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 Reinitializing Sea Surface Temperature in the Ensemble Intermediate Coupled Model for Improved Forecasts

Axioms ◽  
2021 ◽  
Vol 10 (3) ◽  
pp. 189
Author(s):  
Sittisak Injan ◽  
Angkool Wangwongchai ◽  
Usa Humphries ◽  
Amir Khan ◽  
Abdullahi Yusuf
Keyword(s):  
Surface Temperature ◽  
Sea Surface Temperature ◽  
Southern Oscillation ◽  
Coupled Model ◽  
Sea Surface ◽  
In Situ Data ◽  
The Pacific ◽  
Intermediate Coupled Model ◽  
Sea Surface Temperature Anomalies

The Ensemble Intermediate Coupled Model (EICM) is a model used for studying the El Niño-Southern Oscillation (ENSO) phenomenon in the Pacific Ocean, which is anomalies in the Sea Surface Temperature (SST) are observed. This research aims to implement Cressman to improve SST forecasts. The simulation considers two cases in this work: the control case and the Cressman initialized case. These cases are simulations using different inputs where the two inputs differ in terms of their resolution and data source. The Cressman method is used to initialize the model with an analysis product based on satellite data and in situ data such as ships, buoys, and Argo floats, with a resolution of 0.25 × 0.25 degrees. The results of this inclusion are the Cressman Initialized Ensemble Intermediate Coupled Model (CIEICM). Forecasting of the sea surface temperature anomalies was conducted using both the EICM and the CIEICM. The results show that the calculation of SST field from the CIEICM was more accurate than that from the EICM. The forecast using the CIEICM initialization with the higher-resolution satellite-based analysis at a 6-month lead time improved the root mean square deviation to 0.794 from 0.808 and the correlation coefficient to 0.630 from 0.611, compared the control model that was directly initialized with the low-resolution in-situ-based analysis.

Interdecadal Change in Properties of El Niño–Southern Oscillation in an Intermediate Coupled Model

2005 ◽  
Vol 18 (9) ◽  
pp. 1369-1380 ◽  
Author(s):  
Rong-Hua Zhang ◽  
Antonio J. Busalacchi
Keyword(s):  
Surface Temperature ◽  
Sea Surface Temperature ◽  
El Niño ◽  
El Nino ◽  
Southern Oscillation ◽  
Coupled Model ◽  
Ocean Model ◽  
Sea Surface ◽  
Subsurface Temperature ◽  
Intermediate Coupled Model

Abstract The role of subsurface temperature variability in modulating El Niño–Southern Oscillation (ENSO) properties is examined using an intermediate coupled model (ICM), consisting of an intermediate dynamic ocean model and a sea surface temperature (SST) anomaly model. An empirical procedure is used to parameterize the temperature of subsurface water entrained into the mixed layer (Te) from sea level (SL) anomalies via a singular value decomposition (SVD) analysis for use in simulating sea surface temperature anomalies (SSTAs). The ocean model is coupled to a statistical atmospheric model that estimates wind stress anomalies also from an SVD analysis. Using the empirical Te models constructed from two subperiods, 1963–79 (T63–79e) and 1980–96 (T80–96e), the coupled system exhibits strikingly different properties of interannual variability (the oscillation period, spatial structure, and temporal evolution). For the T63–79e model, the system features a 2-yr oscillation and westward propagation of SSTAs on the equator, while for the T80–96e model, it is characterized by a 5-yr oscillation and eastward propagation. These changes in ENSO properties are consistent with the behavior shift of El Niño observed in the late 1970s. Heat budget analyses further demonstrate a controlling role played by the vertical advection of subsurface temperature anomalies in determining the ENSO properties.

scholarly journals Seasonal streamflow forecasting in the upper Indus Basin of Pakistan: an assessment of methods

2018 ◽  
Vol 22 (6) ◽  
pp. 3533-3549 ◽  
Author(s):  
Stephen P. Charles ◽  
Quan J. Wang ◽  
Mobin-ud-Din Ahmad ◽  
Danial Hashmi ◽  
Andrew Schepen ◽  
...  
Keyword(s):  
Southern Oscillation ◽  
Hybrid Approach ◽  
Indus Basin ◽  
Climate Index ◽  
Climate Indices ◽  
Streamflow Forecasting ◽  
Upper Indus Basin ◽  
Probabilistic Forecasts ◽  
Streamflow Forecasts ◽  
Seasonal Streamflow

