Blessed Weather

Here are the current Papers & Articles under the research topic Artificial Intelligence. Click on the title of a paper you are interested in to go straight to the full paper. Papers and articles covering the basics (ideal for learning) are shown in Green. A Sky Full of Data: Weather forecasting in the age of AI An article by Harvard University March 2024. Intro: Imagine a world where weather forecasts are as precise and personalized as the navigation app on your smartphone, and deciding whether to carry a raincoat or planning safe travel routes isn’t a morning dilemma clouded by ambiguous forecasts. This vision isn’t a distant dream– it’s rapidly becoming our reality thanks to the revolutionary impact of artificial intelligence (AI) and machine learning (ML) on meteorology, helping scientists better tackle and conquer the complexities of weather prediction. AI, with its remarkable ability to sift through immense datasets to uncover complicated patterns, heralds a new era in meteorology. Major technology companies like Google Research, Google Deepmind, and Huawei have recently demonstrated the ability of ML-based models to outperform the traditional gold-standard methods in weather predictions, while requiring only a fraction of computational resources. From providing farmers with precise agricultural forecasts to predicting the path of deadly cyclones, AI and ML are transforming how we interact with and understand the weather (Figure 1). In this article, we’ll explore the transformative role of AI and ML in weather forecasting, delving into the underlying science, the potentially revolutionary improvement and potential applications they bring, as well as the challenges that lie ahead in our quest to predict the unpredictable. The quiet AI revolution in weather forecasting Article from Cambridge University Jan 2024. Includes a video of a talk by Richard Turner, Professor of Computer Vision and Machine Learning, discussing the quiet AI revolution that has begun in the field of numerical weather prediction. A Generative Deep Learning Approach to Stochastic Downscaling of Precipitation Forecasts Published Oct 2022 Abstract: Despite continuous improvements, precipitation forecasts are still not as accurate and reliable as those of other meteorological variables. A major contributing factor to this is that several key processes affecting precipitation distribution and intensity occur below the resolved scale of global weather models. Generative adversarial networks (GANs) have been demonstrated by the computer vision community to be successful at super-resolution problems, that is, learning to add fine-scale structure to coarse images. Leinonen et al. previously applied a GAN to produce ensembles of reconstructed high-resolution atmospheric fields, given coarsened input data. In this paper, we demonstrate this approach can be extended to the more challenging problem of increasing the accuracy and resolution of comparatively low-resolution input from a weather forecasting model, using high-resolution radar measurements as a “ground truth.” The neural network must learn to add resolution and structure whilst accounting for non-negligible forecast error. We show that GANs and VAE-GANs can match the statistical properties of state-of-the-art pointwise post-processing methods whilst creating high-resolution, spatially coherent precipitation maps. Our model compares favorably to the best existing downscaling methods in both pixel-wise and pooled CRPS scores, power spectrum information and rank histograms (used to assess calibration). We test our models and show that they perform in a range of scenarios, including heavy rainfall. GraphCast: AI model for faster and more accurate global weather forecasting Blog Published Nov 2023 Abstract: GraphCast is a weather forecasting system based on machine learning and Graph Neural Networks (GNNs), which are a particularly useful architecture for processing spatially structured data. GraphCast makes forecasts at the high resolution of 0.25 degrees longitude/latitude (28km x 28km at the equator). That’s more than a million grid points covering the entire Earth’s surface. At each grid point the model predicts five Earth-surface variables – including temperature, wind speed and direction, and mean sea-level pressure – and six atmospheric variables at each of 37 levels of altitude, including specific humidity, wind speed and direction, and temperature. While GraphCast’s training was computationally intensive, the resulting forecasting model is highly efficient. Making 10-day forecasts with GraphCast takes less than a minute on a single Google TPU v4 machine. For comparison, a 10-day forecast using a conventional approach, such as HRES, can take hours of computation in a supercomputer with hundreds of machines. Learning skillful medium-range global weather forecasting Published Nov 2023 Abstract: Global medium-range weather forecasting is critical to decision-making across many social and economic domains. Traditional numerical weather prediction uses increased compute resources to improve forecast accuracy but does not directly use historical weather data to improve the underlying model. Here, we introduce GraphCast, a machine learning–based method trained directly from reanalysis data. It predicts hundreds of weather variables for the next 10 days at 0.25° resolution globally in under 1 minute. GraphCast significantly outperforms the most accurate operational deterministic systems on 90% of 1380 verification targets, and its forecasts support better severe event prediction, including tropical cyclone tracking, atmospheric rivers, and extreme temperatures. GraphCast is a key advance in accurate and efficient weather forecasting and helps realize the promise of machine learning for modeling complex dynamical systems. Do AI models produce better weather forecasts than physics-based models? A quantitative evaluation case study of Storm Ciarán Published April 2024 Abstract: There has been huge recent interest in the potential of making operational weather forecasts using machine learning techniques. As they become a part of the weather forecasting toolbox, there is a pressing need to understand how well current machine learning models can simulate high-impact weather events. We compare short to medium-range forecasts of Storm Ciarán, a European windstorm that caused sixteen deaths and extensive damage in Northern Europe, made by machine learning and numerical weather prediction models. The four machine learning models considered (FourCastNet, Pangu-Weather, GraphCast and FourCastNet-v2) produce forecasts that accurately capture the synoptic-scale structure of the cyclone including the position of the cloud head, shape of the warm sector and location of the warm conveyor belt jet, and the large-scale dynamical drivers important for the rapid storm development such as the position of the storm relative to the upper-level jet exit. However, their ability to resolve the more detailed structures important for issuing weather warnings is more mixed. All of the machine learning models underestimate the peak amplitude of winds associated with the storm, only some machine learning models resolve the warm core seclusion and none of the machine learning models capture the sharp bent-back warm frontal gradient. Our study shows there is a great deal about the performance and properties of machine learning weather forecasts that can be derived from case studies of high-impact weather events such as Storm Ciarán. ECMWF - First update to the AIFS Blog with update details - published Jan 2024 (contains link to all ECMWF AI blogs) Abstract: On 10 January 2024, we introduced a new version of the AIFS. While the previous version had a spatial resolution of 111 km (1°), the revised AIFS version has a resolution of 28 km (0.25°). Its input and output grids are now the native ERA5 reduced Gaussian grid, which provides near-constant resolution across the globe. There were also architectural changes. The first implementation of the AIFS was built upon Deepmind’s GraphCast approach, based on message-passing graph neural networks and with an internal icosahedral grid with multi-scale edges. In this new version, the encoder and decoder use attention-based graph neural networks, very similar to a transformer (Vaswani et al., 2017) architecture. The processor now works on an octahedral reduced Gaussian grid, the same kind of grid that is used in our operational IFS. The processor is a transformer that processes the 40,320 grid points of the processor grid as a sequence with a sliding attention window (Figure 1). These layers are highly efficient on GPU architecture, meaning the model is faster both to train and to make predictions.