Which combinations of environmental conditions and microphysical parameter values produce a given orographic precipitation distribution?

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  • 1 National Center for Atmospheric Research*, Boulder, CO
  • 2 Jet Propulsion Laboratory, California Institute of Technology, Pasadena, CA
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Abstract

This study applies an idealized modeling framework, alongside a Bayesian Markov chain Monte Carlo (MCMC) algorithm, to explore which combinations of upstream environmental conditions and cloud microphysical parameter values can produce a particular precipitation distribution over an idealized two-dimensional, bell-shaped mountain. Simulations focus on orographic precipitation produced when an atmospheric river interacts with topography. MCMC-based analysis reveals that different combinations of parameter values produce a similar precipitation distribution, with the most influential parameters being relative humidity (RH), horizontal wind speed (U), surface potential temperature (θsfc), and the snow fallspeed coefficient (As). RH, U, and As exhibit inter-dependence: changes in one or more of these factors can be mitigated by compensating changes in the other(s) to produce similar orographic precipitation rates. The results also indicate that the parameter sensitivities and relationships can vary for spatial sub-regions and given different environmental conditions. In particular, high θsfc values are more likely to produce the target precipitation rate and spatial distribution, and thus the ensemble of simulations shows a preference for liquid precipitation at the surface. The results presented here highlight the complexity of orographic precipitation controls, and have implications for flood and water management, observational efforts, and climate change.

Corresponding author address: Annareli Morales, NCAR, 3090 Center Green Drive, Boulder, CO 80301. Email: amorales@ucar.edu

The National Center for Atmospheric Research is sponsored by the National Science Foundation

Abstract

This study applies an idealized modeling framework, alongside a Bayesian Markov chain Monte Carlo (MCMC) algorithm, to explore which combinations of upstream environmental conditions and cloud microphysical parameter values can produce a particular precipitation distribution over an idealized two-dimensional, bell-shaped mountain. Simulations focus on orographic precipitation produced when an atmospheric river interacts with topography. MCMC-based analysis reveals that different combinations of parameter values produce a similar precipitation distribution, with the most influential parameters being relative humidity (RH), horizontal wind speed (U), surface potential temperature (θsfc), and the snow fallspeed coefficient (As). RH, U, and As exhibit inter-dependence: changes in one or more of these factors can be mitigated by compensating changes in the other(s) to produce similar orographic precipitation rates. The results also indicate that the parameter sensitivities and relationships can vary for spatial sub-regions and given different environmental conditions. In particular, high θsfc values are more likely to produce the target precipitation rate and spatial distribution, and thus the ensemble of simulations shows a preference for liquid precipitation at the surface. The results presented here highlight the complexity of orographic precipitation controls, and have implications for flood and water management, observational efforts, and climate change.

Corresponding author address: Annareli Morales, NCAR, 3090 Center Green Drive, Boulder, CO 80301. Email: amorales@ucar.edu

The National Center for Atmospheric Research is sponsored by the National Science Foundation

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