PDF
(12098KB)
Abstract
This study investigates the capabilities of a non-hydrostatic global, variable-resolution model in simulating tropical cyclone precipitation, with historically significant Typhoon Fitow (1323) as a case study. Employing three grid settings (24 km, 60−10 km, 60−3 km) and two microphysical parameterization schemes (WSM6 and Thompson), the study investigates the influence of grid resolution and microphysical parameterization on precipitation simulation. The simulated precipitation intensity and spatial distribution of high-resolution grids exhibit better agreement with the observations compared to the coarse-resolution grids. Specifically, the 60−3 km grid setting shows the greatest improvement in spatial correlation with observed precipitation data compared to the 24 km grid. Through the analysis of the thermal dynamic field, the high-resolution grid configuration more effectively simulates indicators for strong convective weather events, such as convective available potential energy (CAPE), helicity, and nonadiabatic heating. Analysis of TRMM satellite observations reveals that the high-resolution grid simulation results more accurately capture the distribution characteristics of hydrometeor mixing ratio compared to the coarse-resolution grids. Differences in hydrometeor content within convective clouds are more pronounced across grid resolutions than in stratiform clouds, even with the same parameterization scheme. Additionally, at the same resolution, the disparity in ice-phase particle content between the two schemes is much greater than the disparity in liquid-phase particle content. It is also noteworthy that the WSM6 scheme delivers superior performance compared to the Thompson scheme. In summary, this study demonstrates that refining model resolution has a more significant impact on precipitation intensity than the selection of physical parameterization scheme. The Model for Prediction Across Scales (MPAS), using a high-resolution variable-resolution grid, can be effectively used for typhoon precipitation simulation research.
Graphical abstract
Keywords
typhoon simulation
/
microphysical schemes
/
variable-resolution
/
MPAS
Cite this article
Download citation ▾
Jia ZHU, Yuhua YANG, Yan TAN, Wei HUANG.
Impacts of physical parameterization schemes and model resolution on typhoon rainfall simulation with a variable-resolution global model.
Front. Earth Sci., 2025, 19(3): 423-438 DOI:10.1007/s11707-024-1134-x
| [1] |
Bacmeister J T, Reed K A, Hannay C, Lawrence P, Bates S, Truesdale J E, Rosenbloom N, Levy M (2018). Projected changes in tropical cyclone activity under future warming scenarios using a high-resolution climate model.Clim Change, 146(3−4): 547–560
|
| [2] |
Bao X, Davidson N E, Yu H, Hankinson M C N, Sun Z, Rikus L J, Liu J, Yu Z, Wu D (2015). Diagnostics for an extreme rain event near Shanghai during the landfall of Typhoon Fitow (2013).Mon Weather Rev, 143(9): 3377–3405
|
| [3] |
Cha D, Jin C, Lee D, Kuo Y (2011). Impact of intermittent spectral nudging on regional climate simulation using Weather Research and Forecasting model.J Geophys Res, 116(D10): D10103
|
| [4] |
Cha D, Wang Y (2013). A dynamical initialization scheme for real-time forecasts of tropical cyclones using the WRF Model.Mon Weather Rev, 141(3): 964–986
|
| [5] |
Chen F, Dudhia J (2001). Coupling an advanced land surface-hydrology model with the Penn State-NCAR MM5 modeling system. Part I: Model implementation and sensitivity.Monthly Weather Review, 129(4): 569–585
|
| [6] |
