Interpolating GFS winds from isobaric to height coordinates using Metpy

Question

I have been tasked with making plots of winds at various levels of the atmosphere to support aviation. While I have been able to make some nice plots using GFS model data (see code below), I'm really having to make a rough approximation of height using pressure coordinates available from the GFS. I'm using winds at 300 hPA, 700 hPA, and 925 hPA to make an approximation of the winds at 30,000 ft, 9000 ft, and 3000 ft. My question is really for those out there who are metpy gurus...is there a way that I can interpolate these winds to a height surface? It sure would be nice to get the actual winds at these height levels! Thanks for any light anyone can share on this subject!

import cartopy.crs as ccrs
import cartopy.feature as cfeature
import matplotlib.pyplot as plt
from matplotlib.colors import ListedColormap
import numpy as np
from netCDF4 import num2date
from datetime import datetime, timedelta
from siphon.catalog import TDSCatalog
from siphon.ncss import NCSS
from PIL import Image
from matplotlib import cm


# For the vertical levels we want to grab with our queries
# Levels need to be in Pa not hPa
Levels = [30000,70000,92500]
# Time deltas for days
Deltas = [1,2,3]
#Deltas = [1]
# Levels in hPa for the file names
LevelDict = {30000:'300', 70000:'700', 92500:'925'}
# The path to where our banners are stored 
impath = 'C:\\Users\\shell\\Documents\\Python Scripts\\Banners\\'
# Final images saved here
imoutpath = 'C:\\Users\\shell\\Documents\\Python Scripts\\TVImages\\'

# Quick function for finding out which variable is the time variable in the
# netCDF files
def find_time_var(var, time_basename='time'):
    for coord_name in var.coordinates.split():
        if coord_name.startswith(time_basename):
            return coord_name
    raise ValueError('No time variable found for ' + var.name)

# Function to grab data at different levels from Siphon 
def grabData(level):

    query.var = set()
    query.variables('u-component_of_wind_isobaric', 'v-component_of_wind_isobaric')
    query.vertical_level(level)
    data = ncss.get_data(query)
    u_wind_var = data.variables['u-component_of_wind_isobaric']
    v_wind_var = data.variables['v-component_of_wind_isobaric']
    time_var = data.variables[find_time_var(u_wind_var)]
    lat_var = data.variables['lat']
    lon_var = data.variables['lon']

    return u_wind_var, v_wind_var, time_var, lat_var, lon_var

# Construct a TDSCatalog instance pointing to the gfs dataset
best_gfs = TDSCatalog('http://thredds-jetstream.unidata.ucar.edu/thredds/catalog/grib/'
                      'NCEP/GFS/Global_0p5deg/catalog.xml')

# Pull out the dataset you want to use and look at the access URLs
best_ds = list(best_gfs.datasets.values())[1]
#print(best_ds.access_urls)

# Create NCSS object to access the NetcdfSubset
ncss = NCSS(best_ds.access_urls['NetcdfSubset'])
print(best_ds.access_urls['NetcdfSubset'])

# Looping through the forecast times

for delta in Deltas:
    # Create lat/lon box and the time(s) for location you want to get data for
    now = datetime.utcnow()
    fcst = now + timedelta(days = delta)
    timestamp = datetime.strftime(fcst, '%A')
    query = ncss.query()
    query.lonlat_box(north=78, south=45, east=-90, west=-220).time(fcst)
    query.accept('netcdf4')


    # Now looping through the levels to create our plots

    for level in Levels:
        u_wind_var, v_wind_var, time_var, lat_var, lon_var = grabData(level)
        # Get actual data values and remove any size 1 dimensions
        lat = lat_var[:].squeeze()
        lon = lon_var[:].squeeze()
        u_wind = u_wind_var[:].squeeze()
        v_wind = v_wind_var[:].squeeze()
        #converting to knots
        u_windkt= u_wind*1.94384
        v_windkt= v_wind*1.94384
        wspd = np.sqrt(np.power(u_windkt,2)+np.power(v_windkt,2))

