Page 272 - AI for Good Innovate for Impact

AI for Good Innovate for Impact

2�3 Future Work

Proof of Concept:

• Build a prototype integrating AIFS and custom deep learning models.
• Validate against historical benchmarks and compare with conventional NWP performance.

Standardization:

• Define KPIs for forecast performance and system efficiency.
• Collaborate with ITU and WMO to create global AI forecasting guidelines.
• Develop a framework for scalable deployment in smart grids and national weather
services.

Why It Matters:
• Energy Sector: Supports clean energy expansion with minimal waste.
• Climate Resilience: Enhances disaster preparedness and reduces wind-related losses.

Global Leadership: Builds AI-based meteorological innovation capacity in the MENA region
and beyond.

3 Use Case Requirements

• REQ-01: Access to multi-source meteorological data (satellites, stations, sensors)
• REQ-02: Deep learning framework (LSTM, CNN, RNN) integration pipeline
• REQ-03: Cloud-based infrastructure for training and real-time inference
• REQ-04: Standardized accuracy benchmarks for comparing AI vs NWP results
• REQ-05: Partnership with ECMWF and Copernicus to leverage AIFS
• REQ-06: Open-access visualization tools for policymakers and energy operators
• REQ-07: Disaster alert integrations for extreme wind events
• REQ-08: Regulatory alignment with ITU and WMO standards
• REQ-09: AI explainability components for transparency and trust
• REQ-10: Stakeholder training and outreach to utility providers and planners

4 Sequence Diagram

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