MCS Tracking and Microphysics Sensitivity Experiments over East Asia during 2020 JJAS: UDM vs. WDM7 Simulations

Overview This dataset provides detailed tracking results and statistical characteristics of Mesoscale Convective Systems (MCS) over East Asia during the summer of 2020 (June–September). The primary focus of this dataset is to investigate the sensitivity of MCS characteristics to microphysics parameterization schemes in a Convection-Permitting Model (CPM) framework using the WRF model. It includes simulation results from: Unified Forecast System (UFS) Double-Moment Microphysics Scheme (UDM) and WRF Double-Moment 7-class Microphysics Scheme (WDM7) as control experiments. Four sensitivity experiments based on UDM, designed to analyze the impact of key microphysical processes. Additionally, the modified source code used for the UDM sensitivity experiments is included to ensure reproducibility. Experimental Design The dataset includes output from two control runs and four sensitivity experiments designed to isolate the impact of specific microphysical processes. Control Runs: UDM (Hong et al., 2024) WDM7 (Bae et al., 2019) UDM Sensitivity Experiments: UDM-incloud: It disables the in-cloud microphysics concept proposed by Kim and Hong (2018). Rather than calculating cloud fraction to scale process rates, it uses grid-mean values, replicating the traditional method employed by WDM7. UDM-autoconverison: Replaces the rain autoconversion parameterization of UDM (Lee & Baik, 2017) with the formulation used in WDM7 (Tripoli & Cotton, 1980). UDM-semi-lagrangian: Switches the sedimentation scheme for rain from the default forward semi-Lagrangian advection (Juang & Hong, 2010) to the Eulerian sedimentation method used in WDM7. UDM-accretion&collection coefficiency: Modifies the accretion and collection processes to resemble WDM7's behavior. Accretion Rate: Adjusts the exponential coefficients (eacrs, egi, ehi for psaci, pgaci, phaci) for accretion of cloud ice by hail, graupel, and rain to reduce the accretion rates compared to the high rates (0.09 to 0.07 coefficient) typically found in WDM7-based logic. Collection Efficiency: Removes the diagnostic collection efficiency scaling factor for the accretion of cloud ice by snow (psaci), which is defined in the control UDM as: PSACI = PSACI * min(max(0.0,qrs/qci),1.0)^2 Tracking Methodology MCS tracking and feature extraction were performed using PyFLEXTRKR (Feng et al., 2023). Data Contents Source Code: Modified Fortran source codes for UDM sensitivity experiments (e.g., module_mp_udm_....F) Tracking and Statistical Analysis Data (Per Experiment): MCS track files. Monthly accumulated MCS statistics (frequency, intensity, rainfall contribution) for JJAS 2020. Comparison metrics between UDM sensitivity runs and WDM7. Usage & Citation This dataset is intended for research on cloud microphysics and severe weather systems in East Asia. If you use this dataset, please cite the associated publication (once available) and acknowledge the PyFLEXTRKR methodology. Contact Information For inquiries regarding the code or data, please contact [[e-mail]]. References Bae, S. Y., Hong, S. Y., & Tao, W. K. (2019). Development of a single-moment cloud microphysics scheme with prognostic hail for the Weather Research and Forecasting (WRF) model. Asia-Pacific Journal of Atmospheric Sciences, 55(2), 233–245. https://doi.org/10.1007/s13143-018-0066-3 Feng, Z., Hardin, J., Barnes, H. C., Li, J., Leung, L. R., Varble, A., & Zhang, Z. (2023). PyFLEXTRKR: A flexible feature tracking Python software for convective cloud analysis. Geoscientific Model Development, 16, 2753–2776 [Software]. https://doi.org/10.5194/gmd-16-2753-2023 Hong, S., Li, H., Bao, J.-W., Michelson, S., Grell, E., & Dudhia, J. (2024). An advanced double-moment cloud microphysics parameterization scheme for global weather forecasting. Paper presented at 2024 WRF/MPAS Users' Workshop, National Center for Atmospheric Research, Boulder, CO. Juang, H. M. H., & Hong, S. Y. (2010). Forward semi-Lagrangian advection with mass conservation and positive definiteness for falling hydrometeors. Monthly Weather Review, 138(5), 1778–1791. https://doi.org/10.1175/2009MWR3109.1 Kim, S. Y., & Hong, S. Y. (2018). The use of partial cloudiness in a bulk cloud microphysics scheme: Concept and 2D results. Journal of the Atmospheric Sciences, 75(8), 2711–2719. https://doi.org/10.1175/JAS-D-17-0234.1 Lee, H., & Baik, J. J. (2017). A physically based autoconversion parameterization. Journal of the Atmospheric Sciences, 74(5), 1599–1616. https://doi.org/10.1175/JAS-D-16-0207.1 Tripoli, G. J., & Cotton, W. R. (1980). A numerical investigation of several factors contributing to the observed variable intensity of deep convection over south Florida. Journal of Applied Meteorology and Climatology, 19(9), 1037–1063. https://doi.org/10.1175/1520-0450(1980)019<1037:ANIOSF>2.0.CO;2