Microphysical Piggybacking in the Weather Research and Forecasting Model

Journal Article · 19 August 2022 · Journal of Advances in Modeling Earth Systems

DOI: https://doi.org/10.1029/2021MS002890 · OSTI ID:1882714

Sarkadi, Noémi  ^[1]; Xue, Lulin  ^[2]; Grabowski, Wojciech W.  ^[2]; Lebo, Zachary J.  ^[3]; Morrison, Hugh  ^[2]; White, Bethan  ^[4]; Fan, Jiwen  ^[5]; Dudhia, Jimy  ^[2]; Geresdi, István  ^[1]

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1. Faculty of Sciences University of Pécs Pécs Hungary
2. National Center for Atmospheric Research Boulder CO USA
3. University of Wyoming Laramie WY USA
4. Monash University Melbourne VIC Australia
5. Pacific Northwest National Laboratory Richland WA USA

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.

• Research Organization: Oak Ridge National Laboratory (ORNL), Oak Ridge, TN (United States). Atmospheric Radiation Measurement (ARM) Data Center; Pacific Northwest National Laboratory (PNNL), Richland, WA (United States)
• Sponsoring Organization: Hungarian Scientific Research Fund; USDOE; USDOE Office of Science (SC), Biological and Environmental Research (BER); United Arab Emirates (UAE) Rain Enhancement Program
• Contributing Organization: Argonne National Laboratory (ANL); Brookhaven National Laboratory (BNL); Pacific Northwest National Laboratory (PNNL)
• Grant/Contract Number: AC05-76RL01830; SC0020118; SC0020171; SC0021151
• OSTI ID: 1882714
• Report Number(s): PNNL-SA-177555; e2021MS002890
• Journal Information: Journal of Advances in Modeling Earth Systems, Journal Name: Journal of Advances in Modeling Earth Systems Journal Issue: 8 Vol. 14; ISSN 1942-2466
• Publisher: American Geophysical Union (AGU)Copyright Statement
• Country of Publication: United States
• Language: English