A simple adaptive thinning methodology for Atmospheric Infrared Sounder (AIRS) radiances is evaluated through a combination of observing system experiments (OSEs) and adjoint methodologies. The OSEs are performed with the NASA Goddard Earth Observing System (GEOS, version 5) data assimilation and forecast model. In addition, the adjoint-based forecast sensitivity observation impact technique is applied to assess fractional contributions of sensors in different thinning configurations. The adaptive strategy uses a denser AIRS coverage in a moving domain centered around tropical cyclones (TCs) but sparser everywhere else. The OSEs consist of two sets of data assimilation runs that cover the period from 1 September to 10 November 2014, with the first 20 days discarded for spinup. Both sets assimilate all conventional and satellite observations used operationally. In addition, one ingests clear-sky AIRS radiances, the other cloud-cleared radiances, each comprising multiple thinning strategies. Daily 7-day forecasts are initialized from all these analyses and evaluated with a focus on TCs over the Atlantic and Pacific. Evidence is provided on the effectiveness of this simple TC-centered adaptive radiance thinning strategy, in full agreement with previous theoretical studies. Specifically, global skill increases, and tropical cyclone representation is substantially improved. The improvement is particularly strong when cloud-cleared radiances are assimilated. Finally, the article suggests that cloud-cleared radiances, if thinned more aggressively than the currently used clear-sky radiances, could successfully replace them with large improvements in TC forecasting and no loss of global skill.
The inclusion of linearized moist physics can increase the accuracy of 4D-Var data assimilation and adjoint-based sensitivity analysis. Moist processes such as convection can exhibit nonlinear behaviour. As a result, representation of these processes in a linear way requires much care; a straightforward linearization may yield a poor approximation to the behaviour of perturbations of interest and could contain numerical instability. Here, an extensive numerical study of the Jacobian of the relaxed Arakawa-Schubert (RAS) convection scheme is shown. A Jacobian based on perturbations at individual model levels can be used to understand the physical behaviour of the RAS scheme, predict how sensitive that behaviour is to the prognostic variables and determine the stability of a linearization of the scheme. The linearity of the scheme is also considered by making structured perturbations, constructed from the principle components of the model variables. Based on the behaviour of the Jacobian operator and the results when using structured perturbations, a suitable method for linearizing the RAS scheme is determined. For deep, strong convection, the structures of the RAS Jacobian are reasonably simple, the rate at which finite-amplitude estimates of the structures change with respect to input perturbations is small and the eigenmodes of the Jacobian are not prohibitively unstable. For deep convection, an exact linearization is therefore suitable. For shallow convection, the RAS scheme can be more sensitive to the input prognostic variables due to the faster time-scales and proximity to switches. Linearization of the RAS therefore requires some simplifications to smooth the behaviour for shallow convection. It is noted that the physical understanding of the scheme gained from examining the Jacobian provides a useful tool to the developers of nonlinear physical parametrizations.
Inclusion of moist physics in the linearized version of a weather forecast model is beneficial in terms of variational data assimilation. Further, it improves the capability of important tools, such as adjoint-based observation impacts and sensitivity studies. A linearized version of the relaxed Arakawa-Schubert (RAS) convection scheme has been developed and tested in NASA's Goddard Earth Observing System data assimilation tools. A previous study of the RAS scheme showed it to exhibit reasonable linearity and stability. This motivates the development of a linearization of a near-exact version of the RAS scheme. Linearized large-scale condensation is included through simple conversion of supersaturation into precipitation. The linearization of moist physics is validated against the full nonlinear model for 6-and 24-h intervals, relevant to variational data assimilation and observation impacts, respectively. For a small number of profiles, sudden large growth in the perturbation trajectory is encountered. Efficient filtering of these profiles is achieved by diagnosis of steep gradients in a reduced version of the operator of the tangent linear model. With filtering turned on, the inclusion of linearized moist physics increases the correlation between the nonlinear perturbation trajectory and the linear approximation of the perturbation trajectory. A month-long observation impact experiment is performed and the effect of including moist physics on the impacts is discussed. Impacts from moist-sensitive instruments and channels are increased. The effect of including moist physics is examined for adjoint sensitivity studies. A case study examining an intensifying Northern Hemisphere Atlantic storm is presented. The results show a significant sensitivity with respect to moisture.
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