Sensitivity determined simultaneous estimation of multiple parameters in coupled models: part I—based on single model component sensitivities

Zhao, Yuxin; Deng, Xiong; Zhang, Shaoqing; Liu, Zhengyu; Liu, Chang

doi:10.1007/s00382-019-04865-3

Cited by 11 publications

(7 citation statements)

References 65 publications

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“…2 is proved to be feasible in the simple model, and the next step is to apply the method to a physics-based MOC box model. After proving that it is feasible to capture regime transitions by constraining parameters in an idealized conceptual model, we also use a MOCBM (Tardif et al, 2014;Zhao et al, 2019) with a better physical basis to study the problem of AMOC transition.…”

Section: Data Assimilation and Parameter Estimationmentioning

confidence: 99%

A Study of Capturing AMOC Regime Transition through Observation-Constrained Model Parameters

Liu¹,

Zhang²,

Shen³

et al. 2021

Preprint

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Abstract. The multiple equilibria are an outstanding characteristic of the Atlantic meridional overturning circulation (AMOC) that has important impacts on the Earth climate system appearing as regime transitions. The AMOC can be simulated in different models but the behavior deviates from the real world due to the existence of model errors. Here, we first combine a general AMOC model with an ensemble Kalman filter to form an ensemble coupled model data assimilation and parameter estimation (CDAPE) system, and derive the general methodology to capture the observed AMOC regime transitions through utilization of observational information. Then we apply this methodology designed within a twin experiment framework with a simple conceptual model that simulates the transition phenomenon of AMOC multiple equilibria, as well as a more physics-based MOC box model to reconstruct the observed AMOC multiple equilibria. The results show that the coupled model parameter estimation with observations can significantly mitigate the model deviations, thus capturing regime transitions of the AMOC. This simple model study serves as a guideline when a coupled general circulation model is used to incorporate observations to reconstruct the AMOC historical states and make multi-decadal climate predictions.

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Section: Data Assimilation and Parameter Estimationmentioning

confidence: 99%

A Study of Capturing AMOC Regime Transition through Observation-Constrained Model Parameters

Liu¹,

Zhang²,

Shen³

et al. 2021

Preprint

Self Cite

View full text Add to dashboard Cite

show abstract

“…Coupled models usually suffer from unavoidable errors due to biased initial conditions and parameter schemes [1]. These discrepancies continue to develop and subsequently lead to poor forecast quality [2]. Assimilating observations is helpful for correcting biases in the initial conditions.…”

Section: Introductionmentioning

confidence: 99%

“…An integrated modeling observation system involves the combination of three subsystems or algorithms [7]: (1) a prediction model that provides supplementary information for scheme design; (2) an optimization algorithm for generating observation schemes; (3) observation platforms with attribute information. Earlier studies focused more on specific fields.…”

Section: Introductionmentioning

confidence: 99%

Application of the Most Sensitive Initial Error in Sampling Scheme Design for a Mobile Platform

Zhao,

Deng

2023

JMSE

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Since ocean mobile observation equipment and numerical models have achieved remarkable results, the combination of the two has become an influential topic. A numerical model provides auxiliary information for the arrangement of observation equipment. As feedback, observations help produce more accurate initial conditions when incorporated into data assimilation. However, it is still worth investigating the ways to select the most valuable observation sites within the computational domain and the ways to design the observation scheme of the mobile platform. To improve the efficiency of observation, researchers attempt to select the best observation region in the vast ocean. The approach of deploying additional observing assets in selected regions is referred to as targeted observation. By combining the features of the targeted observation and the mobile observing platform, we propose a design approach for the observation scheme. First, based on a model, we estimate the initial perturbation that causes the greatest change in the sea surface temperature in the future. Then, according to the spatial component of the perturbation, we divide the experimental regions into sensitive regions and non-sensitive regions. Observing system simulation experiments are carried out to verify that samples in sensitive regions are more helpful to improve model prediction. Afterward, considering the variation of the perturbation with time, we propose a hybrid sampling scheme design method for an underwater unmanned vehicle combining Q-learning and particle swarm optimization algorithm. Finally, the effectiveness of the hybrid algorithm is verified by comparing the sampling schemes designed in static environment. This approach provides a dynamic basis for the path planning of mobile observing platforms.

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“…The governing equations are given by Eq. (1): (Zhao et al, 2019). A fourth-order Runge-Kutta time difference scheme and a leap-frog timedifference scheme with a Robert-Asselin time filter (Asselin 1972), are used to resolve this 5VCCM, with the time step equaling 0.01 TU (time unit) (1 TU=100 time steps).…”

mentioning

confidence: 99%

A Deep Learning-Based Consistency Test Approach for Earth System Models on Heterogeneous Many-Core Systems

Yu,

Zhang,

et al. 2024

Preprint

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Abstract. Physical and heat limits of the semiconductor technology require the adaptation of heterogeneous architectures in supercomputers to maintain a continuous increase of computing performance. The coexistence of general-purpose cores and accelerator cores, which usually employ different hardware architectures, can lead to bit-level differences, especially when we try to maximize the performance on both kinds of cores. Such differences further lead to unavoidable computational perturbations through temporal integration, which can blend with software or human errors. Software correctness verification in the form of quality assurance is a critically important step in the development and optimization of Earth system models (ESMs) on heterogeneous many-core systems with mixed perturbations of software changes and hardware updates. We have developed a deep learning-based consistency test approach for Earth System Models referred to as ESM-DCT. The ESM-DCT is based on the unsupervised bidirectional gate recurrent unit-autoencoder (BGRU-AE) model, which can still detect the existence of software or human errors when taking hardware-related perturbations into account. We use the Community Earth System Model (CESM) on the new Sunway system as an example of large-scale ESMs to evaluate the ESM-DCT. The results show that facing with the mixed perturbations caused by hardware designs and software changes in heterogeneous computing, the ESM-DCT can detect software or human errors when determining whether or not the model simulation is consistent with the original results in homogeneous computing. Our ESM-DCT tool provides an efficient and objective approach for verifying the reliability of the development and optimization of scientific computing models on the heterogeneous many-core systems.

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Sensitivity determined simultaneous estimation of multiple parameters in coupled models: part I—based on single model component sensitivities

Cited by 11 publications

References 65 publications

A Study of Capturing AMOC Regime Transition through Observation-Constrained Model Parameters

A Study of Capturing AMOC Regime Transition through Observation-Constrained Model Parameters

Application of the Most Sensitive Initial Error in Sampling Scheme Design for a Mobile Platform

A Deep Learning-Based Consistency Test Approach for Earth System Models on Heterogeneous Many-Core Systems

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