Davis C A, Ahijevych D A, Wang W, Skamarock W C (2016). Evaluating medium-range tropical cyclone forecasts in uniform- and variable-resolution global models.Mon Weather Rev, 144(11): 4141–4160
|
| [7] |
Gao Y Y, Xing J Y, Chen Y D (2019). Influence of different cumulus convection parameterization schemes on the simulation of typhoon in the Northwest Pacific in MPAS-A.Marine Forecasts, 36: 10–18
|
| [8] |
Gettelman A, Bresch D N, Chen C C, Truesdale J E, Bacmeister J T (2018). Projections of future tropical cyclone damage with a high-resolution global climate model.Clim Change, 146(3−4): 575–585
|
| [9] |
Gómez-Navarro J J, Raible C C, Dierer S (2015). Sensitivity of the WRF model to PBL parametrisations and nesting techniques: evaluation of wind storms over complex terrain.Geosci Model Dev, 8(10): 3349–3363
|
| [10] |
Grell G A, Freitas S R (2014). A scale and aerosol aware stochastic convective parameterization for weather and air quality modeling.Atmospheric Chemistry and Physics, 14(10): 5233–5250
|
| [11] |
Hashimoto A, Done J M, Fowler L D, Bruyere C L (2016). Tropical cyclone activity in nested regional and global grid-refined simulations.Clim Dyn, 47(1−2): 497–508
|
| [12] |
Hendricks E A, Jin Y, Moskaitis J R, Doyle J D, Peng M S, Wu C, Kuo H (2016). Numerical simulations of Typhoon Morakot (2009) using a multiply nested tropical cyclone prediction model.Weather Forecast, 31(2): 627–645
|
| [13] |
Hong S Y, Lim J J (2006). The WRF single-moment 6-class microphysics scheme (WSM6).Asia-Pacific Journal of Atmospheric Sciences, 42(2): 129–151
|
| [14] |
Hong S, Noh Y, Dudhia J (2006). A new vertical diffusion package with an explicit treatment of entrainment processes.Monthly Weather Review, 134(9): 2318–2341
|
| [15] |
Huang C, Chang C, Kuo H (2022a). Exploring the evolution of Typhoon Lekima (2019) moving offshore northeast of Taiwan with a multi-resolution global model.Atmosphere (Basel), 13(11): 1817
|
| [16] |
Huang C, Huang C, Skamarock W C (2019). Track deflection of Typhoon Nesat (2017) as realized by multiresolution simulations of a global model.Mon Weather Rev, 147(5): 1593–1613
|
| [17] |
Huang C, Lin J, Kuo H, Chen D, Hong J, Hsiao L, Chen S (2022c). A numerical study for Tropical Cyclone Atsani (2020) past offshore of southern Taiwan under topographic influences.Atmosphere (Basel), 13(4): 618
|
| [18] |
Huang C, Lin J, Skamarock W C, Chen S (2022b). Typhoon forecasts with dynamic vortex initialization using an unstructured mesh global model.Mon Weather Rev, 150(11): 3011–3030
|
| [19] |
Huang C, Zhang Y, Skamarock W C, Hsu L (2017). Influences of large-scale flow variations on the track evolution of Typhoons Morakot (2009) and Megi (2010): simulations with a global variable-resolution model.Mon Weather Rev, 145(5): 1691–1716
|
| [20] |
Iacono M J, Delamere J S, Mlawer E J, Shephard M W, Clough S A, Collins W D (2008). Radiative forcing by long-lived greenhouse gases: calculations with the AER radiative transfer models.Journal of Geophysical Research-Atmospheres, 113: D13103
|
| [21] |
Kain J S (2004). The Kain-Fritsch convective parameterization: an update.Journal of Applied Meteorology, 43(1): 170–181
|
| [22] |
Khain A, Lynn B, Shpund J (2016). High resolution WRF simulations of Hurricane Irene: sensitivity to aerosols and choice of microphysical schemes.Atmos Res, 167: 129–145
|
| [23] |
Kim J H, Shin D B, Kummerow C (2013). Impacts of a priori databases using six WRF microphysics schemes on passive microwave rainfall retrievals.J Atmos Ocean Technol, 30(10): 2367–2381
|
| [24] |
Klemp J B (2011). A terrain-following coordinate with smoothed coordinate surfaces.Mon Weather Rev, 139(7): 2163–2169
|
| [25] |
