Grid Levels and Parameters

Notebook This example covers the callable methods of the Python AWIPS DAF when working with gridded data. We start with a connection to an EDEX server, then query data types, then grid names, parameters, levels, and other information. Finally the gridded data is plotted for its domain using Matplotlib and Cartopy.

DataAccessLayer.getSupportedDatatypes()

getSupportedDatatypes() returns a list of available data types offered by the EDEX server defined above.

from awips.dataaccess import DataAccessLayer
import unittest

DataAccessLayer.changeEDEXHost("edex-cloud.unidata.ucar.edu")
dataTypes = DataAccessLayer.getSupportedDatatypes()
dataTypes.sort()
list(dataTypes)

['acars',
 'airep',
 'binlightning',
 'bufrmosAVN',
 'bufrmosETA',
 'bufrmosGFS',
 'bufrmosHPC',
 'bufrmosLAMP',
 'bufrmosMRF',
 'bufrua',
 'climate',
 'common_obs_spatial',
 'gfe',
 'gfeEditArea',
 'grid',
 'maps',
 'modelsounding',
 'obs',
 'pirep',
 'practicewarning',
 'profiler',
 'radar',
 'radar_spatial',
 'satellite',
 'sfcobs',
 'topo',
 'warning']

DataAccessLayer.getAvailableLocationNames()

Now create a new data request, and set the data type to grid to request all available grids with getAvailableLocationNames()

request = DataAccessLayer.newDataRequest()
request.setDatatype("grid")
available_grids = DataAccessLayer.getAvailableLocationNames(request)
available_grids.sort()
list(available_grids)

['AUTOSPE',
 'CMC',
 'ESTOFS',
 'ETSS',
 'FFG-ALR',
 'FFG-FWR',
 'FFG-KRF',
 'FFG-MSR',
 'FFG-ORN',
 'FFG-PTR',
 'FFG-RHA',
 'FFG-RSA',
 'FFG-STR',
 'FFG-TAR',
 'FFG-TIR',
 'FFG-TUA',
 'GEFS',
 'GFS',
 'GFS20',
 'HFR-EAST_6KM',
 'HFR-EAST_PR_6KM',
 'HFR-US_EAST_DELAWARE_1KM',
 'HFR-US_EAST_FLORIDA_2KM',
 'HFR-US_EAST_NORTH_2KM',
 'HFR-US_EAST_SOUTH_2KM',
 'HFR-US_EAST_VIRGINIA_1KM',
 'HFR-US_HAWAII_1KM',
 'HFR-US_HAWAII_2KM',
 'HFR-US_HAWAII_6KM',
 'HFR-US_WEST_500M',
 'HFR-US_WEST_CENCAL_2KM',
 'HFR-US_WEST_LOSANGELES_1KM',
 'HFR-US_WEST_LOSOSOS_1KM',
 'HFR-US_WEST_NORTH_2KM',
 'HFR-US_WEST_SANFRAN_1KM',
 'HFR-US_WEST_SOCAL_2KM',
 'HFR-US_WEST_WASHINGTON_1KM',
 'HFR-WEST_6KM',
 'HPCGuide',
 'HPCqpfNDFD',
 'HRRR',
 'LAMP2p5',
 'MRMS_0500',
 'MRMS_1000',
 'NAM12',
 'NAM40',
 'NOHRSC-SNOW',
 'NationalBlend',
 'RAP13',
 'RTMA',
 'RTOFS-Now-WestAtl',
 'RTOFS-Now-WestConus',
 'RTOFS-WestAtl',
 'RTOFS-WestConus',
 'SPCGuide',
 'SeaIce',
 'TPCWindProb',
 'URMA25',
 'WaveWatch']

DataAccessLayer.getAvailableParameters()

After datatype and model name (locationName) are set, you can query all available parameters with getAvailableParameters()

request.setLocationNames("RAP13")
availableParms = DataAccessLayer.getAvailableParameters(request)
availableParms.sort()
list(availableParms)

