scholarly journals Comparison of Information-Based Measures of Forecast Uncertainty in Ensemble ENSO Prediction

2008 ◽  
Vol 21 (2) ◽  
pp. 230-247 ◽  
Author(s):  
Youmin Tang ◽  
Richard Kleeman ◽  
Andrew M. Moore
Keyword(s):  
Southern Oscillation ◽  
A Priori ◽  
Prediction Skill ◽  
Coupled Models ◽  
Potential Predictability ◽  
Model Skill ◽  
Enso Predictability ◽  
Predictive Skill ◽  
Triangular Relationship ◽  
The Relationship

Abstract In this study, ensemble predictions of the El Niño–Southern Oscillation (ENSO) were conducted for the period 1981–98 using two hybrid coupled models. Several recently proposed information-based measures of predictability, including relative entropy (R), predictive information (PI), predictive power (PP), and mutual information (MI), were explored in terms of their ability of estimating a priori the predictive skill of the ENSO ensemble predictions. The emphasis was put on examining the relationship between the measures of predictability that do not use observations, and the model prediction skills of correlation and root-mean-square error (RMSE) that make use of observations. The relationship identified here offers a practical means of estimating the potential predictability and the confidence level of an individual prediction. It was found that the MI is a good indicator of overall skill. When it is large, the prediction system has high prediction skill, whereas small MI often corresponds to a low prediction skill. This suggests that MI is a good indicator of the actual skill of the models. The R and PI have a nearly identical average (over all predictions) as should be the case in theory. Comparing the different information-based measures reveals that R is a better predictor of prediction skill than PI and PP, especially when correlation-based metrics are used to evaluate model skill. A “triangular relationship” emerges between R and the model skill, namely, that when R is large, the prediction is likely to be reliable, whereas when R is small the prediction skill is quite variable. A small R is often accompanied by relatively weak ENSO variability. The possible reasons why R is superior to PI and PP as a measure of ENSO predictability will also be discussed.

scholarly journals Bred Vector and ENSO Predictability in a Hybrid Coupled Model during the Period 1881–2000

2011 ◽  
Vol 24 (1) ◽  
pp. 298-314 ◽  
Author(s):  
Youmin Tang ◽  
Ziwang Deng
Keyword(s):  
Relative Entropy ◽  
Southern Oscillation ◽  
Heat Content ◽  
Coupled Model ◽  
Prediction Skill ◽  
Good Prediction ◽  
Initial State ◽  
Potential Predictability ◽  
Average Structure ◽  
Enso Predictability

Abstract In this study, a breeding analysis was conducted for a hybrid coupled El Niño–Southern Oscillation (ENSO) model that assimilated a historic dataset of sea surface temperature (SST) for the 120 yr between 1881 and 2000. Meanwhile, retrospective ENSO forecasts were performed for the same period. For a given initial state, 15 bred vectors (BVs) of both SST and upper-ocean heat content (HC) were derived. It was found that the average structure of the 15 BVs was insensitive to the initial states and independent of season and ENSO phase. The average structure of the BVs shared many features already seen in both the final patterns of leading singular vectors and the ENSO BVs of other models. However, individual BV patterns were quite different from case to case. The BV rate (the average cumulative growth rate of BVs) varied seasonally, and the maximum value appeared at the time when the model ran through the boreal spring and summer. It was also sensitive to the strength of the ENSO signal (i.e., the stronger ENSO signal, the smaller the BV rate). Furthermore, ENSO predictability was explored using BV analysis. Emphasis was placed on the relationship between BVs, which are able to characterize potential predictability without requiring observations, and actual prediction skills, which make use of real observations. The results showed that the relative entropy, defined using breeding vectors, was a good measure of potential predictability. Large relative entropy often leads to a good prediction skill; however, when the relative entropy was small, the prediction skill seemed much more variable. At decadal/interdecadal scales, the variations in prediction skills correlated with relative entropy.

scholarly journals PNA Predictability at Various Time Scales

2013 ◽  
Vol 26 (22) ◽  
pp. 9090-9114 ◽  
Author(s):  
Waqar Younas ◽  
Youmin Tang
Keyword(s):  
Time Scales ◽  
Initial Conditions ◽  
Prediction Skill ◽  
Multiple Model ◽  
Coupled Models ◽  
Potential Predictability ◽  
Effective Measure ◽  
Dispersion Component ◽  
Significant Difference ◽  
Better Than

