Showcasing model performance across space and time using single diagrams

Abstract

Model states are often validated against observations to establish the veracity of the model. However, when a model state is compared with multiple in-situ observations scattered in space, no single diagram exists that can showcase the statistical measure of correlation, root mean square error and the standard deviations of the concerned variables across all the locations. We present a simple but efficient representation of correlation, root mean square error and standard deviation of the model and the observation across multiple in-situ locations in a single diagram and name it as the “Performance Across Space (PAS)” diagram. We also present a diagram that showcases the comparison of model state and observation at a single location across multiple time windows in a single diagram and call it “Performance Across Time (PAT)” diagram. We highlight the effectiveness of these diagrams through realistic ocean models and realistic in-situ observations. We show using a PAT diagram that the ocean model NEMO fares better in simulating ocean sea surface temperature during boreal winters compared to boreal summers. We also illustrate how the PAS diagrams can be used in assessing the efficacy of data assimilation. Though the examples shown here are from the field of oceanography, the display mechanism can be extended to any field.