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

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.

Orographic Precipitation Response to Microphysical Parameter Perturbations for Idealized Moist Nearly Neutral Flow

This study explores the sensitivity of clouds and precipitation to microphysical parameter perturbations using idealized simulations of moist, nearly neutral flow over a bell-shaped mountain. Numerous parameters are perturbed within the Morrison microphysics scheme. The parameters that most affect cloud and precipitation characteristics are the snow fall speed coefficient As, snow particle density ρs, rain accretion (WRA), and ice–cloud water collection efficiency (ECI). Surface precipitation rates are affected by As and ρs through changes to the precipitation efficiency caused by direct and indirect impacts on snow fall speed, respectively. WRA and ECI both affect the amount of cloud water removed, but the precipitation sensitivity differs. Large WRA results in increased precipitation efficiency and cloud water removal below the freezing level, indirectly decreasing cloud condensation rates; the net result is little precipitation sensitivity. Large ECI removes cloud water above the freezing level but with little influence on overall condensation rates. Two environmental experiments are performed to test the robustness of the results: 1) reduction of the wind speed profile by 30% (LowU) and 2) decreasing the surface potential temperature to induce a freezing level below the mountain top (LowFL). Parameter perturbations within LowU result in similar mechanisms acting on precipitation, but a much weaker sensitivity compared to the control. The LowFL case shows ρs is no longer a dominant parameter and As now induces changes to cloud condensation, since more of the cloud depth is present above the freezing level. In general, perturbations to microphysical parameters affect the location of peak precipitation, while the total amount of precipitation is more sensitive to environmental parameter perturbations.

An experiment to measure raindrop collection efficiencies: influence of rear capture

In the case of severe accident with loss of containment in a nuclear plant, radionuclides are released into the atmosphere in the form of both gases and aerosol particles (Baklanov and Sørensen, 2001). The analysis of radioactive aerosol scavenged by rain after the Chernobyl accident highlights certain differences between the modelling studies and the environmental measurements. Part of these discrepancies can probably be attributed to uncertainties in the efficiencies used to calculate aerosol particle collection by raindrops, particularly drops with a diameter larger than one millimetre. In order to address the issue of these uncertainties, an experimental study was performed to close the gaps still existing for this key microphysical parameter. In this paper, attention is first focused on the efficiency with which aerosol particles in the accumulation mode are collected by raindrops with a diameter of 2 mm. The collection efficiencies measured for aerosol particle in the sub-micron range are quantitatively consistent with previous theoretical model developed by Beard (1974) and thus highlight the major role of rear capture in the submicron range.

An experiment to measure raindrop collection efficiencies: influence of rear capture

The analysis of radioactive aerosol scavenged by rain after the Chernobyl accident highlights certain differences between the modelling studies and the environmental measurements. Part of these discrepancies can probably be attributed to uncertainties in the efficiencies used to calculate aerosol particle collection by raindrops, particularly drops with a diameter larger than one millimetre. In order to improve the issue of these uncertainties, an experimental study was performed to close the gaps still existing for this key microphysical parameter. In the present article, attention is first focused on the efficiency with which aerosol particles, in the accumulation mode are collected by raindrops with a diameter of 2 mm. The collections efficiencies measured for aerosol particle in the sub-micron range are quantitatively consistent with previous theoretical model developed by Beard (1974) and thus highlight the major role of rear capture in the submicron range.