Kramer M, Heinzeller D, Hartmann H, van den Berg W, Steeneveld G (2020). Assessment of MPAS variable resolution simulations in the grey-zone of convection against WRF model results and observations.Clim Dyn, 55(1−2): 253–276
|
| [26] |
Laprise R, Kornic D, Rapaić M, Šeparović L, Leduc M, Nikiema O, Di Luca A, Diaconescu E, Alexandru A, Lucas-Picher P, de Elía R, Caya D, Biner S (20122012. Considerations of Domain Size and Large-Scale Driving for Nested Regional Climate Models: Impact on Internal Variability and Ability at Developing Small-Scale Details. Springer Vienna, 181–199
|
| [27] |
Li X, Fan K, Yu E (2020). Hindcast of extreme rainfall with high-resolution WRF: model ability and effect of physical schemes.Theor Appl Climatol, 139(1−2): 639–658
|
| [28] |
Liu C, Moncrieff M W (2007). Sensitivity of cloud-resolving simulations of warm-season convection to cloud microphysics parameterizations.Mon Weather Rev, 135(8): 2854–2868
|
| [29] |
Liu , Q , Fu , Y (2007). An examination of summer precipitation over Asia based on TRMM/TMI.Science China Earth Sciences, 50: 430–441
|
| [30] |
Lou L, Li X (2016). Radiative effects on torrential rainfall during the landfall of Typhoon Fitow (2013).Adv Atmos Sci, 33(1): 101–109
|
| [31] |
Lu X Q, Yu H, Ying M, Zhao B K, Zhang S, Lin L M, Bai L N, Wan R J (2021). Western north Pacific tropical cyclone database created by the China Meteorological Administration.Adv Atmos Sci, 38(4): 690–699
|
| [32] |
Lui Y S, Tam C Y, Tse L K S, Ng K K, Leung W N, Cheung C C (2020). Evaluation of a customized variable‐resolution global model and its application for high‐resolution weather forecasts in East Asia.Earth Space Sci, 7(7): e2020EA001228
|
| [33] |
Ma L, Duan Y (2005). Study on structure and rainfall feature of TC Rammasun (2002) using TRMM products. Acta Oceanol Sin: 36–44
|
| [34] |
Mohan P R, Venkata S C, Yesubabu V, Baskaran R, Venkatraman B (2018). Simulation of a heavy rainfall event over Chennai in Southeast India using WRF: sensitivity to microphysics parameterisation.Atmos Res, 210: 83–99
|
| [35] |
Nakanishi M, Niino H (2004). An improved Mellor-Yamada Level-3 model with condensation physics: its design and verification.Boundary-Layer Meteorology, 112: 1–31
|
| [36] |
Otieno G, Mutemi J N, Opijah F J, Ogallo L A, Omondi M H (2019). The sensitivity of rainfall characteristics to cumulus parameterisation schemes from a WRF model. Part I: a case study over East Africa during wet years.Pure Appl Geophys, 1: 1–16
|
| [37] |
Park S, Klemp J B, Skamarock W C (2014). A comparison of mesh refinement in the global MPAS-A and WRF models using an idealized normal-mode baroclinic wave simulation.Mon Weather Rev, 142(10): 3614–3634
|
| [38] |
Ringler T, Ju L, Gunzburger M (2008). A multiresolution method for climate system modeling: application of spherical centroidal Voronoi tessellations.Ocean Dyn, 58(5−6): 475–498
|
| [39] |
Sakaguchi K, Leung L R, Zhao C, Yang Q, Lu J, Hagos S, Rauscher S A, Dong L, Ringler T D, Lauritzen P H (2015). Exploring a multiresolution approach using AMIP simulations.J Clim, 28(14): 5549–5574
|
| [40] |
Shen F, Min J, Xu D (2016). Assimilation of radar radial velocity data with the WRF Hybrid ETKF–3DVAR system for the prediction of Hurricane Ike (2008).Atmos Res, 169: 127–138
|
| [41] |
Shen F, Shu A, Liu Z, Li H, Jiang L, Zhang T, Xu D (2024). Assimilating FY-4A AGRI radiances with a channel-sensitive cloud detection scheme for the analysis and forecasting of multiple typhoons.Adv Atmos Sci, 41(5): 937–958
|
| [42] |
