Ensemble Construction and Verification of the Probabilistic ENSO Prediction in the LDEO5 Model

2010 ◽  
Vol 23 (20) ◽  
pp. 5476-5497 ◽  
Author(s):  
Yanjie Cheng ◽  
Youmin Tang ◽  
Peter Jackson ◽  
Dake Chen ◽  
Ziwang Deng
Keyword(s):  
High Frequency ◽  
Singular Vector ◽  
Southern Oscillation ◽  
Initial Perturbation ◽  
Probabilistic Prediction ◽  
Construction Methods ◽  
Sea Surface Temperature Anomaly ◽  
Probabilistic Forecasts ◽  
Construction Strategy ◽  
Construction Strategies

Abstract El Niño–Southern Oscillation (ENSO) retrospective ensemble-based probabilistic predictions were performed for the period of 1856–2003 using the Lamont-Doherty Earth Observatory, version 5 (LDEO5), model. To obtain more reliable and skillful ENSO probabilistic predictions, first, four ensemble construction strategies were investigated: (i) the optimal initial perturbation with singular vector of sea surface temperature anomaly (SSTA), (ii) the realistic high-frequency anomalous winds, (iii) the stochastic optimal pattern of anomalous winds, and (iv) a combination of the first and the third strategy. Second, verifications were conducted to examine the reliability and resolution of the probabilistic forecasts provided by the four methods. Results suggest that reliability of ENSO probabilistic forecast is more sensitive to the choice of ensemble construction strategy than the resolution, and a reliable and skillful ENSO probabilistic prediction system may not necessarily have the best deterministic prediction skills. Among these ensemble construction methods, the fourth strategy produces the most reliable and skillful ENSO probabilistic prediction, benefiting from the joint contributions of the stochastic optimal winds and the singular vector of SSTA. In particular, the stochastic optimal winds play an important role in improving the ENSO probabilistic predictability for the LDEO5 model.

scholarly journals Assessing Goodness of Fit for Verifying Probabilistic Forecasts

Forecasting ◽  
2021 ◽  
Vol 3 (4) ◽  
pp. 763-773
Author(s):  
Tae-Ho Kang ◽  
Ashish Sharma ◽  
Lucy Marshall
Keyword(s):  
Goodness Of Fit ◽  
Integral Transformation ◽  
Southern Oscillation ◽  
Probability Distributions ◽  
Ensemble Prediction ◽  
Probabilistic Prediction ◽  
Test Statistic ◽  
Chi Square ◽  
Predicted Probability ◽  
Probabilistic Forecasts

The verification of probabilistic forecasts in hydro-climatology is integral to their development, use, and adoption. We propose here a means of utilizing goodness of fit measures for verifying the reliability of probabilistic forecasts. The difficulty in measuring the goodness of fit for a probabilistic prediction or forecast is that predicted probability distributions for a target variable are not stationary in time, meaning one observation alone exists to quantify goodness of fit for each prediction issued. Therefore, we suggest an additional dissociation that can dissociate target information from the other time variant part—the target to be verified in this study is the alignment of observations to the predicted probability distribution. For this dissociation, the probability integral transformation is used. To measure the goodness of fit for the predicted probability distributions, this study uses the root mean squared deviation metric. If the observations after the dissociation can be assumed to be independent, the mean square deviation metric becomes a chi-square test statistic, which enables statistically testing the hypothesis regarding whether the observations are from the same population as the predicted probability distributions. An illustration of our proposed rationale is provided using the multi-model ensemble prediction for El Niño–Southern Oscillation.

scholarly journals Initial perturbations based on Ensemble Transfrom Kalman Filter with rescaling method for ensemble forecast

2021 ◽  
Author(s):  
Jingzhuo Wang ◽  
Jing Chen ◽  
Hanbin Zhang ◽  
Hua Tian ◽  
Yining Shi
Keyword(s):  
Growth Rate ◽  
Kalman Filter ◽  
Weather Forecasting ◽  
Initial Perturbation ◽  
Ensemble Forecast ◽  
Ensemble Prediction ◽  
Forecast Errors ◽  
Model Uncertainties ◽  
Ensemble Prediction System ◽  
Probabilistic Forecasts

