=================== NEXRAD Level3 Radar =================== `Notebook `_ Python-AWIPS Tutorial Notebook -------------- Objectives ========== - Use python-awips to connect to an edex server - Define and filter data request for radar data - Plot NEXRAD 3 algorithm, precipitation, and derived products (not base data) -------------- Table of Contents ----------------- | `1 Imports `__\ | `2 EDEX Connection `__\ | `3 Investigate Data `__\ |     `3.1 Available Locations `__\ |     `3.2 Available Parameters `__\ |     `3.3 Radar Product IDs and Names `__\ | `4 Function: make_map() `__\ | `5 Plot the Data! `__\ | `6 See Also `__\ |     `6.1 Related Notebooks `__\ |     `6.2 Additional Documentation `__\ 1 Imports --------- The imports below are used throughout the notebook. Note the first import is coming directly from python-awips and allows us to connect to an EDEX server. The subsequent imports are for data manipulation and visualization. .. code:: ipython3 import warnings from awips.dataaccess import DataAccessLayer import matplotlib.pyplot as plt import cartopy.crs as ccrs import numpy as np from cartopy.mpl.gridliner import LONGITUDE_FORMATTER, LATITUDE_FORMATTER `Top `__ -------------- 2 EDEX Connection ----------------- First we establish a connection to Unidata’s public EDEX server. This sets the proper server on the **DataAccessLayer**, which we will use numerous times throughout the notebook. .. code:: ipython3 DataAccessLayer.changeEDEXHost("edex-cloud.unidata.ucar.edu") request = DataAccessLayer.newDataRequest("radar") `Top `__ -------------- 3 Investigate Data ------------------ Now that we’ve created a new radar data request, let’s take a look at what locations and parameters are available for our current request. 3.1 Available Locations ~~~~~~~~~~~~~~~~~~~~~~~ We can take a look at what “locations” are available for our radar request. For radar, we’ll see that radar station names are returned when looking at the availalbe location names. For this example we’ll use Baltimore, MD/Washington DC as our region of interest. You can easily look up other station IDs and where they are using `this NWS webpage `__. .. code:: ipython3 available_locs = DataAccessLayer.getAvailableLocationNames(request) available_locs.sort() print(available_locs) # Set our location to Baltimore (klwx) request.setLocationNames("klwx") .. parsed-literal:: ['kabr', 'kabx', 'kakq', 'kama', 'kamx', 'kapx', 'karx', 'katx', 'kbbx', 'kbgm', 'kbhx', 'kbis', 'kblx', 'kbmx', 'kbox', 'kbro', 'kbuf', 'kbyx', 'kcae', 'kcbw', 'kcbx', 'kccx', 'kcle', 'kclx', 'kcrp', 'kcxx', 'kcys', 'kdax', 'kddc', 'kdfx', 'kdgx', 'kdix', 'kdlh', 'kdmx', 'kdox', 'kdtx', 'kdvn', 'kdyx', 'keax', 'kemx', 'kenx', 'keox', 'kepz', 'kesx', 'kevx', 'kewx', 'keyx', 'kfcx', 'kfdr', 'kfdx', 'kffc', 'kfsd', 'kfsx', 'kftg', 'kfws', 'kggw', 'kgjx', 'kgld', 'kgrb', 'kgrk', 'kgrr', 'kgsp', 'kgwx', 'kgyx', 'khdc', 'khdx', 'khgx', 'khnx', 'khpx', 'khtx', 'kict', 'kicx', 'kiln', 'kilx', 'kind', 'kinx', 'kiwa', 'kiwx', 'kjax', 'kjgx', 'kjkl', 'klbb', 'klch', 'klgx', 'klnx', 