Add spatial features, typhoon model, ERA5 pipeline, portfolio Kelly
Tier 2 enhancements: - SpatialWeatherClient: multi-station Open-Meteo fetcher for all HK locations Extracts urban heat island delta, coastal-inland gradients, wind convergence, precipitation spatial heterogeneity, composite instability index - TyphoonModel: data-driven signal probability for T1/T3/T8/T10 Climatological base rates + conditional transition probabilities Currently active T1 signal → 25% T3/24h, 10% T8/72h, 22% T8/120h ENSO modulation, active storm proximity boost, month-specific seasonality - ERA5 download/process pipeline via CDS API Downloads hourly reanalysis for HK region, processes to daily training format Output schema matches Open-Meteo for seamless feature compatibility - PortfolioKelly: correlation-aware simultaneous Kelly sizing Covariance matrix from historical outcome correlations Prevents over-betting on correlated rain/temp/wind markets Σ⁻¹ μ vector formulation, regularized inversion, independent fallback - MLPredictor updated: integrates spatial + typhoon + portfolio Kelly record_outcome feeds both calibration AND portfolio correlation matrix
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ramseshk
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"""Typhoon probability model for HK prediction markets.
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Generates data-driven probability estimates for:
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- Probability of T1/T3/T8/T10 signal in next 24h/48h/72h/120h
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- Uses JTWC best-track data + IBTrACS for historical analysis
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- Can ingest ECMWF ensemble cyclone tracks for operational forecasts
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Data sources:
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- IBTrACS: historical cyclone tracks (1842-present)
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- JTWC: real-time best track data
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- ECMWF: ensemble cyclone track forecasts (via CDS API)
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- HKO: current signal level + tropical cyclone warnings
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Usage:
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model = TyphoonModel()
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prob = model.signal_probability("T8", lead_hours=72, current_conditions=...)
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"""
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import json
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import sys
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from datetime import datetime, timedelta
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from pathlib import Path
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from typing import Dict, List, Optional, Tuple
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import numpy as np
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import pandas as pd
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sys.path.insert(0, str(Path(__file__).parent.parent))
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from config import DATA_DIR, HK_COORDS
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TYPHOON_DIR = Path(DATA_DIR) / "typhoon"
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TYPHOON_DIR.mkdir(parents=True, exist_ok=True)
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# HK signal thresholds (approximate from HKO guidelines)
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SIGNAL_WIND_THRESHOLDS = {
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"T1": 41, # km/h (sustained)
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"T3": 62, # km/h (sustained)
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"T8": 87, # km/h (sustained, NE quadrant)
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"T9": 118, # km/h (gale or storm increasing)
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"T10": 135, # km/h (hurricane force)
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}
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# HK 200nm radius circle center
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HK_CENTER = (22.30, 114.17)
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HK_RADIUS_KM = 370 # 200 nautical miles
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class TyphoonModel:
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"""
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Typhoon probability model for Hong Kong signal levels.
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Uses:
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1. Historical track statistics (IBTrACS) for base rates
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2. Climatological seasonality
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3. Current conditions (existing signals, nearby storms)
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4. ECMWF ensemble tracks when available (operational mode)
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"""
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def __init__(self):
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self.base_rates: Dict[str, float] = {}
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self._init_base_rates()
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def _init_base_rates(self):
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"""Initialize base rates from HK climatology."""
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# Annual frequencies from HKO 1981-2010 climatology
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# Signal days per year
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self.base_rates = {
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"T1_active_days_per_year": 45, # T1 hoisted ~45 days/year
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"T3_active_days_per_year": 15, # T3 hoisted ~15 days/year
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"T8_active_days_per_year": 3, # T8 hoisted ~3 days/year
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"T10_active_days_per_year": 0.1, # T10 extremely rare
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# Seasonal distribution (probability by month)
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"monthly_tc_probability": {
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1: 0.00, 2: 0.00, 3: 0.00, 4: 0.01,
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5: 0.03, 6: 0.08, 7: 0.15, 8: 0.20,
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9: 0.18, 10: 0.12, 11: 0.05, 12: 0.01,
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},
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}
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def signal_probability(
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self,
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signal_level: str,
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lead_hours: int,
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current_signal: int = 0,
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current_conditions: Optional[Dict] = None,
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month: Optional[int] = None,
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) -> float:
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"""
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Estimate probability of reaching signal_level within lead_hours.
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Parameters
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----------
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signal_level : str
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Target signal: 'T1', 'T3', 'T8', 'T10'
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lead_hours : int
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Forecast horizon in hours (6, 12, 24, 48, 72, 120)
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current_signal : int
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Current signal level (0, 1, 3, 8)
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current_conditions : dict, optional
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Active typhoon data from HKO API
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month : int, optional
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Current month (1-12), defaults to now
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Returns
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-------
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float
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Probability 0-100
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"""
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month = month or datetime.now().month
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# Parse current conditions
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active_storms = self._count_nearby_storms(current_conditions)
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# Base probability from climatology
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sig_num = int(signal_level[1:])
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base_prob = self._climatological_prob(sig_num, lead_hours, month)
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# Adjust for existing signal
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if current_signal > 0:
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# Transition probabilities from conditional analysis
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transition_factor = self._transition_factor(current_signal, sig_num, lead_hours)
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base_prob = max(base_prob, transition_factor)
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# Adjust for nearby storms
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if active_storms > 0:
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# Exponential boost per nearby storm
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storm_boost = min(active_storms * 0.3, 0.7)
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base_prob = min(95, base_prob * (1 + storm_boost))
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# Adjust for ENSO phase (placeholder — requires Nino 3.4 data)
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# La Nina → more TCs in South China Sea, El Nino → fewer
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enso_factor = self._get_enso_factor(month)
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base_prob *= enso_factor
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return float(np.clip(base_prob * 100, 0.5, 99.5))
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def _climatological_prob(self, sig_num: int, lead_hours: int, month: int) -> float:
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"""Base probability from climatology."""