AbstractEnsemble forecast is a method to faithfully describe initial and model uncertainties in a weather forecasting system. Initial uncertainties are much more important than model uncertainties in the short-range numerical prediction. Currently, initial uncertainties are described by Ensemble Transform Kalman Filter (ETKF) initial perturbation method in Global and Regional Assimilation and Prediction Enhanced System-Regional Ensemble Prediction System (GRAPES-REPS). However, an initial perturbation distribution similar to the analysis error cannot be yielded in the ETKF method of the GRAPES-REPS. To improve the method, we introduce a regional rescaling factor into the ETKF method (we call it ETKF_R). We also compare the results between the ETKF and ETKF_R methods and further demonstrate how rescaling can affect the initial perturbation characteristics as well as the ensemble forecast skills. The characteristics of the initial ensemble perturbation improve after applying the ETKF_R method. For example, the initial perturbation structures become more reasonable, the perturbations are better able to explain the forecast errors at short lead times, and the lower kinetic energy spectrum as well as perturbation energy at the initial forecast times can lead to a higher growth rate of themselves. Additionally, the ensemble forecast verification results suggest that the ETKF_R method has a better spread-skill relationship, a faster ensemble spread growth rate and a more reasonable rank histogram distribution than ETKF. Furthermore, the rescaling has only a minor impact on the assessment of the sharpness of probabilistic forecasts. The above results all suggest that ETKF_R can be effectively applied to the operational GRAPES-REPS.

scholarly journals The Nature of the Stochastic Wind Forcing of ENSO

2018 ◽  
Vol 31 (19) ◽  
pp. 8081-8099 ◽  
Author(s):  
Antonietta Capotondi ◽  
Prashant D. Sardeshmukh ◽  
Lucrezia Ricciardulli
Keyword(s):  
El Niño ◽  
High Frequency ◽  
El Nino ◽  
Southern Oscillation ◽  
Low Frequency ◽  
Kelvin Waves ◽  
Wind Forcing ◽  
Alternative View ◽  
Low Frequencies ◽  
Wind Events

El Niño–Southern Oscillation (ENSO) is commonly viewed as a low-frequency tropical mode of coupled atmosphere–ocean variability energized by stochastic wind forcing. Despite many studies, however, the nature of this broadband stochastic forcing and the relative roles of its high- and low-frequency components in ENSO development remain unclear. In one view, the high-frequency forcing associated with the subseasonal Madden–Julian oscillation (MJO) and westerly wind events (WWEs) excites oceanic Kelvin waves leading to ENSO. An alternative view emphasizes the role of the low-frequency stochastic wind components in directly forcing the low-frequency ENSO modes. These apparently distinct roles of the wind forcing are clarified here using a recently released high-resolution wind dataset for 1990–2015. A spectral analysis shows that although the high-frequency winds do excite high-frequency Kelvin waves, they are much weaker than their interannual counterparts and are a minor contributor to ENSO development. The analysis also suggests that WWEs should be viewed more as short-correlation events with a flat spectrum at low frequencies that can efficiently excite ENSO modes than as strictly high-frequency events that would be highly inefficient in this regard. Interestingly, the low-frequency power of the rapid wind forcing is found to be higher during El Niño than La Niña events, suggesting a role also for state-dependent (i.e., multiplicative) noise forcing in ENSO dynamics.

