5. Tropical Cyclones

Selection of historical and synthetic Tropical Cyclones (TCs) affecting the site

inputs required:

• Storm tracks records (ibtracks file)

• Synthetic Storm tracks records from Nakajo database (Nakajo et al. 2014)

• longitude and latitude of the site (location of the waves record)

• radius for TCs selection (º)

in this notebook:

• selection of TCs affecting the site for including in Daily Weather Type time series(14º around the site)

• selection of TCs affecting the site for numerical simulation (7.5º around the site)

• obtain TC category, minimum pressure, date when the TC track is closest to site

Workflow:

#!/usr/bin/env python
# -*- coding: utf-8 -*-

# common
import os
import os.path as op
import copy

# pip
import xarray as xr
import numpy as np

# DEV: override installed teslakit
import sys
sys.path.insert(0, op.join(os.path.abspath(''), '..', '..', '..'))

# teslakit
from bluemath.teslakit.tcs.tcs import Extract_Circle, filter_simultaneous_TCs
from bluemath.teslakit.io.aux import save_nc
from bluemath.teslakit.io.matlab import ReadNakajoMats

from bluemath.teslakit.plotting.tcs import Plot_TCs_HistoricalTracks, Plot_TCs_HistoricalTracks_Category
from bluemath.teslakit.plotting.tcs import Plot_TCs_Params_HISTvsSIM, Plot_TCs_Params_HISTvsSIM_histogram

5.1. Files and paths

# project path
p_data = '/Users/albacid/Projects/TeslaKit.2.0_projects/SAMOA'

# output path 
p_out = op.join(p_data,'TCs')
if not os.path.isdir(p_out): os.makedirs(p_out)
    

# SAMOA wave point longitude(0-360) and latitude
site = 'samoa'
pnt_lon = -172.07+360
pnt_lat = -13.76

# input data
tcs_file = op.join(p_data,'resources','IBTrACS.ALL.v04r00.nc')
tcs_path = op.join(p_data,'resources','Nakajo_tracks')


# output data
tcs_hist_r1_file = op.join(p_out,'TCs_hist_r1_' + site + '_raw.nc') # TCs crossing through r1
tcs_hist_r1_params_file = op.join(p_out,'TCs_hist_r1_params_' + site + '_raw.nc') # TCs parameters r1
tcs_hist_r1_params_file_sel = op.join(p_out,'TCs_hist_r1_params_' + site + '.nc') # TCs parameters r1

tcs_hist_r2_params_file = op.join(p_out,'TCs_hist_r2_params_' + site + '_raw.nc') # TCs parameters r2
tcs_hist_r2_params_file_sel = op.join(p_out,'TCs_hist_r2_params_' + site + '.nc') # TCs parameters r2

tcs_sim_r1_file = op.join(p_out,'TCs_r1_sim_' + site + '.nc') # TCs crossing through r1
tcs_sim_r1_params_file = op.join(p_out,'TCs_r1_sim_params_' + site + '.nc') # TCs parameters r1

5.2. Parameters

# --------------------------------------
# load data and set parameters

TCs_wmo = xr.open_dataset(tcs_file)  # noaa IBTrACS.ALL.v04r00.nc

# Load Nakajo simulations
#TCs_Nakajo = ReadNakajoMats(tcs_path)

# save netcdf with Nakajo sims
#TCs_Nakajo.to_netcdf(op.join(p_data,'resources','TCs_nakajo_all.nc'))

TCs_Nakajo = xr.open_dataset(op.join(p_data,'resources','TCs_nakajo_all.nc'))

# radius for TCs selection (º)
r1 = 14
r2 = 7.5

5.3. Historical TCs - extraction

# --------------------------------------
# Select Historical TCs inside circle

# dictionary with needed variable names 
d_vns = {
    'longitude': 'lon',
    'latitude': 'lat',
    'time': 'time',
    'pressure': 'wmo_pres',
}

# Select TCs that crosses a circular area R1
TCs_r1_tracks, TCs_r1_params = Extract_Circle(TCs_wmo, pnt_lon, pnt_lat, r1, d_vns)
TCs_r2_tracks, TCs_r2_params = Extract_Circle(TCs_wmo, pnt_lon, pnt_lat, r2, d_vns)

# store data
save_nc(TCs_r1_tracks, tcs_hist_r1_file)
save_nc(TCs_r1_params, tcs_hist_r1_params_file)
save_nc(TCs_r2_params, tcs_hist_r2_params_file)

# Plot storm tracks world map (requires basemap)

lon1, lon2 = 90, 270
lat1, lat2 = -60, 30

Plot_TCs_HistoricalTracks(
    TCs_r1_tracks, TCs_r2_tracks,
    lon1, lon2, lat1, lat2,
    pnt_lon, pnt_lat, r1, r2, 
);

# zoom map

r_zoom = 20

Plot_TCs_HistoricalTracks(
    TCs_r1_tracks,TCs_r2_tracks,
    pnt_lon-r_zoom, pnt_lon+r_zoom, pnt_lat-r_zoom, pnt_lat+r_zoom,
    pnt_lon, pnt_lat, r1, r2
);

# Plot storm tracks category world map (requires basemap)

lon1, lon2 = 90, 270
lat1, lat2 = -60, 30

# r1
Plot_TCs_HistoricalTracks_Category(
    TCs_r1_tracks, TCs_r1_params.category,
    lon1, lon2, lat1, lat2,
    pnt_lon, pnt_lat, r1,
);

# r2
Plot_TCs_HistoricalTracks_Category(
    TCs_r2_tracks, TCs_r2_params.category,
    lon1, lon2, lat1, lat2,
    pnt_lon, pnt_lat, r2,
);

5.3.1. Filter TCs that happen simultaneously

# Keep TCs of greater category or longest track inside circle

# sort TCs by date
TCs_r1_params_sel = filter_simultaneous_TCs(TCs_r1_params)
TCs_r2_params_sel = filter_simultaneous_TCs(TCs_r2_params)

# save
save_nc(TCs_r1_params_sel, tcs_hist_r1_params_file_sel)
save_nc(TCs_r2_params_sel, tcs_hist_r2_params_file_sel)

5.4. Synthetic TCs - extraction

# Get Nakajo TCs at the study area

# dictionary with needed variable names
d_vns = {
    'longitude':'ylon_TC',
    'latitude':'ylat_TC',
    'time': 'yts',
    'pressure':'yCPRES',
}

# Extract Nakajo TCs inside r1
TCs_r1_sim, TCs_r1_sim_params = Extract_Circle(TCs_Nakajo, pnt_lon, pnt_lat, r1, d_vns)


# Store Nakajo TCs 
TCs_r1_sim.to_netcdf(tcs_sim_r1_file)
TCs_r1_sim_params.to_netcdf(tcs_sim_r1_params_file)

# Historical vs Nakajo Simulation parameters: 
TCs_r1_hist_params = xr.open_dataset(tcs_hist_r1_params_file_sel)  # historical TCs parameters inside radius 1

# scatter plot
Plot_TCs_Params_HISTvsSIM(TCs_r1_hist_params, TCs_r1_sim_params);

# histogram
Plot_TCs_Params_HISTvsSIM_histogram(TCs_r1_hist_params, TCs_r1_sim_params);