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Insights into Convective-scale Predictability in East China: error growth Dynamics and Associated Impact on Precipitation of Warm-Season Convective Events

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Advances in Atmospheric Sciences 2020年第8期37卷 893-911页

作者： Xiaoran ZHUANG Jinzhong MIN Liu ZHANG Shizhang WANG Naigeng WU Haonan ZHU Collaborative Innovation Center on Forecast and Evaluation of Meteorological Disasters Key Laboratory of Meteorological Disaster of EducationNanjing University of Information Science and TechnologyNanjing 210044China Jiangsu Meteorological Observatory Jiangsu Meteorological BureauNanjing 210008China Key Laboratory of Regional Numerical Weather Prediction Institute of Tropical and Marine MeteorologyChina Meteorological AdministrationGuangzhou 210044China Chongqing Climate Center Chongqing Meteorological BureauChongqing 401120China

This study investigated the regime-dependent predictability using convective-scale ensemble forecasts initialized with different initial condition perturbations in the Yangtze and Huai River basin(YHRB)of East *** scale-dependent error growth(ensemble variability)and associated impact on precipitation forecasts(precipitation uncertainties)were quantitatively explored for 13 warm-season convective events that were categorized in terms of strong forcing and weak *** forecast error growth in the strong-forcing regime shows a stepwise increase with increasing spatial scale,while the error growth shows a larger temporal variability with an afternoon peak appearing at smaller scales under weak *** leads to the dissimilarity of precipitation uncertainty and shows a strong correlation between error growth and precipitation across spatial *** lateral boundary condition errors exert a quasi-linear increase on error growth with time at the larger scale,suggesting that the large-scale flow could govern the magnitude of error growth and associated precipitation uncertainties,especially for the strong-forcing *** comparisons between scale-based initial error sensitivity experiments show evident scale interaction including upscale transfer of small-scale errors and downscale cascade of larger-scale ***,small-scale errors are found to be more sensitive in the weak-forcing regime than those under strong ***,larger-scale initial errors are responsible for the error growth after 4 h and produce the precipitation uncertainties at the meso-β-***,these results can be used to explain underdispersion issues in convective-scale ensemble forecasts and provide feedback for ensemble design over the YHRB.

关键词： convective-scale predictability error growth strong forcing weak forcing scale interaction

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Mesoscale Predictability of Moist Baroclinic Waves: Variable and Scale-dependent error growth

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Advances in Atmospheric Sciences 2014年第5期31卷 995-1008页

作者： BEI Naifang Fuqing ZHANG School of Human Settlements and Civil Engineering Xi'an Jiaotong University Department of Meteorology the Pennsylvanian State University University ParkPennsylvania 16802 USA

This study seeks to quantify the predictability of different forecast variables at various scales through spectral analysis of the difference between perturbed and unperturbed cloud-permitting simulations of idealized moist baroclinic waves amplify- ing in a conditionally unstable atmosphere. The error growth of a forecast variable is found to be strongly associated with its reference-state （unperturbed） power spectrum and slope, which differ significantly from variable to variable. The shallower the reference state spectrum, the more spectral energy resides at smaller scales, and thus the less predictable the variable since the error grows faster at smaller scales before it saturates. In general, the variables with more small-scale components （such as vertical velocity） are less predictable, and vice versa （such as pressure）. In higher-resolution simulations in which more rigorous small-scale instabilities become better resolved, the error grows faster at smaller scales and spreads to larger scales more quickly before the error saturates at those small scales during the first few hours of the forecast. Based on the reference power spectrum, an index on the degree of lack （or loss） of predictability （LPI） is further defined to quantify the predictive time scale of each forecast variable. Future studies are needed to investigate the scale- and variable-dependent predictability under different background reference flows, including real case studies through ensemble experiments.

