Physics-Informed ML for Extreme Rainfall Nowcasting: Rapid Review Materials

141.8 MB replication package for a rapid review study on physics-informed machine learning for extreme rainfall nowcasting. The repository includes search strings, selected studies metadata, inclusion/exclusion criteria, and workflow information to improve transparency and reproducibility. It was authored by Gyslla de Vasconcelos and last updated in May 2026.

Use Cases

• Replicate the systematic review process based on the provided search strings and workflow information.
• Analyze trends in physics-informed ML for rainfall forecasting based on the extracted metadata from selected studies.
• Audit the review methodology based on the inclusion/exclusion criteria and qualitative synthesis materials.
• Build a similar literature review corpus based on the structured materials and PRISMA-based workflow.

Strengths

• 141.8 MB of supplementary materials supporting a complete research workflow.
• Includes multiple structured components: search strings, metadata, criteria, and synthesis materials.
• Licensed under CC-BY-4.0 for open reuse and redistribution.

Limitations

• Description metadata is limited; actual data quality requires manual inspection after download.
• Column-level documentation is absent; field semantics must be inferred after download.
• The package contains review materials, not primary observational or forecast data.

Provenance

Source

figshare

Collection Method

Created as a replication package for a rapid review study.

Freshness

Last updated 2026-05-21 20:39:12