scholarly journals Using Conditional Nonlinear Optimal Perturbation to Generate Initial Perturbations in ENSO Ensemble Forecasts

2021 ◽  
Author(s):  
Qian Zhou ◽  
Lei Chen ◽  
Wansuo Duan ◽  
Xu Wang ◽  
Ziqing Zu ◽  
...  
Keyword(s):  
Southern Oscillation ◽  
Initial Perturbation ◽  
Skill Score ◽  
Ensemble Forecasting ◽  
Conditional Nonlinear Optimal Perturbation ◽  
Ensemble Forecasts ◽  
Optimal Perturbation ◽  
Fully Nonlinear ◽  
Enso Events ◽  
Forecasting System

AbstractUsing 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.

scholarly journals An Approach to Generating Mutually Independent Initial Perturbations for Ensemble Forecasts: Orthogonal Conditional Nonlinear Optimal Perturbations

2016 ◽  
Vol 73 (3) ◽  
pp. 997-1014 ◽  
Author(s):  
Wansuo Duan ◽  
Zhenhua Huo
Keyword(s):  
Extreme Events ◽  
Initial Perturbation ◽  
Ensemble Forecasting ◽  
Ensemble Forecasts ◽  
Optimal Perturbation ◽  
Initial Analysis ◽  
Prediction Time ◽  
Lorenz 96 ◽  
Analysis Errors ◽  
Mutually Independent

Abstract Conditional nonlinear optimal perturbation (CNOP) is the initial perturbation that satisfies a certain physical constraint and causes the largest nonlinear evolution at prediction time. To yield mutually independent initial perturbations in ensemble forecasts, orthogonal CNOPs are developed. Orthogonal CNOPs are then applied to a Lorenz-96 model to generate initial perturbations for ensemble forecasting, as compared with orthogonal singular vectors (SVs). When the initial analysis errors are fast growing, the ensemble forecasts generated by orthogonal CNOPs of the control forecasts perform much more skillfully. Nevertheless, for slow-growing initial analysis errors, the ensemble forecasts generated by orthogonal SVs achieve higher skill when the ensemble initial perturbations are large, whereas the ensemble forecasts generated by orthogonal CNOPs achieve almost the same forecast skill as those generated by orthogonal SVs when the ensemble initial perturbations are sufficiently small. The initial analysis errors that possess much faster growth behavior are easily influenced by nonlinearity, and extreme events (extreme here refers to strong), because of strong nonlinear instability, may be much more likely to cause fast growth of initial analysis errors. Therefore, the ensemble forecasts generated by orthogonal CNOPs may have higher skill than those generated by orthogonal SVs for extreme events; in particular, the ensemble forecasts generated by orthogonal CNOPs, compared with those generated by orthogonal SVs, require a much smaller number of ensemble members to achieve high skill. Therefore, orthogonal CNOPs may provide another useful technique to generate initial perturbations for ensemble forecasting.

scholarly journals A Parallel Hybrid Intelligence Algorithm for Solving Conditional Nonlinear Optimal Perturbation to Identify Optimal Precursors of North Atlantic Oscillation

2019 ◽  
Author(s):  
Bin Mu ◽  
Jing Li ◽  
Shijin Yuan ◽  
Xiaodan Luo ◽  
Guokun Dai
Keyword(s):  
North Atlantic Oscillation ◽  
North Atlantic ◽  
Hybrid Algorithm ◽  
Message Passing Interface ◽  
Numerical Models ◽  
Initial Perturbation ◽  
Principal Component ◽  
Local Optimum ◽  
Conditional Nonlinear Optimal Perturbation ◽  
Optimal Perturbation

Abstract. The North Atlantic Oscillation (NAO) is the most prominent atmospheric seesaw phenomenon in North Atlantic Ocean. It has a profound influence on the strength of westerly winds as well as the storm tracks in North Atlantic, thus affecting winter climate in Northern Hemisphere. Therefore, it is necessary to investigate the mechanism related with the NAO events. In this paper, conditional nonlinear optimal perturbation (CNOP), which has been widely used in research on the optimal precursor (OPR) of climatic event, is adopted to investigate which kind of initial perturbation is most likely to trigger the NAO anomaly pattern with the Community Earth System Model (CESM). Since CESM does not have an adjoint model, we propose an adjoint-free parallel principal component analysis (PCA) based genetic algorithm (GA) and particle swarm optimization (PSO) hybrid algorithm (PGAPSO) to solve CNOP in such a high dimensional numerical model. The results demonstrate that the OPRs obtained by CNOP trigger the reference flow into typical NAO mode, which provide the theoretical underpinning in observation and prediction. Furthermore, the hybrid algorithm can accelerate convergence and avoid falling into a local optimum. After parallelization with Message Passing Interface (MPI) and Compute Unified Device Architecture (CUDA), the PGAPSO algorithm achieves a speed-up of 40× compared with its serial version. The results as mentioned above indicate that the proposed algorithm can efficiently and effectively acquire CNOP and can also be generalized to other complex numerical models.

