J6.2 Refining WRF Downscaling from 36-km to 12-km Resolution

Thursday, 10 January 2013: 11:15 AM
Room 10B (Austin Convention Center)
O. Russell Bullock Jr., EPA, Research Triangle Park, NC; and M. S. Mallard, K. Alapaty, J. A. Herwehe, and T. L. Otte

Dynamical downscaling techniques previously developed using WRF at 108- and 36-km resolution are applied for further downscaling to 12-km resolution over the eastern United States and southeastern Canada. This work was motivated by the fact that hydrologic planning and urban air quality management require finer spatial resolution in future climate projections. The WRF model is applied in three modes. The first is the standard WRF application where the simulation is constrained only by the provision of meteorological data at the lateral boundaries and surface conditions (e.g., topography, land surface type, sea-surface temperatures). For the other two modes, internal forcing is also applied. This internal forcing, also called interior nudging, is applied in two different ways called “analysis nudging” and “spectral nudging”. Both methods of nudging are investigated as ways to constrain the WRF simulation towards the NCEP-DOE AMIP-II Reanalysis data set used as a surrogate for future climate information to allow for model evaluation. This work investigates adjustments to the nudging parameters required for optimum results at 12-km resolution. Lake surface temperatures estimated from 2.5-degree latitude/longitude resolved sea surface temperature data produce results suggesting that a more realistic approach to estimating or simulating lake surface temperatures is needed for 12-km resolution. Alternate selections for sub-grid-scale convective parameterization and cloud microphysics are also tested. Simulated surface-level temperature, water vapor mixing ratio, and wind speed are compared to hourly observations collected by the Meteorological Assimilation Data Ingest System (MADIS) to demonstrate the improved accuracy of the 12-km downscaling results. Simulated precipitation is compared to Multisensor Precipitation Estimator (MPE) data showing a general excess of precipitation, especially in the southeast during summer. To help remedy this, we have modified the Kain-Fritsch convective parameterization to take into account radiation effects of sub-grid-scale cumulus. The results show a reduction of precipitation from the shading effects of sub-grid-scale cumulus.
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