An adapted vector autoregressive expectation maximization imputation algorithm for climate data networks

Abstract

Missingness in historical climate data networks is a pervasive phenomenon due to the conditions under which these measurements are made. Accurate estimation of these data is a critical issue as projections of future climate depend on a reliable historical climate record. After all, how can the impact of climate change be reliably forecasted when a large proportion of historical climate records are permeated with missing data? We propose an iterative multivariate infilling algorithm and explore its effectiveness on three United States temperature networks of varying densities (number of stations per unit area). Although other multivariate data are not explored here, the application of this infilling procedure is not restricted to climate networks exclusively. We also explore its utility as a function of the type of missing data (i.e., whether or not there is a mechanism or pattern behind the missing observations themselves) and the proportion of missing data within the network. As one may expect, we observe a slightly smaller root mean square error (RMSE) for temperature networks with more stations and less missingness. Somewhat surprisingly, the RMSE tends to be lower for data which is missing at random (there is some mechanism behind the missing data) rather than for data that is missing completely at random (no mechanism behind the missing data). Not surprisingly, the RMSE is largest for data which is missing not at random (missing data are directly related to the values of the observations themselves). Perhaps the most surprising result observed in these networks is that the inclusion of lagged temperature data does not necessarily improve the accuracy of data imputation. The typical RMSE for monthly minimum temperature networks varies from around 1.5 to 2.5 C.

Principal Component Analysis-Vector Autoregressive Integrated (Pca-Vari) Model Using Data Mining Approach to Climate Data in the West Java Region

“…Using Knowledge Discovery in Database (KDD), the data mining approach includes three steps of preprocessing, data mining, and postprocessing can be used on climate phenomena with some of these variables. Multivariate time series models such as temperature variables observed at several observation stations with a fairly high correlation value can use the Vector Autoregressive (VAR) or Vector Autoregressive Integrated (VARI) models [2]. To model the impact of climate variability (rainfall, maximum temperature, and relative humidity) on the number of malaria sufferers with the estimated variance decomposition showing varying degrees of dependence of the number of malaria sufferers on climate variables that are large enough from the variability at a maximum temperature [3] [4].…”