"""
This module defines functions used to adapt the HURDAT2 cyclone track data as
a ragged-array dataset.
"""
import enum
import os
import re
import tempfile
from dataclasses import dataclass, field, fields
from datetime import datetime, timezone
from io import StringIO
from typing import Literal
import numpy as np
import xarray as xr
from clouddrift.adapters.utils import download_with_progress
from clouddrift.raggedarray import RaggedArray
_DEFAULT_NAME = "hurdat2"
_ATLANTIC_BASIN_URL = "https://www.aoml.noaa.gov/hrd/hurdat/hurdat2.html"
_PACIFIC_BASIN_URL = "https://www.aoml.noaa.gov/hrd/hurdat/hurdat2-nepac.html"
_DEFAULT_FILE_PATH = os.path.join(tempfile.gettempdir(), "clouddrift", _DEFAULT_NAME)
_METERS_IN_NAUTICAL_MILES = 1825
_PASCAL_PER_MILLIBAR = 100
_BasinOption = Literal["atlantic", "pacific", "both"]
[docs]
class RecordIdentifier(str, enum.Enum):
"""
C – Closest approach to a coast, not followed by a landfall
G – Genesis
I – An intensity peak in terms of both pressure and wind
L – Landfall (center of system crossing a coastline)
P – Minimum in central pressure
R – Provides additional detail on the intensity of the cyclone when rapid changes are underway
S – Change of status of the system
T – Provides additional detail on the track (position) of the cyclone
W – Maximum sustained wind speed
"""
CLOSEST_TO_COAST = "C"
GENESIS = "G"
INTENSITY_PEAK = "I"
LANDFALL = "L"
MINIMUM_CENTRAL_PRESSURE = "P"
RAPID_CHANGE = "R"
STATUS_CHANGE = "S"
TRACK_POSITION = "T"
MAX_SUSTAINED_WIND_SPEED = "W"
NOT_AVAILABLE = ""
[docs]
class SystemStatus(str, enum.Enum):
"""
TD – Tropical cyclone of tropical depression intensity (< 34 knots)
TS – Tropical cyclone of tropical storm intensity (34-63 knots)
HU – Tropical cyclone of hurricane intensity (> 64 knots)
EX – Extratropical cyclone (of any intensity)
SD – Subtropical cyclone of subtropical depression intensity (< 34 knots)
SS – Subtropical cyclone of subtropical storm intensity (> 34 knots)
LO – A low that is neither a tropical cyclone, a subtropical cyclone, nor an extratropical cyclone (of any intensity)
WV – Tropical Wave (of any intensity)
DB – Disturbance (of any intensity)
ET - UNKNOWN found in Northeast Pacific Basin
PT - UNKNOWN found in Northeast Pacific Basin
ST - UNKNOWN found in Northeast Pacific Basin
TY - UNKNOWN found in Northeast Pacific Basin
"""
TD = "TD"
TS = "TS"
HU = "HU"
EX = "EX"
SD = "SD"
SS = "SS"
LO = "LO"
WV = "WV"
DB = "DB"
ET = "ET"
PT = "PT"
ST = "ST"
TY = "TY"
[docs]
@dataclass
class DataLine:
time: datetime = field(
metadata={"comments": "Computed property from YYY-MM-DD HH:MM in UTC"}
)
record_identifier: RecordIdentifier = field(
metadata={
"standard_name": "Record Idenfier",
"comments": RecordIdentifier.__doc__,
}
)
system_status: SystemStatus = field(
metadata={"standard_name": "System Status", "comments": SystemStatus.__doc__}
)
lat: float = field(
metadata={
"standard_name": "latitude",
"units": "degree_north",
}
)
lon: float = field(
metadata={
"standard_name": "longitude",
"units": "degree_east",
}
)
wind_speed: float = field(
metadata={
"standard_name": "wind_speed",
"units": "m s-1",
}
)
pressure: float = field(
metadata={
"standard_name": "air_pressure",
"units": "Pa",
}
)
max_low_wind_radius_ne: float = field(
metadata={
"comment": "34 kt Wind maximum radius in the storms NE Quadrant",