Abstract. Timely and skilful seasonal streamflow forecasts are used by water managers in many regions of the world for seasonal water allocation outlooks for irrigators, reservoir operations, environmental flow management, water markets and drought response strategies. In Australia, the Bayesian joint probability (BJP) statistical approach has been deployed by the Australian Bureau of Meteorology to provide seasonal streamflow forecasts across the country since 2010. Here we assess the BJP approach, using antecedent conditions and climate indices as predictors, to produce Kharif season (April–September) streamflow forecasts for inflow to Pakistan's two largest upper Indus Basin (UIB) water supply dams, Tarbela (on the Indus) and Mangla (on the Jhelum). For Mangla, we compare these BJP forecasts to (i) ensemble streamflow predictions (ESPs) from the snowmelt runoff model (SRM) and (ii) a hybrid approach using the BJP with SRM–ESP forecast means as an additional predictor. For Tarbela, we only assess BJP forecasts using antecedent and climate predictors as we did not have access to SRM for this location. Cross validation of the streamflow forecasts shows that the BJP approach using two predictors (March flow and an El Niño Southern Oscillation, ENSO, climate index) provides skilful probabilistic forecasts that are reliable in uncertainty spread for both Mangla and Tarbela. For Mangla, the SRM approach leads to forecasts that exhibit some bias and are unreliable in uncertainty spread, and the hybrid approach does not result in better forecast skill. Skill levels for Kharif (April–September), early Kharif (April–June) and late Kharif (July–September) BJP forecasts vary between the two locations. Forecasts for Mangla show high skill for early Kharif and moderate skill for all Kharif and late Kharif, whereas forecasts for Tarbela also show moderate skill for all Kharif and late Kharif, but low skill for early Kharif. The BJP approach is simple to apply, with small input data requirements and automated calibration and forecast generation. It offers a tool for rapid deployment at many locations across the UIB to provide probabilistic seasonal streamflow forecasts that can inform Pakistan's basin water management.

scholarly journals Application of Data Assimilation and the Relationship between ENSO and Precipitation

2021 ◽  
Vol 26 (1) ◽  
pp. 24
Author(s):  
Sittisak Injan ◽  
Angkool Wangwongchai ◽  
Usa Humphries
Keyword(s):  
Data Assimilation ◽  
Correlation Coefficient ◽  
Southern Oscillation ◽  
Three Dimensional ◽  
Coupled Model ◽  
Heavy Precipitation ◽  
Sea Surface ◽  
Intermediate Coupled Model ◽  
The Relationship ◽  
Better Than

Climate change in Thailand is related to the El Niño and Southern Oscillation (ENSO) phenomenon, in particular drought and heavy precipitation. The data assimilation method is used to improve the accuracy of the Ensemble Intermediate Coupled Model (EICM) that simulates the sea surface temperature (SST). The four-dimensional variational (4D-Var) and three-dimensional variational (3D-Var) schemes have been used for data assimilation purposes. The simulation was performed by the model with and without data assimilation from satellite data in 2011. The result shows that the model with data assimilation is better than the model without data assimilation. The 4D-Var scheme is the best method, with a Root Mean Square Error (RMSE) of 0.492 and a Correlation Coefficient of 0.684. The relationship between precipitation in Thailand and the ENSO area in Niño 3.4 was consistent for seven months, with a correlation coefficient of −0.882.

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 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 Origins of Biases in CMIP5 Models Simulating Northwest Pacific Summertime Atmospheric Circulation Anomalies during the Decaying Phase of ENSO

2018 ◽  
Vol 31 (14) ◽  
pp. 5707-5729 ◽  
Author(s):  
Weichen Tao ◽  
Gang Huang ◽  
Renguang Wu ◽  
Kaiming Hu ◽  
Pengfei Wang ◽  
...  
Keyword(s):  
Atmospheric Circulation ◽  
Southern Oscillation ◽  
Coupled Model ◽  
Tropical Indian Ocean ◽  
Shortwave Radiation ◽  
Cmip5 Models ◽  
Northwest Pacific ◽  
Circulation Anomalies ◽  
Atmospheric Circulation Anomalies ◽  
Sst Anomalies

Abstract The present study documents the biases of summertime northwest Pacific (NWP) atmospheric circulation anomalies during the decaying phase of ENSO and investigates their plausible reasons in 32 models from phase 5 of the Coupled Model Intercomparison Project. Based on an intermodel empirical orthogonal function (EOF) analysis of El Niño–Southern Oscillation (ENSO)-related 850-hPa wind anomalies, the dominant modes of biases are extracted. The first EOF mode, explaining 21.3% of total intermodel variance, is characterized by a cyclone over the NWP, indicating a weaker NWP anticyclone. The cyclone appears to be a Rossby wave response to unrealistic equatorial western Pacific (WP) sea surface temperature (SST) anomalies related to excessive equatorial Pacific cold tongue in the models. On one hand, the cold SST biases increase the mean zonal SST gradient, which further intensifies warm zonal advection, favoring the development and persistence of equatorial WP SST anomalies. On the other hand, they reduce the anomalous convection caused by ENSO-related warming, and the resultant increase in downward shortwave radiation contributes to the SST anomalies there. The second EOF mode, explaining 18.6% of total intermodel variance, features an anticyclone over the NWP with location shifted northward. The related SST anomalies in the Indo-Pacific sector show a tripole structure, with warming in the tropical Indian Ocean and equatorial central and eastern Pacific and cooling in the NWP. The Indo-Pacific SST anomalies are highly controlled by ENSO amplitude, which is determined by the intensity of subtropical cells via the adjustment of meridional and vertical advection in the models.

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.

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