Observed Series Data Research Articles

Outlier detection and sequence reconstruction in continuous time series of ocean observation data based on difference analysis and the Dixon criterion

AbstractIn light of the specific characteristics of the long‐term record and complex sequence nature of the ocean observation data, a new method was developed based on the original Dixon detection criteria to be specifically detect and remove data outliers. This method combines the two traditional methods of data quality control and Dixon detection theory and assumes that the second‐order differential sequence of parameter measurements passes an appropriate stationarity test. Thus, the measurement attributes are considered to be in the same physical state and to occupy a small range of time and space, equivalent to a parallel observation test. Provided that the observations over a small range of time and space correspond to the record of a sequence covering a short period of time, this short time sequence is treated as a sliding window in the proposed new method. Outliers are detected based on lookup‐table after an index parameter Q is calculated within the sliding window. A correlation analysis and the test results show that the proposed new method can effectively instantiate a sequence of outliers characterized by different phases. Compared with other existing methods, the new method proved to be computationally efficient and easily programmable for practical implementation. Further, this method preserves the original data because the outliers are replaced by an inverse distance‐weighted average of the recorded data within the window, while other data were intact.

Climate change and vector-borne diseases of public health significance.

There has been much debate as to whether or not climate change will have, or has had, any significant effect on risk from vector-borne diseases. The debate on the former has focused on the degree to which occurrence and levels of risk of vector-borne diseases are determined by climate-dependent or independent factors, while the debate on the latter has focused on whether changes in disease incidence are due to climate at all, and/or are attributable to recent climate change. Here I review possible effects of climate change on vector-borne diseases, methods used to predict these effects and the evidence to date of changes in vector-borne disease risks that can be attributed to recent climate change. Predictions have both over- and underestimated the effects of climate change. Mostly under-estimations of effects are due to a focus only on direct effects of climate on disease ecology while more distal effects on society's capacity to control and prevent vector-borne disease are ignored. There is increasing evidence for possible impacts of recent climate change on some vector-borne diseases but for the most part, observed data series are too short (or non-existent), and impacts of climate-independent factors too great, to confidently attribute changing risk to climate change.

手持ち LIDAR による地図作成の現場適用に向けた失敗検出法

The paper presents a failure detection of the Gyro-integrated Iterative Closest Point Simultaneous Localization and Mapping (G-ICP SLAM) for useful 3D mapping application. The proposed failure detection use gyroscope input and localization results of SLAM process. It might be effective for other ICP based SLAM algorithms. In SLAM based mapping system, it generates the map by adding time series of observation data sequentially. Some wrong projections affect subsequent calculation and the mapping error detection is important function for practical use. We first define the error pattern of mapping result and propose evaluation functions to detect de- fined mapping errors. The proposed method is implemented to G-ICP SLAM and evaluated with hand-held sensor observation data. The evaluation result show that the system could correctly detect skewed part of mapping process and recover automatically.

Early signs of geodynamic activity before the 2011–2012 El Hierro eruption

The potential relation between mantle plume dynamics, regional tectonics and eruptive activity in the Canary Islands has not been studied yet through the analysis of long-time series of geophysical observational data. The existence of highly reliable seismic and geodetic data has enabled us to study from 1996 to 2014 the geodynamic evolution of the North Atlantic Azores-Gibraltar region (including the NW African margin) and its relationship with recent volcanic activity in El Hierro (Canary Islands). We compiled a new and unified regional seismic catalog and used long time-series of digital 3D surface displacements recorded by permanent GPS stations in the region. A joint regional- and local-scale analysis based on these data enabled us to identify signs of anomalous tectonic activity from 2003 onwards, whose intensity increased in 2007 and finally accelerated three months before the onset of the volcanic eruption on El Hierro in October 2011. Activity included the occurrence of regional extension and an uplift process affecting the southern Iberian Peninsula, NW Africa, and the Canary Islands. We interpret these observations as early signs of the geodynamic activity, which led to El Hierro eruption and the subsequent episodes of magma intrusion. Results point to the significant contribution of the mantle plume dynamics (i.e. external forces) in this renewed volcanic activity in the Canary Islands and emphasize the role of mantle dynamics in controlling regional tectonics.

