Inferring surface and soil variables from assimilation of atmospheric boundary layer observations

通过大气边界层观测的同化推断地表和土壤变量

• 批准号: 246144247
• 负责人: Professor Dr. Felix Ament
• 金额: --
• 依托单位: Meteorologisches Institut (MI)
• 依托单位国家: 德国
• 项目类别: Research Units
• 财政年份: 2013
• 资助国家: 德国
• 起止时间: 2012-12-31 至 2016-12-31
• 项目状态: 已结题
• 来源: https://gepris.dfg.de/gepris/projekt/246144247?language=en
• 关键词: Inferring; surface; soil; variables; assimilation

The evolution of the atmospheric boundary layer is strongly coupled via energy and moisture fluxes with the state of the surface and soil. This interaction is used in meteorological data assimilation to derive soil variables, especially soil moisture, from deviations of forecasts of screen-level atmospheric variables from the observations. This established approach, which is also used operationally by some weather services, will be implemented in the integrated data assimilation scheme of the Research Unit to quantify the information content of atmospheric boundary layer (ABL) observations. We will first consider screen-level temperature and humidity as ABL observations and add later boundary layer height and skin temperature.As a novel research approach, we will extend the assimilation of ABL observations in two main directions: First, model parameters, e.g. leaf area index, will be estimated additionally by the data assimilation to avoid a too strong attribution of forecast errors to state variables of the soil. Second, we will consider not only instantaneous differences of forecasts and observations as so-called innovations, but also take the temporal structure of forecast errors into account. This will account for the very different temporal scales at which surface and soil variables vary: Surface temperature, for example, changes within seconds, whereas soil moisture varies at scales of hours to months (depending on the depth), and parameters, like e.g. the heat conductivity of a dry soil, are constant in time. Accordingly, errors of these quantities result in characteristic atmospheric forecast errors, which we call “fingerprints”, since they allow identifying the error source.The virtual catchment is perfectly suited to determine these fingerprints: At selected grid points, a simplified one-dimensional land-atmosphere model will be used to perform ensemble simulations with systematic perturbations of the fingerprint variable and random perturbations of other model variables. We will identify which atmospheric quantities under which conditions and at which timescales are affected most by the systematic perturbations. As second step, an Ensemble Kalman Filter (EnKF) assimilation system will be installed for the one-dimensional model and expanded to allow for an assimilation of model parameters. Finally, the novel fingerprints will be implemented as observation operator in this framework. This first implementation will be further expanded to account for non-instantaneous observations. We will review existing concepts for this multi-timescale extension and identify the moist suitable approach. The implementation of this enhancement as well as the adaptation of the methodology, which will be developed first for single-column models, to the three-dimensional coupled model system is planned for the second project phase.

大气边界层的演变通过能量和水分通量与地表和土壤的状态密切相关。这种相互作用被用于气象数据同化，以从屏幕水平大气变量预测与观测的偏差中获得土壤变量，特别是土壤湿度。这一已确立的方法也被一些气象服务部门在业务上使用，将在研究股的综合数据同化计划中实施，以量化大气边界层观测的信息内容。作为一种新的研究方法，我们将从两个主要方向扩展大气边界层观测的同化：首先，模式参数，如叶面积指数，将通过数据同化来估计，以避免预报误差过于强烈地归因于土壤的状态变量。第二，我们将不仅考虑作为所谓的创新的预测和观测的瞬时差异，但也考虑到预测误差的时间结构。这将解释地表和土壤变量变化的非常不同的时间尺度：例如，地表温度在几秒钟内变化，而土壤湿度在几小时到几个月的尺度上变化（取决于深度），并且参数，例如干燥土壤的导热性，在时间上是恒定的。因此，这些量的误差会导致大气预报误差的特征，我们称之为“指纹”，因为它们可以识别误差源。虚拟集水区非常适合确定这些指纹：在选定的网格点，一个简化的一维陆地大气模式将用于进行集合模拟，其中包括指纹变量的系统扰动和其他模式变量的随机扰动。我们将确定哪些大气量在哪些条件下和在哪些时间尺度上受系统扰动的影响最大。作为第二步，将为一维模式安装一个Enlarge-Kalman滤波器同化系统，并将其扩大以允许同化模式参数。最后，新的指纹将被实现为该框架中的观察算子。这第一个实现将进一步扩展到非瞬时观测。我们将审查现有的概念，这种多时间尺度的扩展，并确定潮湿的合适的方法。计划在第二个项目阶段实施这一改进以及将首先为单柱模型开发的方法调整为三维耦合模型系统。