Norris Peter M, da Silva Arlindo M
Goddard Earth Sciences Technology and Research, University Space Research Association, Columbia, MD, USA.
Global Modeling and Assimilation Office, NASA Goddard Space Flight Center, Greenbelt, MD, USA.
Q J R Meteorol Soc. 2016 Jul;142(699):2528-2540. doi: 10.1002/qj.2844. Epub 2016 May 31.
Part 1 of this series presented a Monte Carlo Bayesian method for constraining a complex statistical model of global circulation model (GCM) sub-gridcolumn moisture variability using high-resolution Moderate Resolution Imaging Spectroradiometer (MODIS) cloud data, thereby permitting parameter estimation and cloud data assimilation for large-scale models. This article performs some basic testing of this new approach, verifying that it does indeed reduce mean and standard deviation biases significantly with respect to the assimilated MODIS cloud optical depth, brightness temperature and cloud-top pressure and that it also improves the simulated rotational-Raman scattering cloud optical centroid pressure (OCP) against independent (non-assimilated) retrievals from the Ozone Monitoring Instrument (OMI). Of particular interest, the Monte Carlo method does show skill in the especially difficult case where the background state is clear but cloudy observations exist. In traditional linearized data assimilation methods, a subsaturated background cannot produce clouds via any infinitesimal equilibrium perturbation, but the Monte Carlo approach allows non-gradient-based jumps into regions of non-zero cloud probability. In the example provided, the method is able to restore marine stratocumulus near the Californian coast, where the background state has a clear swath. This article also examines a number of algorithmic and physical sensitivities of the new method and provides guidance for its cost-effective implementation. One obvious difficulty for the method, and other cloud data assimilation methods as well, is the lack of information content in passive-radiometer-retrieved cloud observables on cloud vertical structure, beyond cloud-top pressure and optical thickness, thus necessitating strong dependence on the background vertical moisture structure. It is found that a simple flow-dependent correlation modification from Riishojgaard provides some help in this respect, by better honouring inversion structures in the background state.
本系列的第1部分提出了一种蒙特卡洛贝叶斯方法，用于使用高分辨率中分辨率成像光谱仪（MODIS）云数据来约束全球环流模型（GCM）亚网格柱湿度变率的复杂统计模型，从而允许对大规模模型进行参数估计和云数据同化。本文对这种新方法进行了一些基本测试，验证了相对于同化的MODIS云光学厚度、亮温和云顶压力，它确实能显著降低均值和标准差偏差，并且相对于从臭氧监测仪（OMI）进行的独立（非同化）反演，它还能改善模拟的旋转拉曼散射云光学质心压力（OCP）。特别值得关注的是，在背景状态晴朗但存在云观测的特别困难情况下，蒙特卡洛方法确实显示出了技巧。在传统的线性化数据同化方法中，未饱和的背景无法通过任何无穷小的平衡扰动产生云，但蒙特卡洛方法允许基于非梯度的跳跃进入非零云概率区域。在所提供的示例中，该方法能够恢复加利福尼亚海岸附近的海洋层积云，那里的背景状态有一片晴朗区域。本文还研究了新方法的一些算法和物理敏感性，并为其经济高效的实施提供了指导。该方法以及其他云数据同化方法面临的一个明显困难是，除了云顶压力和光学厚度之外，被动辐射计反演的云可观测量中缺乏关于云垂直结构的信息内容，因此需要强烈依赖背景垂直湿度结构。研究发现，Riishojgaard提出的一种简单的与流相关的相关性修正方法在这方面提供了一些帮助，它能更好地体现背景状态中的反演结构。
