**Design a Weather Forecasting System**

### Introduction

In this document, we will explore the design of a system for "Weather Forecasting". The goal is to understand the requirements, challenges, and architectural decisions involved in building such a system.

### Requirements

#### Functional Requirements
The core functionalities the system must provide include:

* Retrieving current weather conditions (temperature, humidity, wind speed, etc.)
* Providing daily and hourly forecasts for a specific location
* Handling user requests for historical weather data (previous 30 days)
* Allowing users to search for weather conditions by location or date

#### Non-Functional Requirements
The system should also meet the following non-functional requirements:

* Performance: The system should respond quickly, ideally within 500ms, to minimize latency.
* Scalability: The system should be able to handle a large number of concurrent requests without experiencing significant performance degradation.
* Reliability: The system should maintain high uptime (99.9%) and ensure that weather data is accurate and up-to-date.

### High-Level Architecture

The system architecture will consist of the following components:

* **Weather Data Provider**: Responsible for collecting and processing weather data from various sources (weather stations, satellites, etc.)
* **Forecasting Engine**: Uses historical weather data to generate daily and hourly forecasts
* **API Gateway**: Handles incoming API requests and routes them to the relevant component
* **Database**: Stores weather data and forecast results

### Database Schema

The database schema will include the following tables:

* **weather_data**:
	+ id (primary key)
	+ location_id (foreign key referencing locations table)
	+ timestamp
	+ temperature
	+ humidity
	+ wind_speed
* **locations**:
	+ id (primary key)
	+ name
	+ latitude
	+ longitude
* **forecasts**:
	+ id (primary key)
	+ location_id (foreign key referencing locations table)
	+ timestamp
	+ temperature
	+ humidity
	+ wind_speed

Indexing strategies will be employed to optimize query performance.

### API Design

#### Key Endpoints

The system will provide the following main API endpoints:

* **GET /weather**: Retrieves current weather conditions for a specific location
* **GET /forecast**: Provides daily and hourly forecasts for a specific location
* **GET /history**: Returns historical weather data for a specific location or date range
* **POST /search**: Searches for weather conditions by location or date

Example requests and responses are as follows:

* GET /weather?location=New York:
	+ Response: { "temperature": 25, "humidity": 60, "wind_speed": 10 }
* GET /forecast?location=Los Angeles&date=2023-03-15:
	+ Response: [{ "timestamp": 1678763200, "temperature": 22 }, ...]

### OpenAPI Specification

The system will be designed according to the OpenAPI specification.

### System Flow

The flow of data and control through the system is as follows:

1. The API gateway receives an incoming request.
2. The request is routed to the relevant component (weather data provider, forecasting engine, or database).
3. The weather data provider retrieves current weather conditions from various sources.
4. The forecasting engine uses historical weather data to generate daily and hourly forecasts.
5. The database stores retrieved weather data and forecast results.

### Challenges and Solutions

Potential challenges in designing and implementing the system include:

* Handling large volumes of weather data
	+ Solution: Employ efficient data processing techniques and optimize database schema
* Ensuring accurate forecasting
	+ Solution: Implement machine learning algorithms to improve forecasting accuracy

### Scalability and Performance

To ensure scalability and performance, the system will be designed with the following strategies in mind:

* Load balancing: Distribute incoming requests across multiple servers to minimize latency.
* Caching: Store frequently accessed data in memory to reduce database queries.

### Security Considerations

Security measures to protect the system and its data include:

* Authentication: Verify user identities before granting access to weather data.
* Authorization: Control access to specific weather data based on user roles.
* Data encryption: Encrypt sensitive weather data during transmission and storage.

### ASCII Diagrams
```
          +---------------+
          |  API Gateway  |
          +---------------+
                  |
                  |  Request
                  v
          +---------------+
          | Weather Data   |
          | Provider       |
          +---------------+
                  |
                  |  Weather Data
                  v
          +---------------+
          | Forecasting    |
          | Engine         |
          +---------------+
                  |
                  |  Forecasts
                  v
          +---------------+
          | Database      |
          +---------------+
```

### Sample SQL Schema
```sql
CREATE TABLE weather_data (
  id INT PRIMARY KEY,
  location_id INT,
  timestamp TIMESTAMP,
  temperature DECIMAL(4,2),
  humidity DECIMAL(3,2),
  wind_speed DECIMAL(4,2)
);

CREATE TABLE locations (
  id INT PRIMARY KEY,
  name VARCHAR(50),
  latitude DECIMAL(10,8),
  longitude DECIMAL(11,8)
);

CREATE TABLE forecasts (
  id INT PRIMARY KEY,
  location_id INT,
  timestamp TIMESTAMP,
  temperature DECIMAL(4,2),
  humidity DECIMAL(3,2),
  wind_speed DECIMAL(4,2)
);
```

### Example JSON API Response
```json
{
  "temperature": 25.5,
  "humidity": 65.1,
  "wind_speed": 12.8,
  "forecast": [
    {
      "timestamp": 1678763200,
      "temperature": 22.9
    },
    ...
  ]
}
```

### Summary

The design of the weather forecasting system involves a combination of functional and non-functional requirements, as well as architectural decisions to ensure scalability, performance, and security. The system will be designed with the OpenAPI specification in mind and will provide a robust and reliable framework for retrieving current and forecasted weather conditions.

Open questions or areas for further exploration include:

* Implementing machine learning algorithms to improve forecasting accuracy
* Integrating additional weather data sources (e.g., radar, satellite imagery)
* Optimizing system performance for large-scale deployments