Wang, Yi-Chi王懌琪

Research Interests

My research interests are especially in understanding spatio-temporal variation of rainfall and atmospheric convection, the underlying physical mechanisms, and how they will respond to future changes of climate systems. To answer the scientific questions, I use extensively observational data of earth systems from both climate research sites and satellites, and do experiments the hierarchy of numerical models to test scientific hypotheses.

The atmospheric convection transports heat, moisture, and momentum vertically, driving atmospheric circulations in the tropics and distribute energy across multiple time scales, from diurnal cycle to interannual oscillations. Given its multi-scale nature, it is very difficult for climate models to represent detailed processes, variations, and feedbacks of convection. Models also have difficulty to provide rainfall with confidence which are coupled closely with surface processes and hydrological cycles. Findings from my research also focus to evaluate and improve the representation of rainfall and atmospheric convection in climate models. Better model capability will further lead to improvements of weather forecast, climate projection, and many societal and economical applications.


Representative Publications

Wang, Y.-C., *S. Xie, S. Tang, and W. Lin (2020): An Improved Convective Trigger for Capturing Summertime Nocturnal Elevated Convection over Lands: Observational Evidence and SCM Test. Accepted by Journal of Geophysical Research: Atmospheres. DOI: 10.1029/2019JD031651

*Xie, S., Wang, Y.‐C., Lin, W., Ma, H.‐Y., Tang, Q., Tang, S., et al. (2019). Improved diurnal cycle of precipitation in E3SM with a revised convective triggering function. Journal of Advances in Modeling Earth Systems, 11, 2290– 2310.

*Wang, Y.‐C., Hsu, H‐H. (2019) Improving diurnal rainfall phase over the Southern Great Plains in warm seasons by using a convective triggering design. International Journal of Climatology. 39: 5181– 5190.

Wu, L.-S., W.-C. Cheng, C.-Y. Chen, M.-C. Wu, Y.-C. Wang, Y.-H. Tseng, *T.-J. Chuang, *C.-J. Shen (2019): Transcriptomopathies of pre- and post-symptomatic frontotemporal dementia-like mice with TDP-43 depletion in forebrain neurons. acta neuropathol commun 7, 50. doi:10.1186/s40478-019-0674-x

*Van Weverberg, K., C.J. Moncrette, J.Petch, S.A. Klein, H.‐Y. Ma, C. Zhang, S. Xie, Q. Tang, W.Gustafson, M. Ahlgrimm, R. Roehrig, J. Cole, F. Cheruy, Y.‐C. Wang, K. Johnson (2018): Attribution of Surface Radiation Errors near the Southern Great Plains in Numerical Weather Prediction and Climate Models. Journal of Geophysical Research: Atmospheres, 123.

*Ma, H.-Y., S. A. Klein, S. Xie, C. Zhang, S. Tang, Q. Tang, C. J. Morcrette, K. Van Weverberg, J. Petch, M. Ahlgrimm, L. K. Berg, F. Cheruy, J. Cole, R. Forbes, W. I. Gustafson Jr, M. Huang, Y. Liu, W. Merryfield, Y. Qian, R. Roehrig, Y.‐C. Wang (2018): CAUSES: On the role of surface energy budget errors to the warm surface air temperature error over the Central United States. Journal of Geophysical Research: Atmospheres, 123, 2888–2909. 

*Morcrette, C.J., K. Van Weverberg, H.‐Y. Ma, M. Ahlgrimm, E. Bazile, L. Berg, A. Cheng, F. Cheruy, J. Cole, R. Forbes, W. Gustafson Jr , M. Huang, W.‐S. Lee, Y. Liu, L. Mellul, W. Merryfield, Y. Qian, R. Roehrig, Y.‐C. Wang, S. Xie, S. Klein, J. Petch (2018): Introduction to CAUSES: Description of weather and climate models and their near‐surface temperature errors in 5 day hindcasts near the Southern Great Plains. Journal of Geophysical Research: Atmospheres, 123, 2655–2683.

*Wang, Y.-C., H.-L. Pan, and H.-H. Hsu (2015): Impacts of the triggering function of cumulus parameterization on warm-season diurnal rainfall cycles at the Atmospheric Radiation Measurement Southern Great Plains site, Journal of Geophysical Research: Atmospheres, 120, 10,681–10,702, doi:10.1002/2015JD023337.

Wang, Y.-C. and W.-w. Tung* (2010): Impacts of Cloud-System Resolving Regional Modeling on the Simulation of Monsoon Depressions, Geophysical Research  Letters, 37, L08806, doi:10.1029/2010GL042734.

Techniques & Development

Understanding key mechanisms of intense nocturnal rainfall events over topographical regions  Our study explores the key mechanisms for nocturnal intense rainfall events over coastal regions, topographical regions, and adjacent plains. Such raining systems are difficult for climate models to represent their strength and variability. Using onsite observations from the Department of Energy (DOE)’s Atmospheric Radiation Measurement (ARM) programs and experiments with single-column version of climate model, we found the initiation of uplifted convection needs to be included in climate models to capture nocturnal raining events [8]. Experiments with global-domain models further show the control of large-scale moisture convergence contributing to the uplifted convection [3].Reference: [8] Wang et al., 2015. [3] Wang and Hsu, 2019.

Developing physical parameterization in Taiwan Earth System Model (TaiESM)  TaiESM is the in-house earth system model developed in RCEC. Based on the previous study in convection initiation, I have assisted the new-developed convective triggering in included in TaiESM. Performance of diurnal cycle is found to be better improved as suppressed spurious daytime rainfall[10]. I also assisted on revising the cloud fraction scheme GFS-TaiESM-Sundqvist scheme in TaiESM. This scheme provides utilized consistent cloud-RH relationship and improves simulation of cloud fraction and many climatic fields in the global models[12].Reference: [10] Lee et al. 2019. [12] Shiu et al. 2020.

Understanding impacts of large-scale interaction on convection initiation and rainfall variability in climate models (Collaborative work with LLNL)  Our study studies how the large-scale interaction on convection initiation and rainfall variability from observations and gains support to use the large-scale dynamic convective available potential energy (dCAPE) trigger design in climate models. Combined with an unrestricted air parcel launch level (ULL) criteria, the dCAPE trigger is able to relax the unrealistic coupling of convection to surface heating and captures nocturnal elevated convection. Both case study and statistical analysis are conducted using data collected from the DOE’s ARM program at its Southern Great Plains (SGP) and Manaus (MAO) sites[1]. They show that dCAPE has a much stronger correlation with precipitation than CAPE and ULL is essential to detect elevated convection above boundary layer under both midlatitude and tropical conditions and for both afternoon and nighttime deep convection regimes. We also found similar improvements in the climate simulations of the Exascale Energy Earth System Model[2]. This part of work is collaborated with the Lawrence Livermore National Lab (LLNL), United States.Reference: [1] Wang et al., 2020. [2] Xie et al. 2019.

  • Ph.D.
    Department of Earth and Atmospheric Sciences
    Purdue University, IN, USA (2011)
  • B.E.
    Department of Electrical Engineering
    National Tsing-Hua University, Taiwan (2005)
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