"units": "m",
}
)
max_low_wind_radius_se: float = field(
metadata={
"comment": "34 kt Wind maximum radius in the storms SE Quadrant",
"units": "m",
}
)
max_low_wind_radius_sw: float = field(
metadata={
"comment": "34 kt Wind maximum radius in the storms SW Quadrant",
"units": "m",
}
)
max_low_wind_radius_nw: float = field(
metadata={
"comment": "34 kt Wind maximum radius in the storms NW Quadrant",
"units": "m",
}
)
max_med_wind_radius_ne: float = field(
metadata={
"comment": "50 kt Wind maximum radius in the storms NE Quadrant",
"units": "m",
}
)
max_med_wind_radius_se: float = field(
metadata={
"comment": "50 kt Wind maximum radius in the storms SE Quadrant",
"units": "m",
}
)
max_med_wind_radius_sw: float = field(
metadata={
"comment": "50 kt Wind maximum radius in the storms SW Quadrant",
"units": "m",
}
)
max_med_wind_radius_nw: float = field(
metadata={
"comment": "50 kt Wind maximum radius in the storms NW Quadrant",
"units": "m",
}
)
max_high_wind_radius_ne: float = field(
metadata={
"comment": "64 kt Wind maximum radius in the storms NE Quadrant",
"units": "m",
}
)
max_high_wind_radius_se: float = field(
metadata={
"comment": "64 kt Wind maximum radius in the storms SE Quadrant",
"units": "m",
}
)
max_high_wind_radius_sw: float = field(
metadata={
"comment": "64 kt Wind maximum radius in the storms SW Quadrant",
"units": "m",
}
)
max_high_wind_radius_nw: float = field(
metadata={
"comment": "64 kt Wind maximum radius in the storms NW Quadrant",
"units": "m",
}
)
max_sustained_wind_speed_radius: float = field(
metadata={
"standard_name": "radius_of_tropical_cyclone_maximum_sustained_wind_speed",
"units": "m",
}
)
[docs]
@dataclass
class TrackData:
global_attrs = {
"title": "HURricane DATa 2nd generation (HURDAT2)",
"date_created": datetime.now().isoformat(),
"publisher_name": "NOAA AOML Hurricane Research Division",
"publisher_email": "AOML.HRDWebmaster@noaa.gov",
"publisher_url": "https://www.aoml.noaa.gov/hrd/hurdat/Data_Storm.html",
"institution": "NOAA Atlantic Oceanographic and Meteorological Laboratory",
"summary": "The National Hurricane Center (NHC) conducts a post-storm analysis of each tropical cyclone in its area of responsibility to determine the official assessment of the cyclone's history",
}
header: HeaderLine
data: list[DataLine]
def get_rowsize(self) -> int:
return len(self.data)
def to_xarray_dataset(self) -> xr.Dataset:
return xr.Dataset(
{
"basin": (["traj"], np.array([self.header.basin])),
"year": (["traj"], np.array([self.header.year])),
"rowsize": (["traj"], np.array([self.header.rowsize])),
"record_identifier": (
["obs"],
np.array([line.record_identifier for line in self.data]),
),
"system_status": (
["obs"],
np.array([line.system_status for line in self.data]),
),
"lat": (["obs"], np.array([line.lat for line in self.data])),
"lon": (["obs"], np.array([line.lon for line in self.data])),
"wind_speed": (
["obs"],
np.array([line.wind_speed for line in self.data]),
),
"pressure": (["obs"], np.array([line.pressure for line in self.data])),
"max_low_wind_radius_ne": (
["obs"],
np.array([line.max_low_wind_radius_ne for line in self.data]),
),
"max_low_wind_radius_se": (
["obs"],
np.array([line.max_low_wind_radius_se for line in self.data]),
),
"max_low_wind_radius_sw": (
["obs"],