'klot', 'klrx', 'klsx', 'kltx', 'klvx', 'klwx', 'klzk', 'kmaf', 'kmax', 'kmbx', 'kmhx', 'kmkx', 'kmlb', 'kmob', 'kmpx', 'kmqt', 'kmrx', 'kmsx', 'kmtx', 'kmux', 'kmvx', 'kmxx', 'knkx', 'knqa', 'koax', 'kohx', 'kokx', 'kotx', 'kpah', 'kpbz', 'kpdt', 'kpoe', 'kpux', 'krax', 'krgx', 'kriw', 'krlx', 'krtx', 'ksfx', 'ksgf', 'kshv', 'ksjt', 'ksox', 'ksrx', 'ktbw', 'ktfx', 'ktlh', 'ktlx', 'ktwx', 'ktyx', 'kudx', 'kuex', 'kvax', 'kvbx', 'kvnx', 'kvtx', 'kvwx', 'kyux', 'pabc', 'pacg', 'paec', 'pahg', 'paih', 'pakc', 'papd', 'phki', 'phkm', 'phmo', 'phwa', 'rkjk', 'rksg', 'tadw', 'tatl', 'tbna', 'tbos', 'tbwi', 'tclt', 'tcmh', 'tcvg', 'tdal', 'tday', 'tdca', 'tden', 'tdfw', 'tdtw', 'tewr', 'tfll', 'thou', 'tiad', 'tiah', 'tich', 'tids', 'tjfk', 'tjua', 'tlas', 'tlve', 'tmci', 'tmco', 'tmdw', 'tmem', 'tmia', 'tmke', 'tmsp', 'tmsy', 'tokc', 'tord', 'tpbi', 'tphl', 'tphx', 'tpit', 'trdu', 'tsdf', 'tsju', 'tslc', 'tstl', 'ttpa', 'ttul'] 3.2 Available Parameters ~~~~~~~~~~~~~~~~~~~~~~~~ Next, let’s look at the parameters returned from the available parameters request. If we look closely, we can see that some of the parameters appear different from the others. .. code:: ipython3 availableParms = DataAccessLayer.getAvailableParameters(request) availableParms.sort() print(availableParms) .. parsed-literal:: ['134', '135', '141', '153', '154', '159', '161', '163', '165', '166', '169', '170', '172', '173', '176', '177', '32', '37', '56', '57', '58', '81', '99', 'CC', 'CZ', 'Composite Refl', 'Correlation Coeff', 'DAA', 'DHR', 'DPA', 'DPR', 'DUA', 'DVL', 'Diff Reflectivity', 'Digital Hybrid Scan Refl', 'Digital Inst Precip Rate', 'Digital Precip Array', 'Digital Vert Integ Liq', 'EET', 'Enhanced Echo Tops', 'HC', 'HHC', 'HV', 'HZ', 'Hybrid Hydrometeor Class', 'Hydrometeor Class', 'KDP', 'MD', 'ML', 'Melting Layer', 'Mesocyclone', 'OHA', 'One Hour Accum', 'One Hour Unbiased Accum', 'Reflectivity', 'SRM', 'STA', 'STI', 'Specific Diff Phase', 'Storm Rel Velocity', 'Storm Total Accum', 'Storm Track', 'User Select Accum', 'V', 'VIL', 'Velocity', 'Vert Integ Liq', 'ZDR'] 3.3 Radar Product IDs and Names ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~ As we saw above, some parameters seem to be describing different things from the rest. The DataAccessLayer has a built in function to parse the available parameters into the separate **Product IDs** and **Product Names**. Here, we take a look at the two different arrays that are returned when parsing the *availableParms* array we just recieved in the previous code cell. .. code:: ipython3 productIDs = DataAccessLayer.getRadarProductIDs(availableParms) productNames = DataAccessLayer.getRadarProductNames(availableParms) print(productIDs) print(productNames) .. parsed-literal:: ['134', '135', '141', '153', '154', '159', '161', '163', '165', '166', '169', '170', '172', '173', '176', '177', '32', '37', '56', '57', '58', '81', '99'] ['Composite Refl', 'Correlation Coeff', 'Diff Reflectivity', 'Digital Hybrid Scan Refl', 'Digital Inst Precip Rate', 'Digital Precip Array', 'Digital Vert