Sea Surface Temperature (SST) is of great importance to study several major phenomena due to ocean interactions with other earth systems. Previous studies on SST based on statistical inference methods were less accurate for longer prediction lengths. A considerable number of studies in recent years involve machine learning for SST modeling. These models were able to mitigate this problem to some length by modeling SST patterns and trends. Sequence analysis by decomposition is used for SST forecasting in several studies. Ensemble Empirical Mode Decomposition (EEMD) has been proven in previous studies as a useful method for this. The application of EEMD in spatiotemporal modeling has been introduced as Multidimensional EEMD (MEEMD). The aim of this study is to employ fast MEEMD methods to decompose the SST spatiotemporal dataset and apply a Convolutional Long Short-Term Memory (ConvLSTM)-based model to model and forecast SST. The results show that the fast MEEMD method is capable of enhancing spatiotemporal SST modeling compared to the Linear Inverse Model (LIM) and ConvLSTM model without decomposition. The model was further validated by making predictions from April to May 2023 and comparing them to original SST values. There was a high consistency between predicted and real SST values.

Amid global energy demands and environmental concerns, the exploration of renewable energy sources has become critically important. This study presents a comprehensive analysis of wind energy dynamics over a 35-year period (1988–2022) using CCMP and ERA5 datasets. It focuses on the spatial and temporal characteristics of wind energy within the region, particularly its distribution. The wind field in Sri Lanka has significant seasonal and regional characteristics. Despite seasonal variations, the overall wind activity remains moderate, with speeds generally above 5.4 m/s and annual average wind power densities exceeding 150 W/m², reaching up to 200 W/m² in certain areas. The two primary wind directions in the waters near Sri Lanka are WSW and NE. The study identifies periods of high stability, particularly from June to August, where effective wind speed occurrence (EWSO) exceeds 80%. A ‘W’-shaped pattern in monthly variations corresponds with changes in wind power density (WPD), highlighting optimal periods for wind energy extraction. Additionally, estimated reserves and technically exploitable wind energy resources (WERs) suggest that wind energy development off Sri Lanka is feasible, with potential capacities of approximately 2.00 GW and 1.60 GW, respectively. The overall coefficient of variation (CV) is small, indicating stable wind energy conditions. This analysis not only provides a scientific basis for evaluating WERs near Sri Lanka but also offers valuable insights for strategic planning and development in the renewable energy sector.

This study addresses the issues of sparse observations from buoys in the tropical Indian Ocean and systematic biases in reanalysis products by proposing a daily-mean wind speed reconstruction framework that integrates multi-source meteorological fields. This study also considers the impact of different source domains on model pre-training, with the goal of providing reliable data support for wind energy assessment. The model was pre-trained using data from the Americas and tropical Pacific buoys as the source domain and then fine-tuned on Indian Ocean buoys as the target domain. Using annual leave-one-out cross-validation, we evaluated the model’s performance against uncorrected ERA5 and CCMP data while comparing three deep reconstruction models. The results demonstrate that deep models significantly reduce reanalysis bias: the RMSE decreases from approximately 1.00 m/s to 0.88 m/s, while R² improves by approximately 8.9% and 7.1% compared to ERA5/CCMP, respectively. The Branch CNN–Transformer outperforms standalone LSTM or CNN models in overall accuracy and interpretability, with transfer learning yielding directional gains for specific wind conditions in complex topography and monsoon zones. The 20-year wind energy data reconstructed using this model indicates wind energy densities 60–150 W/m² higher than in the reanalysis data in open high-wind zones such as the southern Arabian Sea and the Somali coast. This study not only provides a pathway for constructing high-precision wind speed databases for tropical Indian Ocean wind resource assessment but also offers precise quantitative support for delineating priority development zones for offshore wind farms and mitigating near-shore engineering risks.

The Pearl River Estuary is a typical estuary region in southern China, and the study of surface wave occurrence and characteristics is of great importance for shipping management, nearshore engineering, and monitoring shoreline changes and other human activities. Long-term and continuous observational data are critical for achieving a better understanding of waves. In this study, the wave measurements based on a high-precision wave gauge were analyzed and observation data over approximately two years at a sampling frequency of 2 Hz were obtained. The wave system in the Pearl River Estuary was found to deviate from the assumption of a stationary stochastic process similar to that in the open ocean, due to the effects of abnormal waves caused by human activities. Therefore, traditional distribution functions such as Rayleigh and Weibull were not suitable for accurately fitting the main wave parameters ($H_s$, $T_p$, etc.), particularly in the tail. Consequently, abnormal wave signals were extracted from all wave sets, and through the comparison and analysis of the wave spectral features, it was determined that these abnormal waves are caused by the ship wakes. The spectral characterization of these waves was performed to determine the characteristics of different ship wake processes. Ship wakes in the Pearl River Estuary are an important part of the wave system, and their wave height is significantly larger than the normal wave. Based on the spectral characteristics of ship wakes, this study proposed some news characteristics of ship wakes in the main channel of the Pearl River Estuary.

Air–sea exchanges play a crucial role in intense weather events over Sri Lanka, particularly by providing the heat and moisture that fuel heavy rainfall. We present a year-round dataset of meteorological observations from the southern shoreline of Sri Lanka in the equatorial Indian Ocean for 2017, aiming to investigate its seasonal characteristics and evaluate the performance of reanalysis data in this region. The observations reveal distinct diurnal and seasonal patterns. During the winter and spring, higher shortwave (646.2 W/m²) and longwave radiation (−86.9 W/m²) are coupled with higher temperatures (30.6 °C) and lower humidity (67.4% at noon). In contrast, the Indian summer monsoon period features reduced shortwave (579.8 W/m²) and longwave radiation (−58.6 W/m²), lower temperatures (29.2 °C), higher humidity (over 79.7%), and stronger winds (6.25 m/s). The observations were compared with the ERA5 reanalysis dataset to evaluate the regional performance. The reanalysis data correlated well with the observed data for the radiation, temperature, and sensible heat flux, although notable deviations occurred in terms of the wind speed and latent heat flux. During the impact of Tropical Cyclone Ockhi, the reanalysis data tended to underestimate both the wind speed and precipitation. This dataset will provide vital support for studies on monsoons and coastal atmospheric convection, as well as for model initialization and synergistic applications.