Microphysics in Goddard Multi-scale Modeling Systems: A Review

Tao, Wei‐Kuo; Chern, J.; Iguchi, T.; Lang, S.; Lee, M.-I.; Li, X.; Loftus, Adrian M.; Matsui, Toshihisa; Mohr, Karen I.; Nicholls, S.; Peters-Lidard, C.; Posselt, Derek J.; Skofronick-Jackson, Gail

doi:10.1007/978-981-13-3396-5_14

Cited by 4 publications

(3 citation statements)

References 165 publications

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“…By applying a base state temperature and water vapor profile representative of a deep convective environment, then imposing perturbations to temperature, water vapor, and vertical motion consistent with convective system evolution from initiation through maturity to dissipation, the model can be made to approximate the behavior of a single column within a fully three dimensional environment. The model microphysical parameterization is consistent with those used in modern numerical weather prediction models, and is a single moment bulk scheme (Lang et al, 2007;Lin et al, 1983;Rutledge & Hobbs, 1983, 1984Tao et al, 2019) with two liquid (cloud droplets and rain) and three ice (suspended ice particles, falling snow/aggregates, and graupel/hail) hydrometeor species. Clouds interact with longwave and shortwave radiation, and are allowed to be advected in the vertical direction only.…”

Section: D Column Microphysics Model and Casementioning

confidence: 93%

Sensitivity of Modeled Microphysics to Stochastically Perturbed Parameters

Vukićević

Vukicevic

Stanković

2022

J Adv Model Earth Syst

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This study examines the characteristics of several model parameter perturbation methodologies for ensemble simulations of cloud microphysical processes in convection. A simplified 1D model is used to focus the results on cloud microphysics without the complication of feedbacks to the dynamics and environment. Several parameter perturbation methods are tested, including non‐stochastic and stochastic with various distributions and parameter covariance. We find that an ensemble comprised of different time‐invariant parameters (non‐stochastic) exhibits little bias, but small spread. In addition, its behavior does not respect the time evolution of convection through its various phases. Stochastic parameter (SP) methods in which no inter‐parameter covariance is applied produce greater spread, but significant bias. The bias is particularly large for lognormal parameter perturbation distributions. The ensemble spread is retained and the bias reduced when time‐varying parameter covariance is applied. In this case, the SP scheme is able to adapt to the time and state‐dependent covariance structures and produce ensemble characteristics that are consistent with the specific microphysical processes operating at any given time. The results suggest that SP schemes would benefit from inclusion of parameter covariances, and specifically those that vary with the state of the system. It also suggests that a Normal or LogNormal SP scheme with no covariance may significantly impact the ensemble bias. Finally, the results indicate that high temporal and spatial resolution observations may be needed to characterize the variability in parameter values and covariance.

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Section: D Column Microphysics Model and Casementioning

confidence: 93%

Sensitivity of Modeled Microphysics to Stochastically Perturbed Parameters

Vukićević

Vukicevic

Stanković

2022

J Adv Model Earth Syst

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“…The PBLH directly controls the vertical diffusion and mixing of aerosols and pollutants with the planetary boundary layer. These aerosols when interact with the clouds, they result in an increased number density of cloud droplets (Masrour and Rezazadeh, 2023) and sometimes in riming due to formation of super cooled liquids (Tao et al 2017). This in turn affects the amount of latent heat released or absorbed which can impact the stability and mixing processes within the boundary layer (Devan et al 2024;Zhang et al 2021;Shen et al 2022) and hence impacting the PBL structure and height (Skamarock et al 2019).…”

Section: Introductionmentioning

confidence: 99%

Sensitivity of the Planetary Boundary Layer and Cloud Microphysics schemes to the drivers of dispersion and transportation of aerosols

