Model Forecast Error Correction Based on the Local Dynamical Analog Method: An Example Application to the ENSO Forecast by an Intermediate Coupled Model

2020 ◽  
Vol 47 (19) ◽  
Author(s):  
Zhaolu Hou ◽  
Bin Zuo ◽  
Shaoqing Zhang ◽  
Fei Huang ◽  
Ruiqiang Ding ◽  
...  
Keyword(s):  
Error Correction ◽  
Forecast Error ◽  
Coupled Model ◽  
Model Forecast ◽  
Analog Method ◽  
Intermediate Coupled Model

Model forecast error correction based on the Local Dynamical Analog method: an example application to the ENSO forecast

2020 ◽  
Author(s):  
Zhaolu Hou ◽  
Bin Zuo ◽  
Shaoqing Zhang ◽  
Fei Huang ◽  
Ruiqiang Ding ◽  
...  
Keyword(s):  
Error Correction ◽  
Forecast Error ◽  
Forecast Errors ◽  
Model Errors ◽  
Model Forecast ◽  
New Approach ◽  
Correct Model ◽  
State Dependent ◽  
Dependent Model ◽  
Scalar Time Series

<p>Numerical forecasts always have associated errors. Analogue correction methods combine numerical simulations with statistical analyses to reduce model forecast errors. However, identifying appropriate analogues remains a challenging task. Here, we use the Local Dynamical Analog (LDA) method to locate analogues and correct model forecast errors. As an example, an ENSO model forecast error correction experiment confirms that the LDA method locates more dynamical analogues of states of interest and better corrects forecast errors than do other methods. This is because the LDA method ensures similarity of the initial states and the evolution of both states. In addition, the LDA method can be applied using a scalar time series, which reduces the complexity of the dynamical system. Model forecast error correction using the LDA method provides a new approach to correcting state-dependent model errors and can be readily integrated with other advanced models.</p>

scholarly journals Local Coupled Equatorial Variability versus Remote ENSO Forcing in an Intermediate Coupled Model of the Tropical Atlantic

2006 ◽  
Vol 19 (20) ◽  
pp. 5227-5252 ◽  
Author(s):  
Serena Illig ◽  
Boris Dewitte
Keyword(s):  
Mixed Layer ◽  
El Niño ◽  
Time Scale ◽  
El Nino ◽  
Coupled Model ◽  
Tropical Atlantic ◽  
Equatorial Atlantic ◽  
Layer Model ◽  
Intermediate Coupled Model ◽  
Mixed Layer Model

Abstract The relative roles played by the remote El Niño–Southern Oscillation (ENSO) forcing and the local air–sea interactions in the tropical Atlantic are investigated using an intermediate coupled model (ICM) of the tropical Atlantic. The oceanic component of the ICM consists of a six-baroclinic mode ocean model and a simple mixed layer model that has been validated from observations. The atmospheric component is a global atmospheric general circulation model developed at the University of California, Los Angeles (UCLA). In a forced context, the ICM realistically simulates both the sea surface temperature anomaly (SSTA) variability in the equatorial band, and the relaxation of the Atlantic northeast trade winds and the intensification of the equatorial westerlies in boreal spring that usually follows an El Niño event. The results of coupled experiments with or without Pacific ENSO forcing and with or without explicit air–sea interactions in the equatorial Atlantic indicate that the background energy in the equatorial Atlantic is provided by ENSO. However, the time scale of the variability and the magnitude of some peculiar events cannot be explained solely by ENSO remote forcing. It is demonstrated that the peak of SSTA variability in the 1–3-yr band as observed in the equatorial Atlantic is due to the local air–sea interactions and is not a linear response to ENSO. Seasonal phase locking in boreal summer is also the result of the local coupling. The analysis of the intrinsic sustainable modes indicates that the Atlantic El Niño is qualitatively a noise-driven stable system. Such a system can produce coherent interdecadal variability that is not forced by the Pacific or extraequatorial variability. It is shown that when a simple slab mixed layer model is embedded into the system to simulate the northern tropical Atlantic (NTA) SST variability, the warming over NTA following El Niño events have characteristics (location and peak phase) that depend on air–sea interaction in the equatorial Atlantic. In the model, the interaction between the equatorial mode and NTA can produce a dipolelike structure of the SSTA variability that evolves at a decadal time scale. The results herein illustrate the complexity of the tropical Atlantic ocean–atmosphere system, whose predictability jointly depends on ENSO and the connections between the Atlantic modes of variability.

scholarly journals Roles of initial ocean surface and subsurface states on successfully predicting 2006–2007 El Niño with an intermediate coupled model

Ocean Science ◽  
2015 ◽  
Vol 11 (1) ◽  
pp. 187-194 ◽  
Author(s):  
F. Zheng ◽  
J. Zhu
Keyword(s):  
El Niño ◽  
El Nino ◽  
Surface Condition ◽  
Coupled Model ◽  
Ocean Surface ◽  
Sea Surface ◽  
Initial Surface ◽  
El Niño Event ◽  
Successful Prediction ◽  
Intermediate Coupled Model

Abstract. The 2006–2007 El Niño event, an unusually weak event, was predicted by most models only after the warming in the eastern Pacific had commenced. In this study, on the basis of an El Niño prediction system, roles of the initial ocean surface and subsurface states on predicting the 2006–2007 El Niño event are investigated to determine conditions favorable for predicting El Niño growth and are isolated in three sets of hindcast experiments. The hindcast is initialized through assimilation of only the sea surface temperature (SST) observations to optimize the initial surface condition, only the sea level (SL) data to update the initial subsurface state, or both the SST and SL data. Results highlight that the hindcasts with three different initial states can all successfully predict the 2006–2007 El Niño event 1 year in advance and that the hindcast initialized by both the SST and SL data performs best. A comparison between the various sets of hindcast results further demonstrates that successful prediction is more significantly affected by the initial subsurface state than by the initial surface condition. The accurate initial surface state can trigger the easier prediction of the 2006–2007 El Niño, whereas a more reasonable initial subsurface state can contribute to improving the prediction in the growth of the warm event.

