Variational Quality Control of Non-Gaussian Innovations in the GRAPES m3DVAR System: Mass Field Evaluation of Assimilation Experiments

Abstract

The existence of outliers can seriously influence the analysis of variational data assimilation. Quality control allows us to effectively eliminate or absorb these outliers to produce better analysis fields. In particular, variational quality control (VarQC) can process gray zone outliers and is thus broadly used in variational data assimilation systems. In this study, we derived the governing equations of two VarQC algorithms that utilize different contaminated Gaussian distributions (CGDs): Gaussian plus flat distribution and Huber norm distribution. As such, these VarQC algorithms can handle outliers that have non-Gaussian innovations. We then implemented these VarQC algorithms in the Global/Regional Assimilation and PrEdiction System (GRAPES) model-level three-dimensional variational data assimilation (m3DVAR) system. Tests using artificial observations indicated that the VarQC using the Huber distribution has stronger robustness for including outliers to improve posterior analysis than that of the VarQC using the Gaussian plus flat distribution. Furthermore, real observation experiments show that the distribution of observation analysis weights conform well with theory, indicating that the VarQC is effective in the GRAPES m3DVAR system. Subsequent case study and long-period data assimilation experiments show that the spatial distribution and amplitude of the observation analysis weights are related to the mass field’s (geopotential height and temperature) analysis increments. Compared to the control experiment, VarQC experiments have noticeably better posterior mass fields. Finally, the VarQC using the Huber distribution is superior to the VarQC using the Gaussian plus flat distribution, especially at the middle and lower levels.