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  • Author or Editor: Xiang Li x
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Qian Zhou
,
Lei Chen
,
Wansuo Duan
,
Xu Wang
,
Ziqing Zu
,
Xiang Li
,
Shouwen Zhang
, and
Yunfei Zhang

Abstract

Using the latest operational version of the ENSO forecast system from the National Marine Environmental Forecasting Center (NMEFC) of China, ensemble forecasting experiments are performed for El Niño–Southern Oscillation (ENSO) events that occurred from 1997 to 2017 by generating initial perturbations of the conditional nonlinear optimal perturbation (CNOP) and climatically relevant singular vector (CSV) structures. It is shown that when the initial perturbation of the leading CSV structure in the ensemble forecast of the CSVs scheme is replaced by those of the CNOP structure, the resulted ensemble ENSO forecasts of the CNOP+CSVs scheme tend to possess a larger spread than the forecasts obtained with the CSVs scheme alone, leading to a better match between the root-mean-square error and the ensemble spread, a more reasonable Talagrand diagram, and an improved Brier skill score (BSS). All these results indicate that the ensemble forecasts generated by the CNOP+CSVs scheme can improve both the accuracy of ENSO forecasting and the reliability of the ensemble forecasting system. Therefore, ENSO ensemble forecasting should consider the effect of nonlinearity on the ensemble initial perturbations to achieve a much higher skill. It is expected that fully nonlinear ensemble initial perturbations can be sufficiently yielded to produce ensemble forecasts for ENSO, finally improving the ENSO forecast skill to the greatest possible extent. The CNOP will be a useful method to yield fully nonlinear optimal initial perturbations for ensemble forecasting.

Open access
Steven M. Lazarus
,
Michael E. Splitt
,
Michael D. Lueken
,
Rahul Ramachandran
,
Xiang Li
,
Sunil Movva
,
Sara J. Graves
, and
Bradley T. Zavodsky

Abstract

Data reduction tools are developed and evaluated using a data analysis framework. Simple (nonadaptive) and intelligent (adaptive) thinning algorithms are applied to both synthetic and real data and the thinned datasets are ingested into an analysis system. The approach is motivated by the desire to better represent high-impact weather features (e.g., fronts, jets, cyclones, etc.) that are often poorly resolved in coarse-resolution forecast models and to efficiently generate a set of initial conditions that best describes the current state of the atmosphere. As a precursor to real-data applications, the algorithms are applied to one- and two-dimensional synthetic datasets. Information gleaned from the synthetic experiments is used to create a thinning algorithm that combines the best aspects of the intelligent methods (i.e., their ability to detect regions of interest) while reducing the impacts of spatial irregularities in the data. Both simple and intelligent thinning algorithms are then applied to Atmospheric Infrared Sounder (AIRS) temperature and moisture profiles. For a given retention rate, background, and observation error, the optimal 1D analyses (i.e., lowest MSE) tend to have observations that are near regions of large curvature and gradients. Observation error leads to the selection of spurious data in homogeneous regions of the intelligent algorithms. In the 2D experiments, simple thinning tends to perform better within the homogeneous data regions. Analyses produced using AIRS data demonstrate that observations selected via a combination of the simple and intelligent approaches reduce clustering, provide a more even distribution along the satellite swath edges, and, in general, have lower error and comparable computational requirements compared to standard operational thinning methodologies.

Full access
Shenjia Ma
,
Chaohui Chen
,
Hongrang He
,
Jie Xiang
,
Shengjie Chen
,
Yi Li
,
Yongqiang Jiang
,
Dan Wu
, and
Hao Luo

Abstract

In this study, a convection-allowing ensemble prediction experiment was conducted on a strong convective weather process, based on the local breeding growth mode (LBGM) method proposed according to the strongly local nature of the convective-scale weather system. A comparative analysis of the evolution characteristics of the initial perturbation was also performed, considering the results from the traditional breeding growth mode (BGM) method, to enhance understanding and application of this new initial perturbation generation method. The experimental results showed that LBGM results in the perturbation distribution exhibiting characteristics more evident of flow dependence, and an initial perturbation with greater definite kinetic significance was derived. Information entropy theory could well measure the amount of information contained in the perturbation distribution, indicating that the innovative initial perturbation generation method can increase the amount of local information associated with the initial perturbation. With regard to the physical perturbation quantities, the LBGM method can improve the dispersion of the ensemble prediction system, thereby solving the problem of insufficient ensemble spread of prediction systems obtained by the traditional BGM method. Simultaneously, the root-mean-square error of the prediction can be further reduced, and the predicted precipitation distribution is closer to the observed precipitation, thereby improving the prediction effect of the convection-allowing ensemble prediction. The LBGM method has advantages compared to the traditional method and provides a new theoretical basis for further development of initial perturbation technologies for convection-allowing ensemble prediction.

Full access