Applying Conditional Nonlinear Optimal Perturbation (CNOP) in the ensemble ENSO forecast system

2020 ◽  
Author(s):  
Qian Zhou ◽  
Yunfei Zhang ◽  
Junya Hu ◽  
Wansuo Duan
Keyword(s):  
Ensemble Forecast ◽  
Error Growth ◽  
Forecast System ◽  
Ensemble Forecasts ◽  
Vector Method ◽  
Optimal Perturbation ◽  
Initial Errors ◽  
Prediction Time ◽  
Forecasting System ◽  
Marine Environmental

<p><span>      Considering the effects of initial uncertainty on the ENSO forecast, ensemble forecasts method is applied in the latest version of ENSO forecast system in National Marine Environmental Forecasting Center (NMEFC, China). The currently operational ENSO forecasts system of NMEFC is established based on the CESM model, with initialization and data assimilation. </span></p><p><span>      First, leading five Singular Vectors (SV) are obtained using the climatological SST empirical singular vector method, and a SV based ensemble forecasts system is . However, the SVs can only present the initial errors that have the fasted error growth rates in a linear assumption, while ENSO and its forecasting system both are nonlinear. So, Conditional Nonlinear Optimal Perturbations (CNOP), which is has the largest error growth at the prediction time in a nonlinear scenario, is used to replace the leading SV, while other 4 SVs are kept to construct a CNOP-SV based ensemble forecast system. The hindcasts of ENSO from 1982 to 2017 shows that, the ENSO prediction skills of both SV based and CNOP-SV based ENSO ensemble forecasts are improved when compared with the old forecasting system, moreover, the CNOP-SV based ensemble forecast system has a much larger spread, showing higher prediction skills.</span></p>

scholarly journals The potential of radar-based ensemble forecasts for flash-flood early warning in the southern Swiss Alps

2013 ◽  
Vol 17 (10) ◽  
pp. 3853-3869 ◽  
Author(s):  
K. Liechti ◽  
L. Panziera ◽  
U. Germann ◽  
M. Zappa
Keyword(s):  
Early Warning ◽  
Initial Conditions ◽  
Flash Flood ◽  
Weather Prediction ◽  
Radar Data ◽  
Ensemble Forecasting ◽  
Swiss Alps ◽  
Ensemble Forecasts ◽  
Orographic Rainfall ◽  
Forecasting System

Abstract. This study explores the limits of radar-based forecasting for hydrological runoff prediction. Two novel radar-based ensemble forecasting chains for flash-flood early warning are investigated in three catchments in the southern Swiss Alps and set in relation to deterministic discharge forecasts for the same catchments. The first radar-based ensemble forecasting chain is driven by NORA (Nowcasting of Orographic Rainfall by means of Analogues), an analogue-based heuristic nowcasting system to predict orographic rainfall for the following eight hours. The second ensemble forecasting system evaluated is REAL-C2, where the numerical weather prediction COSMO-2 is initialised with 25 different initial conditions derived from a four-day nowcast with the radar ensemble REAL. Additionally, three deterministic forecasting chains were analysed. The performance of these five flash-flood forecasting systems was analysed for 1389 h between June 2007 and December 2010 for which NORA forecasts were issued, due to the presence of orographic forcing. A clear preference was found for the ensemble approach. Discharge forecasts perform better when forced by NORA and REAL-C2 rather then by deterministic weather radar data. Moreover, it was observed that using an ensemble of initial conditions at the forecast initialisation, as in REAL-C2, significantly improved the forecast skill. These forecasts also perform better then forecasts forced by ensemble rainfall forecasts (NORA) initialised form a single initial condition of the hydrological model. Thus the best results were obtained with the REAL-C2 forecasting chain. However, for regions where REAL cannot be produced, NORA might be an option for forecasting events triggered by orographic precipitation.

