A new decomposition model of sea level variability for the sea level anomaly time series prediction

Rising sea level is of great significance to coastal societies;predicting sea level extent in coastal regions is critical.When carrying out predictions,the subsequences obtained using decomposition methods may exhibit a certain regularity and therefore can provide multidimensional information that can be used to improve prediction models.Traditional decomposition methods such as seasonal and trend decomposition using Loess(STL)focus mostly on the fluctuating trend of time series and ignore its impact on prediction.Methods in the signal decomposition domain,such as variational mode decomposition(VMD),have no physical significance.In response to the above problems,a new decomposition method for sea level anomaly time series prediction(DMSLAP)is proposed.With this method,the trend term in a time series can be isolated and the effects of abnormal sea level change behaviors can be attenuated.We decompose multiperiod characteristics using this method while maintaining the smoothness of the analyzed series.Satellite altimetry data from 1993 to 2020 are used in experiments conducted in the study area.The results are then compared with predictions obtained using existing decomposition methods such as the STL and VMD methods and time varying filtering based on empirical mode decomposition(TVF-EMD).The performance of DMSLAP combined with a prediction method resulted in optimal sea level anomaly(SLA)predictions,with a minimum root mean square error(RMSE)of 1.40 cm and a maximum determination coefficient(R^(2))of 0.93 during 2020.The DMSLAP method was more accurate when predicting 1-year data and 3-year data.The TVF-EMD and DMSLAP methods had comparable accuracies,and the periodic term decomposed by the DMSLAP method was more in line with the actual law than that derived using the TVF-EMD method.Thus,DMSLAP can decompose SLA time series better than existing methods and is an effective tool for obtaining short-term SLA prediction.

Adaptive Fourier Decomposition Based Time-Frequency Analysis

The attempt to represent a signal simultaneously in time and frequency domains is full of challenges. The recently proposed adaptive Fourier decomposition （AFD） offers a practical approach to solve this problem. This paper presents the principles of the AFD based time-frequency analysis in three aspects： instantaneous frequency analysis, frequency spectrum analysis, and the spectrogram analysis. An experiment is conducted and compared with the Fourier transform in convergence rate and short-time Fourier transform in time-frequency distribution. The proposed approach performs better than both the Fourier transform and short-time Fourier transform.

Phenology of different types of vegetation and their response to climate change in the Qilian Mountains,China

The Qilian Mountains(QM)possess a delicate vegetation ecosystem,amplifying the evident response of vegetation phenology to climate change.The relationship between changes in vegetation growth and climate remains complex.To this end,we used MODIS NDVI data to extract the phenological parameters of the vegetation including meadow(MDW),grassland(GSD),and alpine vegetation(ALV))in the QM from 2002 to 2021.Then,we employed path analysis to reveal the direct and indirect impacts of seasonal climate change on vegetation phenology.Additionally,we decomposed the vegetation phenology in a time series using the trigonometric seasonality,Box-Cox transformation,ARMA errors,and Trend Seasonal components model(TBATS).The findings showed a distinct pattern in the vegetation phenology of the QM,characterized by a progressive shift towards an earlier start of the growing season(SOS),a delayed end of the growing season(EOS),and an extended length of the growing season(LOS).The growth cycle of MDW,GSD,and ALV in the QM species is clearly defined.The SOS for MDW and GSD occurred earlier,mainly between late April and August,while the SOS for ALVs occurred between mid-May and mid-August,a one-month delay compared to the other vegetation.The EOS in MDW and GSD were concentrated between late August and April and early September and early January,respectively.Vegetation phenology exhibits distinct responses to seasonal temperature and precipitation patterns.The advancement and delay of SOS were mainly influenced by the direct effect of spring temperatures and precipitation,which affected 19.59%and 22.17%of the study area,respectively.The advancement and delay of EOS were mainly influenced by the direct effect of fall temperatures and precipitation,which affected 30.18%and 21.17%of the area,respectively.On the contrary,the direct effects of temperature and precipitation in summer and winter on vegetation phenology seem less noticeable and were mainly influenced by indirect effects.The indirect effect of winter precipitation is the main factor affecting the advance or delay of SOS,and the area proportions were 16.29%and 23.42%,respectively.The indirect effects of fall temperatures and precipitation were the main factors affecting the delay and advancement of EOS,respectively,with an area share of 15.80%and 21.60%.This study provides valuable insight into the relationship between vegetation phenology and climate change,which can be of great practical value for the ecological protection of the Qinghai-Tibetan Plateau as well as for the development of GSD ecological animal husbandry in the QM alpine pastoral area.

Generating hourly electricity demand data for large-scale single-family buildings by a decomposition-recombination method

Household electricity demand has substantial impacts on local grid operation,energy storage and the energy per-formance of buildings.Hourly demand data at district or urban level helps stakeholders understand the demand patterns from a granular time scale and provides robust evidence in energy management.However,such type of data is often expensive and time-consuming to collect,process and integrate.Decisions built upon smart meter data have to deal with challenges of privacy and security in the whole process.Incomplete data due to confiden-tiality concerns or system failure can further increase the difficulty of modeling and optimization.In addition,methods using historical data to make predictions can largely vary depending on data quality,local building envi-ronment,and dynamic factors.Considering these challenges,this paper proposes a statistical method to generate hourly electricity demand data for large-scale single-family buildings by decomposing time series data and recom-bining them into synthetics.The proposed method used public data to capture seasonality and the distribution of residuals that fulfill statistical characteristics.A reference building was used to provide empirical parameter settings and validations for the studied buildings.An illustrative case in a city of Sweden using only annual total demand was presented for deploying the proposed method.The results showed that the proposed method can mimic reality well and represent a high level of similarity to the real data.The average monthly error for the best month reached 15.9%and the best one was below 10%among 11 tested months.Less than 0.6%improper synthetic values were found in the studied region.

