This study explores the controlling factors of the uncertainties and error growth at different spatial and temporal scales in forecasting the high-impact extremely heavy rainfall event that occurred in Zhengzhou,Henan...This study explores the controlling factors of the uncertainties and error growth at different spatial and temporal scales in forecasting the high-impact extremely heavy rainfall event that occurred in Zhengzhou,Henan Province China on 19−20 July 2021 with a record-breaking hourly rainfall exceeding 200 mm and a 24-h rainfall exceeding 600 mm.Results show that the strengths of the mid-level low-pressure system,the upper-level divergence,and the low-level jet determine both the amount of the extreme 24-h accumulated and hourly rainfall at 0800 UTC.The forecast uncertainties of the accumulated rainfall are insensitive to the magnitude and the spatial structure of the tiny,unobservable errors in the initial conditions of the ensemble forecasts generated with Global Ensemble Forecast System(GEFS)or sub-grid-scale perturbations,suggesting that the predictability of this event is intrinsically limited.The dominance of upscale rather than upamplitude error growth is demonstrated under the regime of k^(−5/3) power spectra by revealing the inability of large-scale errors to grow until the amplitude of small-scale errors has increased to an adequate amplitude,and an apparent transfer of the fastest growing scale from smaller to larger scales with a slower growth rate at larger scales.Moist convective activities play a critical role in enhancing the overall error growth rate with a larger error growth rate at smaller scales.In addition,initial perturbations with different structures have different error growth features at larger scales in different variables in a regime transitioning from the k^(−5/3) to k^(−3) power law.Error growth with conditional nonlinear optimal perturbation(CNOP)tends to be more upamplitude relative to the GEFS or sub-grid-scale perturbations possibly owing to the inherited error growth feature of CNOP,the inability of convective parameterization scheme to rebuild the k^(−5/3) power spectra at the mesoscales,and different error growth characteristics in the k^(−5/3) and k^(−3) regimes.展开更多
基金supported by the National Natural Science Foundation of China(Grant Nos.42030604,41875051)the National Science Foundation(Grant No.AGS-1712290)+3 种基金China Postdoctoral Science Foundation(Grant No.2021M702725)sponsored by the MEL Outstanding Postdoctoral Scholarship from Xiamen Universitysupported by the East China Regional Meteorological Science and Technology Collaborative Innovation Fund(Grant No.QYHZ201801)the project from the Qingdao Meteorological Bureau(Grant No.2021qdqxz01)。
文摘This study explores the controlling factors of the uncertainties and error growth at different spatial and temporal scales in forecasting the high-impact extremely heavy rainfall event that occurred in Zhengzhou,Henan Province China on 19−20 July 2021 with a record-breaking hourly rainfall exceeding 200 mm and a 24-h rainfall exceeding 600 mm.Results show that the strengths of the mid-level low-pressure system,the upper-level divergence,and the low-level jet determine both the amount of the extreme 24-h accumulated and hourly rainfall at 0800 UTC.The forecast uncertainties of the accumulated rainfall are insensitive to the magnitude and the spatial structure of the tiny,unobservable errors in the initial conditions of the ensemble forecasts generated with Global Ensemble Forecast System(GEFS)or sub-grid-scale perturbations,suggesting that the predictability of this event is intrinsically limited.The dominance of upscale rather than upamplitude error growth is demonstrated under the regime of k^(−5/3) power spectra by revealing the inability of large-scale errors to grow until the amplitude of small-scale errors has increased to an adequate amplitude,and an apparent transfer of the fastest growing scale from smaller to larger scales with a slower growth rate at larger scales.Moist convective activities play a critical role in enhancing the overall error growth rate with a larger error growth rate at smaller scales.In addition,initial perturbations with different structures have different error growth features at larger scales in different variables in a regime transitioning from the k^(−5/3) to k^(−3) power law.Error growth with conditional nonlinear optimal perturbation(CNOP)tends to be more upamplitude relative to the GEFS or sub-grid-scale perturbations possibly owing to the inherited error growth feature of CNOP,the inability of convective parameterization scheme to rebuild the k^(−5/3) power spectra at the mesoscales,and different error growth characteristics in the k^(−5/3) and k^(−3) regimes.
