"""
This file is part of CLIMADA.
Copyright (C) 2017 ETH Zurich, CLIMADA contributors listed in AUTHORS.
CLIMADA is free software: you can redistribute it and/or modify it under the
terms of the GNU General Public License as published by the Free
Software Foundation, version 3.
CLIMADA is distributed in the hope that it will be useful, but WITHOUT ANY
WARRANTY; without even the implied warranty of MERCHANTABILITY or FITNESS FOR A
PARTICULAR PURPOSE. See the GNU General Public License for more details.
You should have received a copy of the GNU General Public License along
with CLIMADA. If not, see <https://www.gnu.org/licenses/>.
---
Define impact functions for tropical cyclnes .
"""
__all__ = ['ImpfTropCyclone', 'ImpfSetTropCyclone', 'IFTropCyclone']
import logging
from deprecation import deprecated
import numpy as np
import pandas as pd
from climada.entity.impact_funcs.base import ImpactFunc
from climada.entity.impact_funcs.impact_func_set import ImpactFuncSet
from climada.util.constants import SYSTEM_DIR
LOGGER = logging.getLogger(__name__)
[docs]
class ImpfTropCyclone(ImpactFunc):
"""Impact functions for tropical cyclones."""
[docs]
def __init__(self):
ImpactFunc.__init__(self)
self.haz_type = 'TC'
[docs]
def set_emanuel_usa(self, *args, **kwargs):
"""This function is deprecated, use from_emanuel_usa() instead."""
LOGGER.warning("The use of ImpfTropCyclone.set_emanuel_usa is deprecated."
"Use ImpfTropCyclone.from_emanuel_usa instead.")
self.__dict__ = ImpfTropCyclone.from_emanuel_usa(*args, **kwargs).__dict__
[docs]
@classmethod
def from_emanuel_usa(cls, impf_id=1, intensity=np.arange(0, 121, 5),
v_thresh=25.7, v_half=74.7, scale=1.0):
"""
Init TC impact function using the formula of Kerry Emanuel, 2011:
'Global Warming Effects on U.S. Hurricane Damage',
https://doi.org/10.1175/WCAS-D-11-00007.1
Parameters
----------
impf_id : int, optional
impact function id. Default: 1
intensity : np.array, optional
intensity array in m/s. Default:
5 m/s step array from 0 to 120m/s
v_thresh : float, optional
first shape parameter, wind speed in
m/s below which there is no damage. Default: 25.7(Emanuel 2011)
v_half : float, optional
second shape parameter, wind speed in m/s
at which 50% of max. damage is expected. Default:
v_threshold + 49 m/s (mean value of Sealy & Strobl 2017)
scale : float, optional
scale parameter, linear scaling of MDD.
0<=scale<=1. Default: 1.0
Raises
------
ValueError
Returns
-------
impf : ImpfTropCyclone
TC impact function instance based on formula by Emanuel (2011)
"""
if v_half <= v_thresh:
raise ValueError('Shape parameters out of range: v_half <= v_thresh.')
if v_thresh < 0 or v_half < 0:
raise ValueError('Negative shape parameter.')
if scale > 1 or scale <= 0:
raise ValueError('Scale parameter out of range.')
impf = cls()
impf.name = 'Emanuel 2011'
impf.id = impf_id
impf.intensity_unit = 'm/s'
impf.intensity = intensity
impf.paa = np.ones(intensity.shape)
v_temp = (impf.intensity - v_thresh) / (v_half - v_thresh)
v_temp[v_temp < 0] = 0
impf.mdd = v_temp**3 / (1 + v_temp**3)
impf.mdd *= scale
return impf
[docs]
class ImpfSetTropCyclone(ImpactFuncSet):
"""Impact function set (ImpfS) for tropical cyclones."""
[docs]
def __init__(self):
ImpactFuncSet.__init__(self)
[docs]
def set_calibrated_regional_ImpfSet(self, *args, **kwargs):
"""This function is deprecated, use from_calibrated_regional_ImpfSet() instead."""
LOGGER.warning("ImpfSetTropCyclone.set_calibrated_regional_ImpfSet is deprecated."