关键词： predictability baroclinic waves error growth mesoscale

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Is Model Parameter error Related to a Significant Spring Predictability Barrier for El Nio events? Results from a Theoretical Model

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Advances in Atmospheric Sciences 2010年第5期27卷 1003-1013页

作者：段晚锁张蕊 State Key Laboratory of Numerical Modelling for Atmospheric Sciences and Geophysical Fluid Dynamics Institute of Atmospheric Physics Chinese Academy of Sciences Graduate University of the Chinese Academy of Science

Within a theoretical ENSO model, the authors investigated whether or not the errors superimposed on model parameters could cause a significant ＂spring predictability barrier＂（SPB） for El Nio events. First, sensitivity experiments were respectively performed to the air-sea coupling parameter, α and the thermocline effect coefficient μ. The results showed that the uncertainties superimposed on each of the two parameters did not exhibit an obvious season-dependent evolution; furthermore, the uncertainties caused a very small prediction error and consequently failed to yield a significant SPB. Subsequently, the conditional nonlinear optimal perturbation （CNOP） approach was used to study the effect of the optimal mode （CNOP-P） of the uncertainties of the two parameters on the SPB and to demonstrate that the CNOP-P errors neither presented a unified season-dependent evolution for different El Nio events nor caused a large prediction error, and therefore did not cause a significant SPB. The parameter errors played only a trivial role in yielding a significant SPB. To further validate this conclusion, the authors investigated the effect of the optimal combined mode （i.e. CNOP error） of initial and model errors on SPB. The results illustrated that the CNOP errors tended to have a significant season-dependent evolution, with the largest error growth rate in the spring, and yielded a large prediction error, inducing a significant SPB. The inference, therefore, is that initial errors, rather than model parameter errors, may be the dominant source of uncertainties that cause a significant SPB for El Nio events. These results indicate that the ability to forecast ENSO could be greatly increased by improving the initialization of the forecast model.

关键词： ENSO predictability optimal perturbation error growth model parameters

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Which Features of the SST Forcing error Most Likely Disturb the Simulated Intensity of Tropical Cyclones?

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Advances in Atmospheric Sciences 2021年第4期38卷 581-602页

作者： Jiawei YAO Wansuo DUAN Xiaohao QIN State Key Laboratory of Numerical Modeling for Atmospheric Sciences and Geophysical Fluid Dynamics(LASG) Institute of Atmospheric PhysicsChinese Academy of SciencesBeijing 100029China University of Chinese Academy of Sciences Beijing 100049China

Among all of the sources of tropical cyclone(TC) intensity forecast errors, the uncertainty of sea surface temperature(SST) has been shown to play a significant role. In the present study, we determine the SST forcing error that causes the largest simulation error of TC intensity during the entire simulation period by using the WRF model with time-dependent SST forcing. The SST forcing error is represented through the application of a nonlinear forcing singular vector(NFSV)structure. For the selected 12 TC cases, the NFSV-type SST forcing errors have a nearly coherent structure with positive(or negative) SST anomalies located along the track of TCs but are especially concentrated in a particular region. This particular region tends to occur during the specific period of the TCs life cycle when the TCs present relatively strong intensity, but are still intensifying just prior to the mature phase, especially within a TC state exhibiting a strong secondary circulation and very high inertial stability. The SST forcing errors located along the TC track during this time period are verified to have the strongest disturbing effect on TC intensity simulation. Physically, the strong inertial stability of TCs during this time period induces a strong response of the secondary circulation from diabatic heating errors induced by the SST forcing error. Consequently, this significantly influences the subsidence within the warm core in the eye region, which,in turn, leads to significant errors in TC intensity. This physical mechanism explains the formation of NSFV-type SST forcing errors. According to the sensitivity of the NFSV-type SST forcing errors, if one increases the density of SST observations along the TC track and assimilates them to the SST forcing field, the skill of TC intensity simulation generated by the WRF model could be greatly improved. However, this adjustment is most advantageous in improving simulation skill during the time period when TCs become strong but are

关键词： tropical cyclone sea surface temperature sensitivity error growth inertial stability

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Impact of SST Anomaly Events over the Kuroshio-Oyashio Extension on the ＂Summer Prediction Barrier＂

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Advances in Atmospheric Sciences 2018年第4期35卷 397-409页

作者： Yujie WU Wansuo DUAN Laboratory for Climate Studies National Climate Center China Meteorological Administration Beijing 100081 China State Key Laboratory of Numerical Modeling for Atmospheric Sciences and Geophysical Fluid Dynamics (LASG) Institute of Atmospheric Physics Chinese Academy of Sciences Beijing 100029 China CMA-NJU Joint Laboratory for Climate Prediction Studies Nanjing University Nanjing 210023 China University of Chinese Academy of Sciences Beijing 100049 China