Integ Liq', 'Enhanced Echo Tops', 'Hybrid Hydrometeor Class', 'Hydrometeor Class', 'Melting Layer', 'Mesocyclone', 'One Hour Accum', 'One Hour Unbiased Accum', 'Reflectivity', 'Specific Diff Phase', 'Storm Rel Velocity', 'Storm Total Accum', 'Storm Track', 'User Select Accum', 'Velocity', 'Vert Integ Liq'] `Top `__ -------------- 4 Function: make_map() ---------------------- In order to plot more than one image, it’s easiest to define common logic in a function. Here, a new function called **make_map** is defined. This function uses the `matplotlib.pyplot package (plt) `__ to create a figure and axis. The coastlines (continental boundaries) are added, along with lat/lon grids. .. code:: ipython3 def make_map(bbox, projection=ccrs.PlateCarree()): fig, ax = plt.subplots(figsize=(16, 16), 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 `Top `__ -------------- 5 Plot the Data! ---------------- Here we’ll create a plot for each of the Radar Product Names from our *productNames* array from the `previous section `__. .. code:: ipython3 # suppress a few warnings that come from plotting warnings.filterwarnings("ignore",category =RuntimeWarning) warnings.filterwarnings("ignore",category =UserWarning) # Cycle through all of the products to try and plot each one for prod in productNames: request.setParameters(prod) availableLevels = DataAccessLayer.getAvailableLevels(request) # Check the available levels, if there are none, then skip this product if availableLevels: request.setLevels(availableLevels[0]) else: print("No levels found for " + prod) continue cycles = DataAccessLayer.getAvailableTimes(request, True) times = DataAccessLayer.getAvailableTimes(request) if times: print() response = DataAccessLayer.getGridData(request, [times[-1]]) print("Recs : ", len(response)) if response: grid = response[0] else: continue data = grid.getRawData() lons, lats = grid.getLatLonCoords() print('Time :', str(grid.getDataTime())) flat = np.ndarray.flatten(data) print('Name :', str(grid.getLocationName())) print('Prod :', str(grid.getParameter())) print('Range:' , np.nanmin(flat), " to ", np.nanmax(flat), " (Unit :", grid.getUnit(), ")") print('Size :', str(data.shape)) print() cmap = plt.get_cmap('rainbow') bbox = [lons.min()-0.5, lons.max()+0.5, lats.min()-0.5, lats.max()+0.5] fig, ax = make_map(bbox=bbox) cs = ax.pcolormesh(lons, lats, data, cmap=cmap) cbar = fig.colorbar(cs, extend='both', shrink=0.5, orientation='horizontal') cbar.set_label(grid.getParameter() +" " + grid.getLevel() + " " \ + grid.getLocationName() + " (" + prod + "), (" + grid.getUnit() + ") " \ + "valid " + str(grid.getDataTime().getRefTime())) plt.show() .. parsed-literal:: Recs : 1 Time : 2024-05-22 21:53:42 Name : klwx_0.0_464_464 Prod : Composite Refl Range: 5.0 to 60.0 (Unit : dBZ ) Size : (464, 464) .. image:: NEXRAD_Level3_Radar_files/NEXRAD_Level3_Radar_21_1.png .. parsed-literal:: No levels found for Correlation Coeff No levels found for Diff Reflectivity Recs : 1 Time : 2024-05-22 21:57:59 Name : klwx_0.0_230_360_0.0_359.0 Prod : Digital