Abstract In this study, the predictability of the Pacific–North American (PNA) pattern is evaluated on time scales from days to months using state-of-the-art dynamical multiple-model ensembles including the Canadian Historical Forecast Project (HFP2) ensemble, the Development of a European Multimodel Ensemble System for Seasonal-to-Interannual Prediction (DEMETER) ensemble, and the Ensemble-Based Predictions of Climate Changes and their Impacts (ENSEMBLES). Some interesting findings in this study include (i) multiple-model ensemble (MME) skill was better than most of the individual models; (ii) both actual prediction skill and potential predictability increased as the averaging time scale increased from days to months; (iii) there is no significant difference in actual skill between coupled and uncoupled models, in contrast with the potential predictability where coupled models performed better than uncoupled models; (iv) relative entropy (REA) is an effective measure in characterizing the potential predictability of individual prediction, whereas the mutual information (MI) is a reliable indicator of overall prediction skill; and (v) compared with conventional potential predictability measures of the signal-to-noise ratio, the MI-based measures characterized more potential predictability when the ensemble spread varied over initial conditions. Further analysis found that the signal component dominated the dispersion component in REA for PNA potential predictability from days to seasons. Also, the PNA predictability is highly related to the signal of the tropical sea surface temperature (SST), and SST–PNA correlation patterns resemble the typical ENSO structure, suggesting that ENSO is the main source of PNA seasonal predictability. The predictable component analysis (PrCA) of atmospheric variability further confirmed the above conclusion; that is, PNA is one of the most predictable patterns in the climate variability over the Northern Hemisphere, which originates mainly from the ENSO forcing.

scholarly journals ENSO predictability over the past 137 years based on a CESM ensemble prediction system

2021 ◽  
pp. 1-38
Author(s):  
Ting Liu ◽  
Xunshu Song ◽  
Youmin Tang ◽  
Zheqi Shen ◽  
Xiaoxiao Tan
Keyword(s):  
General Circulation ◽  
Southern Oscillation ◽  
Ensemble Prediction ◽  
Coupled Models ◽  
Abrupt Decrease ◽  
Ensemble Prediction System ◽  
Enso Events ◽  
Intermediate Complexity ◽  
Enso Predictability ◽  
Dispersion Component

AbstractIn this study, we conducted an ensemble retrospective prediction from 1881 to 2017 using the Community Earth System Model to evaluate El Niño–Southern Oscillation (ENSO) predictability and its variability on different timescales. To our knowledge, this is the first assessment of ENSO predictability using a long-term ensemble hindcast with a complicated coupled general circulation model (CGCM). Our results indicate that both the dispersion component (DC) and signal component (SC) contribute to the interannual variation of ENSO predictability (measured by relative entropy, RE). In detail, the SC is more important for ENSO events, whereas the DC is of comparable important for short lead times and in weak ENSO signal years. The SC dominates the seasonal variation of ENSO predictability, and an abrupt decrease in signal intensity results in the spring predictability barrier feature of ENSO. At the interdecadal scale, the SC controls the variability of ENSO predictability, while the magnitude of ENSO predictability is determined by the DC. The seasonal and interdecadal variations of ENSO predictability in the CGCM are generally consistent with results based on intermediate complexity and hybrid coupled models. However, the DC has a greater contribution in the CGCM than that in the intermediate complexity and hybrid coupled models.

scholarly journals Benchmarking prediction skill in binary El Niño forecasts

2021 ◽  
Author(s):  
Xinjia Hu ◽  
Jan Eichner ◽  
Eberhard Faust ◽  
Holger Kantz
Keyword(s):  
El Niño ◽  
El Nino ◽  
Southern Oscillation ◽  
Prediction Method ◽  
Surrogate Data ◽  
Prediction Skill ◽  
Lead Times ◽  
Small Data ◽  
Predictive Skill ◽  
Forecast Validation

AbstractReliable El Niño Southern Oscillation (ENSO) prediction at seasonal-to-interannual lead times would be critical for different stakeholders to conduct suitable management. In recent years, new methods combining climate network analysis with El Niño prediction claim that they can predict El Niño up to 1 year in advance by overcoming the spring barrier problem (SPB). Usually this kind of method develops an index representing the relationship between different nodes in El Niño related basins, and the index crossing a certain threshold is taken as the warning of an El Niño event in the next few months. How well the prediction performs should be measured in order to estimate any improvements. However, the amount of El Niño recordings in the available data is limited, therefore it is difficult to validate whether these methods are truly predictive or their success is merely a result of chance. We propose a benchmarking method by surrogate data for a quantitative forecast validation for small data sets. We apply this method to a naïve prediction of El Niño events based on the Oscillation Niño Index (ONI) time series, where we build a data-based prediction scheme using the index series itself as input. In order to assess the network-based El Niño prediction method, we reproduce two different climate network-based forecasts and apply our method to compare the prediction skill of all these. Our benchmark shows that using the ONI itself as input to the forecast does not work for moderate lead times, while at least one of the two climate network-based methods has predictive skill well above chance at lead times of about one year.

scholarly journals Interdecadal Variation of ENSO Predictability in Multiple Models

