scholarly journals Improved ENSO Prediction by Singular Vector Analysis in a Hybrid Coupled Model

2009 ◽  
Vol 26 (3) ◽  
pp. 626-634
Author(s):  
Xiaobing Zhou ◽  
Youmin Tang ◽  
Yanjie Cheng ◽  
Ziwang Deng
Keyword(s):  
Prediction Model ◽  
Singular Vector ◽  
Southern Oscillation ◽  
Coupled Model ◽  
Optimal Growth ◽  
Growth Patterns ◽  
Prediction Skill ◽  
Vector Analysis ◽  
Forecast Errors ◽  
Enso Prediction

Abstract In this study, a method based on singular vector analysis is proposed to improve El Niño–Southern Oscillation (ENSO) predictions. Its essential idea is that the initial errors are projected onto their optimal growth patterns, which are propagated by the tangent linear model (TLM) of the original prediction model. The forecast errors at a given lead time of predictions are obtained, and then removed from the raw predictions. This method is applied to a realistic ENSO prediction model for improving prediction skill for the period from 1980 to 1999. This correction method considerably improves the ENSO prediction skill, compared with the original predictions without the correction.

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 ENSO Prediction in Project Minerva: Sensitivity to Atmospheric Horizontal Resolution and Ensemble Size

2015 ◽  
Vol 28 (5) ◽  
pp. 2080-2095 ◽  
Author(s):  
Jieshun Zhu ◽  
Bohua Huang ◽  
Ben Cash ◽  
James L. Kinter ◽  
Julia Manganello ◽  
...  
Keyword(s):  
Climate Models ◽  
Southern Oscillation ◽  
Horizontal Resolution ◽  
Prediction Skill ◽  
Ensemble Size ◽  
Enso Prediction ◽  
Significant Difference ◽  
Prediction Reliability ◽  
The Impact ◽  
Probabilistic Metrics

Abstract This study examines El Niño–Southern Oscillation (ENSO) prediction in Project Minerva, a recent collaboration between the Center for Ocean–Land–Atmosphere Studies (COLA) and the European Centre for Medium-Range Weather Forecasts (ECMWF). The focus is primarily on the impact of the atmospheric horizontal resolution on ENSO prediction, but the effect from different ensemble sizes is also discussed. Particularly, three sets of 7-month hindcasts performed with ECMWF prediction system are compared, starting from 1 May (1 November) during 1982–2011 (1982–2010): spectral T319 atmospheric resolution with 15 ensembles, spectral T639 with 15 ensembles, and spectral T319 with 51 ensembles. The analysis herein shows that simply increasing either ensemble size from 15 to 51 or atmospheric horizontal resolution from T319 to T639 does not necessarily lead to major improvement in the ENSO prediction skill with current climate models. For deterministic prediction skill metrics, the three sets of predictions do not produce a significant difference in either anomaly correlation or root-mean-square error (RMSE). For probabilistic metrics, the increased atmospheric horizontal resolution generates larger ensemble spread, and thus increases the ratio between the intraensemble spread and RMSE. However, there is little change in the categorical distributions of predicted SST anomalies, and consequently there is little difference among the three sets of hindcasts in terms of probabilistic metrics or prediction reliability.

Skillful prediction of the winter North Atlantic Oscillation

2020 ◽  
Author(s):  
Ke Fan
Keyword(s):  
Prediction Model ◽  
Surface Temperature ◽  
Sea Surface Temperature ◽  
North Atlantic Oscillation ◽  
North Atlantic ◽  
Coupled Model ◽  
Sea Surface ◽  
Prediction Skill ◽  
Statistical Prediction ◽  
Coupled Models