Skamarock W C, Klemp J B, Duda M G, Fowler L D, Park S, Ringler T D (2012). A multiscale nonhydrostatic atmospheric model using centroidal Voronoi Tesselations and C-grid staggering.Mon Weather Rev, 140(9): 3090–3105
|
| [43] |
Thompson G, Rasmussen R M, Manning K (2004). Explicit forecasts of winter precipitation using an improved bulk microphysics scheme. Part I: description and sensitivity analysis.Monthly Weather Review, 132(2): 519–542
|
| [44] |
Tian J, Liu J, Yan D, Li C, Chu Z, Yu F (2017). An assimilation test of Doppler radar reflectivity and radial velocity from different height layers in improving the WRF rainfall forecasts.Atmos Res, 198: 132–144
|
| [45] |
Wicker L J, Skamarock W C (2002). Time-splitting methods for elastic models using forward time schemes.Mon Weather Rev, 130(8): 2088–2097
|
| [46] |
Xu D, Zhang X, Liu Z, Shen F (2023). All-sky infrared radiance data assimilation of FY-4A AGRI with different physical parameterizations for the prediction of an extremely heavy rainfall event.Atmos Res, 293: 106898
|
| [47] |
Xu D, Zhang X, Min J, Shen F (2024). Impacts of assimilating all-sky FY-4A AGRI satellite infrared radiances on the prediction of Super Typhoon In-Fa during the period with abnormal changes.Journal of Geophysical Research: Atmospheres, 129: e2024JD040784
|
| [48] |
Xu G, Fei J, Huang X (2017). Simulation experiments of cloud microphysical parameterization schemes on a squall line and its genesis analysis.J Meteorol Sci, 37: 283–292
|
| [49] |
Xu H, Du B (2015). The impact of Typhoon Danas (2013) on the torrential rainfall associated with Typhoon Fitow (2013) in East China.Adv Meteorol, 2015: 383712
|
| [50] |
Xu H, Li X (2017). Torrential rainfall processes associated with a landfall of Typhoon Fitow (2013): a three-dimensional WRF modeling study.J Geophys Res Atmos, 122(11): 6004–6024
|
| [51] |
Xu H, Liu R, Zhai G, Li X (2016). Torrential rainfall responses of Typhoon Fitow (2013) to radiative processes: a three-dimensional WRF modeling study.J Geophys Res Atmos, 121: 14127–14136
|
| [52] |
Yang Y, Shen X, Lin L, Shou S (2006). Diagnostic analysis and numerical simulation of Typhoon Aili rainstorm.Meteorological Monthly, 32(7): 81–87
|
| [53] |
Yao X, Li W, Chen S (2014). Research on latent heat distributions in tropical cyclones from hydrometeor TMI retrieval data.Chin J Atmos Sci, 38: 909–923
|
| [54] |
Ying M, Zhang W, Yu H, Lu X, Feng J, Fan Y, Zhu Y, Chen D (2014). An overview of the China Meteorological Administration tropical cyclone database.J Atmos Ocean Technol, 31(2): 287–301
|
| [55] |
Yu Z, Chen Y, Wu D, Chen G, Bao X, Yang Q, Yu R, Zhang L, Tang J, Xu M, Zeng Z (2014). Overview of severe Typhoon Fitow and its operational forecasts.Trop Cyclone Res Rev, 3: 22–34
|
| [56] |
Yu Z, Yu H, Chen P, Qian C, Yue C (2009). Verification of tropical cyclone–related satellite precipitation estimates in Mainland China.J Appl Meteorol Climatol, 48(11): 2227–2241
|
| [57] |
Yuk J, Joh M (2019). Prediction of typhoon-induced storm surge, waves and coastal inundation in the Suyeong River Area, South Korea: a case study during Typhoon Chaba.J Coast Res, 91(sp1): 156–160
|
| [58] |
Zhang C, Wang Y, Hamilton K (2011). Improved representation of boundary layer clouds over the southeast Pacific in ARW-WRF using a modified tiedtke cumulus parameterization scheme.Monthly Weather Review, 139(11): 3489–3513
|
| [59] |
Zhao C, Xu M, Wang Y, Zhang M, Guo J, Hu Z, Leung L R, Duda M, Skamarock W (2019). Modeling extreme precipitation over East China with a global variable-resolution modeling framework (MPASv5.2): impacts of resolution and physics.Geosci Model Dev, 12(7): 2707–2726
|
RIGHTS & PERMISSIONS
Higher Education Press