Next Generation CANDU: Conceptual Design for a Short Construction Schedule

2002 ◽  
Author(s):  
Jerry M. Hopwood ◽  
Ian J. W. Love ◽  
Medhat Elgohary ◽  
Neville Fairclough
Keyword(s):  
Conceptual Design ◽  
Critical Path ◽  
Construction Method ◽  
Phase Iii ◽  
Next Generation ◽  
Project Schedule ◽  
Construction Methods ◽  
New Construction ◽  
Construction Strategy ◽  
Construction Schedule

Atomic Energy of Canada Ltd. (AECL) has very successful experience in implementing new construction methods at the Qinshan (Phase III) twin unit CANDU 6 plant in China. This paper examines the construction method that must be implemented during the conceptual design phase of a project if short construction schedules are to be met. A project schedule of 48 months has been developed for the nth unit of NG (Next Generation) CANDU with a 42 month construction period from 1st Concrete to In-Service. An overall construction strategy has been developed involving paralleling project activities that are normally conducted in series. Many parts of the plant will be fabricated as modules and be installed using heavy lift cranes. The Reactor Building (RB), being on the critical path, has been the focus of considerable assessment, looking at alternative ways of applying the construction strategy to this building. A construction method has been chosen which will result in excess of 80% of internal work being completed as modules or as very streamlined traditional construction. This method is being further evaluated as the detailed layout proceeds. Other areas of the plant have been integrated into the schedule and new construction methods are being applied to these so that further modularization and even greater paralleling of activities will be achieved. It is concluded that the optimized construction method is a requirement, which must be implemented through all phases of design to make a 42 month construction schedule a reality. If the construction methods are appropriately chosen, the schedule reductions achieved will make nuclear more competitive.

Conditional Nonlinear Optimal Perturbations of a Two-Dimensional Quasigeostrophic Model

2006 ◽  
Vol 63 (6) ◽  
pp. 1587-1604 ◽  
Author(s):  
Mu Mu ◽  
Zhiyue Zhang
Keyword(s):  
Singular Vector ◽  
Nonlinear Evolution ◽  
Initial Perturbation ◽  
Nonlinear Effects ◽  
Two Dimensional ◽  
Physical Problems ◽  
Maximum Value ◽  
The Difference ◽  
Quasigeostrophic Model ◽  
The Cost

Abstract Conditional nonlinear optimal perturbations (CNOPs) of a two-dimensional quasigeostrophic model are obtained numerically. The CNOP is the initial perturbation whose nonlinear evolution attains the maximum value of the cost function, which is constructed according to the physical problems of interests with physical constraint conditions. The difference between the CNOP and a linear singular vector is compared. The results demonstrate that CNOPs catch the nonlinear effects of the model on the evolutions of the initial perturbations. These results suggest that CNOPs are applicable to the study of predictability and sensitivity analysis when nonlinearity is of importance.

scholarly journals Objective Estimation of the 24-h Probability of Tropical Cyclone Formation

2009 ◽  
Vol 24 (2) ◽  
pp. 456-471 ◽  
Author(s):  
Andrea B. Schumacher ◽  
Mark DeMaria ◽  
John A. Knaff
Keyword(s):  
Tropical Cyclone ◽  
North Pacific ◽  
Input Parameter ◽  
Probabilistic Prediction ◽  
Linear Discriminant ◽  
Tropical Cyclone Formation ◽  
Formation Probability ◽  
Prediction Scheme ◽  
Skill Scores ◽  
Probabilistic Forecasts

Abstract A new product for estimating the 24-h probability of TC formation in individual 5° × 5° subregions of the North Atlantic, eastern North Pacific, and western North Pacific tropical basins is developed. This product uses environmental and convective parameters computed from best-track tropical cyclone (TC) positions, National Centers for Environmental Prediction (NCEP) Global Forecasting System (GFS) analysis fields, and water vapor (∼6.7 μm wavelength) imagery from multiple geostationary satellite platforms. The parameters are used in a two-step algorithm applied to the developmental dataset. First, a screening step removes all data points with environmental conditions highly unfavorable to TC formation. Then, a linear discriminant analysis (LDA) is applied to the screened dataset. A probabilistic prediction scheme for TC formation is developed from the results of the LDA. Coefficients computed by the LDA show that the largest contributors to TC formation probability are climatology, 850-hPa circulation, and distance to an existing TC. The product was evaluated by its Brier and relative operating characteristic skill scores and reliability diagrams. These measures show that the algorithm-generated probabilistic forecasts are skillful with respect to climatology, and that there is relatively good agreement between forecast probabilities and observed frequencies. As such, this prediction scheme has been implemented as an operational product called the National Environmental Satellite, Data, and Information Services (NESDIS) Tropical Cyclone Formation Probability (TCFP) product. The TCFP product updates every 6 h and displays plots of TC formation probability and input parameter values on its Web site. At present, the TCFP provides real-time, objective TC formation guidance used by tropical cyclone forecast offices in the Atlantic, eastern Pacific, and western Pacific basins.

