def mk_rean_sam_index(datapath='./'):
# list in the pre-defined list of files to use.
rean = ['R1', 'R2', '20CR', 'ERA-Int', 'CFSR', 'MERRA', 'HadSLP2r']
rean2 = ['R1', 'R2', '20CR', 'ERA', 'CFSR', 'MERRA', 'HadSLP2r']
tail = '_slp.mon.mean.nc'
names = [ 'zonal-mean_remap_' + r + tail for r in rean ]
df_sam = pd.DataFrame()
for i, name in enumerate(names):
print name
# load the data and make dataframes
ifile = os.path.join(datapath, name)
dims = cd.get_dimensions(ifile, 'slp', toDatetime=True)
dims['time'] = [pd.datetime(d.year, d.month, 1) for d in dims['time']]
sami = calc_sam(ifile, 'slp')
samdf = pd.DataFrame(sami, index=dims['time'])
df_sam = pd.concat([df_sam, samdf], axis=1)
df_sam.columns = rean2
# Store the DataFrame in HDF5
out_file = os.path.join(datapath, 'zonmean_sam-jet_analysis_reanalysis.h5')
store = pd.HDFStore(out_file, 'a')
store['zonmean_sam'] = df_sam
store.close()
def mk_rean_sam_index(datapath='./'):
# list in the pre-defined list of files to use.
rean = ['R1', 'R2', '20CR', 'ERA-Int', 'CFSR', 'MERRA', 'HadSLP2r']
rean2 = ['R1', 'R2', '20CR', 'ERA', 'CFSR', 'MERRA', 'HadSLP2r']
tail = '_slp.mon.mean.nc'
names = ['zonal-mean_remap_' + r + tail for r in rean]
df_sam = pd.DataFrame()
for i, name in enumerate(names):
print name
# load the data and make dataframes
ifile = os.path.join(datapath, name)
dims = cd.get_dimensions(ifile, 'slp', toDatetime=True)
dims['time'] = [pd.datetime(d.year, d.month, 1) for d in dims['time']]
sami = calc_sam(ifile, 'slp')
samdf = pd.DataFrame(sami, index=dims['time'])
df_sam = pd.concat([df_sam, samdf], axis=1)
df_sam.columns = rean2
# Store the DataFrame in HDF5
out_file = os.path.join(datapath, 'zonmean_sam-jet_analysis_reanalysis.h5')
store = pd.HDFStore(out_file, 'a')
store['zonmean_sam'] = df_sam
store.close()
def calc_marshall_sam(psl_file, varname, start_date='1871-01-01', end_date='2013-12-31'):
"""
Compute the SAM index as the pressure difference between 40 and 65S only
using data from the 12 Marshall (2003) locations.
Parameters:
-----------
psl_file : str
The name of the **zonal meaned** SLP netcdf file to compute the SAM
from. Can be a full path.
varname : str
The name of the Sea Level Pressure variable in psl_file.
Returns:
-------
dft : pandas DataFrame
The calculated SAM index, presure at 40 and 65S.
"""
# Marshall locations
mlat40s = np.array([46.9, 37.8, 42.9, 43.5, 39.6, 40.4])*-1
mlon40s = np.array([37.9, 77.5, 147.3, 172.6, -73.1, -9.9])
mlat65s = np.array([70.8, 67.6, 66.6, 66.3, 66.7, 65.2])*-1
mlon65s = np.array([11.8, 62.9, 93.0, 110.5, 140.0, -64.3])
# load the data
dims = cd.get_dimensions(psl_file, varname, toDatetime=True)
#dims['time'] = [pd.datetime(d.year, d.month, 1) for d in dims['time']]
tmin = dims['time'].min()
ds = pd.datetime(tmin.year, tmin.month, 1)
dates = pd.date_range(ds, periods=(len(dims['time']))
, freq='MS')
p40 = np.zeros((len(dims['time']), 6))
p65 = np.zeros((len(dims['time']), 6))
ncvar = Dataset(psl_file).variables[varname]
for k in range(6):
