Uncertainty of Observation Impact Estimation in an Adjoint Model Investigated with an Observing System Simulation Experiment

Abstract

Abstract Adjoint models are often used to estimate the impact of different observations on short-term forecast skill. A common difficulty with the evaluation of short-term forecast quality is the choice of verification fields. The use of self-analysis fields for verification is typical but incestuous, and it introduces uncertainty resulting from biases and errors in the analysis field. In this study, an observing system simulation experiment (OSSE) is used to explore the uncertainty in adjoint model estimations of observation impact. The availability of the true state for verification in the OSSE framework in the form of the nature run allows calculation of the observation impact without the uncertainties present in self-analysis verification. These impact estimates are compared with estimates calculated using self-analysis verification. The Global Earth Observing System, version 5 (GEOS-5), forecast model with the Gridpoint Statistical Interpolation system is used with the National Aeronautics and Space Administration Global Modeling and Assimilation Office (NASA/GMAO) OSSE capability. The adjoint model includes moist processes, with total wet energy selected as the norm for evaluation of observation impacts. The results show that there are measurable but small errors in the adjoint model estimation of observation impact as a result of self-analysis verification. In general, observations of temperature and winds tend to have overestimated impacts with self-analysis verification while observations of humidity and moisture-affected observations tend to have underestimated impacts. The small magnitude of the differences in impact estimates supports the robustness of the adjoint method of estimating observation impacts.