Evaluation of ice particle growth in ICON using statistics of multi‐frequency Doppler cloud radar observations

Ori, Davide; Schemann, Vera; Karrer, Markus; Neto, José Dias; Terzi, Leonie von; Seifert, Axel; Kneifel, Stefan

doi:10.1002/qj.3875

Cited by 27 publications

(27 citation statements)

References 127 publications

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“…Various scattering properties obtained with SSRGA have been compared with DDA simulations (Hogan et al, 2017;Leinonen et al, 2018b;Mech et al, 2020) and revealed a very good agreement even for moderately rimed particles. The scattering properties derived with SSRGA also showed a very good agreement with the observed multi-frequency radar signatures of snowfall (Mason et al, 2019;Dias Neto et al, 2019;Ori et al, 2020a).…”

supporting

confidence: 69%

“…The default assumption can be overridden by specifying the sets of masses and areas to be used in the internal computations. This possibility is particularly useful, because it allows to 225 use snowScatt to forward simulate the outputs of numerical weather prediction models by ensuring internal consistency with the snow microphysical properties assumed in the model (Mech et al, 2020;Ori et al, 2020a).…”

Section: The Snow Librarymentioning

confidence: 99%

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snowScatt 1.0: Consistent model of microphysical and scattering properties of rimed and unrimed snowflakes based on the self-similar Rayleigh-Gans Approximation

Ori¹,

Terzi²,

Karrer³

et al. 2020

Preprint

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Abstract. More detailed observational capabilities in the microwave (MW) and advancements in the details of microphysical schemes for ice and snow demand increasing complexity to be included in scattering databases. The majority of existing databases rely on the Discrete Dipole Approximation (DDA) whose high computational costs limit either the variety of particle types or the range of parameters included, such as frequency, temperature, or particle size. snowScatt is an innovative tool that provides the consistent microphysical and scattering properties of an ensemble of 50 thousand snowflake aggregates generated with different physical particle models. Many diverse snowflake types, including rimed particles and aggregates of different monomer composition, are accounted for. The scattering formulation adopted by snowScatt is based on the Self-Similar Rayleigh-Gans Approximation (SSRGA) which is capable of modeling the scattering properties of large ensembles of particles. Previous comparisons of SSRGA and DDA are extended in this study by including unrimed and rimed aggregates up to cm-sizes and frequencies up to the sub-mm spectrum. The results reveal in general the wide applicability of the SSRGA method for active and passive MW applications. Unlike DDA databases, the set of SSRGA coefficients can be used to infer the scattering properties at any frequency and refractive index. snowScatt also provides tools to derive the SSRGA coefficients for new sets of particle structures which can be easily included in the library. The flexibility of the snowScatt tool with respect to applications that require continuously changing definitions of snow properties is demonstrated in a forward simulation example based on the output of the Predicted Particle Properties (P3) scheme. snowScatt provides the same level of flexibility as commonly used T-matrix solutions while the computed scattering properties reach the level of accuracy of detailed Discrete Dipole Approximation calculations.

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supporting

confidence: 69%

Section: The Snow Librarymentioning

confidence: 99%

snowScatt 1.0: Consistent model of microphysical and scattering properties of rimed and unrimed snowflakes based on the self-similar Rayleigh-Gans Approximation

Ori¹,

Terzi²,

Karrer³

et al. 2020

Preprint

Self Cite

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“…This paper presents the snowScatt software toolkit publicly available at https://github.com/ OPTIMICe-team/snowScatt (last access: 12 March 2021) and released under the terms of the GNU Public License version 3. The exact version used in the paper is archived on Zenodo (Ori et al, 2020b, https://doi.org/10.5281/zenodo.4118245). All codes needed to reproduce the results presented in this paper are included in the examples folder of the snowScatt repository.…”

Section: Discussionmentioning

confidence: 99%

snowScatt 1.0: consistent model of microphysical and scattering properties of rimed and unrimed snowflakes based on the self-similar Rayleigh–Gans approximation

