221 Impacts of Physics Parameterization and Data Assimilation on Synoptic Feature Modeling in Severe Weather Outbreaks

Monday, 11 January 2016
Erin A. Thead, Mississippi State University, Mississippi State, MS; and A. E. Mercer and J. L. Dyer

Handout (1.4 MB)

The use of numerical weather prediction (NWP) has brought significant improvements to the forecasting of severe weather outbreaks. Tornado outbreaks can now be predicted up to four to eight days in advance. Assimilation of atmospheric and oceanic data has been performed for several years as a means of improving NWP forecasts. In particular, it has been shown that WRF-ARW forecasts of severe weather outbreaks are quantitatively improved by the assimilation of conventional and high-resolution satellite radiance observations. In addition, many physics parameterization schemes have been developed, each with its individual strengths and weaknesses.

This research examines the effect of data assimilation (conventional and satellite), microphysics schemes, and planetary boundary layer (PBL) physics schemes on the modeling of synoptic meteorological features in tornadic and nontornadic outbreaks. Building on the work of Mercer et al. 2012 and Thead et al. 2015, twenty synoptically driven United States tornado outbreaks and twenty synoptically driven nontornadic outbreaks from 2008 to 2011 were simulated at 12 km in the WRF-ARW model. These model runs each encompassed 42 hours, from 1800Z on the day before the outbreak day to 1200Z on the day after. Each of the cases was modeled with a set of 23 variations: 8 data assimilation variations (conventional observations and 2 different types of satellite radiances, in all possible combinations, plus a control with no assimilation) and 15 physics variations (5 different microphysics and 3 PBL physics) with no additional data assimilated.

Principal component analysis (PCA) composites of standard synoptic-scale variables such as 300 mb winds and 850 mb temperatures were created for tornadic and nontornadic outbreaks for each of the 23 possible model configurations. Examination of these composites reveals that, while PBL physics and conventional data assimilation heavily influence lower-atmospheric synoptic parameters, microphysics and satellite radiance assimilation play a larger role in the modeling of upper-atmospheric parameters. On the whole, varying the physics parameterization schemes produces greater differentiation between tornadic and nontornadic event modeling than data assimilation.

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