In recent years, Earth system sciences are urgently calling for innovation on improving
accuracy, enhancing model intelligence level, scaling up operation, and reducing costs in …

Vegetation phenology has been viewed as the nature's calendar and an integrative indicator
of plant‐climate interactions. The correct representation of vegetation phenology is important …

Data assimilation (DA) is integrated with machine learning in order to perform entirely data‐
driven online state estimation. To achieve this, recurrent neural networks (RNNs) are …

大气水汽是表征极端天气事件和气候变化的重要参数, 准确监测与分析水汽含量对于精准预测各
类灾害性天气事件与研究气候变化具有显著意义. 作为新兴的大气水汽探测方法, GNSS …

Artificial intelligence (AI) and machine learning (ML) have become important tools for
environmental scientists and engineers, both in research and in applications. Although …

The global mean surface temperature (GMST) record exhibits both interannual to
multidecadal variability and a long‐term warming trend due to external climate forcing. To …

There is a growing interest in developing data‐driven reduced‐order models for
atmospheric and oceanic flows that are trained on data obtained either from high‐resolution …

Recent advances in deep convolutional neural network (CNN)-based super resolution can
be used to downscale atmospheric chemistry simulations with substantially higher accuracy …

Abstract We introduce the National Science Foundation (NSF) AI Institute for Research on
Trustworthy AI in Weather, Climate, and Coastal Oceanography (AI2ES). This AI institute …

Quantitative remote sensing is a technique for quantitatively inferring or inverting earth
environmental variable from the original remote sensing observations, it is an important step …