Enhancing ENSO Prediction Skill by Combining Model‐Analog and Linear Inverse Models (MA‐LIM)

2020 ◽  
Vol 47 (1) ◽  
Author(s):  
Jihoon Shin ◽  
Sungsu Park ◽  
Sang‐Ik Shin ◽  
Matthew Newman ◽  
Michael A. Alexander
Keyword(s):  
Prediction Skill ◽  
Enso Prediction ◽  
Inverse Models

scholarly journals An Analysis of the Temporal Evolution of ENSO Prediction Skill in the Context of the Equatorial Pacific Ocean Observing System

2015 ◽  
Vol 143 (8) ◽  
pp. 3204-3213 ◽  
Author(s):  
Arun Kumar ◽  
Mingyue Chen ◽  
Yan Xue ◽  
David Behringer
Keyword(s):  
Pacific Ocean ◽  
Time Evolution ◽  
Tropical Pacific ◽  
Sea Surface ◽  
Prediction Skill ◽  
Steady Increase ◽  
Coupled Models ◽  
Enso Prediction ◽  
Subsurface Ocean ◽  
Ocean Observations

Abstract Subsurface ocean observations in the equatorial tropical Pacific Ocean dramatically increased after the 1990s because of the completion of the TAO moored array and a steady increase in Argo floats. In this analysis the question explored is whether a steady increase in ocean observations can be discerned in improvements in skill of predicting sea surface temperature (SST) variability associated with El Niño–Southern Oscillation (ENSO)? The analysis is based on the time evolution of skill of sea surface temperatures in the equatorial tropical Pacific since 1982 based on a seasonal prediction system. It is found that for forecasts up to a 6-month lead time, a clear fingerprint of increases in subsurface ocean observations is not readily apparent in the time evolution of prediction skill that is dominated much more by the signal-to-noise consideration of SSTs to be predicted. Finding no clear relationship between an increase in ocean observations and prediction skill of SSTs, various possibilities for why it may be so are discussed. This discussion is to motivate further exploration on the question of the tropical Pacific observing system, its influence on the skill of ENSO prediction, and the capabilities of the current generation of coupled models and ocean data assimilation systems to take advantage of ocean observations.

scholarly journals SEAS5: The new ECMWF seasonal forecast system

2018 ◽  
Author(s):  
Stephanie J. Johnson ◽  
Timothy N. Stockdale ◽  
Laura Ferranti ◽  
Magdalena Alonso Balmaseda ◽  
Franco Molteni ◽  
...  
Keyword(s):  
Sea Ice ◽  
Decadal Variability ◽  
Seasonal Forecast ◽  
Arctic Sea Ice ◽  
Prediction Skill ◽  
The Arctic ◽  
Western Boundary ◽  
Forecast System ◽  
Enso Prediction ◽  
Ice Model

Abstract. In this paper we describe SEAS5, ECMWF’s fifth generation seasonal forecast system, which became operational in November 2017. Compared to its predecessor, System 4, SEAS5 is a substantially changed forecast system. It includes upgraded versions of the atmosphere and ocean models at higher resolutions, and adds a prognostic sea ice model. Here, we describe the configuration of SEAS5 and summarise the most noticeable results from a set of diagnostics including biases, variability, teleconnections and forecast skill. An important improvement in SEAS5 is the reduction of the Equatorial Pacific cold tongue bias, which is accompanied by a more realistic ENSO amplitude and an improvement in ENSO prediction skill over the central-west Pacific. Improvements in two-metre temperature skill are also clear over the tropical Pacific. SST biases in the northern extratropics change due to increased ocean resolution, especially in regions associated with western boundary currents. The increased ocean resolution exposes a new problem in the northwest Atlantic, where SEAS5 fails to capture decadal variability of the North Atlantic subpolar gyre, resulting in a degradation of DJF two-metre temperature prediction skill in this region. The prognostic sea ice model improves seasonal predictions of sea ice cover, although some regions and seasons suffer from biases introduced by employing a fully dynamical model rather than the simple, empirical scheme used in System 4. There are also improvements in two-metre temperature skill in the vicinity of the Arctic sea-ice edge. Cold temperature biases in the troposphere improve, but increase at the tropopause. Biases in the extratropical jets are larger than in System 4: extratropical jets are too strong, and displaced northwards in summer. In summary, development and added complexity since System 4 has ensured SEAS5 is a state-of-the-art seasonal forecast system which continues to display a particular strength in ENSO prediction.