"Use ImpfSetTropCyclone.from_calibrated_regional_ImpfSet instead.")
self.__dict__ = \
ImpfSetTropCyclone.from_calibrated_regional_ImpfSet(*args, **kwargs).__dict__
return ImpfSetTropCyclone.calibrated_regional_vhalf(*args, **kwargs)
[docs]
@classmethod
def from_calibrated_regional_ImpfSet(cls, calibration_approach='TDR', q=.5,
input_file_path=None, version=1):
"""Calibrated regional TC wind impact functions
Based on Eberenz et al. 2021: https://doi.org/10.5194/nhess-21-393-2021
Parameters
----------
calibration_approach : str, optional
The following values are supported:
'TDR' (default)
Total damage ratio (TDR) optimization with
TDR=1.0 (simulated damage = reported damage from EM-DAT)
'TDR1.5'
Total damage ratio (TDR) optimization with
TDR=1.5 (simulated damage = 1.5*reported damage from EM-DAT)
'RMSF'
Root-mean-squared fraction (RMSF) optimization
'EDR'
quantile from individually fitted v_half per event,
i.e. v_half fitted to get EDR=1.0 for each event
q : float, optional
Quantile between 0 and 1.0 to select (EDR only).
Default: 0.5, i.e. median v_half
input_file_path : str or DataFrame, optional
full path to calibration
result file to be used instead of default file in repository
(expert users only)
Returns
-------
impf_set : ImpfSetTropCyclone
TC Impact Function Set based on Eberenz et al, 2021.
"""
reg_v_half = ImpfSetTropCyclone.calibrated_regional_vhalf(
calibration_approach=calibration_approach,
q=q,
input_file_path=input_file_path,
version=version,
)
# define regions and parameters:
v_0 = 25.7 # v_threshold based on Emanuel (2011)
scale = 1.0
regions_long = dict()
regions_long['NA1'] = 'Caribbean and Mexico (NA1)'
regions_long['NA2'] = 'USA and Canada (NA2)'
regions_long['NI'] = 'North Indian (NI)'
regions_long['OC'] = 'Oceania (OC)'
regions_long['SI'] = 'South Indian (SI)'
regions_long['WP1'] = 'South East Asia (WP1)'
regions_long['WP2'] = 'Philippines (WP2)'
regions_long['WP3'] = 'China Mainland (WP3)'
regions_long['WP4'] = 'North West Pacific (WP4)'
regions_long['ROW'] = 'Global'
# init impact function set
impf_set = cls()
for idx, region in enumerate(reg_v_half.keys()):
impf_tc = ImpfTropCyclone.from_emanuel_usa(impf_id=int(idx + 1),
v_thresh=v_0,
v_half=reg_v_half[region],
scale=scale)
impf_tc.name = regions_long[region]
impf_set.append(impf_tc)
return impf_set
[docs]
@staticmethod
def calibrated_regional_vhalf(calibration_approach='TDR', q=.5,
input_file_path=None, version=1):
"""Calibrated TC wind impact function slope parameter v_half per region
Based on Eberenz et al., 2021: https://doi.org/10.5194/nhess-21-393-2021
Parameters
----------
calibration_approach : str, optional
The following values are supported:
'TDR' (default)
Total damage ratio (TDR) optimization with
TDR=1.0 (simulated damage = reported damage from EM-DAT)
'TDR1.5'
Total damage ratio (TDR) optimization with
TDR=1.5 (simulated damage = 1.5*reported damage from EM-DAT)
'RMSF'
Root-mean-squared fraction (RMSF) optimization
'EDR'
quantile from individually fitted v_half per event,
i.e. v_half fitted to get EDR=1.0 for each event
q : float, optional
Quantile between 0 and 1.0 to select (EDR only).
