Construction of continuous local climate zone time series from all available Landsat data for assessing the variability of urban climate

Space missions such as Kepler and CoRoT have led to large numbers of eclipse or transit measurements in nearly continuous time series. This paper shows how to obtain the period error in such measurements from a basic linear least-squares fit, and how to correctly derive the timing error in the prediction of future transit or eclipse events. Assuming strict periodicity, a formula for the period error of such time series is derived: sigma_P = sigma_T (12/( N^3-N))^0.5, where sigma_P is the period error; sigma_T the timing error of a single measurement and N the number of measurements. Relative to the iterative method for period error estimation by Mighell & Plavchan (2013), this much simpler formula leads to smaller period errors, whose correctness has been verified through simulations. For the prediction of times of future periodic events, the usual linear ephemeris where epoch errors are quoted for the first time measurement, are prone to overestimation of the error of that prediction. This may be avoided by a correction for the duration of the time series. An alternative is the derivation of ephemerides whose reference epoch and epoch error are given for the centre of the time series. For long continuous or near-continuous time series whose acquisition is completed, such central epochs should be the preferred way for the quotation of linear ephemerides. While this work was motivated from the analysis of eclipse timing measures in space-based light curves, it should be applicable to any other problem with an uninterrupted sequence of discrete timings for which the determination of a zero point, of a constant period and of the associated errors is needed.

The ocean helps in mitigating climate change by absorbing a large part of the excess heat and atmospheric carbon dioxide (CO2) produced by human activities. A decrease in surface ocean pH, known as ocean acidification, is a consequence of an increase in ocean uptake of CO2 concentrations which presents a significant challenge to various marine organisms, particularly those that rely on calcium for their structures (Metzl et al., 2024; Petton et al., 2024).  More than ever, consistent long-term observations of acidification and carbon cycling variables such as pH, temperature, salinity and CO2 are crucial to provide a quantitative assessment of the vulnerability of an area under climate and anthropogenic stressors. However, up to now, there are only a limited number of coastal observation sites where these parameters are measured simultaneously and at high frequency.In the framework of the ITINERIS project, financed by NextGenerationEU Programme (2022-2025), data on ocean acidification and carbon cycling parameters acquired by the meteo-oceanographic buoy MAMBO-1 (Monitoraggio AMBientale Operativo) were harmonized and standardized in order to obtain a consistent, up-to-date and FAIR (Findable, Accessible, Interoperable and Reusable) continuous time series (1999-2024). The MAMBO-1 buoy was the first meteorological-maritime coastal station to be installed in the northern Adriatic sea capable of recording meteorological and oceanographic parameters in near-real time (Partescano et al., 2014). The buoy is anchored at about 17 m in the seabed within the border of the Miramare Marine Protected Area in the Gulf of Trieste (45.6976667 °N and 13.7083333 °E) and has been operative since January 1999 (M. Lipizer et al., 2017). Over the years, the configuration and instrumentation of the site have changed several times, so it is difficult to obtain a continuous long-term time series from a data management perspective.The importance of the availability of the long-term time series justifies the reconstruction effort for future studies aimed at obtaining a clearer picture of ocean acidification and carbon cycle processes in the northern Adriatic Sea.ReferencesLipizer, M., Iungwirth, R., Arena, F., Brunetti, F., Bubbi, A., Comici, C., Deponte, D., Kuchler, S., Laterza, R., Mansutti, P., Medeot, N., Nair, R. (2017). Sistema di Monitoraggio AMBientale Operativo Boa MAMBO-1: revisione protocolli di acquisizione dati e registro tarature. https://doi.org/10.13120/7d9c292f-bc91-467d-a380-0483e814c000Metzl, N., Lo Monaco, C., Leseurre, C., Ridame, C., Reverdin, G., Chau, T. T. T., Chevallier, F., & Gehlen, M. (2024). Anthropogenic CO 2 , air–sea CO 2 fluxes, and acidification in the Southern Ocean: Results from a time-series analysis at station OISO-KERFIX (51° S–68° E). Ocean Science, 20(3), 725–758. https://doi.org/10.5194/os-20-725-2024Partescano, E., Giorgetti, A., Fanara, C., Crise, A., Oggioni, A., Brosich, A., & Carrara, P. (2014). A (Near) Real-time Validation and Standardization System Tested for MAMBO1 Meteo-marine Fixed Station. https://doi.org/10.13140/2.1.2788.4800Petton, S., Pernet, F., Le Roy, V., Huber, M., Martin, S., Macé, É., Bozec, Y., Loisel, S., Rimmelin-Maury, P., Grossteffan, É., Repecaud, M., Quemener, L., Retho, M., Manach, S., Papin, M., Pineau, P., Lacoue-Labarthe, T., Deborde, J., Costes, L., … Gazeau, F. (2024). French coastal network for carbonate system monitoring: The CocoriCO2 dataset. Earth System Science Data, 16(4), 1667–1688. https://doi.org/10.5194/essd-16-1667-2024

