Assimilating novel ground-based remote sensing observations into a numerical weather prediction model for improving model predictions and advancing knowledge of atmospheric boundary layer processes

将新颖的地面遥感观测纳入数值天气预报模型，以改进模型预测并增进对大气边界层过程的了解

• 批准号: 399851006
• 负责人: Dr. Annika Schomburg
• 金额: --
• 依托单位: Deutscher Wetterdienst (DWD)
• 依托单位国家: 德国
• 项目类别: Research Grants
• 财政年份: 2018
• 资助国家: 德国
• 起止时间: 2017-12-31 至 2022-12-31
• 项目状态: 已结题
• 来源: https://gepris.dfg.de/gepris/projekt/399851006?language=en
• 关键词: Assimilating; novel; ground; based; remote

Although the atmospheric boundary layer plays a crucial role for the diurnal cycle of clouds and precipitation, screen level predictions and severe weather events, its initial state is under-constrained in numerical weather prediction (NWP) models. Especially for nowadays short-range convective-scale NWP models a realistic initialization with respect to the vertical stratification and stability of the boundary layer is expected to prove highly valuable. Ground-based remote sensing devices deliver high-frequent thermodynamic and momentum profile observations of the boundary layer. However no strategies exist yet, how to exploit those data optimally for numerical weather prediction, as the development of affordable high-quality devices by commercial manufacturers has taken place only in the recent years.Moreover many processes in the boundary layer are still not completely understood and, this holds in particular for the stable boundary layer, for subgrid-scale orographic effects, gravity waves, thermal circulations and subgrid-scale surface heterogeneity effects. Often, the simulation of subgrid-scale boundary layer processes in atmospheric models shows deficiencies.The objective of the proposed project is twofold:In a first phase the usefulness of state-of-the-art ground-based remote sensing boundary layer observations (microwave radiometer and Doppler lidar) to improve the initial state and subsequent forecast of the boundary layer in atmospheric models will be exploited, making use of recent developments in data assimilation (DA) and ground-based remote sensing, including the development of an optimal assimilation strategy. The chance to capture small-scale structures typical for the boundary layer in the DA system in such a way is now possible due to developments in the DA community to employ Ensemble Kalman Filters for convective-scale data assimilation, providing high-resolution flow-dependent covariances and allowing high-frequent update cycles.In a second phase we propose to exploit the enormous potential of this expanded NWP framework including data assimilation system and continuously observed quality-checked observations as a novel approach to advance basic boundary layer research. Both, the information obtained from the DA system to correct the model first guess towards the observations but also the analysis itself as a self-consistent three-dimensional state at the kilometer-scale close to the true state, will be exploited. The O-B (observation minus background) statistics point to systematic differences between model first guess and observations, the increments added to the first guess are an indicator of errors in the system leading to deviations from the true state. To obtain information on single processes, the “initial tendency approach” as in the study by Klocke and Rodwell (2013) will be applied.

尽管大气边界层对云和降水的日循环、屏蔽层预报和恶劣天气事件起着至关重要的作用，但在数值天气预报模式中，它的初始状态是欠约束的。特别是对于目前的短程对流尺度数值预报模式而言，关于边界层的垂直层结和稳定性的实际初始化将被证明是非常有价值的。地面遥感设备提供对边界层的高频热力学和动量剖面观测。然而，由于商业制造商最近几年才开发出负担得起的高质量设备，因此还没有战略，如何以最佳方式利用这些数据进行数值天气预报。此外，边界层中的许多过程仍然没有完全了解，特别是对于稳定的边界层，对于亚网格尺度的地形效应、重力波、热环流和亚网格尺度的地表非均匀效应，这一点尤其适用。在第一阶段，将利用最先进的地面遥感边界层观测(微波辐射计和多普勒激光雷达)来改善大气模式中边界层的初始状态和随后的预报，利用数据同化(DA)和地面遥感的最新发展，包括制定最优同化战略。现在有机会以这种方式捕捉DA系统中边界层的典型小尺度结构，这是由于DA社区的发展，使用集合卡尔曼过滤器进行对流尺度数据同化，提供高分辨率的与气流有关的协方差，并允许高频更新周期。在第二阶段，我们建议开发这个扩展的数值预报框架的巨大潜力，包括数据同化系统和连续观测的质量检查观测，作为推进基本边界层研究的新方法。既要利用从DA系统获得的信息来修正模型对观测值的第一次猜测，又要利用分析本身作为千米尺度上接近真实状态的自洽三维状态。O-B(观测值减去本底)统计量指向模型第一次猜测和观测值之间的系统差异，第一次猜测的增量是系统中导致偏离真实状态的误差的指示器。为了获得关于单一过程的信息，将采用KLocke和Rodwell(2013)研究中的“初始趋势法”。