Many research questions in Earth and environmental sciences are inherently causal,
requiring robust analyses to establish whether and how changes in one variable cause …

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 …

We introduce in this survey the major concepts, models, and algorithms proposed so far to
infer causal relations from observational time series, a task usually referred to as causal …

MSWEP V2 Global 3-Hourly 0.1 Precipitation: Methodology and Quantitative Assessment in:
Bulletin of the American Meteorological Society Volume 100 Issue 3 (2019) Jump to …

Droughts and heatwaves cause agricultural loss, forest mortality, and drinking water scarcity,
especially when they occur simultaneously as combined events. Their predicted increase in …

The heart of the scientific enterprise is a rational effort to understand the causes behind the
phenomena we observe. In large-scale complex dynamical systems such as the Earth …

Identifying causal relationships and quantifying their strength from observational time series
data are key problems in disciplines dealing with complex dynamical systems such as the …

Abstract Climate Data Records of soil moisture are fundamental for improving our
understanding of long-term dynamics in the coupled water, energy, and carbon cycles over …

In just the past 5 years, the field of Earth observation has progressed beyond the offerings of
conventional space-agency-based platforms to include a plethora of sensing opportunities …

With an increasing volume and dimensionality of Earth observation data, enhanced
integration of machine-learning methodologies is needed to effectively analyze and utilize …