['36SHRMi',
 '50dbzZ',
 'AV',
 'Along',
 'AppT',
 'BLI',
 'BRN',
 'BRNEHIi',
 'BRNSHR',
 'BRNmag',
 'BRNvec',
 'BdEPT06',
 'BlkMag',
 'BlkShr',
 'CAPE',
 'CFRZR',
 'CICEP',
 'CIn',
 'CP',
 'CP1hr',
 'CPr',
 'CPrD',
 'CRAIN',
 'CSNOW',
 'CURU',
 'CXR',
 'CapeStk',
 'Corf',
 'CorfF',
 'CorfFM',
 'CorfM',
 'CritT1',
 'CumNrm',
 'CumShr',
 'DivF',
 'DivFn',
 'DivFs',
 'DpD',
 'DpT',
 'EHI',
 'EHI01',
 'EHIi',
 'EPT',
 'EPTA',
 'EPTC',
 'EPTGrd',
 'EPTGrdM',
 'EPTs',
 'EPVg',
 'EPVs',
 'EPVt1',
 'EPVt2',
 'ESP',
 'ESP2',
 'FVecs',
 'FeatMot',
 'FnVecs',
 'FsVecs',
 'Fzra1',
 'Fzra2',
 'GH',
 'GHxSM',
 'GHxSM2',
 'Gust',
 'HI',
 'HI1',
 'HI3',
 'HI4',
 'HIdx',
 'HPBL',
 'Heli',
 'HeliC',
 'INV',
 'IPLayer',
 'Into',
 'KI',
 'L-I',
 'LIsfc2x',
 'LM5',
 'LM6',
 'MAdv',
 'MCon',
 'MCon2',
 'MLLCL',
 'MMP',
 'MMSP',
 'MSFDi',
 'MSFi',
 'MSFmi',
 'MSG',
 'MTV',
 'Mix1',
 'Mix2',
 'Mmag',
 'MpV',
 'NBE',
 'NST',
 'NST1',
 'NST2',
 'OmDiff',
 'P',
 'PAdv',
 'PBE',
 'PEC',
 'PFrnt',
 'PGrd',
 'PGrd1',
 'PGrdM',
 'PIVA',
 'PR',
 'PTvA',
 'PTyp',
 'PVV',
 'PW',
 'PW2',
 'PoT',
 'PoTA',
 'QPV1',
 'QPV2',
 'QPV3',
 'QPV4',
 'REFC',
 'RH',
 'RH_001_bin',
 'RH_002_bin',
 'RM5',
 'RM6',
 'RMprop',
 'RMprop2',
 'RRtype',
 'RV',
 'Rain1',
 'Rain2',
 'Rain3',
 'Ro',
 'SH',
 'SHx',
 'SLI',
 'SNSQ',
 'SNW',
 'SNWA',
 'SRMl',
 'SRMlM',
 'SRMm',
 'SRMmM',
 'SRMr',
 'SRMrM',
 'SSP',
 'SSi',
 'STP',
 'STP1',
 'Shear',
 'ShrMag',
 'SnD',
 'Snow1',
 'Snow2',
 'Snow3',
 'SnowT',
 'St-Pr',
 'StrTP',
 'StrmMot',
 'SuCP',
 'T',
 'TAdv',
 'TGrd',
 'TGrdM',
 'TORi',
 'TORi2',
 'TP',
 'TP1hr',
 'TQIND',
 'TShrMi',
 'TV',
 'TW',
 'T_001_bin',
 'Tdef',
 'Tdend',
 'ThGrd',
 'Thom5',
 'Thom5a',
 'Thom6',
 'TmDpD',
 'Tmax',
 'Tmin',
 'Topo',
 'TotQi',
 'Tstk',
 'TwMax',
 'TwMin',
 'Twstk',
 'TxSM',
 'USTM',
 'VAdv',
 'VAdvAdvection',
 'VGP',
 'VSTM',
 'Vis',
 'WCD',
 'WD',
 'WEASD',
 'WEASD1hr',
 'WGS',
 'Wind',
 'WndChl',
 'ageoVC',
 'ageoW',
 'ageoWM',
 'cCape',
 'cCin',
 'cTOT',
 'capeToLvl',
 'dCape',
 'dP',
 'dT',
 'dVAdv',
 'dZ',
 'defV',
 'del2gH',
 'df',
 'fGen',
 'fnD',
 'fsD',
 'gamma',
 'gammaE',
 'geoVort',
 'geoW',
 'geoWM',
 'loCape',
 'maxEPT',
 'minEPT',
 'mixRat',
 'msl-P',
 'muCape',
 'pV',
 'pVeq',
 'qDiv',
 'qVec',
 'qnVec',
 'qsVec',
 'shWlt',
 'snoRat',
 'snoRatCrocus',
 'snoRatEMCSREF',
 'snoRatOv2',
 'snoRatSPC',
 'snoRatSPCdeep',
 'snoRatSPCsurface',
 'staticCoriolis',
 'staticSpacing',
 'staticTopo',
 'swtIdx',
 'tTOT',
 'tWind',
 'tWindU',
 'tWindV',
 'uFX',
 'uW',
 'uWStk',
 'ulSnoRat',
 'vSmthW',
 'vTOT',
 'vW',
 'vWStk',
 'vertCirc',
 'wDiv',
 'wSp',
 'wSp_001_bin',
 'wSp_002_bin',
 'wSp_003_bin',
 'wSp_004_bin',
 'zAGL']

