Alone et al. (2026) Enhancing Precision in Weather Data Extraction: Generalized Algorithm for Small Spatial Domains

Identification

• Journal: Lecture notes in mechanical engineering
• Year: 2026
• Date: 2026-01-01
• Authors: Ashish Alone, Anoop Kumar Shukla, D. R. Pattanaik, Gopal Nandan
• DOI: 10.1007/978-981-96-8496-0_45

Research Groups

• Department of Mechanical Engineering, Amity University Uttar Pradesh, Noida, India
• Indian Institute of Tropical Meteorology, Pune, India
• India Meteorological Department, New Delhi, India
• Department of Mechanical Engineering, Nalanda College of Engineering, Bihar, India

Short Summary

This study proposes a novel, generalized algorithm to enhance the precision of weather data extraction for small spatial domains by utilizing an enhanced interpolation method that averages equispaced points derived from coarser grid climate data. The algorithm aims to provide a robust and efficient solution for applications requiring accurate local weather information.

Objective

• To propose a novel, generalized algorithm to enhance precision in weather data extraction for small spatial domains.

Study Configuration

• Spatial Scale: Small spatial domains, with data derived from coarser grid climate data. No specific numerical dimensions are provided, but the focus is on downscaling to localized areas.
• Temporal Scale: Not explicitly defined, but pertains to weather data extraction which can range from instantaneous to daily averages, suitable for various applications like agriculture and urban planning.

Methodology and Data

• Models used: A novel, generalized algorithm employing an enhanced interpolation method. This method involves identifying domain boundaries, generating equispaced interpolated points within the domain from coarser grid data, and subsequently averaging these points.
• Data sources: Coarser grid climate data. Specific sources (e.g., satellite, observation, reanalysis) are not detailed in the provided text.

Main Results

• The proposed algorithm accurately captures local weather variations within small spatial domains.
• It offers flexibility in defining domain boundaries and demonstrates efficiency in processing large datasets.
• The algorithm allows for seamless integration with existing weather data systems and is customizable for specific application requirements.
• It provides a robust and efficient solution for enhancing precision in weather data extraction, making it valuable for fields such as agriculture, urban planning, and environmental monitoring.

Contributions

• Introduction of a novel, generalized algorithm specifically designed to enhance the precision of weather data extraction for small spatial domains, addressing a common limitation of existing methods.
• Development of an enhanced interpolation method that effectively downscales coarser grid climate data to provide precise local weather information.
• The algorithm's design emphasizes flexibility, efficiency, and ease of integration, offering a practical solution for various application fields.

Funding

Not specified in the provided text.

Citation

@article{Alone2026Enhancing,
  author = {Alone, Ashish and Shukla, Anoop Kumar and Pattanaik, D. R. and Nandan, Gopal},
  title = {Enhancing Precision in Weather Data Extraction: Generalized Algorithm for Small Spatial Domains},
  journal = {Lecture notes in mechanical engineering},
  year = {2026},
  doi = {10.1007/978-981-96-8496-0_45},
  url = {https://doi.org/10.1007/978-981-96-8496-0_45}
}