On Variable Terrains and Diurnal Variations in Surface Data Assimilation

有效地结合单层观测，特别是地球表面附近的气温观测，以准确确定模拟的初始大气条件，是数值天气预报中的一项重大挑战。造成这一困难的确切原因尚不清楚，尽管对日循环的描述不足被认为起到了重要作用。这在地形复杂的地区尤其如此，那里的海拔和相应的表面温度的急剧变化(粗略的模型网格不能完全解决这些问题)可能会导致模型的“初步猜测”字段与插入的局部观测之间的巨大差异。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成果。这里支持的研究重点是将观测系统模拟实验(OSSES)与天气研究和预报(WRF)模型结合使用来解决这一问题。OSSES将用于在正在进行实际观测的地点提供合成观测。在加入已知的代表性误差后，这些合成观测将依次使用传统的变分(3DVAR)和更现代(但计算昂贵)的集合卡尔曼滤波(EnKF)技术进行同化。随后的模型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，并评估减少这些误差的方法。因此，这项工作的目标是(1)识别和了解影响地面观测纳入天气预报模式的基本问题，以及(2)设计和进行数值试验，以克服这些障碍，从而提高预报精度。这项工作的智力优势集中在识别天气预报误差的主要来源和设计缓解方法上。这项工作的更广泛影响将包括：对以社区为基础的世界气候变化框架模式的重大改进；更全面和更有效地利用越来越多的地面观测网络产生的数据；以及在代表不足的群体的PI的指导下对研究生进行教育。

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.