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首页> 外文期刊>Journal of the Atmospheric Sciences >An assessment of the parameterization of subgrid-scale cloud effects on radiative transfer. Part I: Vertical overlap
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An assessment of the parameterization of subgrid-scale cloud effects on radiative transfer. Part I: Vertical overlap

机译：评估亚网格尺度云对辐射传输的影响的参数化。第一部分：垂直重叠

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Different approaches for parameterizing the effects of vertical variability of cloudiness on radiative transfer are assessed using a database constructed from observations derived from lidar and millimeter cloud radar data collected from three different locations. Five different methods for dealing with the vertical overlap of clouds were incorporated into a single radiation model that was applied to the lidar/radar data averaged in time. The calculated fluxes and heating rates derived with this model are compared to broadband fluxes and heating rates calculated with the independent column approximation using the time-resolved cloud data. These comparisons provide a way of evaluating the effects of different overlap assumptions on the calculation of domain-mean fluxes. It was demonstrated how two of the most commonly used overlap schemes, the random and maximum-random methods, suffer a severe problem in that the total cloud amount defined by these methods depends on the vertical resolution of the host model thus creating a vertical-resolution-dependent bias in model total cloudiness and radiative fluxes. A new method is introduced to overcome this problem by preserving the total column cloud amount. Despite these problems, the comparisons presented show that most methods introduce a relatively small bias with respect to the single-column data. This is largely a consequence of the nature of the cloud cover statistics associated with the lidar/radar observations used in this study and might not apply in general. Among the three best-performing methods (random, overcast random, and maximum random), the more commonly used maximum-random method does not perform significantly better than the other two methods with regard to both bias and rms error despite its relative high computational cost. The comparisons also reveal the nature and magnitude of the random errors that are introduced by the subgrid-scale parameterizations. These random errors are large and an inevitable consequence of the parameterization process that treats cloud structure statistically. These errors may be thought of as a source of noise to the general circulation model in which the parameterization is embedded. [References: 36]
机译：使用从从三个不同位置收集的激光雷达和毫米云雷达数据得出的观测结果构建的数据库中，评估了用于参数化浊度垂直变化对辐射传输影响的参数化方法。将用于处理云的垂直重叠的五种不同方法合并到单个辐射模型中，该模型应用于时间平均的激光雷达/雷达数据。将使用此模型得出的计算通量和加热速率与使用时间分辨云数据通过独立柱近似计算的宽带通量和加热速率进行比较。这些比较提供了一种方法，可以评估不同重叠假设对域平均通量计算的影响。演示了两种最常用的重叠方案（随机方法和最大随机方法）如何遭受严重问题，因为这些方法定义的总云量取决于宿主模型的垂直分辨率，从而创建了垂直分辨率模型总浊度和辐射通量的偏差。引入了一种新方法来解决此问题，方法是保留总列云量。尽管存在这些问题，但进行的比较表明，大多数方法相对于单列数据都引入了相对较小的偏差。这很大程度上是由于与这项研究中使用的激光雷达/雷达观测有关的云量统计性质的结果，因此可能并不适用。在三种性能最佳的方法（随机，阴暗随机和最大随机）中，尽管偏向和均方根误差相对较高的计算成本，但最常用的最大随机方法在偏置和均方根误差方面的性能并没有明显好于其他两种方法。比较还揭示了亚网格规模参数化引入的随机误差的性质和大小。这些随机误差很大，这是统计处理云结构的参数化过程的必然结果。这些错误可能被认为是嵌入参数化的一般循环模型的噪声源。 [参考：36]

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