        # Convert number of hours since the reference time into an actual date
        time = num2date(time_var[:].squeeze(), time_var.units)
        print (time)
        # Combine 1D latitude and longitudes into a 2D grid of locations
        lon_2d, lat_2d = np.meshgrid(lon, lat)

        # Create new figure
        #fig = plt.figure(figsize = (18,12))
        fig = plt.figure()
        fig.set_size_inches(26.67,15)
        datacrs = ccrs.PlateCarree()
        plotcrs = ccrs.LambertConformal(central_longitude=-150,
                                       central_latitude=55,
                                       standard_parallels=(30, 60))

        # Add the map and set the extent
        ax = plt.axes(projection=plotcrs)
        ext = ax.set_extent([-195., -115., 50., 72.],datacrs)
        ext2 = ax.set_aspect('auto')
        ax.background_patch.set_fill(False)

        # Add state boundaries to plot
        ax.add_feature(cfeature.STATES, edgecolor='black', linewidth=2)

        # Add geopolitical boundaries for map reference
        ax.add_feature(cfeature.COASTLINE.with_scale('50m'))
        ax.add_feature(cfeature.OCEAN.with_scale('50m'))
        ax.add_feature(cfeature.LAND.with_scale('50m'),facecolor = '#cc9666', linewidth = 4)

        if level == 30000:
            spdrng_sped = np.arange(30, 190, 2)
            windlvl = 'Jet_Stream'
        elif level == 70000:
            spdrng_sped = np.arange(20, 100, 1)
            windlvl = '9000_Winds_Aloft'
        elif level == 92500:
            spdrng_sped = np.arange(20, 80, 1)
            windlvl = '3000_Winds_Aloft'
        else:
            pass


        top = cm.get_cmap('Greens')
        middle = cm.get_cmap('YlOrRd')
        bottom = cm.get_cmap('BuPu_r')
        newcolors = np.vstack((top(np.linspace(0, 1, 128)),
                       middle(np.linspace(0, 1, 128))))
        newcolors2 = np.vstack((newcolors,bottom(np.linspace(0,1,128))))

        cmap = ListedColormap(newcolors2)
        cf = ax.contourf(lon_2d, lat_2d, wspd, spdrng_sped, cmap=cmap,
                         transform=datacrs, extend = 'max', alpha=0.75)

        cbar = plt.colorbar(cf, orientation='horizontal', pad=0, aspect=50,
                            drawedges = 'true')
        cbar.ax.tick_params(labelsize=16)
        wslice = slice(1, None, 4)

        ax.quiver(lon_2d[wslice, wslice], lat_2d[wslice, wslice],
                       u_windkt[wslice, wslice], v_windkt[wslice, wslice], width=0.0015,
                       headlength=4, headwidth=3, angles='xy', color='black', transform = datacrs)

        plt.savefig(imoutpath+'TV_UpperAir'+LevelDict[level]+'_'+timestamp+'.png',bbox_inches= 'tight')

        # Now we use Pillow to overlay the banner with the appropriate day
        background = Image.open(imoutpath+'TV_UpperAir'+LevelDict[level]+'_'+timestamp+'.png')
        im = Image.open(impath+'Banner_'+windlvl+'_'+timestamp+'.png')

        # resize the image
        size = background.size
        im = im.resize(size,Image.ANTIALIAS)

        background.paste(im, (17, 8), im)
        background.save(imoutpath+'TV_UpperAir'+LevelDict[level]+'_'+timestamp+'.png','PNG')

Answer 1

Thanks for the question! My approach here is for each separate column to interpolate the pressure coordinate of GFS-output Geopotential Height onto your provided altitudes to estimate the pressure of each height level for each column. Then I can use that pressure to interpolate the GFS-output u, v onto. The GFS-output GPH and winds have very slightly different pressure coordinates, which is why I interpolated twice. I performed the interpolation using MetPy's interpolate.log_interpolate_1d which performs a linear interpolation on the log of the inputs. Here is the code I used!

