Differences between downscaling with spectral and grid nudging using WRF
Differences between downscaling with spectral and grid nudging using WRF
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DOI：
10.5194/acp-12-3601-2012
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发表时间：
2012-01-01
影响因子：
6.3
通讯作者：
Nenes, A.
中科院分区：
文献类型：
--
作者：
Liu, P.;Tsimpidi, A. P.;Nenes, A.
动力降尺度已被广泛用于研究大尺度全球气候模式强迫下的区域气候。然而，在降尺度过程中，区域气候模式（RCMs）的模拟往往会偏离驱动场。通过保留大尺度特征（来自大尺度场）和小尺度特征（来自RCM）来开发解决该问题的解决方案已经导致了“轻推”技术的发展。在这里，我们研究了两个轻推技术，网格和光谱轻推，在降尺度的NCEP/NCAR数据与天气研究和预报（WRF）模型的性能。模拟结果与北美区域再分析（NARR）数据集在不同尺度的利益，使用相似性的概念。我们发现，通过适当选择波数，谱轻推优于网格轻推的能力，平衡性能的模拟在大尺度和小尺度。
Dynamical downscaling has been extensively used to study regional climate forced by large-scale global climate models. During the downscaling process, however, the simulation of regional climate models (RCMs) tends to drift away from the driving fields. Developing a solution that addresses this issue, by retaining the large scale features (from the large-scale fields) and the small-scale features (from the RCMs) has led to the development of 'nudging' techniques. Here, we examine the performance of two nudging techniques, grid and spectral nudging, in the downscaling of NCEP/NCAR data with the Weather Research and Forecasting (WRF) Model. The simulations are compared against the results with North America Regional Reanalysis (NARR) data set at different scales of interest using the concept of similarity. We show that with the appropriate choice of wave numbers, spectral nudging outperforms grid nudging in the capacity of balancing the performance of simulation at the large and small scales.