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)
7w4.net小蔥技能。

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/

這個天氣 API 技能質量中等偏上。優點是功能針對性強，專門為建築施工場景設計，能評估不同天氣條件下的作業可行性；使用免費資料來源，無需付費。不足之處在於缺少使用說明文件，對於非技術使用者不夠友好；部分細節如異常處理和資料驗證還需完善。總體適合有一定技術背景的使用者使用，普通使用者需要額外學習成本。