2008 ◽  
Vol 21 (18) ◽  
pp. 4811-4833 ◽  
Author(s):  
Youmin Tang ◽  
Ziwang Deng ◽  
Xiaobing Zhou ◽  
Yanjie Cheng ◽  
Dake Chen
Keyword(s):  
Interannual Variability ◽  
Southern Oscillation ◽  
Interdecadal Variation ◽  
Interdecadal Variability ◽  
Prediction Skill ◽  
Late Nineteenth Century ◽  
Enso Predictability ◽  
System A ◽  
Late Twentieth ◽  
High Predictability

Abstract In this study, El Niño–Southern Oscillation (ENSO) retrospective forecasts were performed for the 120 yr from 1881 to 2000 using three realistic models that assimilate the historic dataset of sea surface temperature (SST). By examining these retrospective forecasts and corresponding observations, as well as the oceanic analyses from which forecasts were initialized, several important issues related to ENSO predictability have been explored, including its interdecadal variability and the dominant factors that control the interdecadal variability. The prediction skill of the three models showed a very consistent interdecadal variation, with high skill in the late nineteenth century and in the middle–late twentieth century, and low skill during the period from 1900 to 1960. The interdecadal variation in ENSO predictability is in good agreement with that in the signal of interannual variability and in the degree of asymmetry of ENSO system. A good relationship was also identified between the degree of asymmetry and the signal of interannual variability, and the former is highly related to the latter. Generally, the high predictability is attained when ENSO signal strength and the degree of asymmetry are enhanced, and vice versa. The atmospheric noise generally degrades overall prediction skill, especially for the skill of mean square error, but is able to favor some individual prediction cases. The possible reasons why these factors control ENSO predictability were also discussed.

scholarly journals Benchmarking Prediction Skill in Binary El Niño Forecasts

2021 ◽  
Author(s):  
Xinjia Hu ◽  
Jan Eichner ◽  
Eberhard Faust ◽  
Holger Kantz
Keyword(s):  
El Niño ◽  
El Nino ◽  
Southern Oscillation ◽  
Prediction Method ◽  
Surrogate Data ◽  
Prediction Skill ◽  
Lead Times ◽  
Small Data ◽  
Predictive Skill ◽  
Forecast Validation

Abstract Reliable El Niño Southern Oscillation (ENSO) prediction at seasonal-to-interannual lead times would be critical for different stakeholders to conduct suitable management. In recent years, new methods combining climate network analysis with El Niño prediction claim that they can predict El Niño up to 1 year in advance by overcoming the spring barrier problem (SPB). Usually this kind of method develops an index representing the relationship between different nodes in El Niño related basins, and the index crossing a certain threshold is taken as the warning of an El Niño event in the next few months. How well the prediction performs should be measured in order to estimate any improvements. However, the amount of El Niño recordings in the available data is limited, therefore it is difficult to validate whether these methods are truly predictive or their success is merely a result of chance. We propose a benchmarking method by new surrogate data for a quantitative forecast validation for small data sets. We apply this method to a naïve prediction of El Niño events based on the Oscillation Niño Index (ONI) time series, where we build a data-based prediction scheme using the index series itself as input. In order to assess the network-based El Niño prediction method, we reproduce two different climate network-based forecasts and apply our method to compare the prediction skill of all these. Our benchmark shows that using the ONI itself as input to the forecast does not work for moderate lead times, while at least one of the two climate network-based methods has predictive skill well above 30 chance at lead times of about one year.

scholarly journals Optimal localized observations for advancing beyond the ENSO predictability barrier

2013 ◽  
Vol 20 (2) ◽  
pp. 221-230 ◽  
Author(s):  
W. Kramer ◽  
H. A. Dijkstra
Keyword(s):  
Predictive Power ◽  
Southern Oscillation ◽  
Sea Surface ◽  
Thermocline Depth ◽  
Sequential Importance Sampling ◽  
Ensemble Spread ◽  
Enso Predictability ◽  
Spring Predictability Barrier ◽  
Predictive Skill ◽  
Optimal Observation

Abstract. The existing 20-member ensemble of 50 yr ECHAM5/MPI-OM simulations provides a reasonably realistic Monte Carlo sample of the El Niño–Southern Oscillation (ENSO). Localized observations of sea surface temperature (SST), zonal wind speed and thermocline depth are assimilated in the ensemble using sequential importance sampling to adjust the weight of ensemble members. We determine optimal observation locations, for which assimilation yields the minimal ensemble spread. Efficient observation locations for SST lie in the ENSO pattern, with the optimum located in the eastern and western Pacific for minimizing uncertainty in the NINO3 and NINO4 index, respectively. After the assimilation of the observations, we investigate how the weighted ensemble performs as a nine-month probabilistic forecast of the ENSO. Here, we focus on the spring predictability barrier with observation in the January–March (March–May) period and assess the remaining predictive power in June (August) for NINO3 (NINO4). For the ECHAM5/MPI-OM ensemble, this yields that SST observations around 110° W and 140° W provide the best predictive skill for the NINO3 and NINO4 index, respectively. Forecasts can be improved by additionally measuring the thermocline depth at 150° W.

