""" PQPF (Hong Kong) ======================================================== This example demonstrates how to perform operational PQPF up to three hours from raingauge and radar data, using data from Hong Kong. """ ########################################################### # Definitions # -------------------------------------------------------- # import os import numpy as np import pandas as pd from pyresample import utils import xarray import cartopy.feature as cfeature import cartopy.crs as ccrs import cartopy.io.shapereader as shpreader import matplotlib import imageio from collections import OrderedDict import matplotlib.pyplot as plt from matplotlib.colors import BoundaryNorm, LinearSegmentedColormap from swirlspy.core.resample import grid_resample from swirlspy.qpe.utils import timestamps_ending, locate_file from swirlspy.rad.iris import read_iris_grid from swirlspy.obs import Rain from swirlspy.qpf import rover from swirlspy.qpf import sla from swirlspy.utils import standardize_attr, FrameType from swirlspy.utils.conversion import to_rainfall_rate, to_rainfall_depth, acc_rainfall_depth, temporal_interpolate plt.switch_backend('agg') THIS_DIR = os.getcwd() os.chdir(THIS_DIR) start_time = pd.Timestamp.now() ############################################################# # Initialising # ----------------------------------------------------------- # # This section demonstrates extracting # radar reflectivity data. # # Step 1: Define the basetime. # # Define basetime basetime = pd.Timestamp('201902190800') ############################################################################## # Step 2: Using basetime, generate timestamps of desired radar files # timestamps_ending() and locate files using locate_file(). # Obtain radar files dir = THIS_DIR + '/../tests/samples/iris/ppi' located_files = [] radar_ts = timestamps_ending( basetime, duration=pd.Timedelta(60, 'm') ) for timestamp in radar_ts: located_files.append(locate_file(dir, timestamp)) ############################################################################## # Step 3: Read data from radar files into xarray.DataArray # using read_iris_grid(). reflectivity_list = [] # stores reflec from read_iris_grid() for filename in located_files: reflec = read_iris_grid( filename ) reflectivity_list.append(reflec) ######################################################################## # Step 4: Define the target grid as a pyresample AreaDefinition. # Defining target grid area_id = "hk1980_250km" description = ("A 1km resolution rectangular grid " "centred at HKO and extending to 250 km " "in each direction in HK1980 easting/northing coordinates") proj_id = 'hk1980' projection = ('+proj=tmerc +lat_0=22.31213333333334 ' '+lon_0=114.1785555555556 +k=1 +x_0=836694.05 ' '+y_0=819069.8 +ellps=intl +towgs84=-162.619,-276.959,' '-161.764,0.067753,-2.24365,-1.15883,-1.09425 +units=m ' '+no_defs') x_size = 500 y_size = 500 area_extent = (587000, 569000, 1087000, 1069000) area_def_tgt = utils.get_area_def( area_id, description, proj_id, projection, x_size, y_size, area_extent ) ############################################################################## # Step 5: Reproject the radar data from read_iris_grid() from Centered # Azimuthal (source) projection to HK 1980 (target) projection. # # Extracting the AreaDefinition of the source projection area_def_src = reflectivity_list[0].attrs['area_def'] # Reprojecting reproj_reflectivity_list = [] for reflec in reflectivity_list: reproj_reflec = grid_resample( reflec, area_def_src, area_def_tgt, coord_label=['easting', 'northing'] ) reproj_reflectivity_list.append(reproj_reflec) initialising_time = pd.Timestamp.now() ############################################################################### # Step 6: Concatenate the reflectivity xarrays in the list along the time # dimension. # Concatenating all the (observed) reflectivity # xarray.DataArrays in reproj_reflectivity_list reflectivityObs = xarray.concat(reproj_reflectivity_list, dim='time') ########################################################### # Running ROVER and Semi-Lagrangian