DataAccessLayer.getAvailableLevels()

Selecting “T” for temperature.

request.setParameters("T")
availableLevels = DataAccessLayer.getAvailableLevels(request)
for lvl in availableLevels:
    print(lvl)

0.0SFC
350.0MB
475.0MB
610.0_40000.0FHAG
225.0MB
120.0_150.0BL
900.0MB
125.0MB
0.0_610.0FHAG
450.0MB
575.0MB
325.0MB
100.0MB
1000.0MB
60.0_90.0BL
275.0MB
1.0PV
950.0MB
150.0MB
1.5PV
700.0MB
825.0MB
150.0_180.0BL
250.0MB
375.0MB
1000.0_500.0MB
800.0MB
4000.0FHAG
925.0MB
2.0PV
0.5PV
0.0TROP
750.0MB
500.0MB
625.0MB
400.0MB
0.0FHAG
2.0FHAG
875.0MB
175.0MB
0.0_1000.0FHAG
850.0MB
600.0MB
725.0MB
0.0_6000.0FHAG
975.0MB
550.0MB
0.0_3000.0FHAG
675.0MB
425.0MB
200.0MB
0.0_30.0BL
30.0_60.0BL
650.0MB
525.0MB
300.0MB
90.0_120.0BL
1000.0FHAG
775.0MB
340.0_350.0K
290.0_300.0K
700.0_600.0MB
700.0_300.0MB
320.0Ke
800.0_750.0MB
60.0TILT
5.3TILT
1000.0_900.0MB
340.0K
5500.0_6000.0FHAG
255.0K
255.0_265.0K
3000.0_6000.0FHAG
25.0TILT
2000.0FHAG
0.0_500.0FHAG
1000.0_850.0MB
850.0_250.0MB
280.0_290.0Ke
1524.0FHAG
320.0_330.0K
0.0TILT
310.0_320.0Ke
310.0Ke
330.0K
900.0_800.0MB
550.0_500.0MB
2.4TILT
50.0TILT
3500.0FHAG
35.0TILT
12.0TILT
300.0_310.0K
3000.0_12000.0FHAG
0.9TILT
320.0K
400.0_350.0MB
500.0FHAG
750.0_700.0MB
1000.0_400.0MB
345.0K
250.0_260.0K
300.0Ke
290.0Ke
950.0_900.0MB
4572.0FHAG
275.0_285.0Ke
335.0Ke
295.0_305.0Ke
275.0_285.0K
600.0_550.0MB
310.0K
9000.0FHAG
335.0K
1000.0_7000.0FHAG
700.0_500.0MB
9144.0FHAG
325.0_335.0K
2000.0_8000.0FHAG
0.0_609.6FHAG
300.0K
0.0MAXOMEGA
315.0_325.0K
325.0K
340.0Ke
0.0_4000.0FHAG
5000.0_5500.0FHAG
300.0_250.0MB
1.5TILT
335.0_345.0K
315.0K
3.4TILT
2500.0FHAG
10000.0FHAG
0.0_2000.0FHAG
7000.0FHAG
5000.0FHAG
330.0Ke
500.0_400.0MB
1000.0_1500.0FHAG
305.0K
285.0_295.0Ke
14.0TILT
3000.0_3500.0FHAG
325.0_335.0Ke
2000.0_5000.0FHAG
7620.0FHAG
850.0_800.0MB
6096.0FHAG
6000.0_7000.0FHAG
2000.0_7000.0FHAG
9000.0_10000.0FHAG
295.0Ke
305.0Ke
265.0_275.0K
7000.0_8000.0FHAG
3000.0_8000.0FHAG
700.0_650.0MB
1000.0_6000.0FHAG
0.5TILT
450.0_400.0MB
1.8TILT
330.0_340.0K
