Learn2Vibe

How to Build a Crowdsourced Weather Pattern Analysis App

Develop a weather tracking application that compares current patterns to 50-year historical data, utilizes AI for analysis, and enables users to contribute structured local observations for enhanced regional insights.

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Simple Summary

A collaborative weather tracking app that analyzes historical data to identify unusual patterns and allows users to contribute local observations.

Product Requirements Document (PRD)

Goals

  1. Create a weather tracking app focusing on unusual shifts in weather patterns
  2. Utilize historical data (last ~50 years) for comparison
  3. Implement AI-driven analysis of comprehensive weather data
  4. Enable user contributions of local weather observations
  5. Provide weekly analysis reports to users

Target Audience

  • Residents in areas experiencing emerging disastrous weather patterns
  • Climate researchers and enthusiasts
  • Local communities interested in hyper-local weather trends

Key Features

  1. Historical weather data comparison
  2. AI-powered pattern analysis
  3. Weekly weather analysis reports
  4. User contribution of structured local weather observations
  5. Visualization of weather patterns and trends
  6. Integration with NOAA Weather API

User Stories

  1. As a user, I want to view weekly analysis reports comparing current weather patterns to historical data.
  2. As a user, I want to contribute my local weather observations in a structured format.
  3. As a user, I want to see visualizations of unusual weather patterns in my area.
  4. As a researcher, I want to access a network of user-contributed weather data for a specific region.
  5. As a user, I want to understand what weather conditions led up to past disastrous events in my area.

User Flows

  1. User signs up -> User views weekly weather analysis -> User contributes local weather observation
  2. User logs in -> User explores historical weather patterns -> User compares current conditions to past events
  3. User receives weekly report -> User investigates unusual patterns -> User shares findings with community

Technical Specifications

Recommended Stack

  • Frontend: React with Next.js
  • Backend: Node.js with Express
  • Database: PostgreSQL
  • AI Integration: Claude SDK
  • Weather Data: NOAA Weather API (https://api.weather.gov)
  • Data Visualization: D3.js or Chart.js

Core Technical Decisions

  1. Use React for a responsive and interactive user interface
  2. Implement server-side rendering with Next.js for improved performance and SEO
  3. Utilize Node.js backend for efficient API handling and data processing
  4. Store historical and user-contributed data in PostgreSQL for robust querying capabilities
  5. Integrate Claude SDK for AI-powered weather pattern analysis
  6. Fetch real-time weather data from NOAA Weather API
  7. Implement data visualization libraries for clear presentation of weather patterns

API Endpoints

  • GET /api/weather/historical - Retrieve historical weather data
  • GET /api/weather/current - Fetch current weather conditions
  • POST /api/observations - Submit user weather observation
  • GET /api/analysis/weekly - Retrieve weekly weather analysis report
  • GET /api/patterns/unusual - Fetch unusual weather patterns

Database Schema

  1. Users

    • id (PK)
    • username
    • email
    • password_hash
    • location

  2. WeatherObservations

    • id (PK)
    • user_id (FK)
    • timestamp
    • temperature
    • humidity
    • wind_speed
    • wind_direction
    • pressure
    • precipitation

  3. WeatherAnalysis

    • id (PK)
    • region
    • week_start_date
    • analysis_data (JSON)
    • unusual_patterns (JSON)

File Structure

/src /components Header.js Footer.js WeatherChart.js ObservationForm.js /pages index.js analysis.js contribute.js historical-data.js /api weather.js observations.js analysis.js /utils ai-analysis.js data-processing.js /styles globals.css /public /images /server /models /controllers /routes server.js /tests

Implementation Plan

  1. Set up project structure and install dependencies
  2. Implement basic frontend components and pages
  3. Create backend server and database schema
  4. Integrate NOAA Weather API for current and historical data
  5. Implement user authentication and observation submission
  6. Develop AI analysis functionality using Claude SDK
  7. Create data visualization components for weather patterns
  8. Implement weekly analysis report generation
  9. Develop user contribution system for local observations
  10. Integrate all components and test thoroughly
  11. Optimize performance and user experience
  12. Deploy application

Deployment Strategy

  1. Deploy frontend to Vercel for automatic builds and serverless functions
  2. Host backend on Heroku or DigitalOcean for scalability
  3. Set up PostgreSQL database on a managed service like Amazon RDS
  4. Use Docker containers for consistent deployment across environments
  5. Implement CI/CD pipeline using GitHub Actions

Design Rationale

The technical decisions were made to balance functionality, scalability, and ease of development. React and Next.js provide a robust frontend framework with server-side rendering capabilities, enhancing performance and SEO. Node.js on the backend allows for efficient API handling and integration with various services. PostgreSQL was chosen for its ability to handle complex queries and store structured data effectively. The use of existing AI (Claude SDK) and weather data (NOAA API) resources allows for rapid development and reliable data sources. The modular file structure and use of containerization support scalability and ease of maintenance as the project grows.