Assessment of Multiple Satellite Precipitation Products over Italy

Abstract

Highlights: What are the main findings? The study compared five satellite precipitation products (CHIRPS, GPM, HSAF, PDIRNOW, and SM2RAIN) against ground-based SCIA-ISPRA data across Italy using daily, seasonal, and annual scales. GPM emerged as the most balanced and reliable product overall, while PDIRNOW and SM2RAIN showed high detection but frequent overestimations; CHIRPS performed conservatively, and HSAF exhibited lower reliability in colder seasons. What is the implication of the main finding? The results highlight that no single satellite dataset performs best across all contexts—dataset choice should depend on specific applications, such as flood forecasting or drought monitoring. The findings support the adoption of multi-product or hybrid approaches to enhance precipitation monitoring accuracy in complex terrains like Italy. Accurate rainfall estimation remains a critical challenge in hydrology, particularly in Italy, where complex topography and uneven rain-gauge distribution introduce major uncertainties. To address this gap, this study assessed five widely used satellite precipitation products, CHIRPS, GPM, HSAF, PDIRNOW, and SM2RAIN, against the high-resolution SCIA-ISPRA ground dataset. These products were selected because they represent distinct retrieval approaches (infrared–station hybrid, microwave integration, geostationary blending, neural-network infrared, and soil–moisture inversion) and offer diverse temporal and spatial resolutions suitable for both research and operational monitoring. The evaluation, conducted at daily, seasonal, and annual scales using categorical, continuous, and extreme-event indices, revealed that no single product performs optimally across all metrics. GPM achieved the most balanced and reliable performance overall, whereas PDIRNOW and SM2RAIN provided strong detection but frequent overestimation. CHIRPS yielded conservative estimates with few false alarms, while HSAF was less consistent, especially during winter. The results underscore that product suitability depends on the intended application: detection-oriented systems like PDIRNOW are preferable for flood forecasting, whereas conservative datasets like CHIRPS better support drought monitoring. Overall, integrating multiple products or adopting hybrid approaches is recommended to enhance precipitation assessment accuracy over complex Mediterranean terrains.