Cluster analysis of cloud regimes and characteristic dynamics of midlatitude synoptic systems in observations and a model
Cluster analysis of cloud regimes and characteristic dynamics of midlatitude synoptic systems in observations and a model
复制标题
DOI：
10.1029/2004jd005027
复制
发表时间：
2005-05-25
影响因子：
4.4
通讯作者：
Klein, SA
中科院分区：
文献类型：
--
作者：
Gordon, ND;Norris, JR;Klein, SA
[1]全球气候模式通常不能正确地模拟与中纬度天气系统相关的云量，因为粗网格间距使它们无法解决发生在较小尺度上的动力学问题，并且这些次网格尺度动力学的影响没有足够的参数化。模拟和观测的云特性在相似区域（例如，例如，在一个实施例中，合成）有助于诊断模拟误差和识别造成特定云条件的气象强迫。本研究使用一种k-means聚类算法，根据网格盒平均云分数、云反射率和云顶压力，将卫星云场景客观地分为不同的区域。空间域是大气辐射测量计划的南部大平原站点，时间段是1999 - 2001年的冷季月份（11月-3月）。作为卫星反演云特性的补充，激光雷达和云雷达数据进行了分析，以检查云层的垂直结构。从约束变分分析的气象数据平均为每个集群提供洞察力的大尺度动力学和平流趋势与特定的云类型相一致。与高和低的次网格空间变异性的气象条件进行了调查，为每个集群。云输出单柱模型版本的GFDL AM 2大气模型强迫气象边界条件来自观测和数值天气预报模型进行了比较，以确定准确性的模型再现特定的云制度的属性，为每个集群的观测。
[1] Global climate models typically do not correctly simulate cloudiness associated with midlatitude synoptic systems because coarse grid spacing prevents them from resolving dynamics occurring at smaller scales and there exist no adequate parameterizations for the effects of these subgrid-scale dynamics. Comparison of modeled and observed cloud properties averaged over similar regimes ( e. g., compositing) aids the diagnosis of simulation errors and identification of meteorological forcing responsible for producing particular cloud conditions. This study uses a k-means clustering algorithm to objectively classify satellite cloud scenes into distinct regimes based on grid box mean cloud fraction, cloud reflectivity, and cloud top pressure. The spatial domain is the densely instrumented southern Great Plains site of the Atmospheric Radiation Measurement Program, and the time period is the cool season months ( November - March) of 1999 - 2001. As a complement to the satellite retrievals of cloud properties, lidar and cloud radar data are analyzed to examine the vertical structure of the cloud layers. Meteorological data from the constraint variational analysis is averaged for each cluster to provide insight on the large-scale dynamics and advective tendencies coincident with specific cloud types. Meteorological conditions associated with high and low subgrid spatial variability are also investigated for each cluster. Cloud outputs from a single-column model version of the GFDL AM2 atmospheric model forced with meteorological boundary conditions derived from observations and a numerical weather prediction model were compared to observations for each cluster in order to determine the accuracy with which the model reproduces attributes of specific cloud regimes.