Advection # --------------------------------------------------------- # # This section demonstrates how to run ROVER and # Semi-Lagrangian Advection for multiple members. # # First, write a function to run these steps: # # #. Selecting the two relevant xarrays to be used as the ROVER input. # #. Generate motion field using ROVER, with the selected xarray as the input. # #. Perform Semi-Lagrangian Advection using the motion fields from ROVER. # # Writing the function makes it easier to implement multiprocessing if # required. # def ensemble_rover_sla( reflectivityObs, basetime, interval, duration, member, start_level, max_level, rho, alpha, sigma, track_interval ): """ A function to run ROVER and SLA. Parameters ------------- reflectivityObs: xarray.DataArray The xarray containing observed reflectivity. basetime: pandas.Timestamp The basetime of the forecast. Equivalent to the end time of the latest radar scan. interval: pandas.Timedelta The interval between the radar scans. duration: pandas.Timedelta The duration of the forecast. member: str The name of the member. start_level, max_level, rho, alpha, sigma, track_interval: floats Parameters of the members. Returns ------------ reflectivityFcst: xarray.DataArray Contains foreacsted reflectivity. """ # Generate timestamps of relevant radar scans latestRadarScanTime = basetime - interval roverTimestrings = timestamps_ending( latestRadarScanTime, duration=track_interval, interval=track_interval, format='%Y%m%d%H%M', exclude_end=False ) roverTimestamps = [pd.Timestamp(t) for t in roverTimestrings] qpf_xarray = reflectivityObs.sel(time=roverTimestamps) standardize_attr(qpf_xarray, frame_type=FrameType.dBZ, zero_value=9999.0) # Generate motion field using ROVER motion = rover( qpf_xarray, start_level=start_level, max_level=max_level, rho=rho, sigma=sigma, alpha=alpha ) # Perform SLA steps = int(duration / interval) reflectivityFcst = sla(qpf_xarray, motion, steps) reflectivityFcst = reflectivityFcst.expand_dims( dim=OrderedDict({'member': [member]}) ).copy() return reflectivityFcst ############################################################################# # Then, run the different members by calling the function above. # In this example. only four members are run. # # Define the member parameters # Ensure that values at the same position across # the tuples correspond to the same member startLevelTuple = (1, 2, 1, 1) maxLevelTuple = (7, 7, 7, 7) sigmaTuple = (2.5, 2.5, 1.5, 1.5) rhoTuple = (9., 9., 9., 9.) alphaTuple = (2000., 2000., 2000., 2000.) memberTuple = ('Mem-1', 'Mem-2', 'Mem-3', 'Mem-4') trackIntervalList = [pd.Timedelta(i, 'm') for i in [6, 12, 6, 12]] processes = 4 reflectivityFcstList = [] for i in range(processes): args = ( reflectivityObs, basetime, pd.Timedelta(6, 'm'), pd.Timedelta(3, 'h'), memberTuple[i], startLevelTuple[i], maxLevelTuple[i], rhoTuple[i], alphaTuple[i], sigmaTuple[i], trackIntervalList[i] ) reflectivityFcst = ensemble_rover_sla(*args) reflectivityFcstList.append(reflectivityFcst) rover_sla_time = pd.Timestamp.now() ############################################################## # Concatenating observed and forecasted reflectivities # -------------------------------------------------------- # # From the results from all members: # # #. Change the timestamps of observed reflectivity # and forecasted reflectivity of all members # from start time to end time. # #. Add forecasted reflectivity to reproj_reflectivity_list. # #. Concatenate observed and forecasted reflectivity # xarray.DataArrays along the time dimension. # List to store combined reflectivity arrays of each member reflectivityConcatList = [] # Changing the time coordinates from start time to end time reflectivityObs.coords['time'] = [ pd.Timestamp(t) + pd.Timedelta(6, 'm') for t in reflectivityObs.time.values ] for reflectivityFcst in reflectivityFcstList: # Identify the member member = reflectivityFcst.member.values[0] # Add member dimension to observed reflectivity reflectivityObsExpanded = reflectivityObs.expand_dims( dim=OrderedDict({'member': [member]}) ).copy() # Checking