Hybrid Scan Refl Range: -16.0 to 57.0 (Unit : dBZ ) Size : (230, 360) .. image:: NEXRAD_Level3_Radar_files/NEXRAD_Level3_Radar_21_3.png .. parsed-literal:: Recs : 1 Time : 2024-05-22 21:57:59 Name : klwx_0.0_920_360_0.0_359.0 Prod : Digital Inst Precip Rate Range: 7.0555557e-09 to 4.0117888e-05 (Unit : m*sec^-1 ) Size : (920, 360) .. image:: NEXRAD_Level3_Radar_files/NEXRAD_Level3_Radar_21_5.png .. parsed-literal:: Recs : 1 Time : 2024-05-22 21:57:59 Name : klwx_0.0_13_13 Prod : Digital Precip Array Range: -60.0 to 690.0 (Unit : count ) Size : (13, 13) .. image:: NEXRAD_Level3_Radar_files/NEXRAD_Level3_Radar_21_7.png .. parsed-literal:: Recs : 1 Time : 2024-05-22 21:53:42 Name : klwx_0.0_460_360_0.0_359.0 Prod : Digital Vert Integ Liq Range: 0.0 to 46.34034 (Unit : kg*m^-2 ) Size : (460, 360) .. image:: NEXRAD_Level3_Radar_files/NEXRAD_Level3_Radar_21_9.png .. parsed-literal:: Recs : 1 Time : 2024-05-22 21:53:42 Name : klwx_0.0_346_360_0.0_359.0 Prod : Enhanced Echo Tops Range: nan to nan (Unit : m ) Size : (346, 360) .. image:: NEXRAD_Level3_Radar_files/NEXRAD_Level3_Radar_21_11.png .. parsed-literal:: Recs : 1 Time : 2024-05-22 21:57:59 Name : klwx_0.0_920_360_0.0_359.0 Prod : Hybrid Hydrometeor Class Range: 1.0 to 10.0 (Unit : count ) Size : (920, 360) .. image:: NEXRAD_Level3_Radar_files/NEXRAD_Level3_Radar_21_13.png .. parsed-literal:: No levels found for Hydrometeor Class No levels found for Melting Layer Recs : 0 Recs : 1 Time : 2024-05-22 21:57:59 Name : klwx_0.0_115_360_359.0_359.0 Prod : One Hour Accum Range: 0.0 to 0.0254 (Unit : m ) Size : (115, 360) .. image:: NEXRAD_Level3_Radar_files/NEXRAD_Level3_Radar_21_15.png .. parsed-literal:: Recs : 1 Time : 2024-05-22 21:57:59 Name : klwx_0.0_920_360_0.0_359.0 Prod : One Hour Unbiased Accum Range: 2.54e-05 to 0.030784799 (Unit : m ) Size : (920, 360) .. image:: NEXRAD_Level3_Radar_files/NEXRAD_Level3_Radar_21_17.png .. parsed-literal:: No levels found for Reflectivity No levels found for Specific Diff Phase No levels found for Storm Rel Velocity Recs : 1 Time : 2024-05-22 21:57:59 Name : klwx_0.0_920_360_0.0_359.0 Prod : Storm Total Accum Range: 0.000254 to 0.051054 (Unit : m ) Size : (920, 360) .. image:: NEXRAD_Level3_Radar_files/NEXRAD_Level3_Radar_21_19.png .. parsed-literal:: Recs : 0 No levels found for User Select Accum No levels found for Velocity Recs : 1 Time : 2024-05-22 21:57:59 Name : klwx_0.0_116_116 Prod : Vert Integ Liq Range: 1.0 to 45.0 (Unit : kg*m^-2 ) Size : (116, 116) .. image:: NEXRAD_Level3_Radar_files/NEXRAD_Level3_Radar_21_21.png `Top `__ -------------- 6 See Also ---------- 6.1 Related Notebooks ~~~~~~~~~~~~~~~~~~~~~ - `Grid Levels and Parameters `__ 6.2 Additional Documentation ~~~~~~~~~~~~~~~~~~~~~~~~~~~~ **python-awips** - `DataAccessLayer.changeEDEXHost() `__ - `DataAccessLayer.newDataRequest() `__ - `DataAccessLayer.getRadarProductIDs() `__ - `DataAccessLayer.getRadarProductNames() `__ **matplotlib** - `matplotlib.pyplot() `__ - `matplotlib.pyplot.axes() `__ - `matplotlib.pyplot.figure() `__ `Top `__ --------------