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Evaluating the Impact of Planetary Boundary Layer, Land Surface Model, and Microphysics Parameterization Schemes on Cold Cloud Objects in Simulated GOES-16 Brightness Temperatures
Journal of Geophysical Research: Atmospheres ( IF 4.4 ) Pub Date : 2021-07-14 , DOI: 10.1029/2021jd034709
Sarah M. Griffin 1 , Jason A. Otkin 1 , Sharon E. Nebuda 1 , Tara L. Jensen 2 , Patrick S. Skinner 3, 4, 5 , Eric Gilleland 2 , Timothy A. Supinie 6 , Ming Xue 6
Affiliation  

Infrared brightness temperatures (BTs) from the Geostationary Observing Environmental Satellite-16 Advanced Baseline Imager are used to examine the ability of several microphysics and planetary boundary layer (PBL) schemes, as well as land surface models (LSM) and surface layers, to simulate upper-level clouds. Six parameterization configurations were evaluated. Cloud objects are identified using the Method for Object-Based Diagnostic Evaluation (MODE) and analyzed using the object-based threat score, mean-error distance, and pixel-based metrics including the mean absolute error and mean bias error (MBE) for matched objects where the displacement between objects has been removed. Objects are identified using either a fixed BT threshold of 235 K or the 6.5th percentile of BTs for each model configuration. Analysis of the MODE-identified cloud objects shows that, compared to a configuration with the Thompson microphysics scheme, Mellor-Yamanda-Nakanishi-Niino (MYNN) PBL, Global Forecasting System (GFS) surface layer, and Noah LSM, the configuration employing the National Severe Storms Laboratory microphysics produced more cloud objects with higher BTs. Changing the PBL from MYNN to Shin-Hong or Eddy-Diffusivity Mass-Flux also resulted in a slightly lower accuracy, though these changes result in configurations which more accurately reproduced the number of observation cloud objects and slightly reduced the high MBE. Changing the LSM from Noah to RUC reduces forecast accuracy by producing too many cloud objects with too low BTs. As the forecast hour increases, this accuracy reduction increases at a greater rate than occurred when changing the microphysics or PBL scheme and is further enhanced when using the MYNN surface layer rather than the GFS.

中文翻译:

在模拟的 GOES-16 亮度温度下评估行星边界层、地表模型和微物理参数化方案对冷云物体的影响

来自地球静止观测环境卫星 16 高级基线成像仪的红外亮温 (BT) 用于检查几种微物理和行星边界层 (PBL) 方案以及地表模型 (LSM) 和地表层的模拟能力上层云。评估了六个参数化配置。使用基于对象的诊断评估方法 (MODE) 识别云对象,并使用基于对象的威胁评分、平均误差距离和基于像素的指标进行分析,包括匹配的平均绝对误差和平均偏差误差 (MBE)对象之间的位移已被移除的对象。对于每个模型配置,使用 235 K 的固定 BT 阈值或 BT 的第 6.5 个百分位数来识别对象。对 MODE 识别的云对象的分析表明,与采用 Thompson 微物理方案、Mellor-Yamanda-Nakanishi-Niino (MYNN) PBL、全球预报系统 (GFS) 表面层和 Noah LSM 的配置相比,该配置采用了国家强风暴实验室的微物理产生了更多具有更高 BT 的云物体。将 PBL 从 MYNN 更改为 Shin-Hong 或 Eddy-Diffusivity Mass-Flux 也会导致精度略低,尽管这些更改会导致配置更准确地再现观测云对象的数量并略微降低高 MBE。将 LSM 从 Noah 更改为 RUC 会因生成过多云对象而 BT 过低而降低预测准确性。随着预报时间的增加,
更新日期:2021-07-27
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