Correction of biased climate simulated by biased physics through parameter estimation in an intermediate coupled model

2015 ◽  
Vol 47 (5-6) ◽  
pp. 1899-1912 ◽  
Author(s):  
Xuefeng Zhang ◽  
Shaoqing Zhang ◽  
Zhengyu Liu ◽  
Xinrong Wu ◽  
Guijun Han
Keyword(s):  
Parameter Estimation ◽  
Coupled Model ◽  
Intermediate Coupled Model

scholarly journals Verification of surface minimum, mean, and maximum temperature forecasts in Calabria for summer 2008

2011 ◽  
Vol 11 (2) ◽  
pp. 487-500 ◽  
Author(s):  
S. Federico
Keyword(s):  
Forecast Error ◽  
Background Field ◽  
Horizontal Resolution ◽  
Atmospheric Modeling ◽  
Forecast Skill ◽  
Maximum Temperature ◽  
Model Forecast ◽  
Maximum Temperatures ◽  
Analysis Error ◽  
The Impact

Abstract. Since 2005, one-hour temperature forecasts for the Calabria region (southern Italy), modelled by the Regional Atmospheric Modeling System (RAMS), have been issued by CRATI/ISAC-CNR (Consortium for Research and Application of Innovative Technologies/Institute for Atmospheric and Climate Sciences of the National Research Council) and are available online at http://meteo.crati.it/previsioni.html (every six hours). Beginning in June 2008, the horizontal resolution was enhanced to 2.5 km. In the present paper, forecast skill and accuracy are evaluated out to four days for the 2008 summer season (from 6 June to 30 September, 112 runs). For this purpose, gridded high horizontal resolution forecasts of minimum, mean, and maximum temperatures are evaluated against gridded analyses at the same horizontal resolution (2.5 km). Gridded analysis is based on Optimal Interpolation (OI) and uses the RAMS first-day temperature forecast as the background field. Observations from 87 thermometers are used in the analysis system. The analysis error is introduced to quantify the effect of using the RAMS first-day forecast as the background field in the OI analyses and to define the forecast error unambiguously, while spatial interpolation (SI) analysis is considered to quantify the statistics' sensitivity to the verifying analysis and to show the quality of the OI analyses for different background fields. Two case studies, the first one with a low (less than the 10th percentile) root mean square error (RMSE) in the OI analysis, the second with the largest RMSE of the whole period in the OI analysis, are discussed to show the forecast performance under two different conditions. Cumulative statistics are used to quantify forecast errors out to four days. Results show that maximum temperature has the largest RMSE, while minimum and mean temperature errors are similar. For the period considered, the OI analysis RMSEs for minimum, mean, and maximum temperatures vary from 1.8, 1.6, and 2.0 °C, respectively, for the first-day forecast, to 2.0, 1.9, and 2.6 °C, respectively, for the fourth-day forecast. Cumulative statistics are computed using both SI and OI analysis as reference. Although SI statistics likely overestimate the forecast error because they ignore the observational error, the study shows that the difference between OI and SI statistics is less than the analysis error. The forecast skill is compared with that of the persistence forecast. The Anomaly Correlation Coefficient (ACC) shows that the model forecast is useful for all days and parameters considered here, and it is able to capture day-to-day weather variability. The model forecast issued for the fourth day is still better than the first-day forecast of a 24-h persistence forecast, at least for mean and maximum temperature. The impact of using the RAMS first-day forecast as the background field in the OI analysis is quantified by comparing statistics computed with OI and SI analyses. Minimum temperature is more sensitive to the change in the analysis dataset as a consequence of its larger representative error.

scholarly journals A Multidude of Econometric Tests: Forecasting the Dutsch Guilder

2017 ◽  
Vol 9 (9) ◽  
pp. 94
Author(s):  
Augustine C. Arize ◽  
Ioannis N. Kallianiotis ◽  
Ebere Eme Kalu ◽  
John Malindretos ◽  
Moschos Scoullis
Keyword(s):  
Exchange Rate ◽  
Error Correction ◽  
Forecast Error ◽  
Predictive Performance ◽  
Mean Square ◽  
Rate Model ◽  
Long Run ◽  
Exchange Rate Models ◽  
Econometric Tests ◽  
Nonparametric Techniques

This paper studies a diversity of exchange rate models, applies both parametric and nonparametric techniques to them, and examines said models’ collective predictive performance. We shall choose the forecasting predictor with the smallest root mean square forecast error (RMSE); the empirical evidence for a better type of exchange rate model is in equation (34), although none of our evidence gives an optimal forecast. At the end, these models’ error correction versions will be fit so that plausible long-run elasticities can be imposed on each model’s fundamental variables.

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