<p>The winter North Atlantic oscillation (NAO), is a crucial part of our understanding of Eurasian and Atlantic climate variability and predictability. However, both the statistical forecast model and the coupled model showed the limited forecasting skill for the winter NAO. In this study, we developed effective prediction schemes based on the interannual increment prediction method and verified their performance based on the climate hindcasts of the coupled ocean–atmosphere climate models（DEMETER, ENSEMBLES，CFSV2）. This approach utilizes the year-to-year increment of a variable (i.e. a difference in a variable between the current year and the previous year, e.g. DY of a variable) as the predictand rather than the anomaly of the variable. The results demonstrate that the new schemes can generally improve prediction skill of the winter NAO compared to the raw coupled model’s output（DEMETER, ENSEMBLES，CFSV2）. Also, the new schemes show higher skill in prediction of abnormal NAO cases than the climatological prediction. Scheme-I uses just the NAO in the form of year-to-year increments as a predictor that is derived from the direct outputs of the models. Scheme-II is a hybrid prediction model that contains two predictors: the NAO derived from the coupled models, and the observed preceding autumn Atlantic sea surface temperature in the form of year-to-year increments. Scheme-II shows an even better prediction skill of the winter NAO than Scheme-I. Besides, a new statistical forecast scheme was also developed using observed North Atlantic sea surface temperature and Eurasian snow cover in the preceding autumn to predict the upcoming winter NAO. The statistical prediction model showed high predictive skill in reproducing the interannual and interdecadal variability of NAO in boreal winter.</p>

scholarly journals Improving EnKF-Based Initialization for ENSO Prediction Using a Hybrid Adaptive Method

2016 ◽  
Vol 29 (20) ◽  
pp. 7365-7381 ◽  
Author(s):  
Xinrong Wu
Keyword(s):  
Southern Oscillation ◽  
Hybrid Approach ◽  
Computational Cost ◽  
Coupled Model ◽  
Adaptive Method ◽  
Sampling Errors ◽  
Enso Prediction ◽  
Probabilistic Forecasts ◽  
Intermediate Coupled Model ◽  
Sst Anomaly

Abstract Probabilistic forecasts, which are usually initialized by an ensemble Kalman filter (EnKF), are known to be better than deterministic (or one member) forecasts for the El Niño–Southern Oscillation (ENSO) phenomenon. Because of sampling errors caused by a finite ensemble and the errors related to model biases associated with the physical parameterizations, dynamic core, model resolution, and so on, a state-of-the-art inflation method is commonly used in the standard EnKF to increase the prior variance so as to avoid filter divergence. However, the optimal inflation factor is almost prohibitive in reality because of vast computational cost. An adaptive EnKF and multigrid analysis hybrid approach without inflation is presented to compensate for the abovementioned limitations of the standard EnKF. In this study, the adaptive approach is applied to an intermediate coupled model for ENSO prediction. Gridded observations of daily-mean sea surface temperature (SST) anomalies from the Advanced Very High Resolution Radiometer (AVHRR) during January 1982–December 2012 are assimilated into the model to initialize a 2-yr ENSO hindcast. Results show that compared to the standard EnKF that uses multiplicative variance inflation, the adaptive method can reduce analysis errors by 63% for both the daily SST anomaly and the Niño-1+2 SST anomaly. The prediction skill of Niño-1+2 SST anomaly is consistently enhanced, especially for phase forecast. For SST anomaly forecasting, the advantage of the adaptive method mainly occurs in the eastern equatorial Pacific and the northern boundary of the intermediate coupled model.

scholarly journals Sources of Subseasonal Predictability over CONUS during Boreal Summer

2021 ◽  
pp. 1-52
Author(s):  
V. Krishnamurthy ◽  
Jessica Meixner ◽  
Lydia Stefanova ◽  
Jiande Wang ◽  
Denise Worthen ◽  
...  
Keyword(s):  
Time Scale ◽  
Southern Oscillation ◽  
Intraseasonal Oscillation ◽  
Coupled Model ◽  
Warming Trend ◽  
Adaptive Method ◽  
Horizontal Wind ◽  
Boreal Summer ◽  
Forecast Errors ◽  
Modes Of Variability

AbstractThe predictability of the Unified Forecast System (UFS) Coupled Model Prototype 2 developed by the National Centers for Environmental Prediction is assessed for the boreal summer over the continental United States (CONUS). The retrospective forecasts of low-level horizontal wind, precipitation and 2m temperature for 2011–2017 are examined to determine the predictability at subseasonal time scale. Using a data-adaptive method, the leading modes of variability are obtained and identified to be related to El Niño-Southern Oscillation (ENSO), intraseasonal oscillation (ISO) and warming trend. In a new approach, the sources of enhanced predictability are identified by examining the forecast errors and correlations in the weekly averages of the leading modes of variability. During the boreal summer, the ISO followed by the trend in UFS are found to provide better predictability in weeks 1–4 compared to the ENSO mode and the total anomaly. The western CONUS seems to have better predictability on weekly time scale in all the three modes.

scholarly journals How Much of Monthly Subsurface Temperature Variability in the Equatorial Pacific Can Be Recovered by the Specification of Sea Surface Temperatures?