from datetime import datetime
import numpy as np
import metpy.calc as mpcalc
from metpy.units import units
from metpy.interpolate import log_interpolate_1d
from siphon.catalog import TDSCatalog


gfs_url = 'https://tds.scigw.unidata.ucar.edu/thredds/catalog/grib/NCEP/GFS/Global_0p5deg/catalog.xml'
cat = TDSCatalog(gfs_url)

now = datetime.utcnow()

# A shortcut to NCSS
ncss = cat.datasets['Best GFS Half Degree Forecast Time Series'].subset()

query = ncss.query()
query.var = set()
query.variables('u-component_of_wind_isobaric', 'v-component_of_wind_isobaric', 'Geopotential_height_isobaric')
query.lonlat_box(north=78, south=45, east=-90, west=-220)
query.time(now)
query.accept('netcdf4')

data = ncss.get_data(query)


# Reading in the u(isobaric), v(isobaric), isobaric vars and the GPH(isobaric6) and isobaric6 vars
# These are two slightly different vertical pressure coordinates.
# We will also assign units here, and this can allow us to go ahead and convert to knots
lat = units.Quantity(data.variables['lat'][:].squeeze(), units('degrees'))
lon = units.Quantity(data.variables['lon'][:].squeeze(), units('degrees'))
iso_wind = units.Quantity(data.variables['isobaric'][:].squeeze(), units('Pa'))
iso_gph = units.Quantity(data.variables['isobaric6'][:].squeeze(), units('Pa'))
u = units.Quantity(data.variables['u-component_of_wind_isobaric'][:].squeeze(), units('m/s')).to(units('knots'))
v = units.Quantity(data.variables['v-component_of_wind_isobaric'][:].squeeze(), units('m/s')).to(units('knots'))
gph = units.Quantity(data.variables['Geopotential_height_isobaric'][:].squeeze(), units('gpm'))


# Here we will select our altitudes to interpolate onto and convert them to geopotential meters 
altitudes = ([30000., 9000., 3000.] * units('ft')).to(units('gpm'))

# Now we will interpolate the pressure coordinate for model output geopotential height to 
# estimate the pressure level for our given altitudes at each grid point
pressures_of_alts = np.zeros((len(altitudes), len(lat), len(lon)))

for ilat in range(len(lat)):
    for ilon in range(len(lon)):
        pressures_of_alts[:, ilat, ilon] = log_interpolate_1d(altitudes,
                                                              gph[:, ilat, ilon],
                                                              iso_gph)

pressures_of_alts = pressures_of_alts * units('Pa')


# Similarly, we will use our interpolated pressures to interpolate
# our u and v winds across their given pressure coordinates.
# This will provide u, v at each of our interpolated pressure
# levels corresponding to our provided initial altitudes
u_at_levs = np.zeros((len(altitudes), len(lat), len(lon)))
v_at_levs = np.zeros((len(altitudes), len(lat), len(lon)))

for ilat in range(len(lat)):
    for ilon in range(len(lon)):
        u_at_levs[:, ilat, ilon], v_at_levs[:, ilat, ilon] = log_interpolate_1d(pressures_of_alts[:, ilat, ilon],
                                                                                iso_wind,
                                                                                u[:, ilat, ilon],
                                                                                v[:, ilat, ilon])

u_at_levs = u_at_levs * units('knots')
v_at_levs = v_at_levs * units('knots')


# We can use mpcalc to calculate a wind speed array from these
wspd = mpcalc.wind_speed(u_at_levs, v_at_levs)

I was able to take my output from this and coerce it into your plotting code (with some unit stripping.)

Your 300-hPa GFS winds
My "30000-ft" GFS winds

Here is what my interpolated pressure fields at each estimated height level look like.

Hope this helps!

Answer 2

I am not sure if this is what you are looking for (I am very new to Metpy), but I have been using the metpy height_to_pressure_std(altitude) function. It puts it in units of hPa which then I convert to Pascals and then a unitless value to use in the Siphon vertical_level(float) function.

Answer 3

I don't think you can use metpy functions to convert height to pressure or vice versus in the upper atmosphere. There errors are too when using the Standard Atmosphere to convert say pressure to feet.