# loop over the six stations at each lat and get data at each one.
var = cdo.remapnn('lon=' + str( mlon40s[k] ) + '/lat='\
+ str( mlat40s[k] )
, input=('-selvar,' + varname + ' ' + psl_file)
, returnMaArray=varname).squeeze()
p40[:,k] = scale(ncvar, var)
var2 = cdo.remapnn('lon=' + str( mlon65s[k] ) + '/lat='\
+ str( mlat65s[k] )
, input=('-selvar,' + varname + ' ' + psl_file)
, returnMaArray=varname).squeeze()
p65[:,k] = scale(ncvar, var2)
# Now create the mean pressure at 40S and 65S and return
s40s = pd.Series(p40.mean(axis=1), index=dates)
s65s = pd.Series(p65.mean(axis=1), index=dates)
return s40s, s65s
def mk_20cr_ens_jetprop(datapath='../data_retrieval/data/'):
"""Calculated the kinematic properties of the jet for the 20CR ensemble
"""
#The pre-computed zonal mean u10m file
zmfn = os.path.join(datapath, 'zonal-mean_remap_20CR_ens_u10m.mon.mean.nc')
# load the data and make dataframes
dims = cd.get_dimensions(zmfn, 'u10m', toDatetime=True)
nc = Dataset(zmfn)
u_20cr = nc.variables['u10m'][:].squeeze()
lat = dims['lat']
# initialize some empty arrays for the jet props.
width = np.zeros((len(dims['time']), 56))
umax = np.zeros((len(dims['time']), 56))
uloc = np.zeros((len(dims['time']), 56))
# Calculate properties for each ensemble member
for i in np.arange(56):
uas = np.squeeze(u_20cr[:,i,:])
jetmax, latofmax, latn, lats, jetwidth = jetprop(uas, lat)
umax[:,i] = jetmax
uloc[:,i] = latofmax
width[:,i] = jetwidth
# make a plot at a random time index ri, to see that the defs are working.
#ri = 10
#plt.close()
#plt.plot( lat, uas[ri,:], 'k-o', linewidth=2)
#plt.plot( lat, lat*0, 'k--')
#plt.plot( latofmax[ri], jetmax[ri], 'rx', markersize=8, markeredgewidth=1)
#plt.plot( [-90, 90], [ jetmax[ri],jetmax[ri] ], 'r--')
#plt.plot( [-90, 90], [ jetmax[ri]*0.5, jetmax[ri]*0.5], 'r--')
#print latn[ri], lats[ri]
#plt.plot( [latn[ri], latn[ri]], [-10, 10], 'r--')
#plt.plot( [lats[ri], lats[ri]], [-10, 10], 'r--')
#raw_input('go?')
# Create Pandas DataFrames from the arrays
df_umax = pd.DataFrame(umax, index=dims['time'], columns=np.arange(1,57))
df_uloc = pd.DataFrame(uloc, index=dims['time'], columns=np.arange(1,57))
df_width = pd.DataFrame(width, index=dims['time'], columns=np.arange(1,57))
# Store the DataFrame in HDF5
out_file = os.path.join(datapath, 'zonmean_sam-jet_analysis_20CR_ensemble.h5')
store = pd.HDFStore(out_file, 'a')
store['width'] = df_width
store['maxspd'] = df_umax
store['locmax'] = df_uloc
store.close()
def mk_cmip5_jetprop(datapath='./'):
# List of models to get data for
model_list = [
'ACCESS1-0', 'ACCESS1-3', 'BNU-ESM', 'CMCC-CMS', 'CMCC-CM', 'CNRM-CM5',
'CSIRO-Mk3-6-0', 'CanESM2', 'GISS-E2-H-CC', 'GISS-E2-H',
'GISS-E2-R-CC', 'GISS-E2-R', 'HadCM3', 'HadGEM2-AO', 'HadGEM2-CC',
'HadGEM2-ES', 'IPSL-CM5A-LR', 'IPSL-CM5A-MR', 'IPSL-CM5B-LR',
'MIROC-ESM-CHEM', 'MIROC-ESM', 'MIROC5', 'MPI-ESM-LR', 'MPI-ESM-MR',
