- slug
- weather-api
- display_name
- Weather API
- description
- Fetch weather data for construction scheduling. Historical data, forecasts, and risk assessment for outdoor work.
Weather API for Construction
Overview
Weather impacts 50% of construction activities. This skill fetches weather data for scheduling, risk assessment, and productivity adjustments.
Python Implementation
import requests
import pandas as pd
from typing import Dict, Any, List, Optional
from dataclasses import dataclass
from datetime import datetime, timedelta
from enum import Enum
class WeatherRisk(Enum):
"""Weather risk levels for construction."""
LOW = "low"
MODERATE = "moderate"
HIGH = "high"
CRITICAL = "critical"
@dataclass
class WeatherCondition:
"""Weather condition at a point in time."""
timestamp: datetime
temperature: float # Celsius
humidity: float # Percent
wind_speed: float # m/s
precipitation: float # mm
conditions: str
@dataclass
class WorkabilityAssessment:
"""Assessment of weather workability."""
date: datetime
risk_level: WeatherRisk
workable_hours: int
affected_activities: List[str]
recommendations: List[str]
class WeatherAPIClient:
"""Client for weather APIs."""
# Free tier endpoints
OPEN_METEO_BASE = "https://api.open-meteo.com/v1"
def __init__(self, api_key: Optional[str] = None):
self.api_key = api_key
def get_forecast(self, latitude: float, longitude: float,
days: int = 7) -> List[WeatherCondition]:
"""Get weather forecast."""
url = f"{self.OPEN_METEO_BASE}/forecast"
params = {
'latitude': latitude,
'longitude': longitude,
'hourly': 'temperature_2m,relative_humidity_2m,wind_speed_10m,precipitation',
'forecast_days': days
}
response = requests.get(url, params=params)
if response.status_code != 200:
raise Exception(f"API error: {response.status_code}")
data = response.json()
return self._parse_forecast(data)
def get_historical(self, latitude: float, longitude: float,
start_date: str, end_date: str) -> List[WeatherCondition]:
"""Get historical weather data."""
url = f"{self.OPEN_METEO_BASE}/archive"
params = {
'latitude': latitude,
'longitude': longitude,
'start_date': start_date,
'end_date': end_date,
'hourly': 'temperature_2m,relative_humidity_2m,wind_speed_10m,precipitation'
}
response = requests.get(url, params=params)
if response.status_code != 200:
raise Exception(f"API error: {response.status_code}")
data = response.json()
return self._parse_forecast(data)
def _parse_forecast(self, data: Dict) -> List[WeatherCondition]:
"""Parse API response to WeatherCondition list."""
conditions = []
hourly = data.get('hourly', {})
times = hourly.get('time', [])
temps = hourly.get('temperature_2m', [])
humidity = hourly.get('relative_humidity_2m', [])
wind = hourly.get('wind_speed_10m', [])
precip = hourly.get('precipitation', [])
for i in range(len(times)):
conditions.append(WeatherCondition(
timestamp=datetime.fromisoformat(times[i]),
temperature=temps[i] if i < len(temps) else 0,
humidity=humidity[i] if i < len(humidity) else 0,
wind_speed=wind[i] if i < len(wind) else 0,
precipitation=precip[i] if i < len(precip) else 0,
conditions=self._describe_conditions(
temps[i] if i < len(temps) else 0,
precip[i] if i < len(precip) else 0,
wind[i] if i < len(wind) else 0
)
))
return conditions
def _describe_conditions(self, temp: float, precip: float, wind: float) -> str:
"""Generate weather description."""
conditions = []
if temp < 0:
conditions.append("Freezing")
elif temp > 35:
conditions.append("Extreme heat")
elif temp > 30:
conditions.append("Hot")
elif temp < 10:
conditions.append("Cold")
if precip > 10:
conditions.append("Heavy rain")
elif precip > 2:
conditions.append("Rain")
elif precip > 0:
conditions.append("Light rain")
if wind > 15:
conditions.append("Strong winds")
elif wind > 10:
conditions.append("Windy")
return ", ".join(conditions) if conditions else "Clear"
def to_dataframe(self, conditions: List[WeatherCondition]) -> pd.DataFrame:
"""Convert conditions to DataFrame."""
data = [{
'timestamp': c.timestamp,
'temperature': c.temperature,
'humidity': c.humidity,
'wind_speed': c.wind_speed,
'precipitation': c.precipitation,
'conditions': c.conditions
} for c in conditions]
return pd.DataFrame(data)
class ConstructionWeatherRisk:
"""Assess weather risk for construction activities."""
