Parameter Estimation Based on a Local Ensemble Transform Kalman Filter Applied to El Niño–Southern Oscillation Ensemble Prediction

Yanqiu Gao,Zheqi Shen,Youmin Tang,Xunshu Song

doi:10.3390/rs13193923

Abstract

Parameter estimation plays an important role in reducing model error and thus is of great significance to improve the simulation and prediction capabilities of the model. However, due to filtering divergence, parameter estimation by ensemble-based filters still faces great challenges. Previous studies have shown that a covariance inflation scheme could alleviate the filtering divergence problem by increasing the signal-to-noise ratio of the state-parameter covariance. In this study, we proposed a new inflation scheme based on a local ensemble transform Kalman filter (LETKF). With the new scheme, the Zebiak–Cane (Z-C) model parameters were estimated by assimilating the sea surface temperature anomaly (SSTA) data. The effectiveness of the parameter estimation and its influence on El Niño–Southern Oscillation (ENSO) prediction were evaluated in an observation system simulation experiments (OSSE) framework and real-world scenario, respectively. With the utilization of the OSSE framework, the results showed that the model parameters were successfully estimated. Parameter estimation reduced the model error when compared with only state estimation (onlySE); however, multiple parameter estimation (MPE) further improved the ENSO prediction skill by providing better initial conditions and parameter values than the single parameter estimation (SPE). Parameter estimation could thus alleviate the spring prediction barrier (SPB) phenomenon of ENSO to a certain extent. In real-world experiments, the optimized parameters significantly improved the ENSO forecasting skill, primarily in prediction of warm events. This study provides an effective parameter estimation strategy to improve climate models and further climate predictions in the real world.

Highlights

Many studies have shown that numerical climate prediction models, even the stateof-the-art coupled models, still yield significant errors in predicting the true state of the atmosphere and ocean
Parameter values should vary over time in order to reflect their trait of being state dependent
We proposed a new inflation scheme based on local ensemble transform Kalman filter (LETKF) and assessed its application in parameter estimation and El Niño–Southern Oscillation (ENSO) prediction based on the Z-C model in the observation system simulation experiments (OSSE) framework and real-world experiments, respectively

Summary

Introduction

Many studies have shown that numerical climate prediction models, even the stateof-the-art coupled models, still yield significant errors in predicting the true state of the atmosphere and ocean. Apart from the significant impact of the initial conditions [1,2,3], model errors greatly reduce the accuracy of climate prediction [4,5,6,7]. Model errors stem from three aspects: misfit of the dynamical core, approximation scheme of the physical processes, and errors from the model parameters [8,9]. Due to the difficulty in improving the first two sources, many studies are devoted to reducing model parameter errors [10,11,12]. The process of optimizing parameters by assimilating observed data is known as parameter estimation [13,14]

Methods

Results

Discussion

Conclusion