'MRI-CGCM3', 'NorESM1-ME', 'NorESM1-M', 'bcc-csm1-1-m', 'bcc-csm1-1',
'inmcm4'
]
tail = '_historical-rcp45_r1i1p1_188101-201212.nc'
names = ['zonal-mean_remap_uas_Amon_' + m + tail for m in model_list]
# Initialize some arrays
width = np.zeros((1584, 30))
umax = np.zeros((1584, 30))
uloc = np.zeros((1584, 30))
for i, name in enumerate(names):
# define and load the file dimensions and uas data
ifile = os.path.join(datapath, name)
print ifile
dims = cd.get_dimensions(ifile, 'uas', toDatetime=True)
nc = Dataset(ifile)
uas = nc.variables['uas'][:].squeeze()
lat = dims['lat']
# compute jet props
jetmax, latofmax, latn, lats, jetwidth = jetprop(uas, lat)
umax[:, i] = jetmax
uloc[:, i] = latofmax
width[:, i] = jetwidth
# Assign to pd DataFrames
df_umax = pd.DataFrame(umax, index=dims['time'], columns=np.arange(1, 31))
df_uloc = pd.DataFrame(uloc, index=dims['time'], columns=np.arange(1, 31))
df_width = pd.DataFrame(width,
index=dims['time'],
columns=np.arange(1, 31))
# Store the DataFrame in HDF5
out_file = os.path.join(datapath, 'zonmean_sam-jet_analysis_cmip5.h5')
store = pd.HDFStore(out_file, 'a')
store['width'] = df_width
store['maxspd'] = df_umax
store['locmax'] = df_uloc
store.close()
def mk_cmip5_jetprop(datapath='./'):
# List of models to get data for
model_list = ['ACCESS1-0', 'ACCESS1-3', 'BNU-ESM', 'CMCC-CMS', 'CMCC-CM',
'CNRM-CM5', 'CSIRO-Mk3-6-0', 'CanESM2',
'GISS-E2-H-CC', 'GISS-E2-H', 'GISS-E2-R-CC', 'GISS-E2-R',
'HadCM3', 'HadGEM2-AO', 'HadGEM2-CC', 'HadGEM2-ES',
'IPSL-CM5A-LR', 'IPSL-CM5A-MR', 'IPSL-CM5B-LR', 'MIROC-ESM-CHEM',
'MIROC-ESM', 'MIROC5', 'MPI-ESM-LR','MPI-ESM-MR','MRI-CGCM3',
'NorESM1-ME', 'NorESM1-M', 'bcc-csm1-1-m', 'bcc-csm1-1','inmcm4'
]
tail = '_historical-rcp45_r1i1p1_188101-201212.nc'
names = [ 'zonal-mean_remap_uas_Amon_' + m + tail for m in model_list ]
# Initialize some arrays
width = np.zeros((1584, 30))
umax = np.zeros((1584, 30))
uloc = np.zeros((1584, 30))
for i, name in enumerate(names):
# define and load the file dimensions and uas data
ifile = os.path.join(datapath, name)
print ifile
dims = cd.get_dimensions(ifile, 'uas', toDatetime=True)
nc = Dataset(ifile)
uas = nc.variables['uas'][:].squeeze()
lat = dims['lat']
# compute jet props
jetmax, latofmax, latn, lats, jetwidth = jetprop(uas, lat)
umax[:,i] = jetmax
uloc[:,i] = latofmax
width[:,i] = jetwidth
# Assign to pd DataFrames
df_umax = pd.DataFrame(umax, index=dims['time'], columns=np.arange(1,31))
df_uloc = pd.DataFrame(uloc, index=dims['time'], columns=np.arange(1,31))
df_width = pd.DataFrame(width, index=dims['time'], columns=np.arange(1,31))
# Store the DataFrame in HDF5
out_file = os.path.join(datapath, 'zonmean_sam-jet_analysis_cmip5.h5')
store = pd.HDFStore(out_file, 'a')
store['width'] = df_width
store['maxspd'] = df_umax
store['locmax'] = df_uloc
store.close()
