Data assimilation in atmospheric chemistry models: current status and future prospects for coupled chemistry meteorology models
Data assimilation is used in atmospheric chemistry models to improve air quality forecasts,
construct re-analyses of three-dimensional chemical (including aerosol) concentrations and …
construct re-analyses of three-dimensional chemical (including aerosol) concentrations and …
Rainfall and streamflow sensor network design: a review of applications, classification, and a proposed framework
Sensors and sensor networks play an important role in decision-making related to water
quality, operational streamflow forecasting, flood early warning systems, and other areas. In …
quality, operational streamflow forecasting, flood early warning systems, and other areas. In …
[BOOK][B] Value of information in the earth sciences: Integrating spatial modeling and decision analysis
Gathering the right kind and the right amount of information is crucial for any decision-
making process. This book presents a unified framework for assessing the value of potential …
making process. This book presents a unified framework for assessing the value of potential …
Towards real-time information processing of sensor network data using computationally efficient multi-output Gaussian processes
In this paper, we describe a novel, computationally efficient algorithm that facilitates the
autonomous acquisition of readings from sensor networks (deciding when and which sensor …
autonomous acquisition of readings from sensor networks (deciding when and which sensor …
Measuring atmospheric composition change
Scientific findings from the last decades have clearly highlighted the need for a more
comprehensive approach to atmospheric change processes. In fact, observation of …
comprehensive approach to atmospheric change processes. In fact, observation of …
Sensor configuration optimization based on the entropy of adjoint concentration distribution for stochastic source term estimation in urban environment
Sensor monitoring is foundational to source term estimation (STE). Recently, stochastic STE
methods based mainly on Bayesian inference have been attracting attention. However, few …
methods based mainly on Bayesian inference have been attracting attention. However, few …
Applications of linear and nonlinear models
EW Grafarend, J Awange - Fixed Effects, 2012 - Springer
With the introductory paragraph, we explain the fundamental concepts and basic notions of
this section. For you, the analyst, who has the difficult task to deal with measurements …
this section. For you, the analyst, who has the difficult task to deal with measurements …
Real-time information processing of environmental sensor network data using bayesian gaussian processes
In this article, we consider the problem faced by a sensor network operator who must infer, in
real time, the value of some environmental parameter that is being monitored at discrete …
real time, the value of some environmental parameter that is being monitored at discrete …
Optimal design of air quality monitoring networks: a systematic review
S Verghese, AK Nema - Stochastic Environmental Research and Risk …, 2022 - Springer
The optimal design of air quality monitoring network draws significant attention due to the
severity associated with air pollution and constraints involved with the installation and …
severity associated with air pollution and constraints involved with the installation and …
Sparse sampling: spatial design for monitoring stream networks
MJ Dobbie, BL Henderson, DL Stevens, Jr - 2008 - projecteuclid.org
Spatial designs for monitoring stream networks, especially ephemeral systems, are typically
non-standard,'sparse'and can be very complex, reflecting the complexity of the ecosystem …
non-standard,'sparse'and can be very complex, reflecting the complexity of the ecosystem …