et al. 2021

Self Cite

View full text Add to dashboard Cite

Abstract. More detailed observational capabilities in the microwave (MW) range and advancements in the details of microphysical schemes for ice and snow demand increasing complexity to be included in scattering databases. The majority of existing databases rely on the discrete dipole approximation (DDA) whose high computational costs limit either the variety of particle types or the range of parameters included, such as frequency, temperature, and particle size. The snowScatt tool is innovative in that it provides consistent microphysical and scattering properties of an ensemble of 50 000 snowflake aggregates generated with different physical particle models. Many diverse snowflake types, including rimed particles and aggregates of different monomer composition, are accounted for. The scattering formulation adopted by snowScatt is based on the self-similar Rayleigh–Gans approximation (SSRGA), which is capable of modeling the scattering properties of large ensembles of particles. Previous comparisons of SSRGA and DDA are extended in this study by including unrimed and rimed aggregates up to centimeter sizes and frequencies up to the sub-millimeter spectrum. The results generally reveal the wide applicability of the SSRGA method for active and passive MW applications. Unlike DDA databases, the set of SSRGA parameters can be used to infer scattering properties at any frequency and refractive index; snowScatt also provides tools to derive the SSRGA parameters for new sets of particle structures, which can be easily included in the library. The flexibility of the snowScatt tool with respect to applications that require continuously changing definitions of snow properties is demonstrated in a forward simulation example based on the output of the predicted particle properties (P3) scheme. The snowScatt tool provides the same level of flexibility as commonly used T-matrix solutions, while the computed scattering properties reach the level of accuracy of detailed discrete dipole approximation calculations.

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“…MODIS is a 36-channel imager with a spatial resolution of 250, 500, or 1000 m at nadir and with a swath width of 2330 km. It is the key instrument aboard the Terra and Aqua NASA satellites and provides global coverage every 1 or 2 d. The MODIS cloud microphysical products are also obtained by the Nakajima and King (1990) bispectral method and provide daytime estimates of cloud optical thickness and ice particle effective radius from solar reflectances measured in a non-absorbing visible band and a water-absorbing nearinfrared band (Platnick et al, 2017). Three different spectral cloud retrievals are performed by combining the 0.66 µm channel separately with the 1.6, 2.1, and 3.7 µm channel, although here we only use the primary 0.66-2.1 µm channel pair.…”

Section: Modismentioning

confidence: 99%

The behavior of high-CAPE (convective available potential energy) summer convection in large-domain large-eddy simulations with ICON

et al. 2021

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Abstract. Current state-of-the-art regional numerical weather prediction (NWP) models employ kilometer-scale horizontal grid resolutions, thereby simulating convection within the grey zone. Increasing resolution leads to resolving the 3D motion field and has been shown to improve the representation of clouds and precipitation. Using a hectometer-scale model in forecasting mode on a large domain therefore offers a chance to study processes that require the simulation of the 3D motion field at small horizontal scales, such as deep summertime moist convection, a notorious problem in NWP. We use the ICOsahedral Nonhydrostatic weather and climate model in large-eddy simulation mode (ICON-LEM) to simulate deep moist convection and distinguish between scattered, large-scale dynamically forced, and frontal convection. We use different ground- and satellite-based observational data sets, which supply information on ice water content and path, ice cloud cover, and cloud-top height on a similar scale as the simulations, in order to evaluate and constrain our model simulations. We find that the timing and geometric extent of the convectively generated cloud shield agree well with observations, while the lifetime of the convective anvil was, at least in one case, significantly overestimated. Given the large uncertainties of individual ice water path observations, we use a suite of observations in order to better constrain the simulations. ICON-LEM simulates a cloud ice water path that lies between the different observational data sets, but simulations appear to be biased towards a large frozen water path (all frozen hydrometeors). Modifications of parameters within the microphysical scheme have little effect on the bias in the frozen water path and the longevity of the anvil. In particular, one of our convective days appeared to be very sensitive to the initial and boundary conditions, which had a large impact on the convective triggering but little impact on the high frozen water path and long anvil lifetime bias. Based on this limited set of sensitivity experiments, the evolution of locally forced convection appears to depend more on the uncertainty of the large-scale dynamical state based on data assimilation than of microphysical parameters. Overall, we judge ICON-LEM simulations of deep moist convection to be very close to observations regarding the timing, geometrical structure, and cloud ice water path of the convective anvil, but other frozen hydrometeors, in particular graupel, are likely overestimated. Therefore, ICON-LEM supplies important information for weather forecasting and forms a good basis for parameterization development based on physical processes or machine learning.

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Evaluation of ice particle growth in ICON using statistics of multi‐frequency Doppler cloud radar observations

Cited by 27 publications

References 127 publications

snowScatt 1.0: Consistent model of microphysical and scattering properties of rimed and unrimed snowflakes based on the self-similar Rayleigh-Gans Approximation

snowScatt 1.0: Consistent model of microphysical and scattering properties of rimed and unrimed snowflakes based on the self-similar Rayleigh-Gans Approximation

snowScatt 1.0: consistent model of microphysical and scattering properties of rimed and unrimed snowflakes based on the self-similar Rayleigh–Gans approximation

The behavior of high-CAPE (convective available potential energy) summer convection in large-domain large-eddy simulations with ICON

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