def mk_20cr_ens_sam_index(datapath='.'):
"""Calculates the SAM index for the 20CR ensemble
"""
#The pre-computed zonal mean SLP file
zmfn = os.path.join(datapath, 'zonal-mean_remap_20CR_ens_slp.mon.mean.nc')
# load the data and make dataframes
dims = cd.get_dimensions(zmfn, 'slp', toDatetime=True)
sam_20cr = calc_sam(zmfn, 'slp', start_date='1871-01-01',
end_date='2013-12-31')
df_sam = pd.DataFrame(sam_20cr, index=dims['time'])
# Store the DataFrame in HDF5
out_file = os.path.join(datapath, 'zonmean_sam-jet_analysis_20CR_ensemble.h5')
store = pd.HDFStore(out_file, 'a')
store['zonmean_sam'] = df_sam
store.close()
def mk_20cr_ens_sam_index(datapath='.'):
"""Calculates the SAM index for the 20CR ensemble
"""
#The pre-computed zonal mean SLP file
zmfn = os.path.join(datapath, 'zonal-mean_remap_20CR_ens_slp.mon.mean.nc')
# load the data and make dataframes
dims = cd.get_dimensions(zmfn, 'slp', toDatetime=True)
sam_20cr = calc_sam(zmfn,
'slp',
start_date='1871-01-01',
end_date='2013-12-31')
df_sam = pd.DataFrame(sam_20cr, index=dims['time'])
# Store the DataFrame in HDF5
out_file = os.path.join(datapath,
'zonmean_sam-jet_analysis_20CR_ensemble.h5')
store = pd.HDFStore(out_file, 'a')
store['zonmean_sam'] = df_sam
store.close()
def mk_cmip5_sam_index(datapath='./'):
# List of models to get data for
model_list = ['ACCESS1-0', 'ACCESS1-3', 'BNU-ESM', 'CMCC-CMS', 'CMCC-CM',
'CNRM-CM5', 'CSIRO-Mk3-6-0', 'CanESM2',
'GISS-E2-H-CC', 'GISS-E2-H', 'GISS-E2-R-CC', 'GISS-E2-R',
'HadCM3', 'HadGEM2-AO', 'HadGEM2-CC', 'HadGEM2-ES',
'IPSL-CM5A-LR', 'IPSL-CM5A-MR', 'IPSL-CM5B-LR', 'MIROC-ESM-CHEM',
'MIROC-ESM', 'MIROC5', 'MPI-ESM-LR','MPI-ESM-MR','MRI-CGCM3',
'NorESM1-ME', 'NorESM1-M', 'bcc-csm1-1-m', 'bcc-csm1-1','inmcm4'
]
tail = '_historical-rcp45_r1i1p1_188101-201212.nc'
names = [ 'zonal-mean_remap_psl_Amon_' + m + tail for m in model_list ]
cnames = [ n.replace('-', '_') for n in model_list ]
sam = np.zeros((1584, 30))
for i, name in enumerate(names):
ifile = os.path.join(datapath, name)
# load the data and make dataframes
sam[:,i] = calc_sam(ifile, 'psl', start_date='1881-01-01',
end_date='2013-12-31')
# Get the time axis
dims = cd.get_dimensions(ifile, 'psl', toDatetime=True)
t = dims['time']
a = ( t <= pd.datetime(2013,12,31) ) & ( t >= pd.datetime(1881,1,1) )
dims['time'] = t[a]
df_sam = pd.DataFrame(sam, index=dims['time'], columns=cnames)
# Store the DataFrame in HDF5
out_file = os.path.join(datapath, 'zonmean_sam-jet_analysis_cmip5.h5')
store = pd.HDFStore(out_file, 'a')
store['zonmean_sam'] = df_sam
store.close()
def plot_trend_maps(datapath):
dims = {'lat' : np.arange(-89.5,89.6,1),
'lon' : np.arange(0,360,1)
}
# load the data for all 20CR ense members
ifile_20CR_slopes_all = datapath + 'slope_remap_20CR_ens_slp.mon.mean.nc'
nc = Dataset(ifile_20CR_slopes_all)
psl_slopes_all_20cr = nc.variables['slp'][:].squeeze()*120
