El Niño-Southern Oscillation (ENSO), a natural phenomenon in the Pacific Ocean, is caused by cyclic changes in sea-surface temperature (SST) and the overlying atmosphere in the tropical Pacific. The impact of ENSO varies, ranging from slightly warmer or colder temperatures to extreme weather events such as flash floods, droughts, and hurricanes, affecting various regions around the globe. Therefore, ENSO forecasting has paramount importance in the atmospheric and oceanic sciences. The Oceanic Niño Index (ONI), a three-month running mean of SST anomalies over the east–central equatorial Pacific region, is the commonly used metric for measuring ENSO events. However, the literature shows that the forecasting accuracy of ONI for lead times exceeding one year is low. This study aims to improve the forecast accuracy of ONI for up to 18 months lead time by applying an Adaptive Graph Convolutional Recurrent Neural Network (AGCRNN). The graph-learning module adaptively learns the spatial structure of features during training, while the graph convolution in hidden layers of the recurrent neural network captures the temporal relationships of features with ONI. Experiments conducted on simulation and reanalysis datasets demonstrate that AGCRNN outperforms state-of-art statistical and eight dynamical models for forecasting ONI with up to 18 months’ lead time.

During the COVID-19 pandemic, the value of palliative care has become more evident than ever. The current study quantitatively investigated the perceptions of palliative care emerging from the pandemic experience by analyzing a total of 26,494 English Tweets collected between 1 January 2020 and 1 January 2022. Such an investigation was considered invaluable in the era of more people sharing and seeking healthcare information on social media, as well as the emerging roles of palliative care. Using a web scraping method, we reviewed 6000 randomly selected Tweets and identified four themes in the extracted Tweets: (1) Negative Impact of the Pandemic on Palliative Care; (2) Positive Impact of the Pandemic on Palliative Care; (3) Recognized Benefits of Palliative Care; (4) Myth of Palliative Care. Although a large volume of Tweets focused on the negative impact of COVID-19 on palliative care as expected, we found almost the same volume of Tweets that were focused on the positive impact of COVID-19 on palliative care. We also found a smaller volume of Tweets associated with myths about palliative care. Using these manually classified Tweets, we trained machine learning (ML) algorithms to automatically classify the remaining tweets. The automatic classification of Tweets was found to be effective in classifying the negative impact of the COVID-19.

Accurate precipitation forecasting is essential for emergency management, aviation, and marine agencies to prepare for potential weather impacts. However, traditional radar echo extrapolation has limitations in capturing sudden weather changes caused by convective systems. Deep learning models, an alternative to radar echo extrapolation, have shown promise in precipitation nowcasting. However, the quality of the forecasted radar images deteriorates as the forecast lead time increases due to mean absolute error (MAE, a.k.a L1) or mean squared error (MSE, a.k.a L2), which do not consider the perceptual quality of the image, such as the sharpness of the edges, texture, and contrast. To improve the quality of the forecasted radar images, we propose using the Structural Similarity (SSIM) metric as a regularization term for the Conditional Generative Adversarial Network (CGAN) objective function. Our experiments on satellite images over the region 83° W–76.5° W and 33° S–40° S in 2020 show that the CGAN model trained with both L1 and SSIM regularization outperforms CGAN models trained with only L1, L2, or SSIM regularizations alone. Moreover, the forecast accuracy of CGAN is compared with other state-of-the-art models, such as U-Net and Persistence. Persistence assumes that rainfall remains constant for the next few hours, resulting in higher forecast accuracies for shorter lead times (i.e., <2 h) measured by the critical success index (CSI), probability of detection (POD), and Heidtke skill score (HSS). In contrast, CGAN trained with L1 and SSIM regularization achieves higher CSI, POD, and HSS for lead times greater than 2 h and higher SSIM for all lead times.

El Niño-Southern Oscillation (ENSO) is caused by periodic fluctuations in sea surface temperature and overlying air pressure across the Equatorial Pacific region. ENSO has a global impact on weather patterns and can cause severe weather events, such as heat waves, floods, and droughts, affecting regions far beyond the tropics. Therefore, forecasting ENSO with longer lead times is of great importance. This study utilizes Long Short-Term Memory (LSTM) network to predict ENSO events in the coming year based on environmental variables from previous years, including sea-surface temperature, sea level pressure, zonal wind, meridional wind, and zonal wind flux. These environmental variables are collected only inside certain spatial and temporal windows and used to forecast ENSO events. Furthermore, this study investigates how the size of these spatial and temporal windows influences the generalization accuracy of forecasting ENSO events. The size of spatial and temporal windows is optimized based on the generalization accuracy of the LSTM network in forecasting ENSO events. Our results indicated that the accuracy of the ENSO forecast is significantly sensitive to the extent of spatial and temporal windows. Specifically, increasing the temporal window size from one to nine years and the spatial window from 0 to 17.7 geographical degrees resulted in generalization accuracies, ranging from 40.1% to 83% in forecasting Central Pacific ENSO and 39.2% to 65% in forecasting Eastern Pacific ENSO.