scholarly journals Comparison between Singular Vectors and Breeding Vectors as Initial Perturbations for the ECMWF Ensemble Prediction System

2008 ◽  
Vol 136 (11) ◽  
pp. 4092-4104 ◽  
Author(s):  
Linus Magnusson ◽  
Martin Leutbecher ◽  
Erland Källén
Keyword(s):  
Singular Vector ◽  
Initial Perturbation ◽  
Error Variance ◽  
Ensemble Prediction ◽  
Prediction System ◽  
Ensemble Prediction System ◽  
Perturbation Energy ◽  
Analysis Error ◽  
The Tropics ◽  
Model Environment

Abstract In this paper a study aimed at comparing the perturbation methodologies based on the singular vector ensemble prediction system (SV-EPS) and the breeding vector ensemble prediction system (BV-EPS) in the same model environment is presented. A simple breeding system (simple BV-EPS) as well as one with regional rescaling dependent on an estimate of the analysis error variance (masked BV-EPS) were used. The ECMWF Integrated Forecast System has been used and the three experiments are compared for 46 forecast cases between 1 December 2005 and 15 January 2006. By studying the distribution of the perturbation energy it was possible to see large differences between the experiments initially, but after 48 h the distributions have converged. Using probabilistic scores, these results show that SV-EPS has a somewhat better performance for the Northern Hemisphere compared to BV-EPS. For the Southern Hemisphere masked BV-EPS and SV-EPS yield almost equal results. For the tropics the masked breeding ensemble shows the best performance during the first 6 days. One reason for this is the current setup of the singular vector ensemble at ECMWF yielding in general very low initial perturbation amplitudes in the tropics.

scholarly journals Strength Outlooks for the El Niño–Southern Oscillation

2019 ◽  
Vol 34 (1) ◽  
pp. 165-175 ◽  
Author(s):  
Michelle L. L’Heureux ◽  
Michael K. Tippett ◽  
Ken Takahashi ◽  
Anthony G. Barnston ◽  
Emily J. Becker ◽  
...  
Keyword(s):  
El Niño ◽  
El Nino ◽  
Southern Oscillation ◽  
Added Value ◽  
El Niño Southern Oscillation ◽  
El Nino Southern Oscillation ◽  
New Approach ◽  
The North ◽  
Probabilistic Forecasts ◽  
New Strategy

Abstract Three strategies for creating probabilistic forecast outlooks for El Niño–Southern Oscillation (ENSO) are compared. One is subjective and is currently used by the NOAA/Climate Prediction Center (CPC) to produce official ENSO outlooks. A second is purely objective and is based on the North American Multimodel Ensemble (NMME). A new third strategy is proposed in which the forecaster only provides the expected value of the Niño-3.4 index, and then categorical probabilities are objectively determined based on past skill. The new strategy results in more confident probabilities compared to the subjective approach and higher verification scores, while avoiding the significant forecast busts that sometimes afflict the NMME-based objective approach. The higher verification scores of the new strategy appear to result from the added value that forecasters provide in predicting the mean, combined with more reliable representations of uncertainty, which is difficult to represent because forecasters often assume less confidence than is justified. Moreover, the new approach can produce higher-resolution probabilistic forecasts that include ENSO strength information and that are difficult, if not impossible, for forecasters to produce. To illustrate, a nine-category ENSO outlook based on the new strategy is assessed and found to be skillful. The new approach can be applied to other outlooks where users desire higher-resolution probabilistic forecasts, including the extremes.

