16 research outputs found
Improving collisional growth in Lagrangian cloud models: development and verification of a new splitting algorithm
Lagrangian cloud models (LCMs) are increasingly used in the cloud physics
community. They not only enable a very detailed representation of cloud
microphysics but also lack numerical errors typical for most other models.
However, insufficient statistics, caused by an inadequate number of
Lagrangian particles to represent cloud microphysical processes, can limit
the applicability and validity of this approach. This study presents the
first use of a splitting and merging algorithm designed to improve the warm
cloud precipitation process by deliberately increasing or decreasing the
number of Lagrangian particles under appropriate conditions. This new
approach and the details of how splitting is executed are evaluated in box
and single-cloud simulations, as well as a shallow cumulus test case. The
results indicate that splitting is essential for a proper representation of
the precipitation process. Moreover, the details of the splitting method
(i.e., identifying the appropriate conditions) become insignificant for
larger model domains as long as a sufficiently large number of Lagrangian
particles is produced by the algorithm. The accompanying merging algorithm is
essential to constrict the number of Lagrangian particles in order to
maintain the computational performance of the model. Overall, splitting and
merging do not affect the life cycle and domain-averaged macroscopic
properties of the simulated clouds. This new approach is a useful addition to
all LCMs since it is able to significantly increase the number of Lagrangian
particles in appropriate regions of the clouds, while maintaining a
computationally feasible total number of Lagrangian particles in the entire
model domain.</p
Overview of the PALM model system 6.0
In this paper, we describe the PALM model system 6.0. PALM (formerly an abbreviation for Parallelized Largeeddy Simulation Model and now an independent name) is a Fortran-based code and has been applied for studying a variety of atmospheric and oceanic boundary layers for about 20 years. The model is optimized for use on massively parallel computer architectures. This is a follow-up paper to the PALM 4.0 model description in Maronga et al. (2015). During the last years, PALM has been significantly improved and now offers a variety of new components. In particular, much effort was made to enhance the model with components needed for applications in urban environments, like fully interactive land surface and radiation schemes, chemistry, and an indoor model. This paper serves as an overview paper of the PALM 6.0 model system and we describe its current model core. The individual components for urban applications, case studies, validation runs, and issues with suitable input data are presented and discussed in a series of companion papers in this special issue