Ministry of Earth Sciences

Accurate prediction of extreme rainfall events during the Indian Summer Monsoon (ISM, June to September) is critical for disaster preparedness and mitigation. This study evaluates the performance of two operational numerical weather prediction models, a high-resolution version of Global Forecast System (GFS T1534) and the control member of the Met Office Global and Regional Ensemble Prediction System-Global (MOGREPS-G), in forecasting such events during the ISM from 2020 to 2023. The results demonstrate that, with respect to observations, both models tend to underestimate the mean and variability of rainfall; GFS-T1534 represents the mean and correlation better while MOGREPS-G represents the variability better over the Indian landmass. To assess the models’ performance for extreme rainfall prediction, we fix a rainfall threshold of 50 mm day−1, and the skill scores are computed including Probability of Detection, False Alarm Rate, Bias score and F1 score. Together, these scores indicate that both models show potential in short-range forecasting of extreme rainfall events, particularly within 24 h, but their skills remain limited at longer lead times. Specifically, the model biases vary over different geographical locations, often showing contrasting features. This underscores the need for model-specific post-processing and calibration techniques if these forecasts are to be used effectively for operational decision-making.

The Arabian Sea is ecologically and environmentally significant due to its high biotic diversity and its potential role as a reservoir of emerging resistance determinants. However, molecular-level insights into the taxonomic composition, functional potential, and resistome of sediment associated communities from deep-sea seamount sediments remain limited. A metagenomic approach was employed to investigate the biotic composition, metabolic potential, resistome profiles, and physicochemical characteristics of two seamount sediment samples (SM1 and SM7) collected from the Arabian Sea. Distinct environmental conditions were observed, with SM1 enriched in inorganic nitrogen, whereas SM7 exhibited higher organic carbon content and pigment concentrations, indicating differences in substrate availability. These variations were consistent with differences in the community structure, with SM1 harbouring a less diverse assemblage dominated by Actinomycetota and fungi, while SM7 supported a broader community comprising Actinomycetota, diverse fungi, protists, metazoans, and a richer viral component. Functional annotation revealed enrichment of nitrogen metabolism pathways in SM1, whereas SM7 showed increased representation of carbohydrate metabolism and a higher proportion of novel gene content. Both sediment samples encoded antibiotic and heavy metal resistance genes; however, SM7 exhibited greater abundance and diversity of putative resistance-associated genes, including resistance to mupirocin, triclosan, and sulfonamides, along with broader metal resistance and stress response genes. The results based on two samples demonstrate pronounced sample specific variation in community structure, metabolic potential, and resistome profiles across Arabian Sea seamount sediments. These findings highlight Arabian Sea deep-sea sediments as important molecular reservoirs of microbial diversity and adaptive potential shaped by local environmental conditions.

Abstract Background The reliable earthquake magnitude estimation is a critical component of earthquake early warning (EEW) systems. Conventional P-wave–based amplitude parameters, such as peak vertical displacement ( P d ), are widely used but often suffer from saturation and instability, particularly for larger earthquakes and when low-cost MEMS sensors are employed. The cumulative absolute absement (CAA), a time-integrated displacement parameter, has recently emerged as a promising alternative for improving early magnitude estimation. Methods This study analyzes strong-motion records from the dense P-Alert low-cost MEMS sensor network in Taiwan to evaluate the performance of CAA for earthquake magnitude estimation. CAA values were computed using P-wave windows ranging from 1 to 5 s after P-wave arrival, using stations within a hypocentral distance of 70 km as well as the nearest six stations. Empirical regression relations were developed to estimate magnitude from CAA and P d , and the resulting magnitudes were compared with the catalog moment magnitude ( M w ). A generalized moment magnitude ( M wg ) was additionally used to assess magnitude-scale consistency and bias in small to moderate earthquakes. Results The standard deviation between CAA-derived magnitude ( M caa ) and M w decreases systematically with increasing window length, from ±0.383 for a 3 s window to ±0.333 for a 5 s window when using all stations within 70 km. In contrast, P d -derived magnitudes ( M pd ) show larger deviations, reducing from ±0.504 (3 s) to ±0.398 (5 s). Reliable magnitude estimates are also achieved using only the nearest six stations, with standard deviations of ±0.341 (CAA) and ±0.460 ( P d ) for the 5 s window. CAA exhibits a stable scaling with earthquake magnitude, while P d tends to stagnate and underestimate events approaching M w 7.0. Magnitude scale consistency tests using M wg confirm the robustness of the proposed CAA relations after correcting for M w bias. Conclusions The results demonstrate that CAA provides a more stable and reliable early magnitude estimator than P d , particularly for low-cost MEMS sensor networks and limited station availability. The reduced dispersion, lower saturation tendency, and robustness across different window lengths highlight the strong potential of CAA for operational on-site EEW systems using dense, cost-effective seismic networks.