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- Title
Microphysical Piggybacking in the Weather Research and Forecasting Model.
- Authors
Sarkadi, Noémi; Xue, Lulin; Grabowski, Wojciech W.; Lebo, Zachary J.; Morrison, Hugh; White, Bethan; Fan, Jiwen; Dudhia, Jimy; Geresdi, István
- Abstract
This paper presents incorporation of the microphysical piggybacking into the Weather Research and Forecasting (WRF) model. Microphysical piggybacking is to run a single simulation applying two microphysical schemes, the first scheme driving the simulation and the second piggybacking this simulated flow. "Driving the simulation" means that the simulated microphysical processes, affect the cloud buoyancy and thus force the simulated flow. In contrast, the piggybacking variables are advected by the simulated flow and undergo microphysical transformation, but they do not affect the simulated flow (like in prescribed flow—kinematic—simulations). The two sets of variables (driver and piggybacker) include temperature, water vapor mixing ratio, and all microphysical variables. We provide details of implementing piggybacking into the WRF model, illustrate its applications, and demonstrate the benefits of this methodology in two idealized three‐dimensional cases: (a) a squall line case applying two microphysics schemes, the Thompson bulk microphysics scheme and the University of Pécs/NCAR bin (UPNB) scheme. The piggybacking simulations revealed that the microphysics‐dynamics interaction plays a more important role than the pure microphysical size sorting effect in the transition zone formation. (b) A case of daytime shallow‐to‐deep convective development over land. This case uses the UPNB scheme and contrasts convection developing in environments with either pristine or polluted cloud condensation nuclei (CCN). The piggybacking results indicated that the increase of cloud cover and decrease of supersaturation are mainly associated with the microphysical effect of increasing CCN while the change of precipitation on the ground is also influenced by microphysics‐dynamics interactions. Plain Language Summary: Weather forecast, especially precipitation and cloud formation prediction is a challenging part of numerical modeling. They have to interpret processes that take place on a wide range of scales in space and time (dynamical and microphysical processes). A novel simulation methodology was implemented in a widely used weather prediction model (WRF). This method helps us to understand better the interactions between different processes. On one hand, this unique technique operates as a normal weather prediction model, which drives the simulation (the different processes interact with each other in this "driver" run). On the other hand, a second package of model variables just follows the flow produced by the "driver," which is called piggybacking the simulation. This second set of variables just go through the same calculations as in a normal model run, except that they do not have any feedback on the simulated flow. We have tested various cases to investigate the interactions in different types of weather situations. We have found that it's a useful technique to understand the relationship between dynamic and microphysical processes. It also helps us better understand how weather works so we can make more accurate predictions. Key Points: Piggybacking methodology implemented into the Weather Research and Forecasting model to examine microphysics‐dynamics interactionsIdealized squall line case presented the importance of microphysics‐dynamics interactions in transition zone formationThe advantage of the method is the gridpoint‐by‐gridpoint comparison, for example, to state the cloud condensation nuclei effect on microphysics
- Subjects
METEOROLOGICAL research; CLOUD condensation nuclei; CLOUDINESS; BUOYANCY; WATER vapor
- Publication
Journal of Advances in Modeling Earth Systems, 2022, Vol 14, Issue 8, p1
- ISSN
1942-2466
- Publication type
Article
- DOI
10.1029/2021MS002890