On Variable Terrains and Diurnal Variations in Surface Data Assimilation

有效地结合单层观测，特别是地表附近的气温观测，以准确地确定模拟的初始大气条件，是数值天气预报的一个主要挑战。造成这一困难的确切原因尚不清楚，尽管对昼夜周期的不充分反映被认为起着重要作用。在地形复杂的地区尤其如此，在这些地区，高程和相应的地表温度的急剧变化（粗糙的模式网格无法完全解决）可能导致模式“初步猜测”场与插入的当地观测值之间的巨大差异。这一挑战阻碍了充分利用扩大的地面观测网络带来的大量新观测数据。这里支持的研究重点是利用观测系统模拟实验（OSSEs）结合天气研究与预报（WRF）模式来解决这一问题。osse将用于在正在进行实际观测的场址提供综合观测。在合并了已知的、有代表性的误差之后，这些合成的观测结果将被传统的变分（3DVAR）和更现代的（但计算成本高昂的）集成卡尔曼滤波（EnKF）技术同化。随后的模式预报将与这些选定地点的实际观测结果进行比较，以量化这些误差的影响，并评估减少误差的方法。因此，这项工作的目标是：(1)识别和理解干扰在天气预报模式中纳入地面观测的基本问题；(2)设计和进行数值实验以克服这些障碍，从而提高预报精度。这项工作的智力价值集中在确定天气预报误差的主要来源和设计减轻误差的方法上。这项工作的更广泛影响将包括：显著改进以社区为基础的世界自然资源基金模式；更完整和有效地利用越来越多的地面观测网产生的数据；以及研究生在来自弱势群体的PI的监督下的教育。

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.