Time series analysis of carrier phase differences for dual-frequency GPS high-accuracy positioning

The GPS position based on carrier phase measurement is produced and developed for the exaltation of positioning accuracy. However, in practice, carrier phase observations often include various errors besides essential parameters. Sometimes even satellite signals may be temporarily interrupted and lead to cycle slips. Most of errors can be eliminated with differencing of carrier phase observation. But phase center offsets (PCOs) are a key source of errors in GPS precise measurements and hardly eliminated or weakened by difference methods. Meanwhile as a gross error, cycle slips decreases badly positioning accuracy but can't remove by differencing approach. For all this, based on the feature of cycle slips and time series analysis theory, a quickly and convenient method of eliminating various errors, detecting and correcting cycle slips and PCOs is presented in this paper for high accuracy positioning solution. The key of this method is to build difference series between carrier phase observations of satellites, stations and epochs. These difference series can be used to eliminate various errors implied in observation data, and highlight the features of cycle slips. Based on the time-series analysis theory, the model of difference series of carrier phase observation data is built, cycle slips can be quickly detected by the stability of this time series. At last, the experiment results show that the new method can not only be suitable both for static and dynamic measurements of dual-frequency GPS receivers, but also can eliminate most measurement errors of dual-frequency GPS receivers, improve the efficiency of detection and correction of PCOs and cycle slips less than 1 cycle.

Modeling 25 years of spatio-temporal surface water and inundation dynamics on large river basin scale using time series of Earth observation data

Abstract. The usage of time series of Earth observation (EO) data for analyzing and modeling surface water extent (SWE) dynamics across broad geographic regions provides important information for sustainable management and restoration of terrestrial surface water resources, which suffered alarming declines and deterioration globally. The main objective of this research was to model SWE dynamics from a unique, statistically validated Landsat-based time series (1986–2011) continuously through cycles of flooding and drying across a large and heterogeneous river basin, the Murray–Darling Basin (MDB) in Australia. We used dynamic linear regression to model remotely sensed SWE as a function of river flow and spatially explicit time series of soil moisture (SM), evapotranspiration (ET), and rainfall (P). To enable a consistent modeling approach across space, we modeled SWE dynamics separately for hydrologically distinct floodplain, floodplain-lake, and non-floodplain areas within eco-hydrological zones and 10km × 10km grid cells. We applied this spatial modeling framework to three sub-regions of the MDB, for which we quantified independently validated lag times between river gauges and each individual grid cell and identified the local combinations of variables that drive SWE dynamics. Based on these automatically quantified flow lag times and variable combinations, SWE dynamics on 233 (64 %) out of 363 floodplain grid cells were modeled with a coefficient of determination (r2) greater than 0.6. The contribution of P, ET, and SM to the predictive performance of models differed among the three sub-regions, with the highest contributions in the least regulated and most arid sub-region. The spatial modeling framework presented here is suitable for modeling SWE dynamics on finer spatial entities compared to most existing studies and applicable to other large and heterogeneous river basins across the world.

Wearable Cardioverter-Defibrillator Therapy for the Prevention of Sudden Cardiac Death: A Science Advisory From the American Heart Association.

Sudden cardiac death (SCD) accounts for >300 000 deaths in the United States annually.1 Although the majority of these deaths occur in low-risk populations2 in which aggressive interventions are not practical, some higher-risk populations have been established in whom intervention with an implantable cardioverter-defibrillator (ICD) has been shown in randomized trials to reduce mortality.3–7 Additionally, there is a population of patients who may benefit from automatic emergency cardioversion-defibrillation but are not deemed appropriate candidates for ICD implantation at the time of presentation. This group is defined by 2 populations. The first subgroup comprises those who are at perceived risk but for whom there may be optimism for clinical improvement (eg, patients soon after revascularization or those with a recent diagnosis of myocardial infarction [MI] or cardiomyopathy). Alternatively stated, the optimal management of these patients at risk (or perceived risk) during the waiting period before an ICD is indicated remains unknown. The second subgroup includes those who have a clear indication for ICD but also have a contraindication to immediate ICD placement (eg, active infection or unknown prognosis). The wearable cardioverter-defibrillator (WCD) is a device designed for patients at risk of SCD who are not immediate candidates for ICD therapy. By providing automatic therapy, the WCD does not depend on a second person to defibrillate, as required with a manual or automated external defibrillator (AED). Unlike the ICD (including both transvenous and subcutaneous devices), the WCD requires no surgical operation, can be provided for a short period of time, is temporary, and is easily removed. These characteristics of the WCD, along with safety and efficacy data presented to the US Food and Drug Administration (FDA), resulted in approval in the United States in 2002.8 Because of the increasing use of the WCD and uncertainty of indications …