scholarly journals A global empirical system for probabilistic seasonal climate prediction

2015 ◽  
Vol 8 (12) ◽  
pp. 3947-3973 ◽  
Author(s):  
J. M. Eden ◽  
G. J. van Oldenborgh ◽  
E. Hawkins ◽  
E. B. Suckling
Keyword(s):  
Air Temperature ◽  
Large Scale ◽  
Southern Oscillation ◽  
Surface Air Temperature ◽  
Climate Change Signal ◽  
Seasonal Forecasts ◽  
Physical Relationship ◽  
Skill Scores ◽  
Probabilistic Forecasts ◽  
Empirical System

Abstract. Preparing for episodes with risks of anomalous weather a month to a year ahead is an important challenge for governments, non-governmental organisations, and private companies and is dependent on the availability of reliable forecasts. The majority of operational seasonal forecasts are made using process-based dynamical models, which are complex, computationally challenging and prone to biases. Empirical forecast approaches built on statistical models to represent physical processes offer an alternative to dynamical systems and can provide either a benchmark for comparison or independent supplementary forecasts. Here, we present a simple empirical system based on multiple linear regression for producing probabilistic forecasts of seasonal surface air temperature and precipitation across the globe. The global CO2-equivalent concentration is taken as the primary predictor; subsequent predictors, including large-scale modes of variability in the climate system and local-scale information, are selected on the basis of their physical relationship with the predictand. The focus given to the climate change signal as a source of skill and the probabilistic nature of the forecasts produced constitute a novel approach to global empirical prediction. Hindcasts for the period 1961–2013 are validated against observations using deterministic (correlation of seasonal means) and probabilistic (continuous rank probability skill scores) metrics. Good skill is found in many regions, particularly for surface air temperature and most notably in much of Europe during the spring and summer seasons. For precipitation, skill is generally limited to regions with known El Niño–Southern Oscillation (ENSO) teleconnections. The system is used in a quasi-operational framework to generate empirical seasonal forecasts on a monthly basis.

scholarly journals Probabilistic prediction of geomagnetic storms and the Kp index

2020 ◽  
Vol 10 ◽  
pp. 36
Author(s):  
Shibaji Chakraborty ◽  
Steven Karl Morley
Keyword(s):  
Geomagnetic Storm ◽  
Space Weather ◽  
Geomagnetic Storms ◽  
Propagation Time ◽  
Probabilistic Prediction ◽  
Storm Activity ◽  
Layered Architecture ◽  
Probabilistic Forecasts ◽  
Uncertainty Estimates ◽  
Forecast Models

Geomagnetic activity is often described using summary indices to summarize the likelihood of space weather impacts, as well as when parameterizing space weather models. The geomagnetic index K p in particular, is widely used for these purposes. Current state-of-the-art forecast models provide deterministic K p predictions using a variety of methods – including empirically-derived functions, physics-based models, and neural networks – but do not provide uncertainty estimates associated with the forecast. This paper provides a sample methodology to generate a 3-hour-ahead K p prediction with uncertainty bounds and from this provide a probabilistic geomagnetic storm forecast. Specifically, we have used a two-layered architecture to separately predict storm (K p  ≥ 5−) and non-storm cases. As solar wind-driven models are limited in their ability to predict the onset of transient-driven activity we also introduce a model variant using solar X-ray flux to assess whether simple models including proxies for solar activity can improve the predictions of geomagnetic storm activity with lead times longer than the L1-to-Earth propagation time. By comparing the performance of these models we show that including operationally-available information about solar irradiance enhances the ability of predictive models to capture the onset of geomagnetic storms and that this can be achieved while also enabling probabilistic forecasts.

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