Profiling optimal-Estimates for RaIn-CLoud Efficiency Study (PERICLES)

降水被一致认为是全球水和能量循环的中心变量之一，主要是因为它对人类、农业和地球上的一般生命的水的供应具有直接意义，也因为它通过相关的潜热释放对能量收支和大气循环产生影响。在数值天气预报和大气环流模式中，降水过程在控制和预报天气现象和气候演变方面起着决定性的作用。尽管水对地球上的所有生物和整个地球系统都很重要，但人们对云和降水的生命周期还没有很好的了解；像暖雨迅速形成这样一个看似简单的过程仍然令人费解，而且仍然远远没有一个社区共识的解释或模型。云演变为降雨过程的微物理过程的复杂性是该领域取得重大飞跃的主要障碍，迫切需要努力将模拟和观测结合起来。虽然大涡模拟和云分辨模型的时间和空间尺度现在都适合于研究云的生命周期，但遥感观测(实际上唯一可能观察这种现象)一直受到不适定反演问题带来的不确定性的影响。例如，根据定义，雷达反射率信号强烈依赖于散射体(例如，雨滴)在射束体积中的液滴尺寸分布，因此其解释与负责形成液滴尺寸分布及其演化的微物理过程有关。在所有ARM设施中独特地部署具有辐射测量模式的多波长扫描雷达(将于2010年底完成)，将提供前所未有的独立观测，这将缩小反演过程中的不确定性，并提供对云演变的所有阶段的详细观测，从开始到上升和下降气流的发展，到水气在时间和空间上的演变，再到凝析油分成降水和出流槽。我们建议利用即将到来的机会，开发一种能够以一致的物理方式集成不同传感器的最优估计方法。我们将结合主动(雷达反射率)和被动(亮温)测量，因为这两种测量在垂直延伸过程中都产生不同类型的云微物理信息：云和天气雷达允许距离分辨云结构，而被动微波信号包含关于纵向综合水/冰含量的信息。我们提出的技术将测量(及其误差特征)和先验信息(以及关于其代表性的知识)结合到一个最优估计框架中，以提供大气状态以及不确定性估计。为了最佳地利用遥感观测的信息内容，通过正向模型迭代对大气状态的第一次猜测--将大气状态与测量相联系--直到测量和先验信息与所恢复的大气状态最匹配的点。反演的最终结果是观测到的大气柱的云和降水水分含量剖面，这将在计划于2011年在俄克拉荷马州南部大平原遗址举行的MC3E活动期间通过独立的方法进行广泛验证。这一尖端产品将有助于开发、评估并最终改进数值模式中云降水过程的参数化。作为试验床，将结合MC3E进行面向不同微物理包评估的详细云解析模式研究。

Precipitation is unanimously recognized as one of the central variables of the global water and energy cycle, mainly because of its direct significance for the availability of water for human beings, agriculture and life on Earth in general, but also because of its impact on the energy budget and the atmospheric circulation through the associated latent heat release. Precipitation processes play a decisive role in controlling and thus predicting both weather phenomena and climate evolution in numerical weather prediction and general circulation models. Despite the importance of water to all creatures on Earth and to the Earth system as a whole, the life cycle of clouds and precipitation is not well understood; a seemingly simple process like the rapid formation of warm rain is still puzzling, and remains far from having a community-consensus explanation or model. The complexity of the microphysical processes underpinning the cloud evolution into the rain process represents the major obstacle for a considerable leap forward in this field and urgently calls for an effort towards combining modeling and observations. While the temporal and spatial scales of both Large-Eddy Simulation and Cloud Resolving Models are now suitable for studying cloud lifecycles, remote sensing observations (the only practically possible to look at such phenomena) have always suffered by the uncertainties deriving from ill-posed inversion problems. For instance the radar reflectivity signal is by definition strongly dependent on the drop size distribution of the scatterers, e.g., raindrops, in the beam volume and its interpretation is therefore related to the microphysical processes responsible for the formation of drop size distributions and their evolution. A unique deployment (to be completed by end of 2010) of multi-wavelength scanning radar with radiometric mode at all ARM facilities will provide unprecedented independent observations which should narrow down the uncertainties in the retrieval process and provide detailed observations of all phases of cloud evolution, from initiation, to development of updrafts and downdrafts, to hydrometeor evolution in time and space, to partitioning of condensate into precipitation and outflow anvils. We propose to take advantage of this upcoming opportunity by developing an optimal estimation approach capable of integrating different sensors in a consistent physical way. We will combine active (radar reflectivity) and passive (brightness temperatures) measurements because both yield different kinds of cloud microphysics information throughout the vertical extension: cloud and weather radars allow to range-resolve cloud structure, whereas passive microwave signals contain information about along-sight integrated water/ice contents. Our proposed technique combines measurements (and their error characteristics) with a priori information (and knowledge about its representativeness) into an optimal estimation framework to provide the atmospheric state together with uncertainty estimates. In order to optimally exploit the information content of remote sensing observations a first guess of the atmospheric state is iterated through the forward model - connecting atmospheric state with the measurement - up to a point where measurements and a priori information best match the retrieved atmospheric state. The ultimate product of the retrieval is represented by profiles of cloud and precipitation water content for the observed atmospheric columns, which will be extensively validated by independent methodologies during the MC3E campaign, planned for 2011 at the Oklahoma Southern Great Plain site. This cutting-edge product will help in developing, evaluating, and ultimately improving parameterization of cloud-precipitation processes in numerical models. As a test bed, a detailed cloud resolving model study oriented at the evaluation of different microphysical packages will be conducted in coincidence with MC3E.