Sensitivity and uncertainty analysis on water quality modelling of Aguamilpa reservoir

<p>The use of water quality models is determined to a great extent by their ability to accurately reproduce observed data series and by their predictive capability without the need to adjust the calibrated parameters. However, the observed abiotic variables involved in a system are measured with a level of uncertainty. The sensitivity analysis concepts and generalized methodology of uncertainty analysis were used via a computer tool called UNCSIM. As a first step, a parametric optimization model of the Aguamilpa reservoir water quality was accomplished. The aforementioned analysis identified that the wind sheltering coefficient (WSC), Chezy bottom friction solution (FRICC), and coefficient of bottom heat exchange (CBHE) were the parameters of the CE-QUAL-W2 model that significantly influenced the behaviour of temperature and dissolved oxygen concentration in the reservoir. Afterwards, the uncertainty of the water quality model was evaluated through the modification of the hydrological and climatological information, which had a major influence on the simulation of the system. This analysis showed the possible changes in hydrodynamic and water quality characteristics of the reservoir, including an increase in the thermocline due to a possible air temperature increase and a rainfall decrease in the region. The innovative coupling routine of the different modules for the sensitivity and uncertainty analysis developed in this research establishes the basis for the future development of a modelling platform to conduct water quality simulations of the Aguamilpa reservoir in real time through continuous meteorological and water discharge information.<strong><br clear="all" /> </strong><strong></strong></p>

Application of wavelet-artificial intelligence hybrid models for water quality prediction: a case study in Aji-Chay River, Iran

The accuracy of Artificial Neural Network (ANN), Adaptive Neuro-Fuzzy Inference System (ANFIS), wavelet-ANN and wavelet-ANFIS in predicting monthly water salinity levels of northwest Iran’s Aji-Chay River was assessed. The models were calibrated, validated and tested using different subsets of monthly records (October 1983 to September 2011) of individual solute (Ca2+, Mg2+, Na+, SO4 2− and Cl−) concentrations (input parameters, meq L−1), and electrical conductivity-based salinity levels (output parameter, µS cm−1), collected by the East Azarbaijan regional water authority. Based on the statistical criteria of coefficient of determination (R2), normalized root mean square error (NRMSE), Nash–Sutcliffe efficiency coefficient (NSC) and threshold statistics (TS) the ANFIS model was found to outperform the ANN model. To develop coupled wavelet-AI models, the original observed data series was decomposed into sub-time series using Daubechies, Symlet or Haar mother wavelets of different lengths (order), each implemented at three levels. To predict salinity input parameter series were used as input variables in different wavelet order/level-AI model combinations. Hybrid wavelet-ANFIS (R2 = 0.9967, NRMSE = 2.9 × 10−5 and NSC = 0.9951) and wavelet-ANN (R2 = 0.996, NRMSE = 3.77 × 10−5 and NSC = 0.9946) models implementing the db4 mother wavelet decomposition outperformed the ANFIS (R2 = 0.9954, NRMSE = 3.77 × 10−5 and NSC = 0.9914) and ANN (R2 = 0.9936, NRMSE = 3.99 × 10−5 and NSC = 0.9903) models.

Deterministic Chaos in the X-ray Sources

Hardly any of the observed black hole accretion disks in X-Ray binaries and active galaxies shows constant flux. When the local stochastic variations of the disk occur at specific regions where a resonant behaviour takes place, there appear the Quasi-Periodic Oscillations (QPOs). If the global structure of the flow and its non-linear hydrodynamics affects the fluctuations, the variability is chaotic in the sense of deterministic chaos. Our aim is to solve a problem of the stochastic versus deterministic nature of the black hole binaries vari- ability. We use both observational and analytic methods. We use the recurrence analysis and we study the occurence of long diagonal lines in the recurrence plot of observed data series and compare it to the sur- rogate series. We analyze here the data of two X-Ray binaries - XTE J1550-564, and GX 339-4 observed by Rossi X-ray Timing Explorer. In these sources, the non-linear variability is expected because of the global conditions (such as the mean accretion rate) leading to the pos- sible instability of an accretion disk. The thermal-viscous instability and fluctuations around the fixed-point solution occurs at high accretion rate, when the radiation pressure gives dominant contribution to the stress tensor.

