Impacts of the Initial Perturbation Scale and Magnitude on Convection-Allowing Ensemble Forecasts over Eastern China

Yanan MA^{¹^,²}, Jing CHEN^{³^,⁴^,⁵}(), Jingzhuo WANG^{³^,⁴^,⁵}, Fajing CHEN^{³^,⁴^,⁵}, Jing WANG^⁶, Zhizhen XU^{³^,⁴^,⁵}

Chinese Academy of Meteorological Sciences, China Meteorological Administration (CMA), Beijing 100081

College of Earth and Planetary Sciences, University of Chinese Academy of Sciences, Beijing 100081

CMA Earth System Modeling and Prediction Centre, China Meteorological Administration, Beijing 100081

State Key Laboratory of Severe Weather, China Meteorological Administration, Beijing 100081

Key Laboratory of Earth System Modeling and Prediction, China Meteorological Administration, Beijing 100081

Tianjin Meteorological Observatory, Tianjin 300074

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Abstract

Given the chaotic nature of the atmosphere and inevitable initial condition errors, constructing effective initial perturbations (IPs) is crucial for the performance of a convection-allowing ensemble prediction system (CAEPS). The IP growth in the CAEPS is scale- and magnitude-dependent, necessitating the investigation of the impacts of IP scales and magnitudes on CAEPS. Five comparative experiments were conducted by using the China Meteorological Administration Mesoscale Numerical Weather Prediction System (CMA-MESO) 3-km model for 13 heavy rainfall events over eastern China: smaller-scale IPs with doubled magnitudes, larger-, meso-, and smaller-scale IPs; and a chaos seeding experiment as a baseline. First, the constructed IPs outperform unphysical chaos seeding in perturbation growth and ensemble performance. Second, the daily variation of smaller-scale perturbations is more sensitive to convective activity because smaller-scale perturbations during forecasts reach saturation faster than meso- and larger-scale perturbations. Additionally, rapid downscaling cascade that saturates the smallest-scale perturbation within 6 h for larger- and meso-scale IPs is stronger in the lower troposphere and near-surface. After 9–12 h, the disturbance development of large-scale IPs is the largest in each layer on various scales. Moreover, thermodynamic perturbations, concentrated in the lower troposphere and near-surface with meso- and smaller-scale components being dominant, are smaller and more responsive to convective activity than kinematic perturbations, which are concentrated on the middle–upper troposphere and predominantly consist of larger- and meso-scale components. Furthermore, the increasing magnitude of smaller-scale IPs enables only their smaller-scale perturbations in the first 9 h to exceed those of larger- and meso-scale IPs. Third, for forecast of upper-air and surface variables, larger-scale IPs warrant a more reliable and skillful CAEPS. Finally, for precipitation, larger-scale IPs perform best for light rain at all forecast times, whereas meso-scale IPs are optimal for moderate and heavy rains at 6-h forecast time. Increasing magnitude of smaller-scale IPs improves the probability forecast skills for heavy rains during the first 3–6 h.

Keywords

convection-allowing ensemble prediction system initial perturbation scales initial perturbation magnitudes forecast skill/verification perturbation growth

References

Ancell, B. C., A. Bogusz, M. J. Lauridsen, et al., 2018: Seeding chaos: The dire consequences of numerical noise in NWP perturbation experiments. Bull. Amer. Meteor. Soc., 99, 615–628, https://doi.org/10.1175/BAMS-D-17-0129.1.