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Thank you for your interest in CALIPSO data.
From the filename example that you gave it looks like you are using ASDC's CALIPSO Subsetter . By using this application you can select a geospatial range (Step 3) and decide what specific latitude and/or longitude to analyze. Once you order the data you have subsetted and receive it through email, you will not have to extract any specific latitude and longitude values using the Python script because you already did it through the ASDC's CALIPSO Subsetter . All you have to do is tell the Python script which HDF file you want it to plot.
Please let us know if you have any other questions.
Regards,
ASDC User Services from pyhdf.SD import SD, SDC FILE_NAME = 'CAL_LID_L2_05kmAPro-Standard-V4-20.2015-05-14T23-44-05ZD.hdf' DATAFIELD_NAME = 'Extinction_Coefficient_532' sd = SD(FILE_NAME, SDC.READ) hdf = HDF.HDF(FILE_NAME) vs = hdf.vstart() hdf = SD(FILE_NAME, SDC.READ) #get altitude xid = vs.find('metadata') altid = vs.attach(xid) altid.setfields('Lidar_Data_Altitudes') nrecs, _, _, _, _ = altid.inquire() altitude = altid.read(nRec=nrecs) altid.detach() alti = np.array(altitude[0][0]) alt = np.flip(alti) # Read geolocation datasets. # They are all same for every file so we read only once. lat = hdf.select("Latitude") latitude = np.squeeze(lat[:,0]) #subset for latitude (10-20N) idx_lat = np.where((latitude >= 10) & (latitude <= 20)) idx_subset_lat = idx_lat[0] subset_lat = np.array([latitude[i] for i in idx_subset_lat]) # Read dataset. data2D = hdf.select(DATAFIELD_NAME) data = data2D[:,:].astype(np.float64) # Retrieve the attributes. attrs = data2D.attributes(full=1) # print(attrs) fva=attrs["fillvalue"] fill_value = fva[0] ua=attrs["units"] units = ua[0] # Replace the missing values with NaN. data[data == float(fill_value)] = np.nan #subset data to accomodate the subsetting of latitude data_arrays = [] for x in range(len(data.T)): subset = np.take(data[:,x], idx_subset_lat) data_arrays.append(subset) if x >= len(data.T): break subset_data = np.stack(data_arrays) # Contour the data on a grid of latitude vs. altitude lats, alts = np.meshgrid(subset_lat, alt) #plot data plt.figure(figsize=(7.20,3.60)) plt.contourf(lats, alts, subset_data, cmap=plt.get_cmap('jet')) cb = plt.colorbar() cb.set_label(units) long_name = DATAFIELD_NAME plt.title("{0}\n{1}".format(FILE_NAME, long_name), fontsize=8) plt.xlabel('Latitude (degrees north)') plt.ylabel('Altitude (km)') fig = plt.gcf() pngfile = "{0}.v.py.png".format(FILE_NAME) fig.savefig(pngfile, dpi=200) Thank you for your interest in CALIPSO data.
Just as an oberservation, you are using an older version of the product CAL_LID_L2_01kmCLay. Would recommend using the latest version, V4-51 .
To help get you started here is the link to CALIPSO's nominal browse images for 2022-02-28T23-12-48ZD that includes plots for attenuated backscatter at 532nm: https://www-calipso.larc.nasa.gov/products/lidar/browse_images/show_v451_detail.php?s=production&v=V4-51&browse_date=2022-02-28&orbit_time=23-12-48&page=4&granule_name=CAL_LID_L1-Standard-V4-51.2022-02-28T23-12-48ZD.hdf
There were three variables that you had question in regards to the dimensions, the Attenuated_Backscatter_Statistics_532 (21200, 60), Layer_Top_Altitude (21200, 10) and Layer_Base_Altitude (21200, 10). The first number, 21200, is the time dimension, for the values 60 and 10, I will need to get in contact with a member of the CALIPSO Science Team. It's not clear what the values represent when looking at the documentation.
Apologies for the inconvenience. Hope this helps.
Regards,