Efficient dynamical downscaling of general circulation models using continuous data assimilation
Efficient dynamical downscaling of general circulation models using continuous data assimilation
复制标题
使用连续数据同化对大气环流模型进行有效的动态降尺度
DOI：
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复制
发表时间：
2019
影响因子：
8.9
通讯作者：
I. Hoteit
中科院分区：
文献类型：
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作者：
Srinivas Desamsetti;H. Dasari;S. Langodan;E. Titi;O. Knio;I. Hoteit
连续数据同化(CDA)首次被成功地应用于全球大气再分析的有效动力降尺度。对CDA与标准网格和光谱微调技术在使用天气研究和预报(WRF)模型表示缩小尺度的场中的长尺度和短尺度特征的性能进行了进一步的比较和分析。模式配置为0.2 5° × 0.2 5°水平分辨率，由2.5° × 2.5°再分析场的初始和边界条件驱动。缩小比例实验于2016年1月进行，为期一个月。相似性度量被用来评估大尺度(2,000 公里)和小尺度(300 公里)下尺度方法的性能。用不同的降尺度技术、NCEP/NCAR再分析和NCEP最终分析(FNL，以0.25° × 0.25°水平分辨率提供)比较了全球天气预报模式输出的相似结果。与网格微调相比，频谱微调和CDA都能更好地描述小尺度特征。波数的选择在频谱微调中是至关重要的；增加保留频率的数量通常会产生更好的小尺度特征，但只能达到一定的阈值，在此之后其解逐渐变得更接近网格微调。CDA保持了大尺度和小尺度特征的平衡，类似于最佳光谱微调配置所实现的最佳模拟，而不需要进行光谱分解。不同尺度的大气变量，包括降雨量的分布，与CDA的观测结果最为一致。Brier技能分值进一步表明CDA的附加值分布在整个模型域中。总体结果清楚地表明，CDA通过在全球模式和缩尺度场之间保持更好的平衡，为动力缩尺提供了一种有效的新方法。
Continuous data assimilation (CDA) is successfully implemented for the first time for efficient dynamical downscaling of a global atmospheric reanalysis. A comparison of the performance of CDA with the standard grid and spectral nudging techniques for representing long‐ and short‐scale features in the downscaled fields using the Weather Research and Forecast (WRF) model is further presented and analysed. The WRF model is configured at 0.25° × 0.25° horizontal resolution and is driven by 2.5° × 2.5° initial and boundary conditions from NCEP/NCAR reanalysis fields. Downscaling experiments are performed over a one‐month period in January 2016. The similarity metric is used to evaluate the performance of the downscaling methods for large (2,000 km) and small (300 km) scales. Similarity results are compared for the outputs of the WRF model with different downscaling techniques, NCEP/NCAR reanalysis, and NCEP Final Analysis (FNL, available at 0.25° × 0.25° horizontal resolution). Both spectral nudging and CDA describe better the small‐scale features compared to grid nudging. The choice of the wave number is critical in spectral nudging; increasing the number of retained frequencies generally produced better small‐scale features, but only up to a certain threshold after which its solution gradually became closer to grid nudging. CDA maintains the balance of the large‐ and small‐scale features similar to that of the best simulation achieved by the best spectral nudging configuration, without the need of a spectral decomposition. The different downscaled atmospheric variables, including rainfall distribution, with CDA is most consistent with the observations. The Brier skill score values further indicate that the added value of CDA is distributed over the entire model domain. The overall results clearly suggest that CDA provides an efficient new approach for dynamical downscaling by maintaining better balance between the global model and the downscaled fields.