On Variable Terrains and Diurnal Variations in Surface Data Assimilation

论地面数据同化中的可变地形和日变化

• 批准号: 0833985
• 负责人: Zhaoxia Pu
• 金额: $ 23.57万
• 依托单位: University of Utah
• 依托单位国家: 美国
• 项目类别: Continuing Grant
• 财政年份: 2008
• 资助国家: 美国
• 起止时间: 2008-11-01 至 2012-10-31
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=0833985&HistoricalAwards=false
• 关键词: Variable; Terrains; Diurnal; Variations; Surface

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.

有效地结合单层观测，特别是近地表的气温，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。造成这种困难的确切原因尚不清楚，尽管昼夜循环的代表性不足被认为发挥了重要作用。在地形复杂的地区尤其如此，在这些地区，海拔和相应的地表温度的急剧变化（粗糙的模型网格无法完全解决）可能导致模型“第一次猜测”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所提供的大量新观测资料。这里支持的研究重点是使用观测系统模拟实验（OSSE）结合天气研究和预报（WRF）模型来解决这个问题。在进行实际观测的地点，将使用OSSE提供综合观测。在并入已知的代表性误差之后，这些合成观测将依次使用传统的变分（3DVAR）和更现代的（但计算昂贵的）EnergyKalman滤波器（EnKF）技术来同化。随后的模型预测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）识别和理解干扰天气预报模式中包含地面观测的基本问题，（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。这项工作的智力价值集中在识别天气预报误差的主要来源和设计减轻误差的方法。这项工作的更广泛的影响将包括：显著改善以社区为基础的WRF模型;更完整和有效地利用越来越多的地面观测网络产生的数据;以及在代表性不足的群体的PI的监督下教育研究生。