np.array([line.max_low_wind_radius_sw for line in self.data]),
),
"max_low_wind_radius_nw": (
["obs"],
np.array([line.max_low_wind_radius_nw for line in self.data]),
),
"max_med_wind_radius_ne": (
["obs"],
np.array([line.max_med_wind_radius_ne for line in self.data]),
),
"max_med_wind_radius_se": (
["obs"],
np.array([line.max_med_wind_radius_se for line in self.data]),
),
"max_med_wind_radius_sw": (
["obs"],
np.array([line.max_med_wind_radius_sw for line in self.data]),
),
"max_med_wind_radius_nw": (
["obs"],
np.array([line.max_med_wind_radius_nw for line in self.data]),
),
"max_high_wind_radius_ne": (
["obs"],
np.array([line.max_high_wind_radius_ne for line in self.data]),
),
"max_high_wind_radius_se": (
["obs"],
np.array([line.max_high_wind_radius_se for line in self.data]),
),
"max_high_wind_radius_sw": (
["obs"],
np.array([line.max_high_wind_radius_sw for line in self.data]),
),
"max_high_wind_radius_nw": (
["obs"],
np.array([line.max_high_wind_radius_nw for line in self.data]),
),
"max_sustained_wind_speed_radius": (
["obs"],
np.array(
[line.max_sustained_wind_speed_radius for line in self.data]
),
),
},
coords={
"id": (["traj"], np.array([self.header.id])),
"time": (
["obs"],
np.array(
[int(line.time.timestamp() * 10**9) for line in self.data],
dtype="datetime64[ns]",
),
),
},
)
def _map_heading(coordinate: str) -> float:
heading_map = {"N": 1, "E": 1, "S": -1, "W": -1}
cardinal_direction = coordinate[-1]
heading = coordinate[:-1]
if (sign := heading_map.get(cardinal_direction)) is not None:
return float(heading) * sign
raise ValueError(f"Invalid cardinal direction: {cardinal_direction}")
def _get_download_requests(basin: _BasinOption, tmp_path: str):
download_requests: list[tuple[str, str, None]] = list()
if basin == "atlantic" or basin == "both":
file_name = _ATLANTIC_BASIN_URL.split("/")[-1]
fp = os.path.join(_DEFAULT_FILE_PATH, file_name)
download_requests.append((_ATLANTIC_BASIN_URL, fp, None))
if basin == "pacific" or basin == "both":
file_name = _PACIFIC_BASIN_URL.split("/")[-1]
fp = os.path.join(_DEFAULT_FILE_PATH, file_name)
download_requests.append((_PACIFIC_BASIN_URL, fp, None))
return download_requests
[docs]
def to_raggedarray(
basin: _BasinOption = "both",
tmp_path: str = _DEFAULT_FILE_PATH,
convert: bool = True,
) -> RaggedArray:
os.makedirs(
_DEFAULT_FILE_PATH, exist_ok=True
) # generate temp directory for hurdat related intermerdiary data
download_requests = _get_download_requests(basin, tmp_path)
download_with_progress(download_requests)
track_data = list()
for _, fp, _ in download_requests:
track_data.extend(_extract_track_data(fp, convert))
metadata_fields = list()
data_fields = list()
for f in fields(HeaderLine):
if f.name != "id":
metadata_fields.append(f.name)
for f in fields(DataLine):
if f.name != "time":
data_fields.append(f.name)
ra = RaggedArray.from_files(
indices=list(range(0, len(track_data))),
name_coords=["id", "time"],
name_meta=metadata_fields,
name_data=data_fields,
name_dims={"traj": "rows", "obs": "obs"},
rowsize_func=lambda idx: track_data[idx].get_rowsize(),
preprocess_func=lambda idx: track_data[idx].to_xarray_dataset(),
attrs_global=TrackData.global_attrs,
attrs_variables={
field.name: field.metadata
for field in fields(HeaderLine) + fields(DataLine)
},
)
return ra
def _apply_or_nan(val: str, cond: bool, func) -> float:
"""If `val` is None or condition is true return nan otherwise apply the `func` to `val` and return the result"""
if val is None or cond:
return np.nan
return func(val)
def _extract_track_data(datafile_path: str, convert: bool) -> list[TrackData]:
datapage = StringIO()
with open(datafile_path, "rb") as file:
while (data := file.read()) != b"":
datapage.write(data.decode("utf-8").replace(" ", ""))
datapage.seek(0)
data_line_count = 0
current_header = None
data_lines = list()
track_data = list[TrackData]()
is_html_line = lambda line: re.match(r"[html|head|pre]+", line)
is_header_line = (
lambda cols, data_line_count: len(cols) == 4 and data_line_count == 0
)
is_data_line = lambda cols, data_line_count: len(cols) == 21 and data_line_count > 0
if convert:
nm_to_m = (
lambda x: float(x) * _METERS_IN_NAUTICAL_MILES
) # nautical-miles to meters
k_to_mps = (
lambda x: float(x) * _METERS_IN_NAUTICAL_MILES / 3600
) # knots to meters per second
mb_to_pa = lambda x: float(x) * _PASCAL_PER_MILLIBAR # millibar to pascal
else:
nm_to_m = lambda x: float(x)
k_to_mps = lambda x: float(x)
mb_to_pa = lambda x: float(x)
while (line := datapage.readline()) != "":
if is_html_line(line) or line == "\r\n":
if current_header is not None:
track_data.append(TrackData(current_header, data_lines))
data_lines = list()
current_header = None
continue
cols = line.split(",")
if is_header_line(cols, data_line_count):
data_line_count = int(cols[2])
header = HeaderLine(
id=cols[0],
basin=cols[0][:2],
year=int(cols[0][4:8]),
rowsize=data_line_count,
)
if current_header is None:
current_header = header
else:
track_data.append(TrackData(current_header, data_lines))
data_lines = list()
current_header = header
elif (
is_data_line(cols, data_line_count)
and len(cols[0]) == 8
and len(cols[1]) == 4
):
data_lines.append(
DataLine(
time=datetime(
year=int(cols[0][:4]),
month=int(cols[0][4:6]),
day=int(cols[0][6:8]),
hour=int(cols[1][:2]),
minute=int(cols[1][2:4]),
tzinfo=timezone.utc,
),
record_identifier=RecordIdentifier(cols[2]),
system_status=SystemStatus(cols[3]),
lat=_map_heading(cols[4]),
lon=_map_heading(cols[5]),
wind_speed=_apply_or_nan(cols[6], float(cols[6]) < 0, k_to_mps),
pressure=_apply_or_nan(cols[7], cols[7] == "-999", mb_to_pa),
max_low_wind_radius_ne=_apply_or_nan(
cols[8], cols[8] == "-999", nm_to_m
),
max_low_wind_radius_se=_apply_or_nan(
cols[9], cols[9] == "-999", nm_to_m
),
max_low_wind_radius_sw=_apply_or_nan(
cols[10], cols[10] == "-999", nm_to_m
),
max_low_wind_radius_nw=_apply_or_nan(
cols[11], cols[11] == "-999", nm_to_m
),
max_med_wind_radius_ne=_apply_or_nan(
cols[12], cols[12] == "-999", nm_to_m
),
max_med_wind_radius_se=_apply_or_nan(
cols[13], cols[13] == "-999", nm_to_m
),
max_med_wind_radius_sw=_apply_or_nan(
cols[14], cols[14] == "-999", nm_to_m
),
max_med_wind_radius_nw=_apply_or_nan(
cols[15], cols[15] == "-999", nm_to_m
),
max_high_wind_radius_ne=_apply_or_nan(
cols[16], cols[16] == "-999", nm_to_m
),
max_high_wind_radius_se=_apply_or_nan(
cols[17], cols[17] == "-999", nm_to_m
),
max_high_wind_radius_sw=_apply_or_nan(
cols[18], cols[18] == "-999", nm_to_m
),
max_high_wind_radius_nw=_apply_or_nan(
cols[19], cols[19] == "-999", nm_to_m
),
max_sustained_wind_speed_radius=_apply_or_nan(
cols[20], cols[20] == "-999", nm_to_m
),
)
)
data_line_count -= 1
return track_data
