A web-based support system for biometeorological research.

Area of Science:

• Geosciences
• Environmental Science
• Data Science

Background:

• Scientific studies rely on data for understanding natural phenomena.
• Increasing data sources and formats complicate data reuse across disciplines.
• A standardized approach is needed for processing and accessing atmospheric datasets.

Purpose of the Study:

• To develop a spatial extract-transform-load (ETL) approach for atmospheric datasets.
• To generate novel biometeorological indices from processed data.
• To create a publicly accessible platform for data reuse and visualization.

Main Methods:

• Utilized the GDAL library and Python for geoprocessing tool development.
• Implemented a spatial ETL process to download and transform atmospheric data from NOAA.
• Developed a web application for data download and interactive map visualization.

Main Results:

• Successfully created new biometeorological indices from atmospheric prediction models.
• Established an automated ETL process triggered by NOAA data updates.
• Provided a web application for accessing processed datasets and geoprocessing outcomes.

Conclusions:

• The spatial ETL approach facilitates the creation and dissemination of valuable biometeorological indices.
• The developed tools and web application enhance data accessibility and reuse in scientific research.
• Optimized resource utilization through cloud service implementation is crucial for scalable data processing.