Default: 0.5, i.e. median v_half
input_file_path : str or DataFrame, optional
full path to calibration
result file to be used instead of default file in repository
(expert users only)
Raises
------
ValueError
Returns
-------
v_half : dict
TC impact function slope parameter v_half per region
"""
calibration_approach = calibration_approach.upper()
if calibration_approach not in ['TDR', 'TDR1.0', 'TDR1.5', 'RMSF', 'EDR']:
raise ValueError('calibration_approach is invalid')
if 'EDR' in calibration_approach and (q < 0. or q > 1.):
raise ValueError('Quantile q out of range [0, 1]')
if calibration_approach == 'TDR':
calibration_approach = 'TDR1.0'
# load calibration results depending on approach:
if isinstance(input_file_path, str):
df_calib_results = pd.read_csv(input_file_path,
encoding="ISO-8859-1", header=0)
elif isinstance(input_file_path, pd.DataFrame):
df_calib_results = input_file_path
else:
df_calib_results = pd.read_csv(
SYSTEM_DIR.joinpath(
'tc_impf_cal_v%02.0f_%s.csv' % (version, calibration_approach)),
encoding="ISO-8859-1", header=0)
regions_short = ['NA1', 'NA2', 'NI', 'OC', 'SI', 'WP1', 'WP2', 'WP3', 'WP4']
# loop over calibration regions (column cal_region2 in df):
reg_v_half = dict()
for region in regions_short:
df_reg = df_calib_results.loc[df_calib_results.cal_region2 == region]
df_reg = df_reg.reset_index(drop=True)
reg_v_half[region] = np.round(df_reg['v_half'].quantile(q=q), 5)
# rest of the world (ROW), calibrated by all data:
regions_short = regions_short + ['ROW']
if calibration_approach == 'EDR':
reg_v_half[regions_short[-1]] = np.round(df_calib_results['v_half'].quantile(q=q), 5)
else:
df_reg = df_calib_results.loc[df_calib_results.cal_region2 == 'GLB']
df_reg = df_reg.reset_index(drop=True)
reg_v_half[regions_short[-1]] = np.round(df_reg['v_half'].values[0], 5)
return reg_v_half
[docs]
@staticmethod
def get_countries_per_region(region=None):
"""Returns dictionaries with numerical and alphabetical ISO3 codes
of all countries associated to a calibration region.
Only contains countries that were affected by tropical cyclones
between 1980 and 2017 according to EM-DAT.
Parameters
----------
region : str
regional abbreviation (default='all'),
either 'NA1', 'NA2', 'NI', 'OC', 'SI', 'WP1', 'WP2',
'WP3', 'WP4', or 'all'.
Returns
-------
region_name : dict or str
long name per region
impf_id : dict or int
impact function ID per region
iso3n : dict or list
numerical ISO3codes (=region_id) per region
iso3a : dict or list
numerical ISO3codes (=region_id) per region
"""
if not region:
region = 'all'
iso3n = {'NA1': [660, 28, 32, 533, 44, 52, 84, 60, 68, 132, 136,
152, 170, 188, 192, 212, 214, 218, 222, 238, 254,
308, 312, 320, 328, 332, 340, 388, 474, 484, 500,
558, 591, 600, 604, 630, 654, 659, 662, 670, 534,
740, 780, 796, 858, 862, 92, 850],
'NA2': [124, 840],
'NI': [4, 51, 31, 48, 50, 64, 262, 232,
231, 268, 356, 364, 368, 376, 400, 398, 414, 417,
422, 462, 496, 104, 524, 512, 586, 634, 682, 706,
144, 760, 762, 795, 800, 784, 860, 887],
'OC': [16, 36, 184, 242, 258, 316, 296, 584, 583, 520,
540, 554, 570, 574, 580, 585, 598, 612, 882, 90,
626, 772, 776, 798, 548, 876],
'SI': [174, 180, 748, 450, 454, 466, 480, 508, 710, 834,
716],
'WP1': [116, 360, 418, 458, 764, 704],
'WP2': [608],
'WP3': [156],
