Search Results

You are looking at 1 - 3 of 3 items for

  • Author or Editor: Andrei Pokrovsky x
  • Refine by Access: All Content x
Clear All Modify Search
Vaughan T. J. Phillips, Andrei Pokrovsky, and Alexander Khain

Abstract

Simulations of one maritime and four continental observed cases of deep convection are performed with the Hebrew University Cloud Model that has spectral bin microphysics. The maritime case is from observations made on 18 September 1974 during the Global Atmospheric Research Program’s Atlantic Tropical Experiment (GATE). The continental storm cases are those of summertime Texas clouds observed on 13 August 1999, and green-ocean, smoky, and pyro-clouds observed during the Large-Scale Biosphere–Atmosphere Experiment in Amazonia–Smoke, Aerosols, Clouds, Rainfall, and Climate (LBA–SMOCC) campaign on 1–4 October 2002. Simulations have been performed for these cases with a detailed melting scheme. This scheme allows calculation of liquid water fraction within each mass bin for the melting of graupel, hail, snowflakes, and crystals, as well as alteration of the sedimentation velocity of ice particles in the course of their melting. The results obtained with the detailed melting scheme are compared with corresponding results from simulations involving instantaneous melting at the freezing (0°C) level.

The detailed melting scheme allows penetration of ice from the freezing level down into the boundary layer by distances ranging from a few hundred meters for the numerous, smaller particles to ∼1.5 km for the largest particles, which are much scarcer. In these simulations, most of the mass of ice falling out melts over this short distance of a few hundred meters. The deepening and intensification of the layer of latent cooling enhances the convective destabilization of the troposphere. This effect is especially pronounced under continental conditions, causing significant changes in the accumulated rain amount.

Full access
Barry H. Lynn, Alexander P. Khain, Jimy Dudhia, Daniel Rosenfeld, Andrei Pokrovsky, and Axel Seifert

Abstract

Considerable research investments have been made to improve the accuracy of forecasting precipitation systems in cloud-resolving, mesoscale atmospheric models. Yet, despite a significant improvement in model grid resolution and a decrease in initial condition uncertainty, the accurate prediction of precipitation amount and distribution still remains a difficult problem. Now, the development of a fast version of spectral (bin) microphysics (SBM Fast) offers significant potential for improving the description of precipitation-forming processes in mesoscale atmospheric models.

The SBM Fast is based on solving a system of equations for size distribution functions for water drops and three types of ice crystals (plates, columns, and dendrites), as well as snowflakes, graupel, and hail/frozen drops. Ice processes are represented by three size distributions, instead of six in the original SBM code. The SBM uses first principles to simulate microphysical processes such as diffusional growth and collision. A budget for aerosols is used to obtain the spectrum of condensation nuclei, which is used to obtain the initial drop spectrum. Hence, SBM allows one to take into account aerosol effects on precipitation, and corresponding cloud effects on the atmospheric aerosol concentration and distribution. SBM Fast has been coupled with the three-dimensional fifth-generation Pennsylvania State University–NCAR Mesoscale Model (MM5), which allows SBM Fast to simulate microphysics within a realistic, time-varying mesoscale environment.

This paper describes the first three-dimensional SBM mesoscale model and presents results using 1-km resolution to simulate initial development of a cloud system over Florida on 27 July 1991. The focus is on initial cloud development along the west coast, just prior to sea-breeze formation. The results indicate that the aerosol concentration had a very important impact on cloud dynamics, microphysics, and rainfall.

Vertical cross sections of clouds obtained using SBM Fast are compared to those from a version of the “Reisner2” bulk-parameterization scheme that uses the Kessler autoconversion formula. The results show that this version of “Reisner2” produced vertically upright clouds that progressed very quickly from initial cloud formation to raindrop formation. In contrast, clouds obtained using SBM were relatively long lasting with greater production of stratiform clouds.

Full access
Barry H. Lynn, Alexander P. Khain, Jimy Dudhia, Daniel Rosenfeld, Andrei Pokrovsky, and Axel Seifert

Abstract

Spectral (bin) microphysics (SBM) has been implemented into the three-dimensional fifth-generation Pennsylvania State University–NCAR Mesoscale Model (MM5). The new model was used to simulate a squall line that developed over Florida on 27 July 1991. It is shown that SBM reproduces precipitation rate, rain amounts, and location, radar reflectivity, and cloud structure much better than bulk parameterizations currently implemented in MM5.

Sensitivity tests show the importance of (i) raindrop breakup, (ii) in-cloud turbulence, (iii) different aerosol concentrations, and (iv) inclusion of scavenging of aerosols. Breakup decreases average and maximum rainfall. In-cloud turbulence enhances particle drop collision rates and increases rain rates. A “continental” aerosol concentration produces a much larger maximum rainfall rate versus that obtained with “maritime” aerosol concentration. At the same time accumulated rain is larger with maritime aerosol concentration. The scavenging of aerosols by nucleating water droplets strongly affected the concentration of aerosols in the atmosphere.

The spectral (bin) microphysics mesoscale model can potentially be used for studies of specific phenomena such as severe storms, winter storms, tropical cyclones, etc. The more realistic reproduction of cloud structure than that obtained with bulk parameterization implies that the model will be more useful for remote sensing applications and in the development of advanced rain retrieval algorithms. The model can also simulate the effect of cloud seeding on rain production.

Full access