Exploring the correlation between temperature and crime:A case-crossover study of eight cities in America

Recent years have seen increasing academic interest in exploring the correlation between temperature and crime.However,it is uncertain whether similar long-term trends or seasonality(rather than causal effect)of temper-ature and crime is the major reason for the observed correlation between them.To explore whether there is still a correlation between temperature and crime when long-term trends and seasonal cycles are filtered out,we use the Kalman filter to decompose the time series of temperature and crimes,and then the fast Fourier transform is used to calculate the exact circle of their seasonality separately.Based on that,the box-plot method and linear regression are used to explore the correlation between temperature residuals and crime residuals.The results show that more than half of the crime types have similar seasonal cycles(approximately 1 year)to that of temperature.Moreover,the daily residual analyses show that temperature residuals have a positive correlation with assault and robbery residuals in all cities,whose average slopes are more than 0.1.The other four types of crimes vary greatly from case to case.The temperature residuals show a weak correlation with the residuals of some crime types.

Adaptive Fault Tolerant Control of Multi-time-scale Singularly Perturbed Systems

This paper studies the fault tolerant control, adaptive approach, for linear time-invariant two-time-scale and three-time-scale singularly perturbed systems in presence of actuator faults and external disturbances. First, the full order system will be controlled using v-dependent control law. The corresponding Lyapunov equation is ill-conditioned due to the presence of slow and fast phenomena. Secondly, a time-scale decomposition of the Lyapunov equation is carried out using singular perturbation method to avoid the numerical stiffness. A composite control law based on local controllers of the slow and fast subsystems is also used to make the control law ε-independent. The designed fault tolerant control guarantees the robust stability of the global closed-loop singularly perturbed system despite loss of effectiveness of actuators. The stability is proved based on the Lyapunov stability theory in the case where the singular perturbation parameter is sufficiently small. A numerical example is provided to illustrate the proposed method.

Analysis of temperature time series based on Hilbert-Huang Transform

In this paper, with consideration of the nonlinear and non-stationary properties of the temperature time series, we employ the Hilbert-Huang Transform, based on the empirical mode decomposition（EMD）, to analyze the temperature time series from 1959 to 2012 in the Fengxian district of Shanghai, obtained from a certain monitoring station. The oscillating mode is drawn from the data, and its characteristics of the time series are investigated. The results show that the intrinsic modes of 1, 2 and 6 represent the periodic properties of 1 year, 2.5 years, and 27 years. The mean temperature shows periodic variations, but the main trend of this fluctuation is the rising of the temperature in the recent 50 years. The analysis of the reconstructed modes with the wave pattern shows that the variations are quite large from 1963 to 1964, from 1977 to 1982 and from 2003 to 2006, which indicates that the temperature rises and falls dramatically in these periods. The volatility from 1993 to 1994 is far more dramatic than in other periods. And the volatility is the most remarkable in recent 50 years. The log-linear plots of the mean time scales T and M show that each mode associated with a time scale almost twice as large as the time scale of the preceding mode. The Hilbert spectrum shows that the energy is concentrated in the range of low frequency from 0.05 to 0.1 Hz, and a very small amount of energy is distributed in the range of higher frequency over 0.1 Hz. In conclusion, the HHT is better than other traditional signal analysis methods in processing the nonlinear signals to obtain the periodic variation and volatility＇s properties of different time scales.

Impacts of meteorology and precursor emission change on O_(3) variation in Tianjin, China from 2015 to 2021

Deterioration of surface ozone (O_(3)) pollution in Northern China over the past few years received much attention.For many cities,it is still under debate whether the trend of surface O_(3)variation is driven by meteorology or the change in precursors emissions.In this work,a time series decomposition method (Seasonal-Trend decomposition procedure based on Loess (STL)) and random forest (RF) algorithm were utilized to quantify the meteorological impacts on the recorded O_(3)trend and identify the key meteorological factors affecting O_(3)pollution in Tianjin,the biggest coastal port city in Northern China.After “removing” the meteorological fluctuations from the observed O_(3)time series,we found that variation of O_(3)in Tianjin was largely driven by the changes in precursors emissions.The meteorology was unfavorable for O_(3)pollution in period of 2015-2016,and turned out to be favorable during 2017-2021.Specifically,meteorology contributed 9.3μg/m^(3)O_(3)(13%) in 2019,together with the increase in precursors emissions,making 2019 to be the worst year of O_(3)pollution since 2015.Since then,the favorable effects of meteorology on O_(3)pollution tended to be weaker.Temperature was the most important factor affecting O_(3)level,followed by air humidity in O_(3)pollution season.In the midday of summer days,O_(3)pollution frequently exceeded the standard level (>160μg/m^(3)) at a combined condition with relative humidity in 40%-50%and temperature>31℃.Both the temperature and the dryness of the atmosphere need to be subtly considered for summer O_(3)forecasting.