'WP4': [344, 392, 410, 446, 158],
'ROW': [8, 12, 20, 24, 10, 40, 112, 56, 204, 535, 70, 72,
74, 76, 86, 96, 100, 854, 108, 120, 140, 148, 162,
166, 178, 191, 531, 196, 203, 384, 208, 818, 226,
233, 234, 246, 250, 260, 266, 270, 276, 288, 292,
300, 304, 831, 324, 624, 334, 336, 348, 352, 372,
833, 380, 832, 404, 408, 983, 428, 426, 430, 434,
438, 440, 442, 470, 478, 175, 498, 492, 499, 504,
516, 528, 562, 566, 807, 578, 275, 616, 620, 642,
643, 646, 638, 652, 663, 666, 674, 678, 686, 688,
690, 694, 702, 703, 705, 239, 728, 724, 729, 744,
752, 756, 768, 788, 792, 804, 826, 581, 732, 894,
248]}
iso3a = {'NA1': ['AIA', 'ATG', 'ARG', 'ABW', 'BHS', 'BRB', 'BLZ', 'BMU',
'BOL', 'CPV', 'CYM', 'CHL', 'COL', 'CRI', 'CUB', 'DMA',
'DOM', 'ECU', 'SLV', 'FLK', 'GUF', 'GRD', 'GLP', 'GTM',
'GUY', 'HTI', 'HND', 'JAM', 'MTQ', 'MEX', 'MSR', 'NIC',
'PAN', 'PRY', 'PER', 'PRI', 'SHN', 'KNA', 'LCA', 'VCT',
'SXM', 'SUR', 'TTO', 'TCA', 'URY', 'VEN', 'VGB', 'VIR'],
'NA2': ['CAN', 'USA'],
'NI': ['AFG', 'ARM', 'AZE', 'BHR', 'BGD', 'BTN', 'DJI', 'ERI',
'ETH', 'GEO', 'IND', 'IRN', 'IRQ', 'ISR', 'JOR', 'KAZ',
'KWT', 'KGZ', 'LBN', 'MDV', 'MNG', 'MMR', 'NPL', 'OMN',
'PAK', 'QAT', 'SAU', 'SOM', 'LKA', 'SYR', 'TJK', 'TKM',
'UGA', 'ARE', 'UZB', 'YEM'],
'OC': ['ASM', 'AUS', 'COK', 'FJI', 'PYF', 'GUM', 'KIR', 'MHL',
'FSM', 'NRU', 'NCL', 'NZL', 'NIU', 'NFK', 'MNP', 'PLW',
'PNG', 'PCN', 'WSM', 'SLB', 'TLS', 'TKL', 'TON', 'TUV',
'VUT', 'WLF'],
'SI': ['COM', 'COD', 'SWZ', 'MDG', 'MWI', 'MLI', 'MUS', 'MOZ',
'ZAF', 'TZA', 'ZWE'],
'WP1': ['KHM', 'IDN', 'LAO', 'MYS', 'THA', 'VNM'],
'WP2': ['PHL'],
'WP3': ['CHN'],
'WP4': ['HKG', 'JPN', 'KOR', 'MAC', 'TWN'],
'ROW': ['ALB', 'DZA', 'AND', 'AGO', 'ATA', 'AUT', 'BLR', 'BEL',
'BEN', 'BES', 'BIH', 'BWA', 'BVT', 'BRA', 'IOT', 'BRN',
'BGR', 'BFA', 'BDI', 'CMR', 'CAF', 'TCD', 'CXR', 'CCK',
'COG', 'HRV', 'CUW', 'CYP', 'CZE', 'CIV', 'DNK', 'EGY',
'GNQ', 'EST', 'FRO', 'FIN', 'FRA', 'ATF', 'GAB', 'GMB',
'DEU', 'GHA', 'GIB', 'GRC', 'GRL', 'GGY', 'GIN', 'GNB',
'HMD', 'VAT', 'HUN', 'ISL', 'IRL', 'IMN', 'ITA', 'JEY',
'KEN', 'PRK', 'XKX', 'LVA', 'LSO', 'LBR', 'LBY', 'LIE',
'LTU', 'LUX', 'MLT', 'MRT', 'MYT', 'MDA', 'MCO', 'MNE',
'MAR', 'NAM', 'NLD', 'NER', 'NGA', 'MKD', 'NOR', 'PSE',
'POL', 'PRT', 'ROU', 'RUS', 'RWA', 'REU', 'BLM', 'MAF',
'SPM', 'SMR', 'STP', 'SEN', 'SRB', 'SYC', 'SLE', 'SGP',
'SVK', 'SVN', 'SGS', 'SSD', 'ESP', 'SDN', 'SJM', 'SWE',
'CHE', 'TGO', 'TUN', 'TUR', 'UKR', 'GBR', 'UMI', 'ESH',
'ZMB', 'ALA']}
impf_id = {'NA1': 1, 'NA2': 2, 'NI': 3, 'OC': 4, 'SI': 5,
'WP1': 6, 'WP2': 7, 'WP3': 8, 'WP4': 9, 'ROW': 10}
region_name = dict()
region_name['NA1'] = 'Caribbean and Mexico'
region_name['NA2'] = 'USA and Canada'
region_name['NI'] = 'North Indian'
region_name['OC'] = 'Oceania'
region_name['SI'] = 'South Indian'
region_name['WP1'] = 'South East Asia'
region_name['WP2'] = 'Philippines'
region_name['WP3'] = 'China Mainland'
region_name['WP4'] = 'North West Pacific'
if region == 'all':
return region_name, impf_id, iso3n, iso3a
return region_name[region], impf_id[region], iso3n[region], iso3a[region]
[docs]
@deprecated(details="The class name IFTropCyclone is deprecated and won't be supported in a future "
+"version. Use ImpfTropCyclone instead")
class IFTropCyclone(ImpfTropCyclone):
"""Is ImpfTropCyclone now"""
