Exploring the Microphysical and Environmental Controls on Orographic Precipitation in an Atmospheric River Environment

Abstract

Microphysical parameterizations (MPs) approximate the very small-scale behavior associated with cloud and precipitation processes and their feedbacks on the atmosphere, which cannot be directly resolved by numerical weather and climate prediction models. For numerical simplicity, many of the effects induced by ice crystal shape variability and growth processes are approximated through the use of tunable parameters within MPs. These parameter values have an inherent uncertainty due to limited observations and natural variability which is neglected in MPs. It is thus hypothesized that perturbations to these microphysical parameters may be a source of simulated orographic precipitation uncertainty. Phase changes associated with these microphysical processes can alter the thermodynamic profile and the dynamics of the system, resulting in effects on surface precipitation. This dissertation thus aims to explore the sensitivity of mountain-induced/enhanced (orographic) precipitation to perturbations in microphysical parameters, in conjunction with changes to upstream environmental conditions. To test the hypothesis, an idealized modeling framework is applied using the Cloud Model 1 to simulate a moist, nearly neutral environment flowing over a bell-shaped mountain. The upstream environment is associated with a long, narrow corridor of strong water vapor transport found ahead of the cold front of an extra-tropical cyclone called an atmospheric river (AR). The orographic precipitation produced from ARs being forced upward my mountains provides a source of freshwater but can also lead to flooding upwind. Statistically robust methods including the Morris screening method and a Markov chain Monte Carlo algorithm are applied to understand precipitation sensitivities, as well as to identify which parameter combinations could produce a specific spatial precipitation distribution. Results show that the most influential parameters to orographic precipitation are associated with the fallspeed of snow, collection of supercooled water by ice, collection of cloud droplets by rain, the speed of air flowing towards the mountain, the height of the freezing level, and the relative humidity of the upstream environment. In general, perturbations to microphysical parameters affect the location of peak precipitation, while the total amount of condensate available is more sensitive to environmental parameter perturbations. There is a strong spatial sensitivity to the influential parameters, as changes in freezing level can affect the location of dominant rain processes, i.e., microphysical parameter sensitivities can change for different environments. Although the upstream environment influences the available condensate that microphysical processes can act upon, both processes can result in changes to precipitation of similar magnitudes, especially over the upwind slope of the mountain. Complex relationships between environmental and microphysical parameters are found, demonstrating mitigating factors that can compensate for the effect of perturbing a specific parameter. For example, to yield a similar precipitation intensity and distribution, horizontal wind speed values would need to decrease if the fallspeed of snow is reduced. The results presented herein highlight the complexity of orographic precipitation sensitivity to microphysics and environmental conditions, suggesting a small subset of parameters are responsible for most of the induced precipitation variability. Evaluation of parameter sensitivity is important for ensemble forecasting and data assimilation, as it is generally not computationally feasible to perturb every parameter in the full set of physics parameterizations, thus the small subset of parameters found here could be applied. The results from this dissertation may help future field campaigns focus on observing key processes to better constrain parameter uncertainty and improve our understanding of orographic precipitation.