The modelling of temperature fields, which are crucial to understand a region's climate, can be challenging due to the topography of the study region. In the Pacific Northwest, extensive forests, mountains and proximity to the Pacific Ocean may create sudden changes in climate, contributing to the complexity of the modelling of temperature fields in this area. In this talk, we will firstly describe a modelling strategy for complex temperature fields that addresses non-stationarity via a new approach to modelling the spatial mean field.
Secondly, we will focus on the important task of surveillance of environmental processes. We will introduce a novel strategy for the design of monitoring networks where the goal is to choose a high-quality yet diverse set of locations. The idea is brought to this context via the theory of determinantal point processes (DPPs). We will demonstrate how DPPs, which have traditionally been used in other scientific domains, can also play an important role in statistical sciences, particularly in spatial design.
Time permitting, we will also discuss a recent challenge in spatial statistics applications: the data fusion problem. There has been an increased need for combining information from multiple sources that may have been observed on different spatial scales. We will give an overview of an ensemble modelling strategy which combines observed temperature measurements with outputs from an ensemble of deterministic climate models. This methodology can ultimately be used for calibration of model outputs, spatial mapping, and future forecasting.