Improving the representation of clouds, radiation, and precipitation using spectral nudging in the Weather Research and Forecasting model
Citation:
Spero, T., M. Otte, J. Bowden, AND C. Nolte. Improving the representation of clouds, radiation, and precipitation using spectral nudging in the Weather Research and Forecasting model. JOURNAL OF GEOPHYSICAL RESEARCH: ATMOSPHERES. American Geophysical Union, Washington, DC, 119(20):11682-11694, (2014).
Impact/Purpose:
The National Exposure Research Laboratory’s Atmospheric Modeling Division (AMAD) conducts research in support of EPA’s mission to protect human health and the environment. AMAD’s research program is engaged in developing and evaluating predictive atmospheric models on all spatial and temporal scales for forecasting the Nation’s air quality and for assessing changes in air quality and air pollutant exposures, as affected by changes in ecosystem management and regulatory decisions. AMAD is responsible for providing a sound scientific and technical basis for regulatory policies based on air quality models to improve ambient air quality. The models developed by AMAD are being used by EPA, NOAA, and the air pollution community in understanding and forecasting not only the magnitude of the air pollution problem, but also in developing emission control policies and regulations for air quality improvements.
Description:
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 currently cannot be altered directly by spectral nudging in WRF. 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 timescale 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 improve 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.