scholarly journals Skill of a global forecasting system in seasonal ensemble streamflow prediction

2017 ◽  
Vol 21 (8) ◽  
pp. 4103-4114 ◽  
Author(s):  
Naze Candogan Yossef ◽  
Rens van Beek ◽  
Albrecht Weerts ◽  
Hessel Winsemius ◽  
Marc F. P. Bierkens
Keyword(s):  
Information Service ◽  
Warning System ◽  
Skill Score ◽  
Streamflow Prediction ◽  
Seasonal Forecasts ◽  
Ensemble Forecasts ◽  
Streamflow Forecasts ◽  
Forecasting System ◽  
Set Up ◽  
Global Water

Abstract. In this study we assess the skill of seasonal streamflow forecasts with the global hydrological forecasting system Flood Early Warning System (FEWS)-World, which has been set up within the European Commission 7th Framework Programme Project Global Water Scarcity Information Service (GLOWASIS). FEWS-World incorporates the distributed global hydrological model PCR-GLOBWB (PCRaster Global Water Balance). We produce ensemble forecasts of monthly discharges for 20 large rivers of the world, with lead times of up to 6 months, forcing the system with bias-corrected seasonal meteorological forecast ensembles from the European Centre for Medium-range Weather Forecasts (ECMWF) and with probabilistic meteorological ensembles obtained following the ESP procedure. Here, the ESP ensembles, which contain no actual information on weather, serve as a benchmark to assess the additional skill that may be obtained using ECMWF seasonal forecasts. We use the Brier skill score (BSS) to quantify the skill of the system in forecasting high and low flows, defined as discharges higher than the 75th and lower than the 25th percentiles for a given month, respectively. We determine the theoretical skill by comparing the results against model simulations and the actual skill in comparison to discharge observations. We calculate the ratios of actual-to-theoretical skill in order to quantify the percentage of the potential skill that is achieved. The results suggest that the performance of ECMWF S3 forecasts is close to that of the ESP forecasts. While better meteorological forecasts could potentially lead to an improvement in hydrological forecasts, this cannot be achieved yet using the ECMWF S3 dataset.

scholarly journals A Machine Learning Based Ensemble Forecasting Optimization Algorithm for Preseason Prediction of Atlantic Hurricane Activity

Atmosphere ◽  
2021 ◽  
Vol 12 (4) ◽  
pp. 522
Author(s):  
Xia Sun ◽  
Lian Xie ◽  
Shahil Umeshkumar Shah ◽  
Xipeng Shen
Keyword(s):  
Machine Learning ◽  
Statistical Models ◽  
Skill Score ◽  
Ensemble Member ◽  
Ensemble Forecasting ◽  
Ensemble Forecasts ◽  
Major Hurricane ◽  
Hurricane Activity ◽  
Atlantic Tropical Cyclone ◽  
Response Variables

In this study, nine different statistical models are constructed using different combinations of predictors, including models with and without projected predictors. Multiple machine learning (ML) techniques are employed to optimize the ensemble predictions by selecting the top performing ensemble members and determining the weights for each ensemble member. The ML-Optimized Ensemble (ML-OE) forecasts are evaluated against the Simple-Averaging Ensemble (SAE) forecasts. The results show that for the response variables that are predicted with significant skill by individual ensemble members and SAE, such as Atlantic tropical cyclone counts, the performance of SAE is comparable to the best ML-OE results. However, for response variables that are poorly modeled by individual ensemble members, such as Atlantic and Gulf of Mexico major hurricane counts, ML-OE predictions often show higher skill score than individual model forecasts and the SAE predictions. However, neither SAE nor ML-OE was able to improve the forecasts of the response variables when all models show consistent bias. The results also show that increasing the number of ensemble members does not necessarily lead to better ensemble forecasts. The best ensemble forecasts are from the optimally combined subset of models.

