Spatiotemporal mapping and assessment of daily ground NO2 concentrations in China using high-resolution TROPOMI retrievals
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 …
environment and human health. Ground NO 2 mapping with high spatiotemporal resolution …
[HTML][HTML] The SPDE approach for spatio-temporal datasets with advection and diffusion
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 …
methods, introducing statistical models inspired by the physics of the underlying phenomena …
Large‐domain multisite precipitation generation: Operational blueprint and demonstration for 1,000 sites
SM Papalexiou, F Serinaldi… - Water Resources …, 2023 - Wiley Online Library
Stochastic simulations of spatiotemporal patterns of hydroclimatic processes, such as
precipitation, are needed to build alternative but equally plausible inputs for water‐related …
precipitation, are needed to build alternative but equally plausible inputs for water‐related …
Advancing space‐time simulation of random fields: From storms to cyclones and beyond
SM Papalexiou, F Serinaldi… - Water Resources …, 2021 - Wiley Online Library
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 …
rainfall, is challenging yet of paramount importance to inform environmental risk analysis …
The Mat\'ern Model: A Journey through Statistics, Numerical Analysis and Machine Learning
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 …
More recently, the Mat\'ern model has been central to disciplines as diverse as numerical …
Bayesian spatio-temporal models for stream networks
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 proliferation of the use of in-situ sensors in streams and rivers supports space-time …
Geostatistics and machine learning
S De Iaco, DT Hristopulos, G Lin - Mathematical Geosciences, 2022 - Springer
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 …
learning methods to problems in the earth sciences. Although they have different origins …
Parametric families for complex valued covariance functions: Some results, an overview and critical aspects
D Posa - Spatial Statistics, 2020 - Elsevier
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 …
represent a powerful tool for modeling phenomena which evolve in time, spatial vectorial …
Stationary nonseparable space-time covariance functions on networks
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 …
complexity. This paper addresses the challenge of space-time data where the spatial …
Spatial modeling of precipitation based on data-driven warping of Gaussian processes
VD Agou, A Pavlides, DT Hristopulos - Entropy, 2022 - mdpi.com
Modeling and forecasting spatiotemporal patterns of precipitation is crucial for managing
water resources and mitigating water-related hazards. Globally valid spatiotemporal models …
water resources and mitigating water-related hazards. Globally valid spatiotemporal models …