Stochastic rainfall generators have been commonly used in the field of hydrological and hydrodynamic modeling for a long time. These generators allow for an extensive ensemble of rainfall scenarios and continuous time series that is applicable for risk assessment and response variability studies under current and future climate conditions. Most rainfall generators simulate rainfall at daily scale and at point values. Recently some generators have been developed to produce gridded rainfall products. With advancement in weather radar technology a much more detailed representation of rainfall fields is now possible. This is especially needed in the field of urban hydrology.We developed the stochastic rainfall generator CON-SST-RAIN that is based on traditional dry/wet sequencing using Markov Chains and rainfall field generation by Stochastic Storm Transposition (SST), a time-in-space resampling method. CON-SST-RAIN was developed utilizing a 17-year long C-band radar dataset, with a spatio-temporal resolution of 500m x 500m and 10 minutes, discontinuous in time (discard of data) and Markov Chains are derived from rain gauges.CON-SST-RAIN can recreate continuous areal time series that captures the mean annual precipitation while also retaining seasonal and inter-annual variances. Extreme rain rates are likewise preserved and comparable to rain gauge data with +40 years of record.We test the CON-SST-RAIN on stochastically generated artificial hydrological networks to examine the importance of spatio-temporal dynamic rainfall fields. The networks are generated by a Gibbs sampling approach where the modeler can choose the extent and complexity of the generated network. Runoff from these networks is coupled with a simple detention pond model to estimate return periods for rainfall storage.

Assessing sensitivity to well leakage from three years of continuous reservoir pressure monitoring during CO2 injection at Cranfield, MS, USA

The selection of flow control device (FCD) location is an essential step for designing real-time control (RTC) systems in sewer networks. In this paper, existing storage volume-based approaches for location selection are compared with hydraulic optimisation-based methods using genetic algorithm (GA). A new site pre-screening methodology is introduced, enabling the deployment of optimisation-based techniques in large systems using standard computational resources. Methods are evaluated for combined sewer overflow (CSO) volume reduction using the CENTAUR autonomous local RTC system in a case study catchment, considering overflows under both design and selected historic rainfall events as well as a continuous 3-year rainfall time series. The performance of the RTC system was sensitive to the placement methodology, with CSO volume reductions ranging between −6 and 100% for design and lower intensity storm events, and between 15 and 36% under continuous time series. The new methodology provides considerable improvement relative to storage-based design methods, with hydraulic optimisation proving essential in relatively flat systems. In the case study, deploying additional FCDs did not change the optimum locations of earlier FCDs, suggesting that FCDs can be added in stages. Thus, this new method may be useful for the design of adaptive solutions to mitigate consequences of climate change and/or urbanisation.

