Study of Atmospheric Predictability Based on Periphery Theory

The system periphery （＂jieke＂ in Chinese） is defined as a part of the system and is adjacent to its environment. The periphery is an in- termediary agent between the system and its environment, and has two functions： defending system itself and exchanging with the environment. Generally, the periphery is defined on space dimension. We will investigate the periphery from the time dimension, and study a time jieke based on set theory viewpoint; initial values and forecast lead in weather forecast are clarified. Further predictability of weather forecast on the basis of periph- ery theory is defined; its calculation formulae are given, with which computing for day-to-day forecast were carded out. The results have been com- pared with present researches of atmospheric predictability, and it shows advancing the predictability study. Most interesting is that atmospheric pre- dictability possesses rule of gold ratio 0.618, and it is found firstly in research of weather and climate predictability.

Prediction model for corrosion rate of low-alloy steels under atmospheric conditions using machine learning algorithms

This work constructed a machine learning(ML)model to predict the atmospheric corrosion rate of low-alloy steels(LAS).The material properties of LAS,environmental factors,and exposure time were used as the input,while the corrosion rate as the output.6 dif-ferent ML algorithms were used to construct the proposed model.Through optimization and filtering,the eXtreme gradient boosting(XG-Boost)model exhibited good corrosion rate prediction accuracy.The features of material properties were then transformed into atomic and physical features using the proposed property transformation approach,and the dominant descriptors that affected the corrosion rate were filtered using the recursive feature elimination(RFE)as well as XGBoost methods.The established ML models exhibited better predic-tion performance and generalization ability via property transformation descriptors.In addition,the SHapley additive exPlanations(SHAP)method was applied to analyze the relationship between the descriptors and corrosion rate.The results showed that the property transformation model could effectively help with analyzing the corrosion behavior,thereby significantly improving the generalization ability of corrosion rate prediction models.

ERROR GROWTH IN NUMERICAL PREDICTION AND ATMOSPHERIC PREDICTABILITY

The article is to report some results of numerical experiments on the error growth and the atmospheric predictability Experiments with two-level global baroclinic primitive equation spectral model have main results as follows.The magnitude of initial errors directly affects the error growth,but its distribution form has little effect on the growth.The loss of predictability resulting from small-scale error is much greater than that from large-scale error.The small-scale error rapidly grows and is transferred to the large-scale error by interaction between different scale waves,which stimulates the growth of error for the whole system Orographic forcing restrains planetary-scale error(wavenumbers 0—3)but enhances the small-scale error (wavenumbers 8 or greater).Hence,orographic effects on the error growth closely depend on the characteris- tic scale of initial errors,and there may be a critical wavenumber between 4 and 7.The error growth is great- er in Northern Hemisphere than in Southern Hemisphere if initial errors are the same.In the end we give some discussions about model,initialization scheme,etc.,to improve model prediction.

Nonlinear Local Lyapunov Exponent and Quantification of Local Predictability

Nonlinear local Lyapunov exponent （NLLE） is applied to quantitatively determine the local predictability limit of chaotic systems. As an example, we find that the local predictability limit of Henon attractor varies considerably with time, and some underlying phase-spatial structure does not appear. The local predictability limit of initially adjacent points in phase space may be completely different. This will cause difficulties in making the long-time analogue forecast.

Predictability of the Atmosphere

This paper makes a review on the predictability of the atmosphere. The essential problems of predictability theory, i.e., how a deterministic system changes to an undeterministic system (chaos) and how is the opposite (order within chaos), are discussed. Some applications of predictability theory are given.

Introducing atmospheric angular momentum into prediction of length of day change by generalized regression neural network model

The general regression neural network(GRNN) model was proposed to model and predict the length of day(LOD) change, which has very complicated time-varying characteristics. Meanwhile, considering that the axial atmospheric angular momentum(AAM) function is tightly correlated with the LOD changes, it was introduced into the GRNN prediction model to further improve the accuracy of prediction. Experiments with the observational data of LOD changes show that the prediction accuracy of the GRNN model is 6.1% higher than that of BP network, and after introducing AAM function, the improvement of prediction accuracy further increases to 14.7%. The results show that the GRNN with AAM function is an effective prediction method for LOD changes.

The African Climate as Predicted by the IAP Grid-Point Nine-Layer Atmospheric General Circulation Model (IAP-9L-AGCM)

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An Early Stopping-Based Artificial Neural Network Model for Atmospheric Corrosion Prediction of Carbon Steel

The optimization of network topologies to retain the generalization ability by deciding when to stop overtraining an artificial neural network(ANN)is an existing vital challenge in ANN prediction works.The larger the dataset the ANN is trained with,the better generalization the prediction can give.In this paper,a large dataset of atmospheric corrosion data of carbon steel compiled from several resources is used to train and test a multilayer backpropagation ANN model as well as two conventional corrosion prediction models(linear and Klinesmith models).Unlike previous related works,a grid search-based hyperparameter tuning is performed to develop multiple hyperparameter combinations(network topologies)to train multiple ANNs with mini-batch stochastic gradient descent optimization algorithm to facilitate the training of a large dataset.After that,one selection strategy for the optimal hyperparameter combination is applied by an early stopping method to guarantee the generalization ability of the optimal network model.The correlation coefficients(R)of the ANN model can explain about 80%(more than 75%)of the variance of atmospheric corrosion of carbon steel,and the root mean square errors(RMSE)of three models show that the ANN model gives a better performance than the other two models with acceptable generalization.The influence of input parameters on the output is highlighted by using the fuzzy curve analysis method.The result reveals that TOW,Cl-and SO2 are the most important atmospheric chemical variables,which have a well-known nonlinear relationship with atmospheric corrosion.

The importance of data assimilation components for initial conditions and subsequent error growth

Despite a specific data assimilation method,data assimilation(DA)in general can be decomposed into components of the prior information,observation forward operator that is given by the observation type,observation error covariances,and background error covariances.In a classic Lorenz model,the influences of the DA components on the initial conditions(ICs)and subsequent forecasts are systematically investigated,which could provide a theoretical basis for the design of DA for different scales of interests.The forecast errors undergo three typical stages:a slow growth stage from 0 h to 5 d,a fast growth stage from 5 d to around 15 d with significantly different error growth rates for ensemble and deterministic forecasts,and a saturation stage after 15 d.Assimilation strategies that provide more accurate ICs can improve the predictability.Cycling assimilation is superior to offline assimilation,and a flow-dependent background error covariance matrix(Pf)provides better analyses than a static background error covariance matrix(B)for instantaneous observations and frequent time-averaged observations;but the opposite is true for infrequent time-averaged observations,since cycling simulation cannot construct informative priors when the model lacks predictive skills and the flow-dependent Pf cannot effectively extract information from low-informative observations as the static B.Instantaneous observations contain more information than time-averaged observations,thus the former is preferred,especially for infrequent observing systems.Moreover,ensemble forecasts have advantages over deterministic forecasts,and the advantages are enlarged with less informative observations and lower predictive-skill model priors.