for the track interval of the forecasted # array timeIntervalsSet = set(np.diff(reflectivityFcst.time.values)) if pd.Timedelta(12, 'm').to_timedelta64() in timeIntervalsSet: reflectivityFcst.coords['time'] = [ pd.Timestamp(t) + pd.Timedelta(12, 'm') for t in reflectivityFcst.time.values ] else: reflectivityFcst.coords['time'] = [ pd.Timestamp(t) + pd.Timedelta(6, 'm') for t in reflectivityFcst.time.values ] # Combining reflectivityList = [ reflectivityObsExpanded, reflectivityFcst.isel(time=slice(1, None)) ] reflectivity = xarray.concat(reflectivityList, dim='time') standardize_attr(reflectivity, frame_type=FrameType.dBZ, zero_value=reflectivityFcst.attrs['zero_value']) reflectivityConcatList.append(reflectivity) concat_time = pd.Timestamp.now() ########################################################################## # Accumulating hourly rainfall for 3-hour forecast # ------------------------------------------------ # # Hourly accumulated rainfall is calculated every 30 minutes, the first # endtime is the basetime i.e. T+0min. # # #. Convert reflectivity in dBZ to rainrates in mm/h with dbz2rr(). # #. For rainrates every 12 minutes, interpolate rainfall # temporally to once every 6 minutes to ensure that rainrates are # equally spaced along the time axis. # #. Convert rainrates to rainfalls with rr2rf(). # #. Compute hourly accumulated rainfall every 30 minutes. # #. Concatenate accumulated rainfall of different members along the # member dimension. # accList = [] for reflectivity in reflectivityConcatList: # Converting reflectivity to rainrates rainrates = to_rainfall_rate( reflectivity, logarithmic=False, a=58.53, b=1.56) # Interpolate rainfall to every 12 minutes if # the member has a track interval of 12 minutes if pd.Timedelta(12, 'm').to_timedelta64() in timeIntervalsSet: rainrates = temporal_interpolate(rainrates, basetime - pd.Timedelta(1, 'h'), basetime + pd.Timedelta(3, 'h'), pd.Timedelta(6, 'm'), 'linear') # Convert rainrates to rainfalls every 6 minutes rainfalls = to_rainfall_depth(rainrates) # Accumulate rainfalls every 30 minutes accr = acc_rainfall_depth(rainfalls, basetime, basetime + pd.Timedelta(3, 'h'), pd.Timedelta(30, 'm'), pd.Timedelta(1, 'h')) accList.append(accr) # Concatenating along the mmeber dimension acc_rf = xarray.concat(accList, dim='member') acc_time = pd.Timestamp.now() ###################################################################### # Calculating Probability Exceeding Threshold # ---------------------------------------------- # # In this example, thresholds are 0.5mm, 5mm, # 10mm, 30mm, 50mm and 70 mm. # # Probabilities are 0%, 25%, 50%, 75% and 100%, as there are # four members. # # Steps: # # #. Define threshold. # #. Use a loop to calculate the probability of exceeding threshold # for each gridcell and store results in list. # #. Concatenate the contents of the list. Result is an xarray with # dimensions (threshold, time, y, x). # #. Plot the results using xarray.plot(). In this example, # only the probability exceeding 0.5mm rainfall # every hour will be plotted. # Define threshold threshold = [0.5, 5., 10., 30., 50., 70.] # Define list to store probabilities of exceeding rainfall thresholds # List corresponds to threshold prob_list = [] # Calculate probability for th in threshold: bool_forecast = acc_rf >= th count = bool_forecast.sum(dim='member') prob = count/len(bool_forecast.coords['member'].values) * 100 prob_list.append(prob) # Generate coordinate xarray for threshold th_xarray = xarray.IndexVariable('threshold', threshold) # concatenate prob_rainfall = xarray.concat(prob_list, dim=th_xarray) prob_rainfall.attrs['long_name'] = "Probability of Exceeding Threshold" prob_rainfall.attrs['units'] = "%" # Plot the results # Extracting the crs crs = area_def_tgt.to_cartopy_crs() # Defining cmap cmap = LinearSegmentedColormap.from_list( 'custom blue', ['#FFFFFF', '#000099'] ) # Obtaining xarray slices to be plotted threshold_list = [0.5] timelist = [basetime + pd.Timedelta(hours=i) for i in range(4)] da_plot = prob_rainfall.sel(threshold=threshold_list, time=timelist) # Defining