The ＂summer prediction barrier＂（SPB） of SST anomalies （SSTA） over the Kuroshio--Oyashio Extension （KOE） refers to the phenomenon that prediction errors of KOE-SSTA tend to increase rapidly during boreal summer, resulting in large prediction uncertainties. The fast error growth associated with the SPB occurs in the mature-to-decaying transition phase, which is usually during the August-September-October （ASO） season, of the KOE-SSTA events to be predicted. Thus, the role of KOE-SSTA evolutionary characteristics in the transition phase in inducing the SPB is explored by performing perfect model predictability experiments in a coupled model, indicating that the SSTA events with larger mature-to-decaying transi- tion rates （Category-l） favor a greater possibility of yielding a more significant SPB than those events with smaller transition rates （Category-2）. The KOE-SSTA events in Category-1 tend to have more significant anomalous Ekman pumping in their transition phase, resulting in larger prediction errors of vertical oceanic temperature advection associated with the SSTA events. Consequently, Category-1 events possess faster error growth and larger prediction errors. In addition, the anomalous Ekman upwelling （downwelling） in the ASO season also causes SSTA cooling （warming）, accelerating the transition rates of warm （cold） KOE-SSTA events. Therefore, the SSTA transition rate and error growth rate are both related with the anomalous Ekman pumping of the SSTA events to be predicted in their transition phase. This may explain why the SSTA events transferring more rapidly from the mature to decaying phase tend to have a greater possibility of yielding a more significant SPB.

关键词： Kuroshio-Oyashio Extension SST summer prediction barrier error growth

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Dynamic Analogue Initialization for Ensemble Forecasting

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Advances in Atmospheric Sciences 2013年第5期30卷 1406-1420页

作者：李珊容新尧刘赟刘征宇 Klaus FRAEDRICH Department of Atmospheric and Oceanic Sciences School of Physics Peking University Chinese Academy of Meteorological Sciences Center for Climatic Research and Department of Atmospheric and Oceanic Sciences University of Wisconsin-Madison Max Planck Institute for Meteorology

This paper introduces a new approach for the initialization of ensemble numerical forecasting： Dynamic Analogue Initialization （DAI）. DAI assumes that the best model state trajectories for the past provide the initial conditions for the best forecasts in the future. As such, DAI performs the ensemble forecast using the best analogues from a full size ensemble. As a pilot study, the Lorenz63 and Lorenz96 models were used to test DAI＇s effectiveness independently. Results showed that DAI can improve the forecast significantly. Especially in lower-dimensional systems, DAI can reduce the forecast RMSE by ~50% compared to the Monte Carlo forecast （MC）. This improvement is because DAI is able to recognize the direction of the analysis error through the embedding process and therefore selects those good trajectories with reduced initial error. Meanwhile, a potential improvement of DAI is also proposed, and that is to find the optimal range of embedding time based on the error＇s growing speed.

关键词： initialization ensemble forecast analogue error growth

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Mesoscale Predictability of Mei-yu Heavy Rainfall

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Advances in Atmospheric Sciences 2009年第3期26卷 438-450页

作者：刘建勇谈哲敏 Key Lab of Mesoscale Severe Weather/MOE and School of Atmospheric SciencesNanjing UniversityNanjing 210093

Recently reported results indicate that small amplitude and small scale initial errors grow rapidly and subsequently contaminate short-term deterministic mesoscale forecasts. This rapid error growth is dependent on not only moist convection but also the flow regime. In this study, the mesoscale predictability and error growth of mei-yu heavy rainfall is investigated by simulating a particular precipitation event along the mei-yu front on 4- 6 July 2003 in eastern China. Due to the multi-scale character of the mei-yu front and scale interactions, the error growth of mei-yu heavy rainfall forecasts is markedly different from that in middle-latitude moist baroclinic systems. The optimal growth of the errors has a relatively wide spectrum, though it gradually migrates with time from small scale to mesoscale. During the whole period of this heavy rainfall event, the error growth has three different stages, which similar to the evolution of 6-hour accumulated precipitation. Multi-step error growth manifests as an increase of the amplitude of errors, the horizontal scale of the errors, or both. The vertical profile of forecast errors in the developing convective instability and the moist physics convective system indicates two peaks, which correspond with inside the mei-yu front, and related to moist The error growth for the mei-yu heavy rainfall is concentrated convective instability and scale interaction.