Tendências observadas em indicadores de extremos climáticos de temperatura e precipitação no estado do Paraná

O presente trabalho faz uma análise das tendências de extremos climáticos baseada em indicadores calculados a partir de séries de dados observacionais diários de temperatura e de precipitação durante 35 anos em 20 estações meteorológicas do IAPAR, no Estado do Paraná. O objetivo é contribuir para estudos sobre avaliação de impactos e vulnerabilidade climática, requeridos para elaborar estratégias de adaptação às mudanças climáticas. Inicialmente as climatologias de temperatura do ar e de precipitação no Estado são revisitas. Com relação às tendências de extremos climáticos, os resultados dos indicadores associados à temperatura apontam para um padrão de aquecimento generalizado estatisticamente significativo em grande parte do Paraná. A porcentagem de dias e noites quentes no Estado aumentou consideravelmente a uma taxa de 0,1 a 0,4 %/ano entre 1976 e 2010. As médias anuais das temperaturas mínimas e máximas indicam uma elevação em quase todo o Paraná em torno de +0,02°C/ano no mesmo período. Os indicadores de temperatura mínima apresentam tendências de aumento mais significativas que os de temperatura máxima. Por outro lado, a maior parte dos indicadores de extremos climáticos associados à precipitação não apresenta significância estatística, com exceção de alguns poucos em distintas regiões do Estado. Destes, destacam-se apenas as tendências de elevação nos períodos secos (+0,25 mm/ano) em Pato Branco e Planalto no sudoeste do Paraná e de redução das chuvas fortes (-0,5 mm/ano) em Cambará, Ibiporã e Umuarama no norte do Estado.

An evaluation of the effect of future climate on runoff in the Dongjiang River basin, South China

Abstract. The impact of future climate change on the runoff for the Dongjiang River basin, South China, has been investigated with the Soil and Water Assessment Tool (SWAT). First, the SWAT model was applied in the three sub-basins of the Dongjiang River basin, and calibrated for the period of 1970–1975, and validated for the period of 1976–1985. Then the hydrological response under climate change and land use scenario in the next 40 years (2011–2050) was studied. The future weather data was generated by using the weather generators of SWAT, based on the trend of the observed data series (1966–2005). The results showed that under the future climate change and LUCC scenario, the annual runoff of the three sub-basins all decreased. Its impacts on annual runoff were –6.87%, –6.54%, and –18.16% for the Shuntian, Lantang, and Yuecheng sub-basins respectively, compared with the baseline period 1966–2005. The results of this study could be a reference for regional water resources management since Dongjiang River provides crucial water supplies to Guangdong Province and the District of Hong Kong in China.

Assimilation of satellite data to 3D hydrodynamic model of Lake Säkylän Pyhäjärvi.

To analyze the applicability of direct insertion of total suspended matter (TSM) concentration field based on turbidity derived from satellite data to numerical simulation, dispersion studies of suspended matter in Lake Säkylän Pyhäjärvi (lake area 154 km²; mean depth 5.4 m) were conducted using the 3D COHERENS simulation model. To evaluate the practicality of direct insertion, five cases with different initialization frequencies were conducted: (1) every time, when satellite data were available; (2) every 10 days; (3) 20 days; (4) 30 days; and (5) control run without repeated initialization. To determine the effectiveness of initialization frequency, three methods of comparison were used: simple spatial differences of TSM concentration without biomass in the lake surface layer; averaged spatial differences between initialization data and the forecasts; and time series of TSM concentration and observation data at 1 m depth at the deepest point of the lake. Results showed that direct insertion improves the forecast significantly, even if it is applied less often.

