Utilization of spectral bin microphysics and bulk parameterization schemes to simulate the cloud structure and precipitation in a mesoscale rain event

B. Lynn; A. Khain

doi:10.1029/2007jd008475

Spectral (Bin) Microphysics Coupled with a Mesoscale Model (MM5). Part II: Simulation of a CaPE Rain Event with a Squall Line

Monthly Weather Review ◽

10.1175/mwr-2841.1 ◽

2005 ◽

Vol 133 (1) ◽

pp. 59-71 ◽

Cited By ~ 89

Author(s):

Barry H. Lynn ◽

Alexander P. Khain ◽

Jimy Dudhia ◽

Daniel Rosenfeld ◽

Andrei Pokrovsky ◽

...

Keyword(s):

Mesoscale Model ◽

Three Dimensional ◽

Aerosol Concentration ◽

Squall Line ◽

Rain Event ◽

Maximum Rainfall ◽

Cloud Structure ◽

Sensing Applications ◽

Rate Versus ◽

Bin Microphysics

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.

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Effects of aerosols on the dynamics and microphysics of squall lines simulated by spectral bin and bulk parameterization schemes

Journal of Geophysical Research Atmospheres ◽

10.1029/2009jd011902 ◽

2009 ◽

Vol 114 (D22) ◽

Cited By ~ 43

Author(s):

A. P. Khain ◽

L. R. Leung ◽

B. Lynn ◽

S. Ghan

Keyword(s):

Parameterization Schemes ◽

Squall Lines ◽

Bulk Parameterization

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The Behavior of Number Concentration Tendencies for the Continuous Collection Growth Equation Using One- and Two-Moment Bulk Parameterization Schemes

Journal of Applied Meteorology and Climatology ◽

10.1175/jam2527.1 ◽

2007 ◽

Vol 46 (8) ◽

pp. 1264-1274

Author(s):

Jerry M. Straka ◽

Katharine M. Kanak ◽

Matthew S. Gilmore

Keyword(s):

Growth Process ◽

Number Concentration ◽

Mathematical Explanation ◽

Growth Equation ◽

Parameterization Schemes ◽

Distribution Shape ◽

Bulk Parameterization

Abstract This paper presents a mathematical explanation for the nonconservation of total number concentration Nt of hydrometeors for the continuous collection growth process, for which Nt physically should be conserved for selected one- and two-moment bulk parameterization schemes. Where possible, physical explanations are proposed. The assumption of a constant no in scheme A is physically inconsistent with the continuous collection growth process, as is the assumption of a constant Dn for scheme B. Scheme E also is nonconservative, but it seems this result is not because of a physically inconsistent specification; rather the solution scheme’s equations simply do not satisfy Nt conservation and Nt does not come into the derivation. Even scheme F, which perfectly conserves Nt, does not preserve the distribution shape in comparison with a bin model.

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Preliminary study on terrain uncertainty and its perturbing scheme

10.5194/egusphere-egu2020-1856 ◽

2020 ◽

Author(s):

Li jun ◽

Du jun ◽

Liu yu ◽

Xu jianyu

Keyword(s):

Model Uncertainty ◽

Heavy Rain ◽

Ensemble Prediction ◽

Rain Event ◽

Positive Contribution ◽

Perturbation Scheme ◽

Perturbation Techniques ◽

Ensemble Prediction System ◽

Parameterization Schemes ◽

Ensemble Mean

1.IntroductionA key issue in developing the ensemble prediction technique is the recognition of uncertain factors in numerical forecasting and how to use appropriate perturbation techniques to reflect these uncertain processes and improve ensemble prediction levels.Plenty of corresponding perturbation techniques have been developed. Such as Initial uncertainty and model uncertainty,In addition to the influence of IC and model uncertainty ,precipitation is closely related to terrain.The influence of terrain on the heavy rain includes the following three aspects:(1) The terrain has significant effect on the climatic distribution of precipitation.(2)The windward slope and leeward slope and other dynamic effects generated by terrain impact the triggering and intensity of precipitation.(3)The thermal effect is triggered by the heating of land surface of terrain at different height and latent heat release when airflow rises ,and this thermal action makes mountain precipitation closely related to terrain distribution .What are the terrain uncertainties in the model?(1)Different vertical coordinate systems lead to significant differences in terrain treatment(2)The conversion from real terrain to model terrain is closely associated with the resolution of the model and different terrain interpolation schemes, and it affects the simulation results of precipitation .(3)Measuring error of real terrain, etc.In this report, A terrain perturbation scheme (ter) has been firstly incorporated into an ensemble prediction system (EPS) and preliminarily tested in the simulation of the extremely heavy rain event occurred on 21 July, 2012 in Beijing, along with other three perturbation schemes.2.Case,data and schemes(1)Case: Based on the extremely heavy rain case in Beijing on July 21,2012, maximum precipitation center more than 400mm.(2)Data: GEPS of NCEP were used as initial background fields and lateral boundary condition , surface and upper-level observation of GTS,Rain gauge etc.(3)Model: WRFv4.3, 9km horizontal resolution ,511*511 grid point, 51 vertical layers,KF Eta,WSM6,etc(4)Experiments schemes: Four different perturbation schemes were used in the experiments and six members in each experiment. Sch_1(IC) considered the IC uncertainty ,the parameterization schemes were same but IC/LBC came from different GEPS members. Sch_2(phy) considered the Phy uncertainty ,the IC/LBC were same but PHY schemes were comprised of different parameterization schemes. Sch_3-4(ter and icter) considered the terrain uncertainty ,the second aspect of terrain uncertainty was considered in this study. Two different model terrain smoothing schemes and 3 terrain interpolation schemes were used to reflect the forecast error caused by terrain height. Icter is the mixed scheme of ter and ic.3.Preliminary test and results(1)Precipitation is closely related to terrain, terrain uncertainties have significant effect on the intensity and falling area of precipitation.(2) Only a simple terrain perturbation can produce a significant forecast spread , and its ensemble mean forecast is also improved compared with control forecast. for this case, it has a slightly positive contribution to the spread and probability forecast of precipitation on the basis of not impacting the quality of ensemble mean forecast.(3) In this case, the magnitude of spread generated by the terrain perturbation scheme is significantly smaller than that generated by the initial perturbation and physics process perturbation schemes.

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