coastlines map_shape_file = os.path.join(THIS_DIR, "./../tests/samples/shape/rsmc") ocean_color = np.array([[[178, 208, 254]]], dtype=np.uint8) land_color = cfeature.COLORS['land'] coastline = cfeature.ShapelyFeature( list(shpreader.Reader(map_shape_file).geometries()), ccrs.PlateCarree() ) for th in threshold_list: # Plotting p = da_plot.sel(threshold=th).plot( col='time', col_wrap=2, subplot_kws={'projection': crs}, cbar_kwargs={'ticks': [0, 25, 50, 75, 100], 'format': '%.3g'}, cmap=cmap ) for idx, ax in enumerate(p.axes.flat): # ocean ax.imshow(np.tile(ocean_color, [2, 2, 1]), origin='upper', transform=ccrs.PlateCarree(), extent=[-180, 180, -180, 180], zorder=-1) # coastline, color ax.add_feature(coastline, facecolor=land_color, edgecolor='none', zorder=0) # overlay coastline without color ax.add_feature(coastline, facecolor='none', edgecolor='gray', linewidth=0.5, zorder=3) ax.gridlines() # gridlines ax.set_title( f"% Exceeding {th}mm rainfall\n" f"Based @ {basetime.strftime('%H:%MH')}", loc='left', fontsize=8 ) ax.set_title( '' ) ax.set_title( f"{basetime.strftime('%Y-%m-%d')} \n" f"Valid @ {timelist[idx].strftime('%H:%MH')} ", loc='right', fontsize=8 ) plt.savefig( THIS_DIR + f"/../tests/outputs/p_{th}.png", dpi=300 ) prob_time = pd.Timestamp.now() ############################################################################### # Rainfall percentiles # ---------------------------- # # #. Using xarray.DataArray.mean(), # calculate the mean rainfall of all gridpoints. # #. Using xarray.DataArray.min(), # find the minimum rainfall of all gridpoints. # #. Using xarray.DataArray.max(), # find the maximum rainfall of all gridpoints. # #. Using xarray.DataArray.quantile() # find the 25th, 50th and 75th percentile rainfall # of all gridpoints. # #. Concatenate rainfall along percentile dimension. # #. Plot results using xarray.plot(). In this example # only the maximum percentile rainfall # every hour will be plotted. # mean mean_rainfall = acc_rf.mean(dim='member', keep_attrs=True) # max max_rainfall = acc_rf.max(dim='member', keep_attrs=True) # min min_rainfall = acc_rf.min(dim='member', keep_attrs=True) # quartiles q_rainfall = acc_rf.quantile( [.25, .5, .75], dim='member', interpolation='nearest', keep_attrs=True) # generate index percentile = xarray.IndexVariable( 'percentile', ['Minimum', '25th Percentile', '50th Percentile', 'Mean', '75th Percentile', 'Maximum'] ) # concatenating p_rainfall = xarray.concat( [min_rainfall, q_rainfall.sel(quantile=.25).squeeze().drop('quantile'), q_rainfall.sel(quantile=.50).squeeze().drop('quantile'), mean_rainfall, q_rainfall.sel(quantile=.75).squeeze().drop('quantile'), max_rainfall], dim=percentile ) p_rainfall.attrs['long_name'] = "Hourly Accumulated Rainfall" p_rainfall.attrs['units'] = "mm" # Defining levels # Defining the colour scheme levels = [ 0, 0.5, 2, 5, 10, 20, 30, 40, 50, 70, 100, 150, 200, 300, 400, 500, 600, 700 ] cmap = matplotlib.colors.ListedColormap([ '#ffffff', '#9bf7f7', '#00ffff', '#00d5cc', '#00bd3d', '#2fd646', '#9de843', '#ffdd41', '#ffac33', '#ff621e', '#d23211', '#9d0063', '#e300ae', '#ff00ce', '#ff57da', '#ff8de6', '#ffe4fd' ]) norm = BoundaryNorm(levels, ncolors=cmap.N, clip=True) # Obtaining xarray slices to be plotted plot_positions = ['Maximum'] timelist = [basetime + pd.Timedelta(hours=i) for i in range(4)] da_plot = p_rainfall.sel(percentile=plot_positions, time=timelist) for pos in plot_positions: # Plotting p = da_plot.sel(percentile=pos).plot( col='time', col_wrap=2, subplot_kws={'projection': crs}, cbar_kwargs={'ticks': levels, 'format': '%.3g'}, cmap=cmap, norm=norm ) for idx, ax in enumerate(p.axes.flat): # ocean ax.imshow(np.tile(ocean_color, [2, 2, 1]), origin='upper', transform=ccrs.PlateCarree(), extent=[-180, 180, -180, 180], zorder=-1) # coastline, color ax.add_feature(coastline, facecolor=land_color, edgecolor='none', zorder=0) # overlay coastline without color ax.add_feature(coastline, facecolor='none', edgecolor='gray', linewidth=0.5, zorder=3) ax.gridlines() ax.set_title( f"{pos} Radar-Based Rainfall\n" f"Based @ {basetime.strftime('%H:%MH')}", loc='left', fontsize=8 ) ax.set_title( '' ) ax.set_title( f"{basetime.strftime('%Y-%m-%d')} \n" f"Valid @ {timelist[idx].strftime('%H:%MH')} ", loc='right', fontsize=8 ) position = pos.split(" ")[0] plt.savefig( THIS_DIR + f"/../tests/outputs/rainfall_{position}.png", dpi=300 ) ptime = pd.Timestamp.now() ############################################################################## # Extract the rainfall values at a specified location # ------------------------------------------------------------------ # # In this example, the rainfall values at the location is assumed # to be the same as the nearest gridpoint. # # #. Read information regarding the radar stations into a pandas.DataFrame. # #. Extract the rainfall values at the nearest gridpoint to location # for given timesteps (in this example, 30 minute intervals). # #. Store rainfall values over time in an xarray.DataArray. # #. Plot the time series of rainfall with boxplots at desired station. # In this case, the 15th percentile member is plotted. # Getting radar station coordinates df = pd.read_csv( os.path.join(THIS_DIR, "../tests/samples/hk_raingauge.csv"), usecols=[0, 1, 2, 3, 4] ) # Extract rainfall values at gridpoint closest to the # location specified for given timesteps and storing it # in xarray.DataArray. station_rf_list = [] station_name = [] for index, row in df.iterrows(): station_rf_list.append(p_rainfall.sel( northing=row[1], easting=row[2], method='nearest' ).drop('northing').drop('easting')) station_name.append(row[0]) station_name_index = xarray.IndexVariable( 'ID', station_name ) station_rf = xarray.concat( station_rf_list, dim=station_name_index ) # Extracting the 15th ranked station xr_15_percentile = station_rf.quantile( .15, dim='ID', interpolation='nearest').drop('quantile') # Plotting _, tax = plt.subplots(figsize=(20, 15)) plt.plot( np.arange(1, len(xr_15_percentile.coords['time'].values) + 1), xr_15_percentile.loc['Mean'].values, 'ko-' ) # plot line # Storing percentiles as dictionary to call ax.bxp for boxplot stats_list = [] for i in range(len(xr_15_percentile.coords['time'].values)): stats = { 'med': xr_15_percentile.loc['50th Percentile'].values[i], 'q1': xr_15_percentile.loc['25th Percentile'].values[i], 'q3': xr_15_percentile.loc['75th Percentile'].values[i], 'whislo': xr_15_percentile.loc['Maximum'].values[i], 'whishi': xr_15_percentile.loc['Minimum'].values[i] } stats_list.append(stats) # Plot boxplot tax.bxp( stats_list, showfliers=False ) # Labels xcoords = xr_15_percentile.coords['time'].values xticklabels = [pd.to_datetime(str(t)).strftime("%H:%M") for t in xcoords] tax.set_xticklabels(xticklabels) tax.xaxis.set_tick_params(labelsize=20) tax.yaxis.set_tick_params(labelsize=20) plt.title('Time Series of Hourly Accumulated Rainfall', fontsize=25) plt.ylabel("Hourly Accumulated Rainfall [mm]", fontsize=22) plt.xlabel("Time", fontsize=18) plt.savefig(THIS_DIR+"/../tests/outputs/pqpf_time_series.png") extract_time = pd.Timestamp.now() ###################################################################### # Checking run time of each component # -------------------------------------------------------------------- # print(f"Start time: {start_time}") print(f"Initialising time: {initialising_time}") print(f"Rover and SLA time: {rover_sla_time}") print(f"Concatenating time: {concat_time}") print(f"Accumulating rainfall time: {acc_time}") print("Calculate and plot probability exceeding threshold: " f"{prob_time}") print(f"Plotting rainfall maps: {ptime}") print(f"Extracting and plotting time series time: {extract_time}") print(f"Time to initialise: {initialising_time-start_time}") print(f"Time to run rover and SLA: {rover_sla_time-initialising_time}") print(f"Time to concatenate xarrays: {concat_time - rover_sla_time}") print(f"Time to accumulate rainfall: {acc_time - concat_time}") print("Time to calculate and plot probability exceeding threshold: " f"{prob_time-acc_time}") print(f"Time to plot rainfall maps: {ptime-prob_time}") print(f"Time to extract station data and plot time series: " f"{extract_time-ptime}")