Evaluating satellite precipitation products for data-scarce mountainous regions: A case study from Armenia

Accurate precipitation estimation in data-scarce mountainous regions is essential for effective water resource management and hazard monitoring. However, complex orographic processes present significant challenges to satellite retrieval algorithms. This study provides the first systematic evaluation of CMORPH and IMERG precipitation products against 42 ground stations across Armenia’s elevation-stratified terrain (2018–2022), using continuous ( R 2 , RMSE, bias) and categorical (POD, FAR, CSI) metrics at daily, monthly, and seasonal scales. CMORPH systematically overestimated precipitation (mean bias +68%; 78.6% of stations), while IMERG underestimated precipitation above 1000 m elevation (74% of stations). Both products showed weak-to-moderate correlation with gauge observations ( R 2 ≈ 0.36 ), with CMORPH showing limited event detection skill (POD = 0.45, FAR = 0.50) relative to IMERG (POD = 0.61). These biases followed physically explainable patterns: summer overestimation at low elevations was associated with dry sub-cloud evaporation, while spring underestimation at high elevations was related to orographic stratiform cloud structures. Despite these limitations, the elevation- and season-dependent error patterns identified in this study offer a transferable diagnostic framework for developing targeted bias correction in mountainous regions worldwide. Nevertheless, the modest correlation and detection skill of both products suggest that substantial correction is necessary before they can be reliably used in event-based applications such as flood forecasting