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

虽然大气边界层在云和降水的日循环、屏面预报和恶劣天气事件中起着至关重要的作用，但在数值天气预报（NWP）模式中，其初始状态受到限制。特别是对于当今的短程对流尺度NWP模式来说，关于边界层垂直分层和稳定性的现实初始化预计将被证明非常有价值。地面遥感装置提供边界层的高频率热力学和动量剖面观测。然而，由于商业制造商仅在最近几年才开发出负担得起的高质量设备，因此还不存在如何最佳地利用这些数据进行数值天气预报的策略。此外，边界层中的许多过程仍然没有完全理解，特别是对于稳定边界层，对于亚网格尺度地形效应，重力波，热循环和次网格尺度表面非均匀性效应。在第一阶段，将利用数据同化和地面遥感方面的最新发展，包括制定最佳同化战略，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）来改进大气模型中边界层的初始状态和随后的预报。由于DA社区的发展采用集合卡尔曼滤波器进行对流尺度数据同化，因此现在有可能以这种方式捕获DA系统中边界层典型的小尺度结构，提供高分辨率的流量相关协方差，在第二阶段，我们建议利用这个扩展的数值预报框架，包括数据同化系统的巨大潜力和连续观测的质量检查观测作为一种新的方法，以推进基本的边界层研究。这两个，从DA系统中获得的信息，以纠正模型的第一次猜测对观察，而且分析本身作为一个自洽的三维状态，在更大的规模接近真实的状态，将被利用。O-B（观测值减去背景值）统计量指出模型第一次猜测和观测值之间的系统差异，添加到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标。为了获得关于单个过程的信息，将应用Klocke和Rodwell（2013）的研究中的“初始趋势方法”。

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.