In this paper, we study hybrid neural representations for spherical data, a
domain of increasing relevance in scientific research. In particular, our work
focuses on weather and climate data as well as comic microwave background (CMB)
data. Although previous studies have delved into coordinate-based neural
representations for spherical signals, they often fail to capture the intricate
details of highly nonlinear signals. To address this limitation, we introduce a
novel approach named Hybrid Neural Representations for Spherical data (HNeR-S).
Our main idea is to use spherical feature-grids to obtain positional features
which are combined with a multilayer perception to predict the target signal.
We consider feature-grids with equirectangular and hierarchical equal area
isolatitude pixelization structures that align with weather data and CMB data,
respectively. We extensively verify the effectiveness of our HNeR-S for
regression, super-resolution, temporal interpolation, and compression tasks.

本文研究球面数据的混合神经表示方法，特别关注天气和气候数据以及宇宙微波背景数据。我们提出了一种名为 HNeR-S 的新方法，通过使用球面特征网格和多层感知器结合来预测目标信号，经过广泛验证在回归、超分辨率、时间插值和压缩任务方面具有有效性。

球面数据的混合神经表征

Hybrid Neural Representations for Spherical Data

As artificial intelligence (AI) continues to rapidly evolve, the realm of
Earth and atmospheric sciences is increasingly adopting data-driven models,
powered by progressive developments in deep learning (DL). Specifically, DL
techniques are extensively utilized to decode the chaotic and nonlinear aspects
of Earth systems, and to address climate challenges via understanding weather
and climate data. Cutting-edge performance on specific tasks within narrower
spatio-temporal scales has been achieved recently through DL. The rise of large
models, specifically large language models (LLMs), has enabled fine-tuning
processes that yield remarkable outcomes across various downstream tasks,
thereby propelling the advancement of general AI. However, we are still
navigating the initial stages of crafting general AI for weather and climate.
In this survey, we offer an exhaustive, timely overview of state-of-the-art AI
methodologies specifically engineered for weather and climate data, with a
special focus on time series and text data. Our primary coverage encompasses
four critical aspects: types of weather and climate data, principal model
architectures, model scopes and applications, and datasets for weather and
climate. Furthermore, in relation to the creation and application of foundation
models for weather and climate data understanding, we delve into the field's
prevailing challenges, offer crucial insights, and propose detailed avenues for
future research. This comprehensive approach equips practitioners with the
requisite knowledge to make substantial progress in this domain. Our survey
encapsulates the most recent breakthroughs in research on large, data-driven
models for weather and climate data understanding, emphasizing robust
foundations, current advancements, practical applications, crucial resources,
and prospective research opportunities.

人工智能在地球和大气科学领域日益采用数据驱动模型，通过深度学习技术对地球系统的混沌和非线性方面进行解码，以及通过理解天气和气候数据来应对气候挑战。这篇综述全面介绍了针对天气和气候数据专门设计的最先进的人工智能方法，特别关注时间序列和文本数据，同时深入研究了该领域的挑战，并提出了未来研究的详细意见。