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Impacts of New Implementing Strategies for Surface and Model Physics Perturbations in TREPS on Forecasts of Landfalling Tropical Cyclones

Fund Project:

the National Key R&D Program of China through Grant 2017YFC1501603, the National Natural Science Foundation of China through Grant 41975136,the Guangdong Basic and Applied Basic Research Foundation through Grant 2019A1515011118


doi:  10.1007/s00376-021-1222-8

  • To improve the ensemble prediction system of the tropical regional atmosphere model for the South China Sea (TREPS) in predicting landfalling tropical cyclones (TCs), the impacts of three new implementing strategies for surface and model physics perturbations in TREPS were evaluated for 19 TCs making landfall in China during 2014–2016. For sea surface temperature (SST) perturbations, spatially uncorrelated random perturbations were replaced with spatially correlated ones; the multiplier f, which is used to form perturbed tendency in the Stochastically Perturbed Parameterization Tendency (SPPT) scheme, was inflated in regions with evident convective activity (f-inflated SPPT); and the Stochastically Perturbed Parameterization (SPP) scheme with 14 perturbed parameters selected from the planetary boundary layer, surface layer, microphysics, and cumulus convection parameterizations was added. Overall, all of these methods improved forecasts more significantly for non-intensifying than intensifying TCs. Compared with f-inflated SPPT, the spatially correlated SST perturbations generally showed comparable performance but were more and less skillful for intensifying and non-intensifying TCs, respectively. The advantages of the spatially correlated SST perturbations and f-inflated SPPT were mainly present in the deterministic guidance for both track and wind and in the probabilistic guidance for reliability of wind. For intensifying TCs, adding SPP led to mixed impacts with significant improvements in probability-matched mean of modest wind and in probabilistic forecasts of rainfall; while for non-intensifying TCs, adding SPP led to frequently positive impacts on the deterministic guidance for track, intensity, strong wind and moderate rainfall and on the probabilistic guidance for wind and discrimination of rainfall.
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Manuscript History

Manuscript received: 09 June 2021
Manuscript revised: 27 October 2021
Manuscript accepted: 23 November 2021
通讯作者: 陈斌, bchen63@163.com
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Impacts of New Implementing Strategies for Surface and Model Physics Perturbations in TREPS on Forecasts of Landfalling Tropical Cyclones

Abstract: To improve the ensemble prediction system of the tropical regional atmosphere model for the South China Sea (TREPS) in predicting landfalling tropical cyclones (TCs), the impacts of three new implementing strategies for surface and model physics perturbations in TREPS were evaluated for 19 TCs making landfall in China during 2014–2016. For sea surface temperature (SST) perturbations, spatially uncorrelated random perturbations were replaced with spatially correlated ones; the multiplier f, which is used to form perturbed tendency in the Stochastically Perturbed Parameterization Tendency (SPPT) scheme, was inflated in regions with evident convective activity (f-inflated SPPT); and the Stochastically Perturbed Parameterization (SPP) scheme with 14 perturbed parameters selected from the planetary boundary layer, surface layer, microphysics, and cumulus convection parameterizations was added. Overall, all of these methods improved forecasts more significantly for non-intensifying than intensifying TCs. Compared with f-inflated SPPT, the spatially correlated SST perturbations generally showed comparable performance but were more and less skillful for intensifying and non-intensifying TCs, respectively. The advantages of the spatially correlated SST perturbations and f-inflated SPPT were mainly present in the deterministic guidance for both track and wind and in the probabilistic guidance for reliability of wind. For intensifying TCs, adding SPP led to mixed impacts with significant improvements in probability-matched mean of modest wind and in probabilistic forecasts of rainfall; while for non-intensifying TCs, adding SPP led to frequently positive impacts on the deterministic guidance for track, intensity, strong wind and moderate rainfall and on the probabilistic guidance for wind and discrimination of rainfall.

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