Mechanism Study of Two-Dimensional Precipitation Diagnostic Models Within a Dynamic Framework

被引:1
作者
Wei, Xiangqian [1 ]
Liu, Yi [1 ]
Chang, Xinyu [1 ]
Guo, Jun [1 ]
Li, Haochuan [1 ]
机构
[1] Huazhong Univ Sci & Technol, Sch Civil & Hydraul Engn, Wuhan 430074, Peoples R China
基金
中国国家自然科学基金; 国家重点研发计划;
关键词
precipitation processes; atmospheric modeling; water vapor condensation; MICROPHYSICS; PARAMETERIZATION; SIMULATION; EXTENSION; SCHEME; BULK;
D O I
10.3390/atmos16040380
中图分类号
X [环境科学、安全科学];
学科分类号
08 ; 0830 ;
摘要
This study investigates the formation and triggering mechanisms of precipitation processes. Given the substantial effort required to construct a 3D model, we developed an idealized 2D precipitation scenario, using a simplified dynamical framework with vortex wind fields as the background atmospheric flow field. By modeling the transport, uplift, and subsidence of water vapor and liquid water, a condensation model was developed to simulate air parcel uplift and high-altitude water vapor condensation. Further, a cloud microphysics precipitation scheme was incorporated to simulate precipitation triggering and falling processes following water vapor condensation. Model results demonstrate that the approach accurately reproduces key processes of water vapor transport, condensation, and precipitation formation. With a time step of 15 s and a total of 120 steps, the simulation of a 30-min scenario was completed in just 158.5 s, indicating the high computational efficiency of the model. This paper introduces an innovative research scheme for a diagnostic model. Upon technological maturity, the model will utilize radar wind field data as its input to evaluate and enhance the performance of precipitation diagnostic models in real weather processes. This research lays a solid foundation for the further refinement and optimization of precipitation forecasting models, thereby advancing the accuracy of weather prediction.
引用
收藏
页数:18
相关论文
共 50 条
[1]   South East Pacific atmospheric composition and variability sampled along 20° S during VOCALS-REx [J].
Allen, G. ;
Coe, H. ;
Clarke, A. ;
Bretherton, C. ;
Wood, R. ;
Abel, S. J. ;
Barrett, P. ;
Brown, P. ;
George, R. ;
Freitag, S. ;
McNaughton, C. ;
Howell, S. ;
Shank, L. ;
Kapustin, V. ;
Brekhovskikh, V. ;
Kleinman, L. ;
Lee, Y-N ;
Springston, S. ;
Toniazzo, T. ;
Krejci, R. ;
Fochesatto, J. ;
Shaw, G. ;
Krecl, P. ;
Brooks, B. ;
McMeeking, G. ;
Bower, K. N. ;
Williams, P. I. ;
Crosier, J. ;
Crawford, I. ;
Connolly, P. ;
Allan, J. D. ;
Covert, D. ;
Bandy, A. R. ;
Russell, L. M. ;
Trembath, J. ;
Bart, M. ;
McQuaid, J. B. ;
Wang, J. ;
Chand, D. .
ATMOSPHERIC CHEMISTRY AND PHYSICS, 2011, 11 (11) :5237-5262
[2]  
Arabas S., 2014, Geosci. Model. Dev. Discuss, V7, P8275
[3]   On the CCN (de) activation nonlinearities [J].
Arabas, Sylwester ;
Shima, Shin-ichiro .
NONLINEAR PROCESSES IN GEOPHYSICS, 2017, 24 (03) :535-542
[4]  
Axel S., 2020, J. Adv. Model. Earth Syst, V12, pe2020MS002301
[5]  
Berry E.X., 1968, Amer. Meteor. Soc, V49, P154
[6]  
BOLTON D, 1980, MON WEATHER REV, V108, P1046, DOI 10.1175/1520-0493(1980)108<1046:TCOEPT>2.0.CO
[7]  
2
[8]  
Bott A, 1998, J ATMOS SCI, V55, P2284, DOI 10.1175/1520-0469(1998)055<2284:AFMFTN>2.0.CO
[9]  
2
[10]   Single-Objective and Multi-Objective Flood Interval Forecasting Considering Interval Fitting Coefficients [J].
Chang, Xinyu ;
Guo, Jun ;
Qin, Hui ;
Huang, Jingwei ;
Wang, Xinying ;
Ren, Pingan .
WATER RESOURCES MANAGEMENT, 2024, 38 (10) :3953-3972