dims20cr = cd.get_dimensions(ifile_20CR_slopes_all, 'slp')
ifile_20CR_slopes_all_u10m = datapath + 'slope_remap_20CR_ens_u10m.mon.mean.nc'
nc = Dataset(ifile_20CR_slopes_all_u10m)
u10m_slopes_all_20cr = nc.variables['u10m'][:].squeeze()*120
dims20cr2 = cd.get_dimensions(ifile_20CR_slopes_all_u10m, 'u10m')
# Look at the uncertainty in the trends
fig, (axt, axm, axb) = plt.subplots(3,1, sharex=True, figsize=(7,7))
fig.subplots_adjust(right=0.75, top=0.8, hspace=0.2)
# For SLP
vmin = 0
vmax = 20
ncols = 9
# For u10m
vmin2 = 0
vmax2 = 0.1*100
cmap = brewer2mpl.get_map('Reds', 'sequential', ncols,
reverse=False).mpl_colormap
cmap = discrete_cmap(ncols, cmap)
m =\
Basemap(llcrnrlon=0,llcrnrlat=-90,urcrnrlon=360,urcrnrlat=0,projection='mill',
suppress_ticks=True)
lons, lats = np.meshgrid(dims20cr['lon'], dims20cr['lat'])
lons2, lats2 = np.meshgrid(dims20cr2['lon'], dims20cr2['lat'])
x, y = m(lons, lats)
x2, y2 = m(lons2, lats2)
xpt, ypt = m(20,-86)
cot = m.pcolor(x, y, psl_slopes_all_20cr.std(axis=0)*2,vmin=vmin, vmax=vmax,
cmap=cmap, ax=axt, rasterized=True, zorder=0)
#CS = m.contour(x, y, psl_slopes_all_20cr.std(axis=0)*2,np.arange(0,20,2),
#ax=axt, colors='k', zorder=0)
#plt.clabel(CS, CS.levels, inline=True, fmt='%d', fontsize=10, zorder=1)
axt.text(xpt, ypt, r'20CR SLP trend $2\sigma$', zorder=5)
com = m.pcolor(x2, y2, u10m_slopes_all_20cr.std(axis=0)*200,vmin=vmin2,
vmax=vmax2, cmap=cmap, ax=axm, rasterized=True, zorder=0)
axm.text(xpt, ypt, r'20CR u10m trend $2\sigma$', zorder=5)
#CS2 = m.contour(x2, y2, u10m_slopes_all_20cr.std(axis=0)*2,np.arange(0,0.1,0.01),
#ax=axm, colors='k', zorder=0)
#plt.clabel(CS2, CS2.levels, inline=True, fmt='%1.2f', fontsize=10, zorder=1)
xtp = np.arange(0,360,60)
m.drawmeridians(xtp,labels=[0,0,0,1], linewidth=0,yoffset=-0.4e6
, ax=axm)
xt, yt = m(np.arange(0,360,60), np.repeat(-90,6))
axm.set_xticks(xt)
for ax in fig.axes:
ax.autoscale(enable=True, axis='both', tight=True)
m.drawcoastlines(linewidth=1.25, ax=ax)
m.fillcontinents(color='0.8',ax=ax, zorder=4)
m.drawparallels(np.arange(-80,81,20),labels=[1,0,0,0], linewidth=0, ax=ax)
for k, spine in ax.spines.items():
spine.set_zorder(10)
box = axt.get_position()
tl = fig.add_axes([box.x0*1.1 + box.width * 1., box.y0, 0.02, box.height])
bounds = np.linspace(vmin, vmax, ncols)
plt.colorbar(cot, cax=tl, label='Pa decade$^{-1}$',spacing='proportional',
boundaries=bounds)
box = axm.get_position()
tl = fig.add_axes([box.x0*1.1 + box.width * 1., box.y0, 0.02, box.height])
bounds = np.linspace(vmin2, vmax2, ncols)
plt.colorbar(com, cax=tl, label=r'$\times 10^{-2}$ m s$^{-1}$ decade$^{-1}$',
spacing='proportional', boundaries=bounds)
#fig.delaxes(axm)
fig.delaxes(axb)
plt.savefig('../plots/psl_maps_20CR_trends-uncertainty_1951-2011.pdf',
bbox_inches='tight', dpi=300)
def mk_rean_jetprop(datapath='./'):