Understanding the diurnal cycle in fluvial dissolved organic carbon – The interplay of in-stream residence time, day length and organic matter turnover

There is increasing interest in characterising the diurnal fluctuation of stream solute concentrations because observed data series derived from spot samples may be highly subjective if such diurnal fluctuations are large. This can therefore lead to large uncertainties, bias or systematic errors in calculation of fluvial solute fluxes, depending upon the particular sampling regime. A simplistic approach would be to assume diurnal fluctuations are constant throughout the water year, but this study proposes diurnal cycles in stream water quality can only be interpreted in the context of stream residence time and changing day length. Three years of hourly dissolved organic carbon (DOC) concentration and flow data from the River Dee catchment (1674 km2) were analysed, and statistical analysis of the entire record shows there is no consistent diurnal cycle in the record. From the 3-year record (1095 days) there were only 96 diurnal cycles could be analysed. Cycles were quantified in terms of their: relative and absolute amplitude; duration; time to maximum concentration; asymmetry; percentile flow and in-stream residence time. The median diurnal cycle showed an amplitude that was 9.2% of the starting concentration; it was not significantly asymmetric; and occurred at the 19th percentile flow. The median DOC removal rate was 0.07 mg C/l/hr with an inter-quartile range of 0.052–0.100 mg C/l/hr. Results were interpreted as controlled by two, separate, zero-order kinetic rate laws, one for the day and one for the night. There was no single diurnal cycle present across the record, rather a number of different cycles controlled by the combination of in-stream residence time and exposure to contrasting light conditions. Over the 3-year period the average in-stream loss of DOC was 32%. The diurnal cycles evident in high resolution DOC data are interpretable, but require contextual information for their influence on in-stream processes to be understood or for them to be utilised.

Retrieval of the maximum horizontal current speed induced by ocean internal solitary waves from low resolution time series mooring data based on the KdV theory

Internal solitary waves (ISWs) are the nonlinear large amplitude waves existing in the oceanic pycnocline. Normally, it is difficult to obtain the maximum horizontal current speed induced by ISW since most of the passage periods of ISWs are no more than 30min while the observational sampling interval is too low; meanwhile, to avoid power shortage of the mooring Acoustic Doppler Current Profiler-carried electric battery, oceanographers who are only concerned about the study of circulation or internal tide would choose a sampling interval of no less than 5min. Can we use these low-time-resolution observational data to retrieve the maximum horizontal current speed induced by ISW? In this article, based on Korteweg-de Vries (KdV) equation theory (shallow-water theory), we propose a method to retrieve the maximum horizontal current speed induced by ISW. According to the calculated results based on the observational current data of ISWs near the Dongsha Islands in the northern South China Sea, it is found that, our method could well retrieve the maximum horizontal current speed induced by ISW from the low-time-resolution observational current data series, meanwhile, when the sampling interval of observational data or the ISW characteristic half-width increases, or when the ISW nonlinear phase speed decreases, the relative error of the retrieved maximum horizontal current speed will reduce. The applicability of the method to ISWs in finite-depth is also discussed.

A New Method of Dam Safety Monitoring for Identifying Displacement Mutation

For the phenomenon of data pulsating indefinitely in observed data series, a safety monitoring model for concrete dam displacement is constructed in consideration of dynamic mutation’s blind spot based on dynamic cross-correlation factor index and cloud model theory. On the basis of evolvement analysis on dam deformation using uncertain dynamic system,while comprehensively applying numerical methods of phase space reconstruction, cloud model, wavelet analysis, a diagnosis method to predict abnormality variation of working state of dam is established. Project examples show the method can identify the dynamic mutation blind spot and be a better solution to eliminate its adverse influence on deformation series. Hereby, the new method can improve the reliability and scientific of dam displacement safety monitoring model.

Uncertainties in assessing hydrological drought using streamflow drought index for the upper Yangtze River basin

Drought is an environmental disaster which is frequently and world-widely occurred in recent years. Precisely assessment and prediction of drought is important for water resources planning and management. Sampling uncertainty commonly exists in frequency analysis-based hydrological drought assessment due to the limited length of observed data series. Based on the daily streamflow data of the Yichang hydrological station from 1882 to 2009, the streamflow drought index (SDI) series with 12-month time scale was calculated and the hydrological drought of the upper Yangtze River was assessed. By employing the bootstrap method, the impact of sample size on the sampling uncertainty of the SDI was analyzed. The longer record is used to derive the SDI, the narrower the shifting ranges of the parameters of the streamflow volume probability distribution functions and corresponding interval estimators of SDI are. The upper Yangtze River basin has experienced successive alternation of wet and dry years, and the spring seems to be the driest season within a year. The current difficulty in fighting against increasing droughts in upper Yangtze River basin is upgrading. Considering the possible misjudgment of drought degree results from the sampling uncertainty, attention should be paid to the preparation of drought relief strategies in order to reduce the potential losses.

