Development of “Physical Parametrizations with PYthon” (PPPY, version 1.1)  and its usage to reduce the time-step dependency in a microphysical scheme

Riette, Sébastien

doi:10.5194/gmd-13-443-2020

Cited by 3 publications

(3 citation statements)

References 17 publications

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“…The operational model AROME implements the ICE3 single‐moment bulk microphysical scheme (Pinty and Jabouille, 1998; Riette, 2020). In addition to the water vapour, cloud microphysics are represented using three ice species (pristine ice, snow which typically represents crystals larger than 200 μm, and graupel representing dense ice) and two liquid‐water species (cloud droplets and rain drops).…”

Section: The French Mesoscale Forecast System Arome and Its Microphys...mentioning

confidence: 99%

Improvements to the parametrization of snow in AROME in the context of ice crystal icing

Wurtz

Bouniol

Vié

2023

Quart J Royal Meteoro Soc

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Ice crystal icing (ICI) poses a threat nowadays for airplane pilots crossing the anvils of tropical mesoscale convective systems (MCSs). The use of fine‐scale operational numerical weather predictions as provided by the French limited‐area model AROME could help to better understand this phenomenon and to help its anticipation. To enable AROME to simulate ICI‐prone conditions, modifications of its single‐moment microphysical scheme Intercity‐Express 3 (ICE3) are tested. Using a temperature‐dependent snow particle distribution deeply impacts the organization and the ice phase of the simulated MCS. Notably, while the size of convective regions decreases, the size of anvil clouds increases and the low stratiform rain increases as well. As a result, by increasing the quantity of snow and decreasing the quantity of graupel, the simulation of ICI‐prone conditions in the anvils of convective systems is enabled. Using this parametrization, further modifications fine‐tune the representation of snow and further increase the size of the anvil cloud. The Marshall–Palmer snow distribution is replaced by a generalized gamma and the terminal fall velocities of snow hydrometeors are parametrized so that they are in closer agreement with observations.

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Section: The French Mesoscale Forecast System Arome and Its Microphys...mentioning

confidence: 99%

Improvements to the parametrization of snow in AROME in the context of ice crystal icing

Wurtz

Bouniol

Vié

2023

Quart J Royal Meteoro Soc

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“…For each hydrometeor type, the only prognostic variable is the mass mixing ratio. The ICE3 has been further improved by Riette (2020) with a reduction to the time-step dependence. The warm phase is described by the Kessler (1969) scheme.…”

Section: 2mentioning

confidence: 99%

Evaluation of the AROME model's ability to represent ice crystal icing using in situ observations from the HAIC 2015 field campaign

Wurtz

Bouniol

Vié

et al. 2021

Quart J Royal Meteoro Soc

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Since pilots generally avoid intense convective areas, ice crystals icing (ICI) is an aeronautical weather incident that mainly occurs in the anvil of tropical deep convective clouds. Samples of favorable conditions for the occurrence of ICI and data from the High Altitude Ice Crystals (HAIC) 2015 field campaign in French Guiana are investigated and compared with simulations of the French operational mesoscale forecast system Application of Research to Operations at Mesoscales (AROME). To this end, a contextualization of convective systems into convective, stratiform, and cirriform regions is employed for both observations and AROME. General features of the microphysics of deep tropical convective systems are identified. The number concentration of crystals larger than 125 μm and total water content (TWC) are strongly correlated at each temperature level, and both decrease with increasing distance from convective cores. AROME can reproduce the general behavior of the observed microphysics, especially TWC, but seems unable to simulate extreme ICI events. Reasons are sought in the assumptions performed in the microphysical scheme ICE3, and guidelines are proposed to enhance its skills in the context of ICI. In particular, the representation of the snow particle size distribution is adjusted across observations using a generalized gamma shape. This shape is found to outperform the usual Marshall–Palmer and gamma shapes. Additionally, a temperature and snow content dependence of generalized gamma parameters is found. These changes are found to significantly improve the snow concentration diagnostic of ICE3, and these modifications open the way for improvements in the ICE3 scheme.

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“…(2015) examined the time step sensitivities of version 2 of the Morrison‐Gettelman scheme (MG2) in combination with different cloud macrophysics schemes in the Community Atmosphere Model (CAM). The time step sensitivity of microphysics schemes has also been tested in 1D kinematic drivers (Chosson et al., 2014; Gettelman & Morrison, 2015) and other 0D and 1D frameworks (Riette, 2020; Santos et al., 2020).…”

Section: Introductionmentioning

confidence: 99%

Cloud Process Coupling and Time Integration in the E3SM Atmosphere Model

Santos

Caldwell

Bretherton

2021

J Adv Model Earth Syst

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Introduction Time Step Sensitivity in Atmospheric General Circulation ModelsTime integration strategies for atmospheric general circulation models (AGCMs) have grown more complex, both due to an increase in the number of separate processes within a model (allowing individual parameterizations to adopt different time integration strategies from the "host" model) and due to changes in process coupling. The latter include modifications that allow different processes to be run concurrently (Balaji et al., 2016;Donahue & Caldwell, 2020), as well as process coupling methods that apply the effects of faster processes separately from the effects of slower processes (

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Development of “Physical Parametrizations with PYthon” (PPPY, version 1.1) and its usage to reduce the time-step dependency in a microphysical scheme

Cited by 3 publications

References 17 publications

Improvements to the parametrization of snow in AROME in the context of ice crystal icing

Improvements to the parametrization of snow in AROME in the context of ice crystal icing

Evaluation of the AROME model's ability to represent ice crystal icing using in situ observations from the HAIC 2015 field campaign

Cloud Process Coupling and Time Integration in the E3SM Atmosphere Model

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