2014 ◽  
Vol 27 (4) ◽  
pp. 1559-1577 ◽  
Author(s):  
Arun Kumar ◽  
Hui Wang ◽  
Yan Xue ◽  
Wanqiu Wang
Keyword(s):  
Initial Conditions ◽  
Southern Oscillation ◽  
Coupled Model ◽  
Temperature Variability ◽  
Sea Surface ◽  
Model Integration ◽  
Ocean Temperature ◽  
Enso Prediction ◽  
Subsurface Ocean ◽  
Ocean Observing

Abstract The focus of the analysis is to investigate the question to what extent the specification of sea surface temperature (SST) in coupled model integration can impart realistic evolution of subsurface ocean temperature in the equatorial tropical Pacific. In the context of El Niño–Southern Oscillation (ENSO) prediction, the analysis is of importance from two aspects: such a system can be considered as a simple coupled ocean data assimilation system that can provide ocean initial conditions; and what additional components of the ocean observing system may be crucial for skillful ENSO prediction. The results indicate that coupled model integration where SST is continuously nudged toward the observed state can generate a realistic evolution of subsurface ocean temperature. The evolution of slow variability related to ENSO, in particular, has a good resemblance against the observational counterpart. The realism of subsurface ocean temperature variability is highest near the date line and least in the far eastern Pacific where the thermocline is shallowest. The results are also discussed in the context of ocean observing system requirements for ENSO prediction.

scholarly journals Parameter Optimization for Real-World ENSO Forecast in an Intermediate Coupled Model

2019 ◽  
Vol 147 (5) ◽  
pp. 1429-1445 ◽  
Author(s):  
Yuchu Zhao ◽  
Zhengyu Liu ◽  
Fei Zheng ◽  
Yishuai Jin
Keyword(s):  
Data Assimilation ◽  
Parameter Optimization ◽  
Real World ◽  
Climate Models ◽  
Coupled Model ◽  
Prediction Skill ◽  
Model Parameters ◽  
Real World Data ◽  
The Real ◽  
Advection Term

Abstract We performed parameter estimation in the Zebiak–Cane model for the real-world scenario using the approach of ensemble Kalman filter (EnKF) data assimilation and the observational data of sea surface temperature and wind stress analyses. With real-world data assimilation in the coupled model, our study shows that model parameters converge toward stable values. Furthermore, the new parameters improve the real-world ENSO prediction skill, with the skill improved most by the parameter of the highest climate sensitivity (gam2), which controls the strength of anomalous upwelling advection term in the SST equation. The improved prediction skill is found to be contributed mainly by the improvement in the model dynamics, and second by the improvement in the initial field. Finally, geographic-dependent parameter optimization further improves the prediction skill across all the regions. Our study suggests that parameter optimization using ensemble data assimilation may provide an effective strategy to improve climate models and their real-world climate predictions in the future.

scholarly journals Deep learning for bias correction of MJO prediction

2021 ◽  
Vol 12 (1) ◽  
Author(s):  
H. Kim ◽  
Y. G. Ham ◽  
Y. S. Joo ◽  
S. W. Son
Keyword(s):  
Deep Learning ◽  
Bias Correction ◽  
Numerical Models ◽  
Weather Prediction ◽  
Primary Source ◽  
Correction Method ◽  
Prediction Skill ◽  
Convective System ◽  
Forecast Errors ◽  
Potential Predictability

AbstractProducing accurate weather prediction beyond two weeks is an urgent challenge due to its ever-increasing socioeconomic value. The Madden-Julian Oscillation (MJO), a planetary-scale tropical convective system, serves as a primary source of global subseasonal (i.e., targeting three to four weeks) predictability. During the past decades, operational forecasting systems have improved substantially, while the MJO prediction skill has not yet reached its potential predictability, partly due to the systematic errors caused by imperfect numerical models. Here, to improve the MJO prediction skill, we blend the state-of-the-art dynamical forecasts and observations with a Deep Learning bias correction method. With Deep Learning bias correction, multi-model forecast errors in MJO amplitude and phase averaged over four weeks are significantly reduced by about 90% and 77%, respectively. Most models show the greatest improvement for MJO events starting from the Indian Ocean and crossing the Maritime Continent.

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