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Raquel Lorente-Plazas and Joshua P. Hacker

augmentation in ensemble filter data assimilation, is explored for simultaneously estimating and correcting observation biases and a bias in model forcing. The emphasis is on understanding the effectiveness of observation bias estimation in the presence of varying levels of model error. It is impossible to a priori determine whether biases in the state estimates result from biased observations or model deficiencies, because both can cause systematic departures of the predicted state from observations. An

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Robert S. Arthur, Katherine A. Lundquist, Jeffrey D. Mirocha, and Fotini K. Chow

WRF, version 3.6.1) is modified to include topographic effects on radiation, expanding on the implementation of Lundquist et al. (2010) . Changes to the model are validated by confirming agreement in radiation and land surface fluxes, as well as temperature and velocity fields, between WRF-IBM and standard WRF when the same initialization and forcing conditions are applied. The validation is performed in a domain with an idealized two-dimensional valley that is forced by incoming solar radiation

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Joshua P. Hacker and Lili Lei

between the initial conditions and a forecast metric is spuriously large because of sampling error, and the analysis error statistics do not overestimate the covariance as severely, the sensitivity can be overestimated. Most results in the literature so far have used an approximation to the analysis error covariance, where it is assumed diagonal (e.g., Ancell and Hakim 2007 ; Torn and Hakim 2008 ). Across a broad range of problems, and in particular for mesoscale sensitivities lacking strong forcing

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