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🌧️ Understanding Extreme Rainfall: Lessons from Medicane Daniel and Satellite Precipitation Data

🌍 Introduction

Accurately estimating rainfall is critical for predicting and mitigating the impacts of extreme weather events, particularly in regions vulnerable to Mediterranean cyclones (Medicanes). One such event, Medicane Daniel, struck Central Greece in September 2023, causing devastating floods across the Thessaly Plain. This study investigates how advanced satellite-based precipitation products, specifically the Integrated Multi-Satellite Retrievals for GPM (IMERG), can be combined with ground-based observations to improve accuracy in rainfall monitoring.

🛰️ What is IMERG and Why Does It Matter?

The IMERG system, developed under NASA’s Global Precipitation Measurement (GPM) mission, integrates multiple satellite observations to estimate global rainfall in near real time. During extreme weather, such as tropical storms, cyclones, or monsoon floods, having timely and reliable rainfall data is vital for:

• Disaster preparedness – giving authorities early warning for evacuation.

• Agricultural resilience – helping farmers anticipate crop damage.

• Urban planning – enabling better flood control and water management.

Example: In India’s 2018 Kerala floods, satellite rainfall estimates supported quick decision-making when ground stations were damaged by rising waters.

📊 How the Study Was Conducted

To evaluate IMERG’s accuracy during Medicane Daniel, three versions were tested:

• Final Run (FR) – highest-quality dataset after all calibrations.

• Early Run (ER) – quick estimates available soon after the event.

• Late Run (LR) – improved accuracy but still intermediate.

These were compared with ground-based rain gauge data, interpolated using two spatial methods:

• Inverse Distance Weighting (IDW)

• Ordinary Kriging

Statistical indicators such as the Probability of Detection (POD), False Alarm Ratio (FAR), Critical Success Index (CSI), and Peirce Skill Score (PSS) were used to verify performance.

🔎 lKey Findings

• The Final Run (FR) product showed the highest agreement with ground data, reaching a correlation (R) of 0.87 and CSI up to 0.995 at 100 mm thresholds.

• Kriging outperformed IDW in spatial accuracy, achieving an almost perfect correlation (R = 0.99).

• Combining satellite and ground-based data provided more localized, precise rainfall estimates, particularly useful in complex terrains like Thessaly.

Example: Similar approaches are used in California’s Sierra Nevada region, where blending satellite snowpack data with ground sensors improves water resource management.

🌱 Why Does This Research Matter?

Accurate rainfall monitoring is not just about science—it directly affects communities and ecosystems. By improving rainfall estimates:

• Emergency response teams can deploy resources more effectively.

• Hydrologists can better model flood risks.

• Farmers can plan irrigation and crop cycles during extreme drought or rainfall events.

Example: In Bangladesh, integrating satellite rainfall data with river gauge networks has improved flood forecasting, saving thousands of lives annually.

🚀 Looking Ahead

This study highlights the importance of combining satellite precipitation data with ground observations through advanced spatial methods like kriging. As climate change intensifies storms, adopting such approaches will enhance resilience in vulnerable regions.

• Kriging offers superior spatial accuracy for detailed hydrological modeling.

• IMERG-FR provides a reliable quick-response tool during emergencies.

Ultimately, blending these methods can become a gold standard for disaster preparedness worldwide.

✨ Conclusion

The case of Medicane Daniel illustrates how technology, when paired with local data, can transform how we understand and respond to extreme weather events. As climate change continues to amplify storms, investing in satellite-ground hybrid monitoring systems will be essential to safeguard communities, agriculture, and infrastructure.

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