✏️ Custom Data Sources
A neat and very usesul aspect of SounderPy is the clean_data dictionary that stores sounding data. This dictionary is fed into a SounderPy plotting function. The plotting functions are designed in such a way that any data can be passed to it, so long as its in the SounderPy clean_data dictionary format.
So if you have custom data that you want to plot with SounderPy, you can manually create a clean_data dictionary and pass it to a plot!
Your data could be field campaign observations, a CM1 input_sounding, WRF output, and anything else in between. So long as you set it up right, it can be integrated into SounderPy.
Understanding the SounderPy data format
What’s contained in the clean_data dictionary…
- The actual profile data…
clean_data['p']: an array of pressure dataclean_data['z']: an array of height dataclean_data['T']: an array of temperature dataclean_data['Td']: an array of dewpoint dataclean_data['u']: an array of u-component of wind dataclean_data['v']: an array of v-component of wind dataclean_data['omega']: an array of vertical velocity – model data only
- The profile metadata (via clean_data[‘site_info’])…
clean_data['site_info']['site-name']a str representing the name of a profile site, if available (e.g. ‘DTX’)
clean_data['site_info']['site-lctn']a str representing additional site location information (e.g. ‘MI US’)
clean_data['site_info']['site-latlon']a latitude-longitude pair of floats in a list
clean_data['site_info']['site-elv']elevation of the profile
clean_data['site_info']['source']a str representing the data source name (e.g. ‘RAOB OBSERVED PROFILE’)
other sources are… ‘ACARS OBSERVED AIRCRAFT PROFILE’, ‘BUFKIT FORECAST PROFILE’, ‘MODEL REANALYSIS PROFILE’, ‘RAOB OBSERVED PROFILE’
clean_data['site_info']['model']a str representing the model name, if available (e.g., ‘no-model’ or ‘hrrr’)
clean_data['site_info']['fcst-hour']if a model is used, the forecast hour of the model run as a str (e.g. ‘no-fcst-hour’ or ‘F01’)
clean_data['site_info']['run-time']if a model is used, the model run time as a list of strs
clean_data['site_info']['valid-time']the data’s valid time as a list of strs
- And finally,
clean_dataalso contains pre-built plot titles (via clean_data[‘titles’]). clean_data['titles']['top_title']the primary, or “main” title that appears above everything else
clean_data['titles']['left_title']the left title that typically contains time/date information
clean_data['titles']['right_title']the right title that typically contains location information
Below is an example:
{'p': <Quantity([984.7 981.8 976.4 967.1 953.4 935.8 914.8 892. 867.2 839.6 809.2 775.8 739.6 700.4 658.4 613.4 566.1 520.1 478.7 441.6 408.1 377.8 350.5 325.8 303.6 283.6 265.5 248.7 233.5 219.6 205.7 191.7 177.7 163.8 149.8 135.9 121.9 108. 94. 81. 70.6 62.2 54.3], 'hectopascal')>, 'z': <Quantity([ 262.29 287.38 334.72 417.39 540.9 701.94 897.92 1116.39 1360.38 1638.85 1954.13 2311.53 2712.99 3165.6 3673.36 4247.56 4889.99 5558.47 6202. 6817.56 7410.43 7981.44 8527.44 9050.51 9548. 10021.78 10474.5 10917.67 11339.77 11745.44 12172.28 12626.51 13109.32 13623.36 14184.85 14794.86 15466.97 16205.52 17052.44 17961.73 18802.02 19579.02 20415.56], 'meter')>, 'T': <Quantity([ 14.84 17.54 20.04 21.34 21.44 20.44 21.44 22.74 21.64 19.94 17.54 14.84 11.94 8.64 5.24 1.84 -1.86 -6.06 -10.56 -14.66 -18.36 -22.36 -26.66 -30.66 -34.06 -37.26 -40.06 -43.06 -45.96 -48.76 -51.56 -54.46 -56.86 -58.26 -58.66 -59.66 -64.26 -65.26 -64.26 -64.66 -63.86 -63.36 -62.16], 'degree_Celsius')>, 'Td': <Quantity([ 9.3 8.79 8.32 7.88 7.01 6.38 -2.2 -13.24 -14.49 -24.86 -17.51 -9.57 -7.31 -9.9 -12.11 -12.99 -16.41 -20.62 -25.37 -30.93 -36.12 -40.09 -42.47 -45.57 -49.39 -52.06 -54.3 -55.81 -58.64 -62.27 -64.93 -65.45 -68.93 -74.24 -74.84 -101.09 -101.6 -102.16 -102.8 -103.49 -104.11 -104.68 -105.28], 'degree_Celsius')>, 'u': <Quantity([-2.33041109 -2.91567705 -2.3323691 0.77626972 3.5007069 5.63835178 9.13497097 10.6914363 13.41432206 20.79403892 23.12721208 21.57710061 20.41478107 21.38502534 22.54815854 23.51844683 24.29878342 23.71105505 20.98924152 22.35724617 28.57257978 28.18931303 25.65927737 28.56994968 28.57863291 29.73848546 31.87877692 31.48766321 29.74415098 27.98817845 27.59730449 26.43360693 23.32832754 21.76679295 23.12850533 25.85409643 33.62524652 19.4383033 8.94253878 9.13760323 6.0283743 6.02452813 0.38886559], 'knot')>, 'v': <Quantity([ 3.88459575 9.13602361 12.24993691 13.79818123 13.99892679 13.41310886 10.68811982 9.33002089 9.33014275 6.99796725 2.91344493 1.36129697 -1.35952707 -4.85855855 -5.83135029 -5.44263343 -4.85978647 -4.08282603 -4.27412453 -5.44184192 -4.46923758 -0.19680173 -0.19257433 -4.0813693 -2.52543878 -1.75116608 -3.49692463 -2.13557144 0.97112424 2.71960789 2.13803291 0.5813991 1.55355538 5.2458388 6.21890192 20.41100923 13.40831073 9.91285856 15.74811418 9.3310132 9.72277241 7.38416961 6.99920592], 'knot')>, 'omega': <Quantity([ 0. 0. 0. 0. 0. 0.1 0.1 0.1 -0.1 -0.3 -0.4 -0.2 -0.1 0. 0. 0.1 0.1 0. 0. 0. 0. 0. 0. 0.1 0.1 0. 0. 0. -0.1 0. 0. 0.1 0.1 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. ], 'pascal / second')>, 'site_info': {'site-id': 'KGFK', 'site-name': 'GRAND FORKS INTL', 'site-lctn': 'ND', 'site-latlon': [47.95, -97.18], 'site-elv': 257, 'source': 'BUFKIT FORECAST PROFILE', 'model': 'RAP', 'fcst-hour': 'F00', 'run-time': ['2024', '09', '28', '04'], 'valid-time': ['2024', '09', '28', '04']}, 'titles': {'top_title': 'BUFKIT MODEL FORECAST PROFILE | 04Z RAP F00', 'left_title': ' RUN: 09/28/2024 04Z | VALID: 09/28/2024 04Z', 'right_title': 'KGFK - GRAND FORKS INTL, ND | 47.95, -97.18 '}}
Building a custom clean_data dictionary
After importing your custom sounding data, (perhaps using pandas or numpy) you can define a clean_data dictionary with it.
Here is an example. raw_data would be your custom data.
# declare the clean_data dictionary, leave it empty for the moment clean_data = {} # add profile data | make sure you have p, z, T, Td, u, & v clean_data['p'] = np.array(raw_data[2])*units.hPa clean_data['z'] = np.array(raw_data[7])*units.m clean_data['T'] = np.array(raw_data[3])*units.degC clean_data['Td'] = np.array(raw_data[11])*units.degC clean_data['u'] = np.array(raw_data[5])*units.kts clean_data['v'] = np.array(raw_data[6])*units.kts # declare site metadata clean_data['site_info'] = { 'site-id' : 'UND', # could be a station ID, site ID, launch ID, mission ID, etc 'site-name' : 'GRAND FORKS' # a location's "name", usually the city or town 'site-lctn' : 'ND', # could be another name, or the state 'site-latlon' : [47.9213, -97.087] # location lat/lon, list of floats 'site-elv' : 257, # the profile's elevation in meters (int or float) (easily found on google) 'source' : 'UND AEROSPACE' # the 'source' which will be the main title component of the plot 'model' : 'none', # model name if a model was involved 'fcst-hour' : f'none', # forecast hour if a model was involved 'run-time' : ['none', 'none', 'none', 'none'], # model run date if a model was involved 'valid-time' : ["2024", "09", "28", "16:15"]} # the profile's valid date/time. # declare the plot titles clean_data['titles'] = { 'top_title': 'UNIVERSITY OF NORTH DAKOTA AEROSPACE | OBSERVED SOUNDING', 'left_title': 'VALID: 09/28/2024 - 16:15Z', 'right_title': 'GRAND FORKS, ND [47.9213, -97.087] '}
Done! Now your custom data source is integrated into a SounderPy clean_data dictionary.
Finally, you can pass it into the build_sounding() to visualize your data.
spy.build_sounding(clean_data *kwargs)