Climate change, air quality, and environmental degradation are important societal
challenges for the Twenty-first Century. These challenges require an intelligent response …

Chemical data assimilation is the process by which models use measurements to produce
an optimal representation of the chemical composition of the atmosphere. Leveraging …

Air quality prediction plays an important role in the management of our environment.
Computational power and efficiencies have advanced to the point where chemical transport …

We establish an on-line optimization framework to exploit weather forecast information in the
operation of energy systems. We argue that anticipating the weather conditions can lead to …

In operational air‐quality forecasting, initial concentrations of chemical species are often
obtained using previous‐day forecasts with limited or no account for the observations. In this …

The objective of this paper is to evaluate the performances of different data assimilation
schemes with the aim of designing suitable assimilation algorithms for short‐range ozone …

Data assimilation is the process of integrating observational data and model predictions to
obtain an optimal representation of the state of the atmosphere. As more chemical …

In a series of experiments we issue forecasts of fine aerosol concentration over the
coterminous USA and southern Canada using the Weather Research and Forecasting …

This paper develops an efficient implementation of the ensemble Kalman filter based on a
modified Cholesky decomposition for inverse covariance matrix estimation. This …

In uncertainty quantification of computational models (eg, turbulence modeling) with
Bayesian inferences, Gaussian processes are commonly used as the prior for model …