Wednesday, 15 January 2020
Hall B1 (Boston Convention and Exhibition Center)
It is well known that cumulus convection parameterization (CP) is an important component in numerical weather prediction models at coarser resolutions. Some recent studies have shown that a CP may have a significant role in improving surface rainfall prediction in models at convection-permitting grid sizes. In addition a CP is necessary in models like the Model for Prediction Across Scales (MPAS) where the distances between cell centers on the model mesh can vary from relatively coarse to fine resolutions across the globe. Following the similar ideas proposed by Zheng et al. (2016), this work attempts to adapt a version of the Tiedtke (Zhang and Wang 2017) CP scheme to be 'scale-aware'. The goal is to reduce the effect of CP as the model grid size decreases but not eliminating it completely. The modified scheme is tested in several cases at 1.5 to 3 km grid sizes in the Weather Research and Forecasting (WRF) Model and the results show some improvement in the simulated surface rainfall, especially over a tropical region. Tests in MPAS show that the behavior of the scheme changes smoothly from coarse mesh where convection is largely driven by the CP to fine mesh where the effect of the CP is reduced, and convection is predominantly modeled by microphysics. More tests and quantitive evaluation of the precipitation forecast are planned and results will be presented at the conference.
Zhang, C. and Y. Wang, 2017: Projected Future Changes of Tropical Cyclone Activity over the Western North and South Pacific in a 20-km-Mesh Regional Climate Model. J. Climate, 30, 5923-5941. doi:10.1175/JCLI-D-16-0597.1.
Zheng, Yue, K. Alapaty, J. A. Herwehe, A. D. Del Genio, and D. Niyogi, 2016: Improving high-resolution weather forecasts using the Weather Research and Forecasting (WRF) Model with an updated Kain–Fritsch scheme. Mon. Wea. Rev.,117-3, 833-860. doi:10.1175/MWR-D-15-0005.1.
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