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 (R2, 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 (R2≈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
Climate change and rainfall variability have increased the frequency and intensity of droughts worldwide. It is therefore important to identify when and where droughts are likely to occur to implement effective drought management strategies. The drought conditions investigated in this research are based on long-term agro-meteorological drought conditions (2000 to 2025) for Prince Edward Island (PEI), Canada. This study has used satellite-based drought indices from MODIS (VCI, TCI, VHI) and CHIRPS (SPI-3), along with other drought metrics such as SDF and ADF. The analysis was done on a cloud-based GEE platform. The Moving Average Test was applied to determine the trend and seasonality of time-series data. The Mann–Kendall test indicated a significant decreasing trend in annual rainfall (τ = −0.317, p = 0.025), whereas the long-term trend in SPI-3 was not statistically significant (τ = −0.028, p = 0.677). The results revealed that 2001 and 2020 were the two driest years of the period studied, with almost 80–90% of the area under effect, and in 2025, PEI experienced its third major drought. Based on drought frequency, 65.67% of the area is in a very low drought-frequency zone, and 15.81% is in an extreme drought-frequency zone. Locations where both SDF and ADF consistently appeared helped identify drought-prone hotspots. Based on long-term trends in precipitation and SPI, there was a general decline in rainfall and an increase in drought events after 2018. These findings will assist in developing improved drought-preparedness and adaptive agricultural management strategies for PEI.
Errors in GPS receivers arise from multiple sources, including thermal noise, antenna phase center variations, inter-channel and inter-frequency biases, and receiver clock offsets relative to GPS time. A major challenge is estimating inter-frequency biases (IFBs) for stations outside the International GNSS Service (IGS) network, where predefined IFB products are unavailable. This limitation introduces significant uncertainty in bias correction and degrades the accuracy of derived positioning solutions. Reliable estimation of receiver IFBs for non-IGS stations is therefore essential to maintain precise positioning performance. In this work, we introduce the IFB Estimation Using Regional Modeling (IFBERM) framework, which enables single-station estimation and comparative Analysis of satellite and receiver IFBs across various GNSS receiver architectures over Egypt by combining three regional ionospheric models (GTSM, SCHM, and polynomial). The proposed method estimates satellite and receiver Instrumental Frequency Biases (IFBs) using the Generalized Trigonometric Series Model (GTSM) and a polynomial model applied to standalone dual-frequency carrier-smoothed GPS Pseudorange measurements via elevation-weighted least squares. We evaluated model performance across seven tracking stations in Egypt using 2015 GPS data by comparing estimates against the Spherical Cap Harmonic Model (SCHM) and official International GNSS Service (IGS) products. The results indicate that GTSM and SCHM achieve comparable, consistently high temporal stability (average satellite IFB standard deviations of ~0.30 ns and ~ 0.31 ns, respectively). In contrast, the polynomial model exhibits higher, site-dependent variability (averaging ~0.69 ns and reaching up to 3.77 ns at station Said). Following zero-mean constellation datum alignment, both GTSM and SCHM demonstrate strong quantitative agreement with IGS products, achieving Root Mean Square (RMS) errors of ~0.40 ns and Pearson correlation coefficients exceeding 0.996, proving statistically indistinguishable from global IGS baselines (p > 0.05).
Automated hazard detection in high-resolution satellite imagery of electrical substations presents significant challenges, including complex background clutter, high intra-class defect variability, and the prohibitive cost of pixel-level annotation. Existing methods are limited by their reliance on extensive manual labeling and their inability to generalize across diverse substation configurations and equipment types. This paper presents SubSAM-Detect, a novel integrated framework that synergizes the Segment Anything Model (SAM) with domain-adapted large vision-language models (LVLMs) to enable annotation-efficient, zero-shot hazard detection in substation satellite imagery. The framework operates as a unified three-stage pipeline: (1) SAM-LoRA fine-tuning with substation-specific prompt engineering for precise infrastructure component segmentation (transformers, bushings, insulators, conductor arrays); (2) CLIP-enhanced hierarchical hazard classification propagating SAM-generated segments through a multi-granularity defect taxonomy covering vegetation encroachment, oil leakage, thermal anomalies, and structural deformations; and (3) confidence-calibrated active learning for iterative pseudo-label refinement. Compared against five state-of-the-art baselines on the SubStation-Sat (12,000 images, 150 substations) and PowerGrid-Global (8500 images, 85 substations) datasets, SubSAM-Detect achieves 89.3% mAP@0.5, a 6.9% improvement over the best competing method (GDPGO-SAM at 82.4% mAP@0.5), while reducing manual annotation requirements by 85% and improving small-scale defect detection by 15.7%.
