UT-MeteoGAN: A Next-Generation AI Model for High-Resolution Numerical Weather Prediction

We introduce UT-MeteoGAN, a cutting-edge, AI-driven model for high-resolution numerical weather prediction. UT-MeteoGAN enhances prediction accuracy and resolution by transitioning from Super Resolution Convolutional Neural Networks to the Swin Transformer, leveraging adversarial loss to refine its outputs. The model integrates data assimilation techniques, directly incorporating station observations into the training process, which significantly boosts forecast reliability. By utilizing a global model as input and targeting high-resolution gridded ground truth observations, UT-MeteoGAN excels in generating high-resolution gridded products, even in the absence of detailed high-resolution data. We conducted a comprehensive case study over the continental United States, when UT-MeteoGAN provided 36-hour lead forecasts at 1-km spatial and 1-hour temporal resolution. The global model Graphcast served as the input, with the National Oceanic and Atmospheric Administration (NOAA) Analysis of Record for Calibration (AORC) as the high-resolution target and NOAA’s Global Historical Climatological Network hourly (GHCNh) for station observations. UT-MeteoGAN consistently delivered forecasts that were comparable to or exceeded those of other models, offering rapid predictions with high accuracy. The development and operational deployment of UT-MeteoGAN are supported by the advanced NVIDIA H100 GPUs available through the Texas Advanced Computing Center. UT-MeteoGAN represents a significant advancement in numerical weather prediction, providing a robust and scalable solution for precise and timely weather forecasts. Its successful application in the continental United States case study highlights its potential for broader adoption in meteorological forecasting.Texas Advanced Computing Center (TACC

Mind meets machine: Unravelling GPT-4’s cognitive psychology

Cognitive psychology delves on understanding perception, attention, memory, language, problem-solving, decision-making, and reasoning. Large Language Models (LLMs) are emerging as potent tools increasingly capable of performing human-level tasks. The recent development in the form of Generative Pre-trained Transformer 4 (GPT-4) and its demonstrated success in tasks complex to humans exam and complex problems has led to an increased confidence in the LLMs to become perfect instruments of intelligence. Although GPT-4 report has shown performance on some cognitive psychology tasks, a comprehensive assessment of GPT-4, via the existing well-established datasets is required. In this study, we focus on the evaluation of GPT-4’s performance on a set of cognitive psychology datasets such as CommonsenseQA, SuperGLUE, MATH and HANS. In doing so, we understand how GPT-4 processes and integrates cognitive psychology with contextual information, providing insight into the underlying cognitive processes that enable its ability to generate the responses. We show that GPT-4 exhibits a high level of accuracy in cognitive psychology tasks relative to the prior state-of-the-art models. Our results strengthen the already available assessments and confidence on GPT-4’s cognitive psychology abilities. It has significant potential to revolutionise the field of Artificial Intelligence (AI), by enabling machines to bridge the gap between human and machine reasoning