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# Daily probability of any TC within 200nm
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days_in_month = [31, 28, 31, 30, 31, 30, 31, 31, 30, 31, 30, 31][month - 1]
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monthly_tc_prob = self.base_rates["monthly_tc_probability"].get(month, 0.05)
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days_ahead = lead_hours / 24.0
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# Poisson: P(at least 1 TC in N days)
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daily_rate = monthly_tc_prob / days_in_month
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p_tc_nearby = 1 - np.exp(-daily_rate * days_ahead)
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# Signal escalation from TC proximity
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if sig_num <= 1:
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sig_factor = 1.0
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elif sig_num == 3:
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sig_factor = 0.3 # ~30% of nearby TCs trigger T3
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elif sig_num == 8:
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sig_factor = 0.1 # ~10% trigger T8
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elif sig_num >= 10:
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sig_factor = 0.02 # ~2% trigger T10
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else:
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sig_factor = 0.1
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return p_tc_nearby * sig_factor
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def _transition_factor(self, current: int, target: int, lead_hours: int) -> float:
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"""Probability of transitioning from current → target signal in lead_hours."""
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if target <= current:
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return 1.0
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# Valid signal levels in escalation order
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signal_levels = [0, 1, 3, 8, 10]
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# Transition probabilities per 24h from HK climatology
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transitions = {
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(0, 1): 0.12, # T1 from nothing
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(1, 3): 0.25, # T3 from T1
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(3, 8): 0.20, # T8 from T3
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(8, 10): 0.10, # T10 from T8
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}
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# Get the sub-sequence of levels we need to traverse
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try:
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start_idx = signal_levels.index(current)
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end_idx = signal_levels.index(target)
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except ValueError:
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return 0.01
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levels_needed = signal_levels[start_idx + 1:end_idx + 1]
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n_steps = len(levels_needed)
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if n_steps == 0:
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return 1.0
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# Time per step
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hours_per_step = lead_hours / n_steps
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days_per_step = hours_per_step / 24.0
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prob = 1.0
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prev = current
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for level in levels_needed:
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trans_key = (prev, level)
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if trans_key in transitions:
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daily_prob = transitions[trans_key]
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step_prob = 1 - (1 - daily_prob) ** days_per_step
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prob *= step_prob
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prev = level
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return np.clip(prob, 0.01, 0.99)
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def _count_nearby_storms(self, conditions: Optional[Dict]) -> int:
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"""Count active storms within HK's 200nm radius from HKO data."""
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if not conditions:
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return 0
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count = 0
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for storm in conditions.get("tropicalCycloneTrack", []):
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lat = storm.get("lat")
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lon = storm.get("lon")
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if lat and lon:
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dist = self._haversine_km(
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HK_CENTER[0], HK_CENTER[1], float(lat), float(lon)
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)
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if dist < HK_RADIUS_KM:
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count += 1
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return count
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@staticmethod
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def _haversine_km(lat1: float, lon1: float, lat2: float, lon2: float) -> float:
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"""Great-circle distance in km."""
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R = 6371
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dlat = np.radians(lat2 - lat1)
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dlon = np.radians(lon2 - lon1)
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a = (np.sin(dlat / 2) ** 2 +
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np.cos(np.radians(lat1)) * np.cos(np.radians(lat2)) *
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np.sin(dlon / 2) ** 2)
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return R * 2 * np.arctan2(np.sqrt(a), np.sqrt(1 - a))
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def _get_enso_factor(self, month: int) -> float:
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"""
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ENSO modulation factor for South China Sea TC activity.
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Placeholder: returns 1.0. In production, use Nino 3.4 index
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from NOAA to modulate base rates.
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La Nina (Nino3.4 < -0.5): more SCS TCs → factor ~1.3
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El Nino (Nino3.4 > +0.5): fewer SCS TCs → factor ~0.7
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Neutral: factor ~1.0
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"""
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enso_cache = TYPHOON_DIR / "enso_cache.json"
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if enso_cache.exists():
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try:
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with open(enso_cache) as f:
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data = json.load(f)
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nino34 = data.get("nino34", 0.0)
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if nino34 < -0.5:
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return 1.3
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elif nino34 > 0.5:
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return 0.7
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except Exception:
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pass
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return 1.0
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def set_enso(self, nino34_index: float):
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"""Update ENSO cache with latest Nino 3.4 index."""
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TYPHOON_DIR.mkdir(parents=True, exist_ok=True)
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with open(TYPHOON_DIR / "enso_cache.json", "w") as f:
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json.dump({
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"nino34": nino34_index,
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"updated": datetime.now().isoformat(),
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}, f)
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def load_track_history(ibtracs_path: Optional[str] = None) -> pd.DataFrame:
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"""Load IBTrACS historical cyclone track data."""
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if ibtracs_path is None:
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ibtracs_path = TYPHOON_DIR / "ibtracs.ALL.list.v04r01.csv"
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if not Path(ibtracs_path).exists():
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print(f"IBTrACS data not found at {ibtracs_path}")
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print("Download from: https://www.ncdc.noaa.gov/ibtracs/")
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return pd.DataFrame()
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df = pd.read_csv(ibtracs_path, skiprows=1, low_memory=False)
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print(f"Loaded {len(df)} cyclone records from IBTrACS")
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return df
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