关键词： mesoscale predictability error growth scale interaction mei-yu front precipitation

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Role of Parameter errors in the Spring Predictability Barrier for ENSO Events in the Zebiak–Cane Model

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Advances in Atmospheric Sciences 2014年第3期31卷 647-656页

作者： YU Liang MU Mu Yanshan YU Key Laboratory of Ocean Circulation and Waves Institute of Oceanology Chinese Academy of Sciences University of Chinese Academy of Sciences School of Mathematical Sciences Monash University Clayton Victoria

ABSTRACT The impact of both initial and parameter errors on the spring predictability barrier （SPB） is investigated using the Zebiak Cane model （ZC model）. Previous studies have shown that initial errors contribute more to the SPB than parameter errors in the ZC model. Although parameter errors themselves are less important, there is a possibility that nonlinear interactions can occur between the two types of errors, leading to larger prediction errors compared with those induced by initial errors alone. In this case, the impact of parameter errors cannot be overlooked. In the present paper, the optimal combination of these two types of errors [i.e., conditional nonlinear optimal perturbation （CNOP） errors] is calculated to investigate whether this optimal error combination may cause a more notable SPB phenomenon than that caused by initial errors alone. Using the CNOP approach, the CNOP errors and CNOP-I errors （optimal errors when only initial errors are considered） are calculated and then three aspects of error growth are compared：（1） the tendency of the seasonal error growth; （2） the prediction error of the sea surface temperature anomaly; and （3） the pattern of error growth. All three aspects show that the CNOP errors do not cause a more significant SPB than the CNOP-I errors. Therefore, this result suggests that we could improve the prediction of the E1 Nifio during spring by simply focusing on reducing the initial errors in this model.

关键词： ENSO predictability spring predictability barrier initial errors parameter errors error growth

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Analysis Study on Perturbation Energy and Predictability of Heavy Precipitation in South China

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Advances in Atmospheric Sciences 2010年第2期27卷 382-392页

作者：朱本璐林万涛张云 State Key Laboratory of Numerical Modeling for Atmospheric and Geophysical Fluid Dynamics Institute of Atmospheric Physics Chinese Academy of Sciences Graduate University of Chinese Academy of Sciences Institute of Meteorology PLA University of Science and Technology

The AREMv2.3 mesoscale numerical model is used to explore storm processes in South China during the pre-rainy season in 2006 by imposing perturbations on the initial fields of physical variables （temperature, humidity, and wind fields）. Sensitivity experiments are performed to examine the impacts of initial uncertainties on precipitation, on the error growth, and on the predictability of mesoscale precipitation in South China. The primary conclusion is that inherent initial condition uncertainties can significantly limit the predictability of storm. The 24-h accumulated precipitation is most sensitive to temperature perturbations. Larger-amplitude initial uncertainties generally lead to larger perturbation energies than those with smaller amplitude, but these kinds of differences decrease with time monotonically so the mechanism for the growth of perturbation energy is nonlinear. The power spectra of precipitation differences indicate that predictability increases with accumulated time. This also indicates the difficulties faced for short-term, small-scale precipitation forecasting.

关键词： initial perturbation error growth DTE predictability

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The Mesoscale Predictability of a Heavy Precipitation Event

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Acta meteorologica Sinica 2009年第4期23卷 403-412页

作者：翟丹华林永辉 State Key Laboratory of Severe Weather Chinese Academy of Meteorological Sciences Chongqing Meteorological Bureau

Using the mesoscale model MM5, the development of initial condition uncertainties at different scales and amplitudes and their influences on the mesoscale predictability of the ＂0185＂ Shanghai heavy precipitation event are investigated. It is found that different initial conditions obtained from different globe model analyses lead to large variations in the simulated location and strength of the heavy precipitation, and the scales and amplitudes of the initial condition perturbations significantly influence the model error growth. The power spectrum evolution of the difference total energy （DTE） between a control simulation and a sensitivity experiment indicates that the error growth saturates after 12 h, which is the predictable time limit of the heavy precipitation event. The power spectrum evolution of the accumulated precipitation difference between the control and sensitivity simulations suggests a loss of the mesoscale predictability for precipitation systems of scales smaller than 300 kin, i.e., the predictable space for the heavy precipitation event is beyond 300 km. The results also show that the initial uncertainties at larger scales and amplitudes generally result in larger forecast divergence than the uncertainties at smaller scales and amplitudes. The predictable forecasting time and space can be expanded （e.g., from 12 to 15 h, and from beyond 300 kin to beyond 200 km） under properly prescribed initial perturbations at smaller scales and amplitudes.

关键词： mesoscale predictability error growth heavy precipitation

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