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Improving the representation of clouds, radiation, and precipitation using spectral nudging in the Weather Research and Forecasting model
Author(s) -
Spero Tanya L.,
Otte Martin J.,
Bowden Jared H.,
Nolte Christopher G.
Publication year - 2014
Publication title -
journal of geophysical research: atmospheres
Language(s) - English
Resource type - Journals
eISSN - 2169-8996
pISSN - 2169-897X
DOI - 10.1002/2014jd022173
Subject(s) - weather research and forecasting model , environmental science , mesoscale meteorology , climate model , precipitation , climatology , forcing (mathematics) , meteorology , scale (ratio) , climate change , representation (politics) , tropopause , geography , troposphere , geology , politics , political science , law , ecology , cartography , biology
Spectral nudging—a scale‐selective interior constraint technique—is commonly used in regional climate models to maintain consistency with large‐scale forcing while permitting mesoscale features to develop in the downscaled simulations. Several studies have demonstrated that spectral nudging improves the representation of regional climate in reanalysis‐forced simulations compared with not using nudging in the interior of the domain. However, in the Weather Research and Forecasting (WRF) model, spectral nudging tends to produce degraded precipitation simulations when compared to analysis nudging—an interior constraint technique that is scale indiscriminate but also operates on moisture fields which until now could not be altered directly by spectral nudging. Since analysis nudging is less desirable for regional climate modeling because it dampens fine‐scale variability, changes are proposed to the spectral nudging methodology to capitalize on differences between the nudging techniques and aim to improve the representation of clouds, radiation, and precipitation without compromising other fields. These changes include adding spectral nudging toward moisture, limiting nudging to below the tropopause, and increasing the nudging time scale for potential temperature, all of which collectively improve the representation of mean and extreme precipitation, 2 m temperature, clouds, and radiation, as demonstrated using a model‐simulated 20 year historical period. Such improvements to WRF may increase the fidelity of regional climate data used to assess the potential impacts of climate change on human health and the environment and aid in climate change mitigation and adaptation studies.