Comparison of Analysis and Spectral Nudging Techniques for Dynamical Downscaling with the WRF Model over China

Abstract

To overcome the problem that the horizontal resolution of global climate models may be too low to resolve features which are important at the regional or local scales, dynamical downscaling has been extensively used. However, dynamical downscaling results generally drift away from large-scale driving fields. The nudging technique can be used to balance the performance of dynamical downscaling at large and small scales, but the performances of the two nudging techniques (analysis nudging and spectral nudging) are debated. Moreover, dynamical downscaling is now performed at the convection-permitting scale to reduce the parameterization uncertainty and obtain the finer resolution. To compare the performances of the two nudging techniques in this study, three sensitivity experiments (with no nudging, analysis nudging, and spectral nudging) covering a period of two months with a grid spacing of 6 km over continental China are conducted to downscale the 1-degree National Centers for Environmental Prediction (NCEP) dataset with the Weather Research and Forecasting (WRF) model. Compared with observations, the results show that both of the nudging experiments decrease the bias of conventional meteorological elements near the surface and at different heights during the process of dynamical downscaling. However, spectral nudging outperforms analysis nudging for predicting precipitation, and analysis nudging outperforms spectral nudging for the simulation of air humidity and wind speed.

Assessment of the Performance of Three Dynamical Climate Downscaling Methods Using Different Land Surface Information over China

“…In this study, the WRF domain size is about 6600 × 7800 km in the zonal and meridional directions, respectively. Hence, a wave number of 5 is employed in both directions in order to capture reanalysis data features with scales of 1300-1500 km [18,24].…”

State and Perspectives of the Concept of Large‐Scale Conditioning of Regional Climate Modelling

“…Obviously such a constraint is limiting the development of the state much more than the selective nudging, since not only the large-scales, which are supposed to be less affected by the finite grid resolution, but all scales including those heavily affected by the grid truncation and the interrupted energy cascade are affected. A number of studies have considered the two techniques (e.g., Liu et al, 2012;Ma et al, 2016;Spero et al, 2018). The latter, the grid-point ansatz, is useful, when sub-grid-scale processes are examined and fitted, while for hindcasting the spectral nudging is to be preferred, as it allows the improved small-scale dynamics and better descriptions of physiographic detail.…”

The Concept of Large-Scale Conditioning of Climate Model Simulations of Atmospheric Coastal Dynamics: Current State and Perspectives

“…Obviously such a constraint is limiting the development of the state much more than that of selective nudging, since not only the large scales, which are supposed to be less affected by the finite grid resolution, but all scales, including those heavily affected by the grid truncation and the interrupted energy cascade, are affected. A number of studies have considered the two techniques (e.g., References [35][36][37]). The latter, the grid-point ansatz, is useful when sub-grid-scale processes are examined and fitted, while for hindcasting spectral nudging is to be preferred, as it allows improved small-scale dynamics and better descriptions of physiographic detail.…”