The concept of a digital twin of Earth envisages the convergence of Big Earth Data with
physics-based models in an interactive computational framework that enables monitoring …

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 …

Recent progress in machine learning (ML) inspires the idea of improving (or learning) earth
system models directly from the observations. Earth sciences already use data assimilation …

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 …

It remains difficult to disentangle the relative influences of aerosols and greenhouse gases
on regional surface temperature trends in the context of global climate change. To address …

Performing data assimilation (DA) at low cost is of prime concern in Earth system modeling,
particularly in the era of Big Data, where huge quantities of observations are available …

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 …

In recent years, tropospheric products obtained from ground-based global navigation
satellite system (GNSS) measurements, especially the zenith total delay (ZTD) and …

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