Nonlinear character analysis for bistability in virus–immune dynamics

2019 ◽  
Vol 14 (10) ◽  
pp. 655-662
Author(s):  
Xiaofang Liu ◽  
Guodong Sun
Keyword(s):  
Equilibrium State ◽  
Nonlinear Effect ◽  
Stable Equilibrium ◽  
Initial Perturbation ◽  
Equilibrium States ◽  
Conditional Nonlinear Optimal Perturbation ◽  
Vector Method ◽  
Optimal Perturbation ◽  
Abrupt Changes ◽  
Nonlinear Character

Structured abstract Aim: The nonlinear characters of two linearly stable equilibrium states (virus and immune) for a theoretical virus-immune model are analyzed. Methods: Conditional nonlinear optimal perturbation (CNOP), Lyapunov method and linear singular vector method. Results & conclusion: Two linearly stable equilibrium states (immune-free and immune) with linear methods are nonlinearly unstable using the CNOP method. When the CNOP-type of initial perturbation is used in the model, the immune-free (immune) equilibrium state will be made into the immune (immune-free) equilibrium state. Through computing the variations of nonlinear terms of the model, the nonlinear effect of immune proliferation plays an important role in abrupt changes of the immune-free equilibrium state compared with the linear term. For the immune equilibrium state, the nonlinear effect of viral replication is also an important factor.

scholarly journals Evaluating Tropical Cyclone Forecasts from the NCEP Global Ensemble Forecasting System (GEFS) Reforecast Version 2

2016 ◽  
Vol 31 (3) ◽  
pp. 895-916 ◽  
Author(s):  
Weiwei Li ◽  
Zhuo Wang ◽  
Melinda S. Peng
Keyword(s):  
Tropical Cyclone ◽  
North Pacific ◽  
Large Scale ◽  
Southern Oscillation ◽  
Regional Scale ◽  
Ensemble Forecasting ◽  
Tropical Cyclogenesis ◽  
Easterly Waves ◽  
The North ◽  
Forecasting System

Abstract Tropical cyclone (TC) forecasts from the NCEP Global Ensemble Forecasting System (GEFS) Reforecast version 2 (1985–2012) were evaluated from the climate perspective, with a focus on tropical cyclogenesis. Although the GEFS captures the climatological seasonality of tropical cyclogenesis over different ocean basins reasonably well, large errors exist on the regional scale. As different genesis pathways are dominant over different ocean basins, genesis biases are related to biases in different aspects of the large-scale or synoptic-scale circulations over different basins. The negative genesis biases over the western North Pacific are associated with a weaker-than-observed monsoon trough in the GEFS, the erroneous genesis pattern over the eastern North Pacific is related to a southward displacement of the ITCZ, and the positive genesis biases near the Cape Verde islands and negative biases farther downstream over the Atlantic can be attributed to the hyperactive Africa easterly waves in the GEFS. The interannual and subseasonal variability of TC activity in the reforecasts was also examined to evaluate the potential skill of the GEFS in providing subseasonal and seasonal predictions. The GEFS skillfully captures the interannual variability of TC activity over the North Pacific and the North Atlantic, which can be attributed to the modulation of TCs by the El Niño–Southern Oscillation (ENSO) and the Atlantic meridional mode (AMM). The GEFS shows promising skill in predicting the active and inactive periods of TC activity over the Atlantic. The skill, however, has large fluctuations from year to year. The analysis presented herein suggests possible impacts of ENSO, the Madden–Julian oscillation (MJO), and the AMM on the TC subseasonal predictability.

scholarly journals The Sensitivity Analysis of a Lake Ecosystem with the Conditional Nonlinear Optimal Perturbation Method

2012 ◽  
Vol 2012 ◽  
pp. 1-7 ◽  
Author(s):  
Bo Wang ◽  
Peijun Zhang ◽  
Zhenhua Huo ◽  
Qianqian Qi
Keyword(s):  
Nutrient Loading ◽  
Loading Rate ◽  
Initial Perturbation ◽  
Finite Amplitude ◽  
Lake Ecosystem ◽  
Conditional Nonlinear Optimal Perturbation ◽  
Optimal Perturbation ◽  
Amplitude Parameter ◽  
Nutrient Loading Rate ◽  
Method Show

The instability and sensitivity of a lake ecosystem to the finite-amplitude perturbations related to the initial condition and the parameter correspondingly are studied. The CNOP-I and CNOP-P methods are adopted to investigate this nonlinear system. The numerical results with CNOP-I method show that the lake ecosystem can be nonlinearly unstable with finite-amplitude initial perturbations when the nutrient loading rate is between the two bifurcation points. A large enough finite amplitude initial perturbation, that is, CNOP-I, can induce a transition from an oligotrophic (eutrophic) state to an eutrophic (oligotrophic) state. With CNOP-P method, it is shown that the lake ecosystem can be transformed from an oligotrophic (eutrophic) state to an eutrophic (oligotrophic) state with a large enough finite amplitude parameter perturbation, that is, CNOP-P, no matter how large the nutrient loading rate is.

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