Generating data ensembles over a model grid from sparse climate point measurements

Abstract

Parameterizations in high-end climate models can be evaluated with observed climate and meteorological data. Often this evaluation is achieved by averaging measured quantities over time and space to match the spatial and temporal resolution of a gridded climate model without any regard for the statistical errors in doing so. We present a statistical method to interpolate sparsely located surface measurements into a uniform spatial grid representative of global and regional models of the atmosphere and climate. This method provides estimates of mean values over the entire domain containing the measurement sites as well as the uncertainty in the estimated quantities at the grid locations through multiple simulations to create data ensembles. We demonstrate this method using measurements of surface sensible heat flux over the southern Great Plains region gathered through the Department of Energy's Atmospheric Radiation Measurement (ARM) program. Application to the numerous other climate variables measured through the ARM program and the development of a software tool to streamline data management and implementation of the statistical models is also discussed. © 2008 IOP Publishing Ltd.