Earth system models (ESMs) are our main tools for quantifying the physical state of the Earth
and predicting how it might change in the future under ongoing anthropogenic forcing. In …

Process-based models are widely used to predict the agroecosystem dynamics, but such
modeled results often contain considerable uncertainty due to the imperfect model structure …

Promising new opportunities to apply artificial intelligence (AI) to the Earth and
environmental sciences are identified, informed by an overview of current efforts in the …

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 …

The planetary boundary layer height (zi) is a key parameter used in atmospheric models for
estimating the exchange of heat, momentum, and moisture between the surface and the free …

The outstanding breakthroughs of deep learning in computer vision and natural language
processing have been the horn of plenty for many recent developments in the climate …

The ocean mixed layer plays an important role in the coupling between the upper ocean and
atmosphere across a wide range of time scales. Estimation of the variability of the ocean …

Using generative models from the machine learning literature to create artificial ensemble
members for use within data assimilation schemes has been introduced in Grooms (2021)[1] …

X-ray computed microtomography (μCT) is an innovative and nondestructive versatile
technique that has been used extensively to investigate bio-based systems in multiple …

Data assimilation (DA) can provide the more accurate initial state for numerical forecasting
models. But traditional DA algorithms has the problem of long calculation time. This paper …