Nitrogen dioxide (NO 2) is an important air pollutant that causes direct harms to the
environment and human health. Ground NO 2 mapping with high spatiotemporal resolution …

In the task of predicting spatio-temporal fields in environmental science using statistical
methods, introducing statistical models inspired by the physics of the underlying phenomena …

Stochastic simulations of spatiotemporal patterns of hydroclimatic processes, such as
precipitation, are needed to build alternative but equally plausible inputs for water‐related …

Realistic stochastic simulation of hydro‐environmental fluxes in space and time, such as
rainfall, is challenging yet of paramount importance to inform environmental risk analysis …

The Mat\'ern model has been a cornerstone of spatial statistics for more than half a century.
More recently, the Mat\'ern model has been central to disciplines as diverse as numerical …

Spatio-temporal models are widely used in many research areas including ecology. The
recent proliferation of the use of in-situ sensors in streams and rivers supports space-time …

Recent years have seen a steady growth in the number of papers that apply machine
learning methods to problems in the earth sciences. Although they have different origins …

Complex valued random fields, a natural generalization of real valued random fields,
represent a powerful tool for modeling phenomena which evolve in time, spatial vectorial …

The advent of data science has provided an increasing number of challenges with high data
complexity. This paper addresses the challenge of space-time data where the spatial …

Modeling and forecasting spatiotemporal patterns of precipitation is crucial for managing
water resources and mitigating water-related hazards. Globally valid spatiotemporal models …