scholarly journals The relationship among probabilistic, deterministic and potential skills in predicting the ENSO for the past 161 years

2019 ◽  
Vol 53 (11) ◽  
pp. 6947-6960
Author(s):  
Ting Liu ◽  
Youmin Tang ◽  
Dejian Yang ◽  
Yanjie Cheng ◽  
Xunshu Song ◽  
...  
Keyword(s):  
Theoretical Perspective ◽  
Prediction Skill ◽  
Probabilistic Prediction ◽  
Model Assumption ◽  
Potential Predictability ◽  
The Past ◽  
Perfect Model ◽  
The Relationship ◽  
Theoretical Analyses

Abstract Here, we explored in depth the relationship among the deterministic prediction skill, the probabilistic prediction skill and the potential predictability. This was achieved by theoretical analyses and, in particular, by an analysis of long-term ensemble ENSO hindcast over 161 years from 1856 to 2016. First, a nonlinear monotonic relationship between the deterministic prediction skill and the probabilistic prediction skill, derived by theoretical analysis, was examined and validated using the ensemble hindcast. Further, the co-variability between the potential predictability and the deterministic prediction skill was explored in both perfect model assumption and actual model scenario. On these bases, we investigated the relationship between the potential predictability and probabilistic prediction skill from both the practice of ENSO forecast and theoretical perspective. The results of the study indicate that there are nonlinear monotonic relationships among these three kinds of measures. The potential predictability is considered to be a good indicator for the actual prediction skill in terms of both the deterministic measures and the probabilistic framework. The relationships identified here exhibit considerable significant practical sense to conduct predictability researches, which provide an inexpensive and moderate approach for inquiring prediction uncertainties without the requirement of costly ensemble experiments.

Effects of a Priori Liking on the Elicitation of Mimicry

2010 ◽  
Vol 57 (6) ◽  
pp. 412-418 ◽  
Author(s):  
Mariëlle Stel ◽  
Rick B. van Baaren ◽  
Jim Blascovich ◽  
Eric van Dijk ◽  
Cade McCall ◽  
...  
Keyword(s):  
A Priori ◽  
The Relationship

Mimicry and prosocial feelings are generally thought to be positively related. However, the conditions under which mimicry and liking are related largely remain unspecified. We advance this specification by examining the relationship between mimicry and liking more thoroughly. In two experiments, we manipulated an individual’s a priori liking for another and investigated whether it influenced mimicry of that person. Our experiments demonstrate that in the presence of a reason to like a target, automatic mimicry is increased. However, mimicry did not decrease when disliking a target. These studies provide further evidence of a link between mimicry and liking and extend previous research by showing that a certain level of mimicry even occurs when mimicry behavior is inconsistent with one’s goals or motivations.

The Impact of Competitive Quasi-Market on Service Delivery in Benue State University, Makurdi Nigeria

GIS Business ◽  
2019 ◽  
Vol 14 (4) ◽  
pp. 85-98
Author(s):  
Idoko Peter
Keyword(s):  
Service Delivery ◽  
A Priori ◽  
Least Square ◽  
Secondary Source ◽  
State University ◽  
Ordinary Least Square ◽  
Negative Effect ◽  
The Impact ◽  
The Relationship

This research the impact of competitive quasi market on service delivery in Benue State University, Makurdi Nigeria. Both primary and secondary source of data and information were used for the study and questionnaire was used to extract information from the purposively selected respondents. The population for this study is one hundred and seventy three (173) administrative staff of Benue State University selected at random. The statistical tools employed was the classical ordinary least square (OLS) and the probability value of the estimates was used to tests hypotheses of the study. The result of the study indicates that a positive relationship exist between Competitive quasi marketing in Benue State University, Makurdi Nigeria (CQM) and Transparency in the service delivery (TRSP) and the relationship is statistically significant (p<0.05). Competitive quasi marketing (CQM) has a negative effect on Observe Competence in Benue State University, Makurdi Nigeria (OBCP) and the relationship is not statistically significant (p>0.05). Competitive quasi marketing (CQM) has a positive effect on Innovation in Benue State University, Makurdi Nigeria (INVO) and the relationship is statistically significant (p<0.05) and in line with a priori expectation. This means that a unit increases in Competitive quasi marketing (CQM) will result to a corresponding increase in innovation in Benue State University, Makurdi Nigeria (INVO) by a margin of 22.5%. It was concluded that government monopoly in the provision of certain types of services has greatly affected the quality of service experience in the institution. It was recommended among others that the stakeholders in the market has to be transparent so that the system will be productive to serve the society effectively

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