Parametric Estimation of the Stochastic Dynamics of Sea Surface Winds

Abstract In this study, the parameters of a stochastic–dynamical model of sea surface winds are estimated from long time series of sea surface wind observational data. The model was introduced by A. H. Monahan, who developed an idealized model from a highly simplified representation of the momentum budget of a surface atmospheric layer of fixed depth. Such estimation of model parameters is challenging, in particular for a multivariate model with nonlinear terms as is considered here. The authors use a method developed recently by Crommelin and Vanden-Eijnden, which approaches the estimation problem variationally, finding the spectrally “best fit” stochastic differential equation to a time series of observations. While the estimation procedure assumes forcing that is white in time, observed time series are generally better approximated as forced by red noise. Using a red-noise-forced linear system, the authors first show that the estimation procedure can still be used to estimate model parameters. Because the assumption of white noise is violated, these estimates lead to model autocorrelation functions that differ from the observed time series. Application of the estimation procedure to the wind data is further complicated by the fact that the boundary layer model is inconsistent with certain observed features of the wind. When these mismatches between the model and observations are accounted for, the estimation procedure generally results in parameter estimates consistent with the climatological features of the associated meteorological fields. Important exceptions to this result are the layer thickness and layer-top eddy diffusivity, which are poorly estimated where the vector winds are close to Gaussian.

The variability of the snow and ice melt in alpine rivers in northwestern China

The study of snow and ice melt (SIM) is important in water-scarce arid regions for the assessment of water supply and quality. These studies involve unique difficulties, especially in the calibration of hydro-models because there is no direct way to continuously measure the SIM at hydrostations. The recursive digital filter (RDF) and the isotopic hydro-geochemical method (IHM) were coupled to separate the SIM from eight observed series of alpine streamflows in northwestern China. Validation of the calibrated methods suggested a good capture of the SIM characteristics with fair accuracy in both space and time. Applications of the coupled methods in the upper reaches of the Hei River Basin (HRB) suggested a double peak curve of the SIM fraction to streamflow for the multi-component recharged (MCR) rivers, while a single peak curve was suggested for the rainfall-dominant recharged (RDR) rivers. Given inter-annual statistics of the separation, both types of the alpine rivers have experienced an obvious decrease of SIM since 1960s. In the past 10 years, the SIM in the two types of rivers has risen to the levels of the 1970s, but has remained lower than the level of the 1960s. The study provided a considerable evidence to quantify the alpine SIM based on the separation of observed data series at gauge stations. Application of the coupled method could be helpful in the calibration and validation of SIM-related hydro-models in alpine regions.

Time series analysis of contaminant transport in the subsurface: Applications to conservative tracer and engineered nanomaterials

Accurately predicting the transport of contaminants in the field is subject to multiple sources of uncertainty due to the variability of geological settings, the complexity of field measurements, and the scarcity of data. Such uncertainties can be amplified when modeling some emerging contaminants, such as engineered nanomaterials, when a fundamental understanding of their fate and transport is lacking. Typical field work includes collecting concentration at a certain location for an extended period of time, or measuring the movement of plume for an extended period time, which would result in a time series of observation data. This work presents an effort to evaluate the possibility of applying time series analysis, particularly, autoregressive integrated moving average (ARIMA) models, to forecast contaminant transport and distribution in the subsurface environment. ARIMA modeling was first assessed in terms of its capability to forecast tracer transport at two field sites, which had different levels of heterogeneity. After that, this study evaluated the applicability of ARIMA modeling to predict the transport of engineered nanomaterials at field sites, including field measured data of nanoscale zero valent iron and (nZVI) and numerically generated data for the transport of nano-fullerene aggregates (nC60). This proof-of-concept effort demonstrates the possibility of applying ARIMA to predict the contaminant transport in the subsurface environment. Like many other statistical models, ARIMA modeling is only descriptive and not explanatory. The limitation and the challenge associated with applying ARIMA modeling to contaminant transport in the subsurface are also discussed.