Bayesian Exploration of Multivariate Orographic Precipitation Sensitivity

Orographic precipitation is a key component of the hydrologic cycle. The amount and distribution are known to depend on mountain and atmospheric sounding characteristics: mountain height and width, wind speed, stability, temperature, and humidity. Numerical models, including the Weather Research and Forecasting (WRF) model and the CM1 cloud-resolving model (Bryan and Fritsch, 2002), are useful tools for exploring the response of precipitation to atmosphere and mountain characteristics, and have been used to model moist neutral flow over a mountain (Miglietta and Rotunno, 2005, 2006, 2009, 2010). However, exploring all possible combinations of mountain and sounding configurations with a numerical model is computationally exhausting. Bayesian techniques, namely Markov chain Monte Carlo (MCMC) algorithms, were recently used to explore parameter sensitivities in remote sensing retrievals and model parameterizations in a computationally efficient way (Posselt et al., 2008; Posselt and Vukicevic, 2010; Posselt and Mace, 2014). However, use of MCMC to explore interactions in the physical system is an original concept. Here we introduce a MCMC-based sensitivity analysis that, in conjunction with the CM1 model, provides insight into which combinations of atmospheric and mountain parameters produce a given upslope precipitation distribution. In the process, we identify key multivariate interactions and tipping points in the physical system. References: Bryan, G. H., J. M. Fritsch, 2002: A Benchmark Simulation for Moist Nonhydrostatic Numerical Models. Mon. Wea. Rev., 130, 2917–2928. Miglietta, M. M., R. Rotunno, 2005: Simulations of Moist Nearly Neutral Flow over a Ridge. J. Atmos. Sci., 62, 1410–1427. Miglietta, M. M., R. Rotunno, 2006: Further Results on Moist Nearly Neutral Flow over a Ridge. J. Atmos. Sci., 63, 2881–2897. Miglietta, M.M., R. Rotunno, 2009: Numerical Simulations of Conditionally Unstable Flows over a Mountain Ridge. J. Atmos. Sci., 66, 1865–1885. Miglietta, M.M., R. Rotunno, 2010: Numerical Simulations of Low-CAPE Flows over a Mountain Ridge. J. Atmos. Sci., 67, 2391–2401. Posselt, D. J., T. S. L'Ecuyer, and G. L. Stephens, 2008: Exploring the Error Characteristics of Thin Ice Cloud Property Retrievals Using a Markov Chain Monte Carlo Algorithm. J. Geophys. Res., 113, D24206. Posselt, D. J., and T. Vukicevic, 2010: Robust Characterization of Model Physics Uncertainty for Simulations of Deep Moist Convection. Mon. Wea. Rev., 138, 1513–1535.