Climate Analysis Project

Overview

This comprehensive climate analysis project utilizes OpenWeather API and NASA climate data to analyze temperature trends, precipitation patterns, and extreme weather events across multiple cities globally from 2015-2024. The project includes time series decomposition, trend analysis, seasonal pattern identification, predictive modeling for temperature forecasting, statistical tests for climate change detection, and interactive visualizations.

Features

• Multi-source Data Collection: Retrieves climate data from OpenWeather API and NASA POWER API
• Comprehensive Analysis:
  • Time series decomposition of temperature and precipitation data
  • Trend analysis for detecting long-term climate changes
  • Seasonal pattern identification and analysis
  • Extreme weather event detection and characterization
• Predictive Modeling:
  • Temperature forecasting models using machine learning
  • Precipitation prediction models
  • Extreme weather event prediction
• Statistical Testing:
  • Mann-Kendall test for trend detection
  • Statistical significance testing for climate change
  • Distribution change analysis
• Interactive Visualization:
  • Regional climate variation maps
  • Time series visualizations of climate trends
  • Comparative analysis dashboards
  • Correlation heatmaps for geographic factors
• Geographic Correlation:
  • Analysis of relationships between weather patterns and geographic factors
  • Elevation, proximity to water, urbanization effects analysis

Installation

Prerequisites

• Python 3.8+
• pip (Python package installer)

Setup

1. Clone the repository:

git clone https://github.com/Saloni-Agg/Climate-Analysis.git
cd climate-analysis-project

2. Create a virtual environment (recommended):

python -m venv venv
source venv/bin/activate  # On Windows: venv\Scripts\activate

3. Install dependencies:

pip install -r requirements.txt

4. Set up API keys:
   • Create a .env file in the project root
   • Add your API keys:

   OPENWEATHER_API_KEY=your_openweather_api_key
   NASA_API_KEY=your_nasa_api_key
   

Project Structure

climate_analysis_project/
├── README.md                 # Main project documentation
├── requirements.txt          # Python dependencies
├── config.yaml               # Configuration file
├── setup.py                  # Package setup file
├── .gitignore                # Git ignore file
├── src/                      # Source code
│   ├── data_collection/      # Data collection modules
│   ├── analysis/             # Analysis modules
│   ├── visualization/        # Visualization modules
│   └── utils/                # Utility functions
├── data/                     # Data storage
│   ├── raw/                  # Raw data
│   ├── processed/            # Processed data
│   └── external/             # External data
├── notebooks/                # Jupyter notebooks
├── tests/                    # Test suite
├── docs/                     # Documentation
└── scripts/                  # Utility scripts

Usage

Data Collection

To collect climate data from OpenWeather API and NASA POWER API:

python scripts/update_data.py --start-date 2015-01-01 --end-date 2024-06-01 --cities cities.json

Running Analysis

To perform comprehensive climate analysis:

python scripts/run_analysis.py --config config.yaml

Generating Reports

To generate visualizations and reports:

python scripts/generate_reports.py --output-dir reports/

Interactive Dashboard

To launch the interactive dashboard:

python -m src.visualization.dashboard

Examples

Time Series Decomposition

from src.analysis.time_series_analysis import decompose_time_series
import pandas as pd

# Load temperature data
temp_data = pd.read_csv('data/processed/temperature_data.csv', parse_dates=['date'], index_col='date')

# Perform time series decomposition
trend, seasonal, residual = decompose_time_series(temp_data['temperature'], period=365)

# Plot components
plot_decomposition(temp_data['temperature'], trend, seasonal, residual)

Temperature Forecasting

from src.analysis.forecasting_models import train_temperature_model, forecast_temperature
import pandas as pd

# Load training data
train_data = pd.read_csv('data/processed/temperature_train.csv', parse_dates=['date'], index_col='date')

# Train model
model = train_temperature_model(train_data, features=['temp', 'humidity', 'pressure'], target='next_day_temp')

# Forecast future temperatures
forecast = forecast_temperature(model, current_data, days=7)

Interactive Climate Map

from src.visualization.climate_maps import create_interactive_map
import pandas as pd

# Load climate data with geographic coordinates
climate_data = pd.read_csv('data/processed/global_climate_data.csv')

# Create interactive map
map_fig = create_interactive_map(climate_data, value_column='temperature_anomaly', 
                               title='Global Temperature Anomalies 2015-2024')
map_fig.show()

Configuration

The project uses a config.yaml file for configuration. Here's an example:

# Data collection settings
data_collection:
  openweather:
    api_url: "https://api.openweathermap.org/data/3.0/onecall"
    units: "metric"
    variables:
      - "temp"
      - "humidity"
      - "precipitation"
      - "wind_speed"
  nasa_power:
    api_url: "https://power.larc.nasa.gov/api/temporal/daily/point"
    parameters:
      - "T2M"  # Temperature at 2 Meters
      - "PRECTOTCORR"  # Precipitation
      - "WS10M"  # Wind Speed at 10 Meters

# Analysis settings
analysis:
  time_series:
    decomposition_method: "additive"
    trend_detection_threshold: 0.05
  forecasting:
    model_type: "lstm"
    train_test_split: 0.8
    features:
      - "temp"
      - "humidity"
      - "pressure"
      - "month"
      - "day"
  statistical_tests:
    significance_level: 0.05

# Visualization settings
visualization:
  color_palette: "viridis"
  map_projection: "mercator"
  default_height: 600
  default_width: 900

# Cities to analyze
cities:
  - name: "New York"
    country: "US"
    lat: 40.7128
    lon: -74.0060
  - name: "London"
    country: "GB"
    lat: 51.5074
    lon: -0.1278
  - name: "Tokyo"
    country: "JP"
    lat: 35.6762
    lon: 139.6503
  - name: "Sydney"
    country: "AU"
    lat: -33.8688
    lon: 151.2093
  - name: "Rio de Janeiro"
    country: "BR"
    lat: -22.9068
    lon: -43.1729

Contributing

Contributions are welcome! Please feel free to submit a Pull Request.

1. Fork the repository
2. Create your feature branch (git checkout -b feature/amazing-feature)
3. Commit your changes (git commit -m 'Add some amazing feature')
4. Push to the branch (git push origin feature/amazing-feature)
5. Open a Pull Request

License

This project is licensed under the MIT License - see the LICENSE file for details.

Acknowledgments

• OpenWeather API for providing weather data
• NASA POWER Project for climate data
• Python data science community for the amazing tools and libraries