Technical note: A noniterative approach to modelling moist thermodynamics

Moisseeva, Nadya; Stull, Roland B.

doi:10.5194/acp-17-15037-2017

Cited by 2 publications

(1 citation statement)

References 7 publications

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“…This oscillation causes a number of transformations within the air particle. As the air particle is rising, as long as its relative humidity is below 100%, it is considered a dry air particle and will cool at the adiabatic lapse rate with a constant mixing ratio and potential temperature [21]. Potential temperature is defined as the temperature a particle of air would be at if it were brought to a standard reference pressure P 0 , defined as 1000 hPa.…”

Section: Convectionmentioning

confidence: 99%

Fortran Coarray Implementation of Semi-Lagrangian Convected Air Particles within an Atmospheric Model

et al. 2021

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This work added semi-Lagrangian convected air particles to the Intermediate Complexity Atmospheric Research (ICAR) model. The ICAR model is a simplified atmospheric model using quasi-dynamical downscaling to gain performance over more traditional atmospheric models. The ICAR model uses Fortran coarrays to split the domain amongst images and handle the halo region communication of the image’s boundary regions. The newly implemented convected air particles use trilinear interpolation to compute initial properties from the Eulerian domain and calculate humidity and buoyancy forces as the model runs. This paper investigated the performance cost and scaling attributes of executing unsaturated and saturated air particles versus the original particle-less model. An in-depth analysis was done on the communication patterns and performance of the semi-Lagrangian air particles, as well as the performance cost of a variety of initial conditions such as wind speed and saturation mixing ratios. This study found that given a linear increase in the number of particles communicated, there is an initial decrease in performance, but that it then levels out, indicating that over the runtime of the model, there is an initial cost of particle communication, but that the computational benefits quickly offset it. The study provided insight into the number of processors required to amortize the additional computational cost of the air particles.

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Section: Convectionmentioning

confidence: 99%

Fortran Coarray Implementation of Semi-Lagrangian Convected Air Particles within an Atmospheric Model

et al. 2021

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A consistent treatment of mixed‐phase saturation for atmospheric thermodynamics

Warren

2024

Quart J Royal Meteoro Soc

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Ice processes are integral to the formation and maintenance of high‐level clouds and can contribute substantially to the buoyancy of deep moist convection. These processes are represented in modern microphysics parameterisations but are commonly ignored in theoretical treatments of moist thermodynamics. While many thermodynamic variables can be defined uniquely for saturation with respect to liquid water and ice, it is desirable to account for the presence of mixed‐phase condensate, which is common in real clouds. Here, a simple yet novel representation of mixed‐phase saturation is introduced. Using this approach, analytical equations are derived for a variety of thermodynamic variables under mixed‐phase conditions. These include mixed‐phase versions of the saturation vapour pressure, saturated lapse rates, isobaric wet‐bulb temperature, and equivalent potential temperature. A new formulation of the ice–liquid water potential temperature is also presented, together with a method for calculating the mixed‐phase pseudo wet‐bulb temperature and wet‐bulb potential temperature. In addition, two new thermodynamic quantities are introduced: the isobaric saturation‐point temperature, a mixed‐phase analogue of the dew‐point and frost‐point temperatures, and the lifting saturation level, a mixed‐phase analogue of the lifting condensation and lifting deposition levels. Differences between each mixed‐phase variable and its liquid‐ and ice‐only counterparts are quantified across a wide range of atmospheric conditions. A Python library for evaluating these variables, developed during the course of this work, is also briefly introduced.

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Technical note: A noniterative approach to modelling moist thermodynamics

Cited by 2 publications

References 7 publications

Fortran Coarray Implementation of Semi-Lagrangian Convected Air Particles within an Atmospheric Model

Fortran Coarray Implementation of Semi-Lagrangian Convected Air Particles within an Atmospheric Model

A consistent treatment of mixed‐phase saturation for atmospheric thermodynamics

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