# list in the pre-defined list of files to use.
rean = ['R1', 'R2', '20CR', 'ERA-Int', 'CFSR', 'MERRA']
rean2 = ['R1', 'R2', '20CR', 'ERA', 'CFSR', 'MERRA']
tail = '_u10m.mon.mean.nc'
names = [ 'zonal-mean_remap_' + r + tail for r in rean ]
# Initialize some jetprop arrays
df_umax = pd.DataFrame()
df_uloc = pd.DataFrame()
df_width = pd.DataFrame()
# Loop over the reanalyses
for i, name in enumerate(names):
# define the input
ifile = os.path.join(datapath, name)
print ifile
# Get the dimensions
dims = cd.get_dimensions(ifile, 'u10m', toDatetime=True)
dims['time'] = [pd.datetime(d.year, d.month, 1) for d in dims['time']]
# load the data
nc = Dataset(ifile)
u10m = nc.variables['u10m'][:].squeeze()
lat = dims['lat']
# Compute the properties
jetmax, latofmax, latn, lats, jetwidth = jetprop(u10m, lat)
# Assign to pd DataFrames
df = pd.DataFrame(jetmax, index=dims['time'])
df_umax = pd.concat([df_umax, df], axis=1)
df = pd.DataFrame(latofmax, index=dims['time'])
df_uloc = pd.concat([df_uloc, df], axis=1)
df = pd.DataFrame(jetwidth, index=dims['time'])
df_width = pd.concat([df_width, df], axis=1)
# Test plots to make sure props were calculated right
#ri = 49
#plt.close()
#plt.plot( lat, uwnd[ri,:], 'k-o', linewidth=2)
#plt.plot( lat, lat*0, 'k--')
#plt.plot( latofmax[ri], jetmax[ri], 'rx', markersize=8, markeredgewidth=1)
#plt.plot( [-90, 90], [ jetmax[ri],jetmax[ri] ], 'r--')
#plt.plot( [-90, 90], [ jetmax[ri]*0.5, jetmax[ri]*0.5], 'r--')
##print latn[ri], lats[ri]
#plt.plot( [latn[ri], latn[ri]], [-10, 10], 'r--')
#plt.plot( [lats[ri], lats[ri]], [-10, 10], 'r--')
#raw_input('go?')
df_umax.columns = rean2
df_uloc.columns = rean2
df_width.columns = rean2
# Store the DataFrame in HDF5
out_file = os.path.join(datapath, 'zonmean_sam-jet_analysis_reanalysis.h5')
store = pd.HDFStore(out_file, 'a')
store['width'] = df_width
store['maxspd'] = df_umax
store['locmax'] = df_uloc
store.close()
def mk_rean_jetprop(datapath='./'):
# list in the pre-defined list of files to use.
rean = ['R1', 'R2', '20CR', 'ERA-Int', 'CFSR', 'MERRA']
rean2 = ['R1', 'R2', '20CR', 'ERA', 'CFSR', 'MERRA']
tail = '_u10m.mon.mean.nc'
names = ['zonal-mean_remap_' + r + tail for r in rean]
# Initialize some jetprop arrays
df_umax = pd.DataFrame()
df_uloc = pd.DataFrame()
df_width = pd.DataFrame()
# Loop over the reanalyses
for i, name in enumerate(names):
# define the input
ifile = os.path.join(datapath, name)
print ifile
# Get the dimensions
dims = cd.get_dimensions(ifile, 'u10m', toDatetime=True)
dims['time'] = [pd.datetime(d.year, d.month, 1) for d in dims['time']]
# load the data
nc = Dataset(ifile)
u10m = nc.variables['u10m'][:].squeeze()
lat = dims['lat']
# Compute the properties
jetmax, latofmax, latn, lats, jetwidth = jetprop(u10m, lat)
# Assign to pd DataFrames
df = pd.DataFrame(jetmax, index=dims['time'])
df_umax = pd.concat([df_umax, df], axis=1)
df = pd.DataFrame(latofmax, index=dims['time'])
df_uloc = pd.concat([df_uloc, df], axis=1)
df = pd.DataFrame(jetwidth, index=dims['time'])
df_width = pd.concat([df_width, df], axis=1)
# Test plots to make sure props were calculated right
#ri = 49
#plt.close()
#plt.plot( lat, uwnd[ri,:], 'k-o', linewidth=2)
#plt.plot( lat, lat*0, 'k--')
#plt.plot( latofmax[ri], jetmax[ri], 'rx', markersize=8, markeredgewidth=1)
#plt.plot( [-90, 90], [ jetmax[ri],jetmax[ri] ], 'r--')
#plt.plot( [-90, 90], [ jetmax[ri]*0.5, jetmax[ri]*0.5], 'r--')
##print latn[ri], lats[ri]
#plt.plot( [latn[ri], latn[ri]], [-10, 10], 'r--')
#plt.plot( [lats[ri], lats[ri]], [-10, 10], 'r--')
#raw_input('go?')
df_umax.columns = rean2
df_uloc.columns = rean2
df_width.columns = rean2
# Store the DataFrame in HDF5
out_file = os.path.join(datapath, 'zonmean_sam-jet_analysis_reanalysis.h5')
store = pd.HDFStore(out_file, 'a')
store['width'] = df_width
store['maxspd'] = df_umax
store['locmax'] = df_uloc
store.close()