# Activity-specific thresholds
THRESHOLDS = {
'concrete_pour': {
'min_temp': 5, 'max_temp': 35,
'max_wind': 12, 'max_precip': 0.5
},
'crane_work': {
'min_temp': -10, 'max_temp': 40,
'max_wind': 10, 'max_precip': 5
},
'exterior_paint': {
'min_temp': 10, 'max_temp': 35,
'max_wind': 8, 'max_precip': 0
},
'roofing': {
'min_temp': 5, 'max_temp': 38,
'max_wind': 12, 'max_precip': 0
},
'earthwork': {
'min_temp': -5, 'max_temp': 40,
'max_wind': 20, 'max_precip': 10
}
}
def assess_workability(self, condition: WeatherCondition,
activities: List[str] = None) -> WorkabilityAssessment:
"""Assess workability for given conditions."""
if activities is None:
activities = list(self.THRESHOLDS.keys())
affected = []
recommendations = []
for activity in activities:
if activity in self.THRESHOLDS:
thresh = self.THRESHOLDS[activity]
reasons = []
if condition.temperature < thresh['min_temp']:
reasons.append(f"Too cold ({condition.temperature}°C)")
if condition.temperature > thresh['max_temp']:
reasons.append(f"Too hot ({condition.temperature}°C)")
if condition.wind_speed > thresh['max_wind']:
reasons.append(f"High wind ({condition.wind_speed} m/s)")
if condition.precipitation > thresh['max_precip']:
reasons.append(f"Precipitation ({condition.precipitation} mm)")
if reasons:
affected.append(activity)
recommendations.append(f"{activity}: " + ", ".join(reasons))
# Determine overall risk level
if len(affected) >= len(activities) * 0.8:
risk = WeatherRisk.CRITICAL
workable = 0
elif len(affected) >= len(activities) * 0.5:
risk = WeatherRisk.HIGH
workable = 4
elif len(affected) > 0:
risk = WeatherRisk.MODERATE
workable = 6
else:
risk = WeatherRisk.LOW
workable = 8
return WorkabilityAssessment(
date=condition.timestamp,
risk_level=risk,
workable_hours=workable,
affected_activities=affected,
recommendations=recommendations
)
def weekly_forecast_risk(self, conditions: List[WeatherCondition],
activities: List[str] = None) -> pd.DataFrame:
"""Assess risk for week of weather data."""
# Group by date
daily_conditions = {}
for c in conditions:
date = c.timestamp.date()
if date not in daily_conditions:
daily_conditions[date] = []
daily_conditions[date].append(c)
assessments = []
for date, day_conditions in daily_conditions.items():
# Use midday condition as representative
midday = [c for c in day_conditions
if 10 <= c.timestamp.hour <= 16]
representative = midday[len(midday)//2] if midday else day_conditions[0]
assessment = self.assess_workability(representative, activities)
assessments.append({
'date': date,
'risk_level': assessment.risk_level.value,
'workable_hours': assessment.workable_hours,
'affected_count': len(assessment.affected_activities)
})
return pd.DataFrame(assessments)Quick Start
# Initialize client
weather = WeatherAPIClient()
# Get forecast for site
conditions = weather.get_forecast(latitude=52.52, longitude=13.41, days=7)
df = weather.to_dataframe(conditions)
print(df.head())
# Assess construction risk
risk = ConstructionWeatherRisk()
weekly_risk = risk.weekly_forecast_risk(conditions)
print(weekly_risk)Common Use Cases
1. Schedule Planning
conditions = weather.get_forecast(52.52, 13.41, days=14)
risk = ConstructionWeatherRisk()
# Check concrete pour window
for c in conditions:
assessment = risk.assess_workability(c, ['concrete_pour'])
if assessment.risk_level == WeatherRisk.LOW:
print(f"Good for concrete: {c.timestamp}")2. Historical Analysis
historical = weather.get_historical(52.52, 13.41, '2024-01-01', '2024-03-31')
df = weather.to_dataframe(historical)
# Count rain days
rain_days = df[df['precipitation'] > 2]['timestamp'].dt.date.nunique()
print(f"Rain days in Q1: {rain_days}")Resources
- DDC Book: Chapter 2.2 - Open Data Integration
- Open-Meteo API: https://open-meteo.com/