if (subtract_mean == True):
den_arr = den_arr - np.nanmean(den_arr)
if (subtract_mean_standardize == True):
den_arr = (den_arr - np.nanmean(den_arr)) / np.std(den_arr)
else:
infile1 = np.load(fLoad)
infile2 = np.load(fLoad2)
den_arr1 = infile1['den_arr']
# How to read in a file and extract the years of analysis
###########################
ab = np.loadtxt(datadir+infile1, skiprows=1)
years_april = ab[:,0]
slp_april = ab[:,1]
inds = np.where( (years_april>=analysis_years[0]) & (years_april<=analysis_years[1]))
tmp = slp_april[inds]
del slp_april
slp_april = tmp
del tmp
years = years_april[inds]
# to check statistics, make sure mstats.m is in this directory and type:
mstats(slp_april)
###########################
# If you want to calculate a running mean, do something like this:
###########################
#new_array = np.zeros(len(slp_april)) # create a new array with all zeros
#new_array[0:nyears] = float('NaN') # change to zeros in the years where you can't compute a 5-y mean to NaNs
#for ii in range(nyears,len(new_array)):
# new_array[ii] = np.nanmean(slp_april[ii:ii+nyears+1])
###########################
# To calculate the long-term climatology, try something like:
###########################
#inds_climo = np.where( (years>=climo_years[0]) & (years<=climo_years[1]))
#myarray_climo = np.nanmean(myarray[inds_climo])
cmap_opt = plt.cm.RdBu_r
else:
cint = 3
cbar_min = -45
cbar_max = 45 + (cint / 2)
cbar_labels = 'meters'
titlestring = "Geopotential height " + date_string
cmap_opt = plt.cm.RdBu_r
figname = "erainterim_cross_section_analysis_ghgt_" + orient
elif plot_option == 10:
plot_cross = w_cross
mstats(plot_cross)
if plot_anomaly == 'true':
plot_cross = w_cross_anom
cint = 0.01
cbar_min = -0.1
cbar_max = 0.1 + (cint / 2)
cbar_labels = 'Pa s-1'
titlestring = "Vertical velocity anomaly " + date_string
cmap_opt = plt.cm.RdBu
if standardize_anomaly == 'true':
cint = 0.1
cbar_min = -2
cbar_max = 2 + (cint / 2)
cbar_labels = 'Standard deviations'
titlestring = "Standardized Eliassen-Palm flux divergence " + date_string
cmap_opt = plt.cm.RdBu
else:
cint = -0.01
cbar_min = -0.1
cbar_max = 0.1+(cint/2)
cbar_labels = ''
titlestring = "Eliassen-Palm flux divergence " + date_string
cmap_opt = plt.cm.RdBu
mstats(plot_cross)
elif plot_option == 9:
plot_cross = geop_cross / 9.81
if plot_anomaly == 'true':
plot_cross = geop_cross_anom / 9.81
cint = 50
cbar_min = -500
cbar_max = 500+(cint/2)
cbar_labels = 'meters'
titlestring = "Geopotential height anomaly " + date_string
cmap_opt = plt.cm.RdBu_r
if standardize_anomaly == 'true':
Lv = 2.5*10**(6.0) # J kg-1
###########################
# Read file, compute stuff
###########################
if read_temperature_fromfile == 'True':
ab = np.loadtxt(datadir+infile1, skiprows=0)
lats = ab[:,0]
T1 = ab[:,1]
[refind] = np.where(lats==reference_latitude)
print refind
else:
T1 = np.arange(-40,41,0.25)+273.15 # Virtual temperatures
[refind] = np.where(T1 == 273.15)
# Check to make sure it read in correctly
mstats(T1)
es1 = wm.claus_clap(T1) # saturation vapor pressure
ws1 = (epsi*es1)/(pres1-es1) # saturation mixing ratio
# Compute moist adiabatic lapse rate, first in z-coords
sat_lapse = (9.81/cp)*(( (1 + (Lv*ws1)/(Rd*T1))) / (1 + (ws1*(Lv**2)/(cp*Rv*T1**2.0)) ) )
Rho=pres1/(Rv*T1) # Density, moist air
sat_lapse_isobaric = -sat_lapse/(Rho*9.81) # Convert to p-coords
# Temperature at top of ascent layer (T2) is equal to what it was initially (T1) plus slope (-gamma_m) * deltap
T2 = T1 - (sat_lapse_isobaric)*(pres2-pres1)
# Convert to Celsius (needed for plot if not reading from file)
T1C = T1 - 273.15
T2C = T2 - 273.15
cmap_opt = plt.cm.RdBu_r
else:
cint = 3
cbar_min = -45
cbar_max = 45+(cint/2)
cbar_labels = 'meters'
titlestring = "Geopotential height " + date_string
cmap_opt = plt.cm.RdBu_r
figname = "erainterim_cross_section_analysis_ghgt_" + orient
elif plot_option == 10:
plot_cross = w_cross
mstats(plot_cross)
if plot_anomaly == 'true':
plot_cross = w_cross_anom
cint = 0.01
cbar_min = -0.1
cbar_max = 0.1+(cint/2)
cbar_labels = 'Pa s-1'
titlestring = "Vertical velocity anomaly " + date_string
cmap_opt = plt.cm.RdBu
if standardize_anomaly == 'true':
cint = 0.1
cbar_min = -2
cbar_max = 2 + (cint/2)
x] <= 2: # only getting tpv tracks for winter months
plotvar_summer.append(np.median(
data.variables[str(varname)][x, :]))
elif trackStartMon[x] > 5 and trackStartMon[x] < 9:
plotvar_winter.append(np.median(data.variables[str(varname)][
x, :])) # only getting tpv tracks for summer months
else:
continue
data.close()
plotvar = np.array(plotvar)
plotvar_winter = np.array(plotvar_winter)
plotvar_summer = np.array(plotvar_summer)
mstats(plotvar)
mstats(plotvar_winter)
mstats(plotvar_summer)
############################
# Plots
############################
plotvar_cntl = plotvar
plotvar_exp1 = plotvar_winter
plotvar_exp2 = plotvar_summer
golden = (pylab.sqrt(5) + 1.) / 2.
figprops = dict(figsize=(8., 8. / golden), dpi=128)
adjustprops = dict(left=0.15,
bottom=0.1,
right=0.90,
cint_trth = 1
cflevs_trth = np.arange(cbar_min_trth, cbar_max_trth, cint_trth)
#cflevs_trth_cntrs = cflevs_trth[0:12]
cflevs_trth_cntrs = cflevs_trth
cflevs_trth_ticks = np.arange(cbar_min_trth, cbar_max_trth,
4 * cint_trth)
cmap_opt = plt.cm.jet
#cmap_opt = plt.cm.Blues_r
trth = ndimage.gaussian_filter(trth, 0.75)
trth = um.filter_numeric_nans(trth, trth_thresh + cint_trth,
trth_thresh + cint_trth, 'high')
plotvar = trth
mstats(plotvar)
#plotvar, lons = um.addcyclic(trth[tt,:,:].squeeze(), lonin)
#plotvar = ndimage.gaussian_filter(plotvar,0.75)
titletext1 = 'Tropopause potential temperature gradient %s at %s UTC' % (
dt.strftime('%d %b %Y'), dt.strftime('%H00'))
if plot_field == 'trpr':
cbarlabel = 'Pressure (hPa)'
cint_trth = 20
cbar_max_trth = 800.
cbar_min_trth = 300.
cflevs_trth = np.arange(cbar_min_trth, cbar_max_trth, cint_trth)
#cflevs_trth_cntrs = cflevs_trth[0:12]