This work introduces GPTCast, a generative deep-learning method for ensemble
nowcast of radar-based precipitation, inspired by advancements in large
language models (LLMs). We employ a GPT model as a forecaster to learn
spatiotemporal precipitation dynamics using tokenized radar images. The
tokenizer is based on a Quantized Variational Autoencoder featuring a novel
reconstruction loss tailored for the skewed distribution of precipitation that
promotes faithful reconstruction of high rainfall rates. The approach produces
realistic ensemble forecasts and provides probabilistic outputs with accurate
uncertainty estimation. The model is trained without resorting to randomness,
all variability is learned solely from the data and exposed by model at
inference for ensemble generation. We train and test GPTCast using a 6-year
radar dataset over the Emilia-Romagna region in Northern Italy, showing
superior results compared to state-of-the-art ensemble extrapolation methods.

GPTCast 是一种基于大型语言模型（LLMs）的生成式深度学习方法，用于雷达降水的集成预测，该方法使用 GPT 模型作为预测器，使用标记化的雷达图像学习时空降水动态，模型通过学习数据中的变化性并在推理时以模型暴露的形式生成集成，无需使用随机性，提供了具有准确不确定性估计的逼真集成预测，通过在意大利北部的 Emilia-Romagna 地区的 6 年雷达数据集上进行训练和测试，展示了与最先进的集成外推方法相比的卓越结果。

GPTCast：一种用于降水预报的天气语言模型

GPTCast: a weather language model for precipitation nowcasting

Geological parameterization entails the representation of a geomodel using a
small set of latent variables and a mapping from these variables to grid-block
properties such as porosity and permeability. Parameterization is useful for
data assimilation (history matching), as it maintains geological realism while
reducing the number of variables to be determined. Diffusion models are a new
class of generative deep-learning procedures that have been shown to outperform
previous methods, such as generative adversarial networks, for image generation
tasks. Diffusion models are trained to "denoise", which enables them to
generate new geological realizations from input fields characterized by random
noise. Latent diffusion models, which are the specific variant considered in
this study, provide dimension reduction through use of a low-dimensional latent
variable. The model developed in this work includes a variational autoencoder
for dimension reduction and a U-net for the denoising process. Our application
involves conditional 2D three-facies (channel-levee-mud) systems. The latent
diffusion model is shown to provide realizations that are visually consistent
with samples from geomodeling software. Quantitative metrics involving spatial
and flow-response statistics are evaluated, and general agreement between the
diffusion-generated models and reference realizations is observed. Stability
tests are performed to assess the smoothness of the parameterization method.
The latent diffusion model is then used for ensemble-based data assimilation.
Two synthetic "true" models are considered. Significant uncertainty reduction,
posterior P$_{10}$-P$_{90}$ forecasts that generally bracket observed data, and
consistent posterior geomodels, are achieved in both cases.

地质参数化中使用低维潜变量进行降维，结合扩散模型和隐含扩散模型，通过去噪实现从含有噪声的输入场生成新的地质实现，该模型经过稳定性测试，并用于基于集合的数据同化，实现了显著的不确定性降低和一致的地质模型。