800.0_700.0MB
850.0_300.0MB
6.0TILT
900.0_850.0MB
3657.6FHAG
0.0_5000.0FHAG
320.0_330.0Ke
8.7TILT
650.0_600.0MB
600.0_400.0MB
55.0TILT
270.0_280.0Ke
30.0TILT
310.0_320.0K
1500.0FHAG
1000.0_950.0MB
5500.0FHAG
250.0_200.0MB
500.0_1000.0FHAG
400.0_300.0MB
500.0_100.0MB
1000.0_3000.0FHAG
8000.0FHAG
285.0Ke
290.0K
305.0_315.0K
285.0_295.0K
0.0_2500.0FHAG
925.0_850.0MB
275.0Ke
1500.0_2000.0FHAG
300.0_200.0MB
260.0_270.0K
2743.2FHAG
3000.0FHAG
315.0_325.0Ke
600.0_500.0MB
16.7TILT
280.0K
500.0_250.0MB
40.0TILT
3048.0FHAG
400.0_200.0MB
300.0_310.0Ke
270.0_280.0K
1000.0_700.0MB
45.0TILT
850.0_500.0MB
2500.0_3000.0FHAG
609.6FHAG
0.0_8000.0FHAG
295.0K
4.3TILT
295.0_305.0K
330.0_340.0Ke
270.0K
4000.0_4500.0FHAG
280.0_290.0K
925.0_700.0MB
0.0_1500.0FHAG
260.0K
10.0TILT
3500.0_4000.0FHAG
325.0Ke
285.0K
290.0_300.0Ke
7.5TILT
1828.8FHAG
280.0Ke
500.0_450.0MB
305.0_315.0Ke
250.0K
4500.0FHAG
1250.0FHAG
0.0_10000.0FHAG
4500.0_5000.0FHAG
250.0_350.0K
270.0Ke
275.0K
315.0Ke
500.0_300.0MB
350.0_300.0MB
750.0FHAG
19.5TILT
2000.0_2500.0FHAG
850.0_700.0MB
350.0K
265.0K
6000.0FHAG
8000.0_9000.0FHAG
700.0_300.0LYRMB
850.0_700.0LYRMB

  • 0.0SFC is the Surface level

  • FHAG stands for Fixed Height Above Ground (in meters)

  • NTAT stands for Nominal Top of the ATmosphere

  • BL stands for Boundary Layer, where 0.0_30.0BL reads as 0-30 mb above ground level

  • TROP is the Tropopause level

request.setLevels()

For this example we will use Surface Temperature

request.setLevels("2.0FHAG")

DataAccessLayer.getAvailableTimes()

  • getAvailableTimes(request, True) will return an object of run times - formatted as YYYY-MM-DD HH:MM:SS

  • getAvailableTimes(request) will return an object of all times - formatted as YYYY-MM-DD HH:MM:SS (F:ff)

  • getForecastRun(cycle, times) will return a DataTime array for a single forecast cycle.

cycles = DataAccessLayer.getAvailableTimes(request, True)
times = DataAccessLayer.getAvailableTimes(request)
fcstRun = DataAccessLayer.getForecastRun(cycles[-1], times)
list(fcstRun)

