Dynamics of Atmospheres and Oceans最新文献

A comparative study has been conducted between genetic algorithm (GA) and deep learning models to predict swell wave heights in the Bay of Bengal (BOB) region. To simulate the required parameter SWAN (Simulating Waves Nearshore) model is integrated with daily 25 km wind from 2009 to 2018 for July and December separately representing the southwest and northeast monsoons respectively. For the BOB region empirical orthogonal function (EOF) analysis is applied on the swell parameter to study the spatial and temporal patterns. GA is applied on the principal component of swell wave heights to generate a forecast explicit equation and thus a basin scale EOF-GA model is established. Next a grid (20⁰ N, 90⁰E) is chosen in the head bay region and the outcomes of the standalone GA model and the deep learning models are compared to predict the time series data of swell wave heights (SWS). It is observed that the performances of the deep learning model is better during the calm conditions in December than the rough seas in July. Another grid (15⁰ N, 82⁰E) is chosen along the east coast through which the severe cyclonic storm PHETHAI (13–18 December 2018) passed and the model accuracies are tested. The EOF-GA model serves as an effective computationally cheap basin scale forecast model. Thus, both the genetic algorithm and deep learning models can be developed and utilized for normal and extreme wave prediction having wide application in the ocean engineering domains.

In this study, the anomalies of dynamical tropopause associated with blocking events in wintertime of the period 1959–2020 are analyzed using the JRA-55 re-analysis data with focus on the Southwest Asia. The identification and analysis of blocking properties is based on a wave breaking index. To this end, at first, the periods of occurrence of blocking are identified, and then the anomalies of the tropopause in the upstream and downstream of the relevant blocking locations in the two sectors of West Asia (Aral) and Euro–Atlantic are investigated. The analysis is carried out for the whole blocking events irrespective of their strength and blocking events with the large blocking index. Results show that the general characteristics obtained for blocking, such as location and frequency of occurrence, are in agreement with most previous studies. In addition, with the occurrence of blocking in the above-mentioned sectors, the characteristics of tropopause in the geographical area of blocking occurrence generally undergo well-defined changes. However, in the downstream of the respective atmospheric blockings, corresponding to the Southwest Asia, the changes in the characteristics of tropopause are relatively small. Although the changes are small compared to those in blocking event area itself, but they are expected to have important implications for the evolution of synoptic systems. For both the two sectors, results for the population of events with large blocking index indicate a significant eastward displacement of the location of blocking relative to that of the whole population of blocking events.

In the context of climate change and human activities, the global sea level is facing a rising trend, which poses serious challenges to the ecological environment of coastal areas. In this study, we selected the monthly mean sea level (MSL) time series of 9 stations in the coastal areas of China as the research object. First, we analyzed the spatiotemporal distribution characteristics of the monthly MSL in the coastal areas of China. Secondly, we analyzed the ability of ensemble empirical mode decomposition (EEMD) to decompose the monthly MSL series. Finally, we choose three machine learning models, namely Back Propagation (BP), K-Nearest Neighbor (KNN), and Long Short-Term Memory (LSTM) neural network models to compare model prediction effect between single machine learning models with machine learning models combined with EEMD. The results show that except for the YANTAI (YT) station, which showed an insignificant downward trend, the monthly MSL of other stations showed an upward trend, indicating that the coastal areas of China are facing the risk of sea level rise. EEMD can effectively reduce the complexity of the original monthly MSL time series, and different intrinsic mode functions (IMFs) reflect changes in monthly MSL at different frequencies. Comparing the single machine learning model and the machine learning model combined with EEMD, it is found that the simulation effect of the machine learning model combined with EEMD is better than that of the single model. The model with the best prediction effect on monthly MSL in the coastal areas of China is LSTM-EEMD, followed by KNN-EEMD. This study provides an important reference for systematically understanding sea level changes and selecting an appropriate monthly MSL prediction model in the coastal areas of China.