Spatial Modeling of Temperature and Humidity Using Systems of Stochastic Partial Differential Equations

• Activity Number: 14
• Type: Topic Contributed
• Date/Time: Sunday, August 3, 2014 : 2:00 PM to 3:50 PM
• Sponsor: Section on Statistics and the Environment
• Author(s): Ingelin Steinsland*+ and Xiangping Hu and Sara Martino and Daniel Simpson and Håvard Rue
• Companies: NTNU and NTNU and SINTEF Energy and NTNU and NTNU
• Keywords: bivariate spatial model ; SPDE ; Markovian geostatistics ; temperature ; humidity

Abstract

This work is motivated by constructing a weather simulator for precipitation. Temperature and humidity are two of the most important driving forces of precipitation, and the strategy is to have a stochastic model for temperature and humidity, and use a deterministic model to go from these variables to precipitation. Temperature and humidity are empirically positively correlated. Generally speaking, if variables are empirically dependent, then multivariate models should be considered. In this work we model humidity and temperature in southern Norway. We want to construct bivariate Gaussian random fields (GRFs) based on this dataset. The aim of our work is to use the bivariate GRFs to capture both the dependence structure between humidity and temperature as well as their spatial dependencies. One important feature for the dataset is that the humidity and temperature are not necessarily observed at the same locations. Both univariate and bivariate spatial models are fitted and compared. For modeling and inference the SPDE approach for univariate models and the systems of SPDEs approach for multivariate models have been used. To evaluate the performance of the difference between the un