Singular Vector Downscaling Initial Perturbation Based on Convective Adjustment Targeted Areas and Its Application in Convection‐Permitting Ensemble Prediction

Abstract The China Meteorological Administration convection‐permitting ensemble prediction system (CMA‐CPEPS) adopted a singular vector (SV) downscaling from the global ensemble prediction system because of its simplicity and applicability. The key to this method is reasonably describing the initial perturbation structures and growth characteristics, with targeted areas being crucial factors. However, previous studies using fixed targeted areas failed to capture perturbation structures at low latitudes and those associated with convection, leading to mismatches with convective weather development, insufficient ensemble spreads, and low precipitation predictability. To address these issues, we constructed convective adjustment targeted areas using composite parameters of convective available potential energy and shear. The results show that (a) the energy norms of SV for experiments with targeted areas across China exhibit a two‐peaked structure, effectively capturing perturbation structures at low and middle levels, similar to the largest areas for experiments with convective adjustment targeted areas. Both experiments have larger potential energy norms than kinetic energy norms at the initial time. (b) The new method largely improves the spread‐skill relationships and slightly enhances the probabilistic forecasting abilities for both precipitation and nonprecipitation variables, effectively describing uncertainties associated with heavy precipitation. (c) The new method yields more broadly distributed initial perturbation structures and larger perturbation magnitudes, particularly those associated with moist convection, thereby enhancing ensemble prediction skills in the CMA‐CPEPS. Moist convection is the primary physical mechanism driving the perturbation growth. Overall, these findings provide a foundation for improving the SV dynamical downscaling method in CMA‐CPEPS.

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Publication Details

Journal
Journal of Geophysical Research Atmospheres
Published
2025-10-27
DOI
https://doi.org/10.1029/2025jd044303
Citations
1
Primary Topic
Meteorological Phenomena and Simulations
Type
article
Field-Weighted Citation Impact
1.30
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article

Singular Vector Downscaling Initial Perturbation Based on Convective Adjustment Targeted Areas and Its Application in Convection‐Permitting Ensemble Prediction

Fajing Chen, Jingzhuo Wang, Hanbin Zhang, Hongqi Li et al.
1 citations
Journal of Geophysical Research Atmospheres
Meteorological Phenomena and Simulations
1.30
article

Singular Vector Downscaling Initial Perturbation Based on Convective Adjustment Targeted Areas and Its Application in Convection‐Permitting Ensemble Prediction

Fajing Chen, Jingzhuo Wang, Hanbin Zhang, Hongqi Li, Jing Chen
article en
1 citations

Abstract

Abstract The China Meteorological Administration convection‐permitting ensemble prediction system (CMA‐CPEPS) adopted a singular vector (SV) downscaling from the global ensemble prediction system because of its simplicity and applicability. The key to this method is reasonably describing the initial perturbation structures and growth characteristics, with targeted areas being crucial factors. However, previous studies using fixed targeted areas failed to capture perturbation structures at low latitudes and those associated with convection, leading to mismatches with convective weather development, insufficient ensemble spreads, and low precipitation predictability. To address these issues, we constructed convective adjustment targeted areas using composite parameters of convective available potential energy and shear. The results show that (a) the energy norms of SV for experiments with targeted areas across China exhibit a two‐peaked structure, effectively capturing perturbation structures at low and middle levels, similar to the largest areas for experiments with convective adjustment targeted areas. Both experiments have larger potential energy norms than kinetic energy norms at the initial time. (b) The new method largely improves the spread‐skill relationships and slightly enhances the probabilistic forecasting abilities for both precipitation and nonprecipitation variables, effectively describing uncertainties associated with heavy precipitation. (c) The new method yields more broadly distributed initial perturbation structures and larger perturbation magnitudes, particularly those associated with moist convection, thereby enhancing ensemble prediction skills in the CMA‐CPEPS. Moist convection is the primary physical mechanism driving the perturbation growth. Overall, these findings provide a foundation for improving the SV dynamical downscaling method in CMA‐CPEPS.

Journal of Geophysical Research AtmospheresVol. 130(20)
China Meteorological Administration (CN), Chinese Academy of Meteorological Sciences (CN)
Openalex Percentile: Top 17%
Meteorological Phenomena and Simulations
1.30
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