Rengaraju,

Tiwari,

Ghita

et al. 2024

Preprint

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The Planetary Boundary Layer (PBL) is the lowest layer of the atmosphere that interacts with the Earth’s surface, and all major meteorology happens within this layer. This paper investigates the simulation of PBL Height (PBLH) and its sensitivity to meteorological parameters as a driver of aerosol dispersion and transport. Four simulations at 6 Km resolution using the Weather Research and Forecasting (WRF) model are run with two PBL schemes (ACM2 and YSU) combined with two Microphysics schemes (LIN and Morrison) for the year 2015 over the European domain. The simulated PBLH and other meteorological parameters that drive the PBL genesis, like temperature and wind speed, are analysed seasonally. In addition to the domain, a few locations from European Aerosol Lidar Network (EARLINET) representing various topography across the domain are chosen for analysis. The model performance in simulating the PBLH, temperature, and wind are examined using various statistical metrics involved in the Taylor diagram and Standard Deviation Error (STDE) methods. The study illustrates the comparisons of model performances for each variable over the domain and at selected station locations near the surface as well as the interfacial layer. We analyze how high (often overestimated) wind speeds affect the development of the Planetary Boundary Layer (PBL), particularly in complex terrains, on a seasonal scale. Our findings attribute unbalanced wind dynamics as a significant factor in this process. The STDE of microphysics schemes combination with YSU scheme shows a relatively 8-20% lesser error than other combinations across the seasons. Despite encountering notable errors over complex terrains, the YSU PBL scheme has performed better due to its ability to handle a wide range of atmospheric conditions.

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“…To illustrate this, for rainfall estimation with satellite-based methods or groundbased retrieval algorithms, DSDs are critical for determining the relations in the reflectivityrain rate [10][11][12][13][14] and improving dual-polarization rainfall retrievals [15][16][17][18]. Additionally, the characterization of DSDs in multi-scale models is vital for representing the processes of cloud dynamics and rainfall prediction in numerical simulations and forecasts [19][20][21][22]. It is also essential for broader studies of soil erosion due to rainfall and irrigation systems in agricultural engineering [23][24][25][26][27][28].…”

Section: Introductionmentioning

confidence: 99%

Raindrop Size Spectrum in Deep Convective Regions of the Americas

et al. 2021

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This study compared drop size distribution (DSD) measurements on the surfaces, the corresponding properties, and the precipitation modes among three deep convective regions within the Americas. The measurement compilation corresponded to two sites in the midlatitudes: the U.S. Southern Great Plains and Córdoba Province in subtropical South America, as well as to one site in the tropics: Manacapuru in central Amazonia; these are all areas where intense rain-producing systems contribute to the majority of rainfall in the Americas’ largest river basins. This compilation included two types of disdrometers (Parsivel and 2D-Video Disdrometer) that were used at the midlatitude sites and one type of disdrometer (Parsivel) that was deployed at the tropical site. The distributions of physical parameters (such as rain rate R, mass-weighted mean diameter Dm, and normalized droplet concentration Nw) for the raindrop spectra without rainfall mode classification seemed similar, except for the much broader Nw distributions in Córdoba. The raindrop spectra were then classified into a light precipitation mode and a precipitation mode by using a cutoff at 0.5 mm h−1 based on previous studies that characterized the full drop size spectra. These segregated rain modes are potentially unique relative to previously studied terrain-influenced sites. In the light precipitation and precipitation modes, the dominant higher frequency observed in a broad distribution of Nw in both types of disdrometers and the identification of shallow light precipitation in vertically pointing cloud radar data represent unique characteristics of the Córdoba site relative to the others. As a result, the co-variability between the physical parameters of the DSD indicates that the precipitation observed in Córdoba may confound existing methods of determining the rain type by using the drop size distribution.

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Microphysics in Goddard Multi-scale Modeling Systems: A Review

Cited by 4 publications

References 165 publications

Sensitivity of Modeled Microphysics to Stochastically Perturbed Parameters

Sensitivity of Modeled Microphysics to Stochastically Perturbed Parameters

Sensitivity of the Planetary Boundary Layer and Cloud Microphysics schemes to the drivers of dispersion and transportation of aerosols

Raindrop Size Spectrum in Deep Convective Regions of the Americas

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