Observation Impacts for Longer Forecast Lead-Times

Observation impact on forecasts evaluated using adjoint-based techniques (e.g. Langland and Baker, 2004) are limited by the validity of the assumptions underlying the forecasting model adjoint. Most applications of this approach have focused on deriving observation impacts on short-range forecasts (e.g. 24-hour) in part to stay well within linearization assumptions. The most widely used measure of observation impact relies on the availability of the analysis for verifying the forecasts. As pointed out by Gelaro et al. (2007), and more recently by Todling (2013), this introduces undesirable correlations in the measure that are likely to affect the resulting assessment of the observing system. Stappers and Barkmeijer (2012) introduced a technique that, in principle, allows extending the validity of tangent linear and corresponding adjoint models to longer lead-times, thereby reducing the correlations in the measures used for observation impact assessments. The methodology provides the means to better represent linearized models by making use of Gaussian quadrature relations to handle various underlying non-linear model trajectories. The formulation is exact for particular bi-linear dynamics; it corresponds to an approximation for general-type nonlinearities and must be tested for large atmospheric models. The present work investigates the approach of Stappers and Barkmeijer (2012)in the context of NASA's Goddard Earth Observing System Version 5 (GEOS-5) atmospheric data assimilation system (ADAS). The goal is to calculate observation impacts in the GEOS-5 ADAS for forecast lead-times of at least 48 hours in order to reduce the potential for undesirable correlations that occur at shorter forecast lead times. References [1]Langland, R. H., and N. L. Baker, 2004: Estimation of observation impact using the NRL atmospheric variational data assimilation adjoint system. Tellus, 56A, 189-201. [2] Gelaro, R., Y. Zhu, and R. M. Errico, 2007: Examination of various-order adjoint-based approximations of observation impact. Meteoroloische Zeitschrift, 16, 685-692. [3]Stappers, R. J. J., and J. Barkmeijer, 2012: Optimal linearization trajectories for tangent linear models. Q. J. R. Meteorol. Soc., 138, 170-184. [4] Todling, R. 2013: Comparing two approaches for assessing observation impact. Mon. Wea. Rev., 141, 1484-1505.