Genest and Remillard have recently studied tests of randomness based on a decomposition of the serial independence empirical copula process into a finite number of asymptotically independent sub-processes. A generalization of this decomposition that can be used to test serial independence in the continuous multivariate time series framework is investigated. The weak limits of the Cramer–von Mises statistics derived from the various processes under consideration are determined. As these statistics are not distribution-free, the consistency of the bootstrap methodology is investigated. Extensive simulations are used to study the finite-sample behavior of the tests for continuous time series of dimension one to three, and comparisons with the portmanteau test are provided, as well as, in the one-dimensional case, with the ranked-based version of the Brock, Dechert, and Scheinkman test. Finally, the studied tests are applied to a real trivariate financial time series.

We have already proposed a nonlinear modeling method which uses event sizes and event timings. In this paper, we consider availability of our method for continuous time series by predicting occurrence timing of maxima and their sizes from continuous time series. In order to evaluate availability of our scheme, we introduce the prediction accuracy by the following two methods. The first one is to predict continuous time series, using all information of the continuous time series. The second is to extract maxima from continuous time series and apply our proposed modeling scheme to the maxima of time series. Comparing these results, we show that our method has higher predictability if there exists an underlying dynamics of observed complex behavior.

Noise analysis of continuous GPS coordinate time series for CMONOC

We propose an approach for stochastic simulation of realistic continuous snow depth time series using a snow depth estimation model and a stochastic weather generation model. The snow depth estimation model consists of three steps: (1) determination of the precipitation type, (2) estimation of  the snow ratio, and (3) estimation of the decreased snow depth. In the first step, air temperature and relative humidity are used as indicators to determine the type of precipitation when precipitation occurs. In the second step, when the type is determined as snow, the snow ratio is estimated, converting the depth of precipitation into depth of fresh snow. Here, the air temperature is used as an indicator to estimate the snow ratio using sigomidal relationship with the snow ratio. In the last step, the amount of decreased snow depth was estimated using a novel temperature index snowmelt equation considering a trend of depth-dependent decreasing snow depth. The snow depth estimation model was applied to the four snowiest meteorological stations of Korea and yielded high Nash Sutcliffe efficiency values which ranged between 0.745 and 0.875 for calibration, and ranged between 0.432 and 0.753 for validation. This calibrated snow depth estimation model was then applied to the simulated weather time series (precipitation, temperature, and relative humidity) from the stochastic weather generation model to simulate continuous snow depth time series. The simulated snow depth data accurately reproduced standard and extreme value statistics of the observed data, the latter of which were consistent with the estimates provided in Korean Building Code. Then, the model was extended to investigate the influence of climate change on the future snow depth. For this, future weather statistics were obtained by applying factor of change to the current weather statistics and then were used to calibrate the weather generation model. Lastly, the future snow depth time series for three future time windows (2021-2040, 2041-2070, and 2071-2100) were simulated using future weather time series and snow depth estimation model. This research was supported by a grant(2022-MOIS61-003) of Development Risk Prediction Technology of Storm and Flood for Climate Change based on Artificial Intelligence funded by Ministry of Interior and Safety(MOIS, Korea).

Pulse oximetry is routinely used to non-invasively monitor oxygen saturation levels. A low oxygen level in the blood means low oxygen in the tissues, which can ultimately lead to organ failure. Yet, contrary to heart rate variability measures, a field which has seen the development of stable standards and advanced toolboxes and software, no such standards and open tools exist for continuous oxygen saturation time series variability analysis. The primary objective of this research was to identify, implement and validate key digital oximetry biomarkers (OBMs) for the purpose of creating a standard and associated reference toolbox for continuous oximetry time series analysis. We review the sleep medicine literature to identify clinically relevant OBMs. We implement these biomarkers and demonstrate their clinical value within the context of obstructive sleep apnea (OSA) diagnosis on a total of n = 3806 individual polysomnography recordings totaling 26,686 h of continuous data. A total of 44 digital oximetry biomarkers were implemented. Reference ranges for each biomarker are provided for individuals with mild, moderate, and severe OSA and for non-OSA recordings. Linear regression analysis between biomarkers and the apnea hypopnea index (AHI) showed a high correlation, which reached overline R ^2 = 0.82. The resulting python OBM toolbox, denoted “pobm”, was contributed to the open software PhysioZoo (physiozoo.org). Studying the variability of the continuous oxygen saturation time series using pbom may provide information on the underlying physiological control systems and enhance our understanding of the manifestations and etiology of diseases, with emphasis on respiratory diseases.