[<DataTime instance: 2020-09-04 18:00:00 >,
 <DataTime instance: 2020-09-04 18:00:00 >,
 <DataTime instance: 2020-09-04 18:00:00 >,
 <DataTime instance: 2020-09-04 18:00:00 >,
 <DataTime instance: 2020-09-04 18:00:00 >,
 <DataTime instance: 2020-09-04 18:00:00 >,
 <DataTime instance: 2020-09-04 18:00:00 >,
 <DataTime instance: 2020-09-04 18:00:00 >,
 <DataTime instance: 2020-09-04 18:00:00 >,
 <DataTime instance: 2020-09-04 18:00:00 >,
 <DataTime instance: 2020-09-04 18:00:00 >,
 <DataTime instance: 2020-09-04 18:00:00 >,
 <DataTime instance: 2020-09-04 18:00:00 >,
 <DataTime instance: 2020-09-04 18:00:00 >,
 <DataTime instance: 2020-09-04 18:00:00 >,
 <DataTime instance: 2020-09-04 18:00:00 >,
 <DataTime instance: 2020-09-04 18:00:00 >,
 <DataTime instance: 2020-09-04 18:00:00 >,
 <DataTime instance: 2020-09-04 18:00:00 >,
 <DataTime instance: 2020-09-04 18:00:00 >,
 <DataTime instance: 2020-09-04 18:00:00 >,
 <DataTime instance: 2020-09-04 18:00:00 >]

DataAccessLayer.getGridData()

Now that we have our request and DataTime fcstRun arrays ready, it’s time to request the data array from EDEX.

response = DataAccessLayer.getGridData(request, [fcstRun[-1]])
for grid in response:
    data = grid.getRawData()
    lons, lats = grid.getLatLonCoords()
    print('Time :', str(grid.getDataTime()))

print('Model:', str(grid.getLocationName()))
print('Parm :', str(grid.getParameter()))
print('Unit :', str(grid.getUnit()))
print(data.shape)

Time : 2020-09-04 18:00:00
Model: RAP13
Parm : T
Unit : K
(337, 451)

Plotting with Matplotlib and Cartopy

1. pcolormesh

%matplotlib inline
import matplotlib.pyplot as plt
import matplotlib
import cartopy.crs as ccrs
import cartopy.feature as cfeature
from cartopy.mpl.gridliner import LONGITUDE_FORMATTER, LATITUDE_FORMATTER
import numpy as np
import numpy.ma as ma
from scipy.io import loadmat
from scipy.constants import convert_temperature
def make_map(bbox, projection=ccrs.PlateCarree()):
    fig, ax = plt.subplots(figsize=(16, 9),
                           subplot_kw=dict(projection=projection))
    ax.set_extent(bbox)
    ax.coastlines(resolution='50m')
    gl = ax.gridlines(draw_labels=True)
    gl.top_labels = gl.right_labels = False
    gl.xformatter = LONGITUDE_FORMATTER
    gl.yformatter = LATITUDE_FORMATTER
    return fig, ax

#convert temp from K to F
dataf = convert_temperature(data, 'K', 'F')

cmap = plt.get_cmap('rainbow')
bbox = [lons.min(), lons.max(), lats.min(), lats.max()]
fig, ax = make_map(bbox=bbox)
cs = ax.pcolormesh(lons, lats, dataf, cmap=cmap)
cbar = fig.colorbar(cs, extend='both', shrink=0.5, orientation='horizontal')
cbar.set_label(grid.getLocationName() +" " + grid.getLevel() + " " \
               + grid.getParameter() + " (F) " \
               + "valid " + str(grid.getDataTime().getRefTime()))
../../_images/Grid_Levels_and_Parameters_16_0.png

2. contourf

fig2, ax2 = make_map(bbox=bbox)
cs2 = ax2.contourf(lons, lats, dataf, 80, cmap=cmap,
                  vmin=dataf.min(), vmax=dataf.max(), extend='both')
cbar2 = fig2.colorbar(cs2, shrink=0.5, orientation='horizontal')
cbar2.set_label(grid.getLocationName() +" " + grid.getLevel() + " " \
               + grid.getParameter() + " (F) " \
               + "valid " + str(grid.getDataTime().getRefTime()))
../../_images/Grid_Levels_and_Parameters_18_0.png