On Variable Terrains and Diurnal Variations in Surface Data Assimilation

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Effective incorporation of single-level observations, especially those of air temperature near the earth's surface, to accurately determine modeled initial atmospheric conditions represents a major challenge in numerical weather prediction. The exact reasons for this difficulty remain unclear, though inadequate representation of the diurnal cycle is thought to play an important role. This is particularly true in regions of complex terrain, where sharp variations of elevation and corresponding surface temperature (which are imperfectly resolved by coarse model grids) may lead to large differences between model "first guess" fields and inserted local observations. This challenge stands in the way of capitalizing fully on the bounty of new observations coming from expanded surface observing networks. The research supported here focuses on the use of observing system simulation experiments (OSSEs) in conjunction with the Weather Research and Forecasting (WRF) model to address this problem. OSSEs will be used to supply synthetic observations at sites where actual observations are being made. After incorporation of known, representative errors these synthesized observations will in turn be assimilated using both traditional variational (3DVAR) and more modern (but computationally expensive) Ensemble Kalman filter (EnKF) techniques. Ensuing model forecasts will be compared with actual observations at these selected sites to quantify the impact of such errors as well as assess methods for their reduction. The goals of this effort are thus to (1) identify and understand fundamental problems interfering with the inclusion of surface observations in weather forecast models, and (2) design and conduct numerical experiments to overcome these obstacles and thereby improve forecast accuracy.The intellectual merit of this work centers upon identification of leading sources of weather forecast errors and design of methods for their mitigation. Broader impacts of this work will include: significant improvements to the community-based WRF model; more complete and efficient utilization of data emerging from a growing array of surface observational networks; and the education of a graduate student under supervision of a PI from an underrepresented group.