Wind Data Processing Project

This project processes meteorological wind data by converting units, resampling data, and identifying high wind speed instances. The final output provides a summary of maximum wind speeds at specific intervals.

Steps Performed

1. Data Loading and Conversion

   • Loaded wind data from a CSV file.
   • Converted wind speed values from knots (kt) to kilometers per hour (km/h).
   • Rounded converted values to two decimal places.

2. Data Cleaning and Preparation

   • Selected relevant columns: DATE, TIME(UTC), WIND_MAX/GUST_10m(km/hr), WIND_SPEED_10m(km/hr).
   • Merged DATE and TIME(UTC) into a single TIME column.
   • Set TIME as the DataFrame index.
   • Removed redundant DATE and TIME(UTC) columns.

3. Resampling and Analysis

   • Resampled data at 3-hour intervals to find maximum wind speeds.
   • Created a binary max_speed column to flag instances where wind speed exceeded 50 km/h.
   • Extracted and formatted Month and Time information for easier analysis.

4. Data Aggregation

   • Grouped data by Month and Time to sum high wind speed occurrences.

5. Final Output

   • Generated a summary DataFrame showing the number of high wind speed events for each month and time interval.
   • Saved the processed data to a new CSV file (Final_p_Met_data_1.csv).

Dependencies

• Python libraries: pandas, numpy, matplotlib, seaborn, datetime

File Structure

• Met_data_1.csv: Original dataset
• updated_Met_data_1.csv: Intermediate processed dataset
• Final_p_Met_data_1.csv: Final output dataset

Usage

1. Ensure all dependencies are installed.
2. Run the provided Python script to process the data.
3. Review the output CSV file for summarized wind speed information.

Author

Satyarth Ranjan