Current status of ENSO prediction skill in coupled ocean–atmosphere models

2008 ◽  
Vol 31 (6) ◽  
pp. 647-664 ◽  
Author(s):  
Emilia K. Jin ◽  
James L. Kinter ◽  
B. Wang ◽  
C.-K. Park ◽  
I.-S. Kang ◽  
...  
Keyword(s):  
Prediction Skill ◽  
Current Status ◽  
Enso Prediction ◽  
Ocean Atmosphere

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.

scholarly journals Impact of Systematic GCM Errors on Prediction Skill as Estimated by Linear Inverse Modeling

2020 ◽  
Vol 33 (23) ◽  
pp. 10073-10095
Author(s):  
Ingo Richter ◽  
Ping Chang ◽  
Xue Liu
Keyword(s):  
General Circulation ◽  
Circulation Model ◽  
Equatorial Pacific ◽  
Prediction Skill ◽  
Statistical Prediction ◽  
Equatorial Atlantic ◽  
Sst Variability ◽  
Inverse Models ◽  
Tropical Sea ◽  
Mean State

Statistical prediction of tropical sea surface temperatures (SSTs) is performed using linear inverse models (LIMs) that are constructed from both observations and general circulation model (GCM) output of SST. The goals are to establish a baseline for tropical SST predictions, to examine the extent to which the skill of a GCM-derived LIM is indicative of that GCM’s skill in forecast mode, and to examine the linkages between mean state bias and prediction skill. The observation-derived LIM is more skillful than a simple persistence forecasts in most regions. Its skill also compares well with some GCM forecasts except in the equatorial Pacific, where the GCMs are superior. The observation-derived LIM is matched or even outperformed by the GCM-derived LIMs, which may be related to the longer data record available for GCMs. The GCM-derived LIMs provide a fairly good measure for the skill achieved by their parent GCMs in forecast mode. In some cases, the skill of the LIM is actually superior to that of its parent GCM, indicating that the GCM predictions may suffer from initialization problems. A weak-to-moderate relation exists between model mean state error and prediction skill in some regions. An example is the eastern equatorial Atlantic, where an erroneously deep thermocline reduces SST variability, which in turn affects prediction skill. Another example is the equatorial Pacific, where skill appears to be linked to cold SST biases in the western tropical Pacific, which may reduce the strength of air–sea coupling.

Decadal and Seasonal Dependence of ENSO Prediction Skill

1995 ◽  
Vol 8 (11) ◽  
pp. 2705-2715 ◽  
Author(s):  
Magdalena A. Balmaseda ◽  
Michael K. Davey ◽  
David L. T. Anderson
Keyword(s):  
Prediction Skill ◽  
Enso Prediction ◽  
Seasonal Dependence

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.

Re-conceptualizing Machiavellianism and Social-Cognitive Skills

2020 ◽  
pp. 1-8
Author(s):  
William Hart ◽  
Christopher J. Breeden ◽  
Charlotte Kinrade
Keyword(s):  
Cognitive Skills ◽  
Person Perception ◽  
Social Cognitive ◽  
Cognitive Skill ◽  
Prediction Skill ◽  
Current Evidence ◽  
College Sample ◽  
Social Prediction

Abstract. Machiavellianism is presumed to encompass advanced social-cognitive skill, but research has generally suggested that Machiavellian individuals are rather deficient in social-cognitive skill. However, previous research on the matter has been limited to measures of (a) Machiavellianism that are unidimensional and saturated with both antagonism and disinhibition and measures (b) only one type of social-cognitive skill. Using a large college sample ( N = 461), we examined how various dimensions of Machiavellianism relate to two types of social-cognitive skill: person-perception skill and general social prediction skill. Consistent with some prior theorizing, the planful dimension of Machiavellianism was positively related to both person-perception and general social prediction skills; antagonistic dimensions of Machiavellianism were negatively related to both skills; either agentic or cynical dimensions of Machiavellianism were generally unrelated to both skills. Overall, the current evidence suggests a complicated relationship between Machiavellianism and social-cognitive skill because Machiavellianism encompasses features that blend deficiency, proficiency, and average levels of social-cognitive skills.

Curve Estimation Modeling for Predictions of the Novel Coronavirus (Covid-19) Epidemic in Nigeria

2020 ◽  
Vol 4 (1) ◽  
pp. 1
Author(s):  
Christian Ebere Enyoh ◽  
Andrew Wirnkor Verla ◽  
Chidi Edbert Duru ◽  
Emmanuel Chinedu Enyoh ◽  
Budi Setiawan
Keyword(s):  
State Governments ◽  
The Novel ◽  
Curve Estimation ◽  
Exponential Models ◽  
Inverse Models ◽  
Forward Prediction ◽  
S Curve ◽  
Novel Coronavirus ◽  
Best Fit ◽  
Official Data

Based on the official Nigeria Centre for Disease Control (NCDC) data, the current research paper modeled the confirmed cases of the novel coronavirus disease 2019 (COVID-19) in Nigeria. Ten different curve regression models including linear, logarithmic, inverse, quadratic, cubic, compound, power, S-curve, growth, and exponential were used to fit the obtained official data. The cubic (R2 = 0.999) model gave the best fit for the entire country. However, the growth and exponential had the lowest standard error of estimate (0.958) and thus may best be used. The equations for these models were e0.78897+0.0944x and 2.2011e0.0944x respectively. In terms of confirmed cases in individual State, quadratic, cubic, compound, growth, power and exponential models generally best describe the official data for many states except for the state of Kogi which is best fitted with S-curve and inverse models.  The error between the model and the official data curve is quite small especially for compound, power, growth and exponential models. The computed models will help to realized forward prediction and backward inference of the epidemic situation in Nigeria, and the relevant analysis help Federal and State governments to make vital decisions on how to manage the lockdown in the country.

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