Shrinking cities—cities suffering from population and economic decline—has become a pressing societal issue of worldwide concern. While night-time light (NTL) data have been applied as an important tool for the identification of shrinking cities, the current methods are constrained and biased by the lack of using long-term continuous NTL time series and the use of unidimensional indices. In this study, we proposed a novel method to identify and classify shrinking cities by long-term continuous NTL time series and population data, and applied the method in northeastern China (NEC) from 1996 to 2020. First, we established a long-term consistent NTL time series by applying a geographically weighted regression model to two distinct NTL datasets. Then, we generated NTL index (NI) and population index (PI) by random forest model and the slope of population data, respectively. Finally, we developed a shrinking city index (SCI), based on NI and PI to identify and classify city shrinkage. The results showed that the shrinkage pattern of NEC in 1996–2009 (stage 1) and 2010–2020 (stage 2) was quite different. From stage 1 to stage 2, the shrinkage situation worsened as the number of shrinking cities increased from 102 to 162, and the proportion of severe shrinkage increased from 9.2% to 30.3%. In stage 2, 85.4% of the cities exhibited population decline, and 15.7% of the cities displayed an NTL decrease, suggesting that the changes in NTL and population were not synchronized. Our proposed method provides a robust and long-term characterization of city shrinkage and is beneficial to provide valuable information for sustainable urban planning and decision-making.

A hybrid machine learning model of the principal component analysis and neural network is described for the continuous microarray gene expression time series. The methodology can model numerically the continuous gene expression time series. The proposed model can give us the extracted features from the gene expressions time series with higher prediction accuracies. It can help practitioners to gain a better understanding of a cell cycle, and to find the dependency of genes, which is useful for drug discoveries. In this chapter, we describe the background, the machine learning algorithms, and then the application of the hybrid machine learning in the microarray analysis. The machine learning model is compared with other popular continuous prediction methods. Based on the results of two public microarray datasets, the hybrid method outperforms the other continuous prediction methods.Machine learningNeural networkMicroarrayGene expressionTime series prediction

Predictions of Taylor's power law, density dependence and pink noise from a neutrally modeled time series

The Astronomical Institute of the University of Berne is hosting one of the Analysis Centers (AC) of the International GPS Service (IGS). A network of a few GPS stations in Europe and North America is routinely analyzed for time transfer purposes, using the carrier phase observations. This work is done in the framework of a joint project with the Swiss Federal Office of Metrology and Accreditation (METAS). The daily solutions are computed independently. The resulting time transfer series show jumps of up to 1 ns at the day boundaries. A method to concatenate the daily time transfer solutions to a continuous series was developed. A continuous time series is available for a time span of more than 4 mo. The results were compared with the time transfer results from other techniques such as two-way satellite time and frequency transfer. This concatenation improves the results obtained in a daily computing scheme because a continuous time series better reflects the characteristics of continuously working clocks.

In this study, a stochastic rainfall generator was developed to create continuous rainfall time series with a high temporal resolution of 10 min. The rainfall-generation process involved Monte Carlo simulation for stochastically generating rainfall parameters such as rainfall quantity, duration, inter-event time, and type. A bivariate copula was used to preserve the correlation between rainfall quantity and rainfall duration in the generated rainfall series. A modified Huff curve method was used to overcome the drawbacks of rainfall type classification by using the conventional Huff curve method. The number of discarded rainfall events was lower in the modified Huff curve method than in the conventional Huff curve method. Moreover, the modified method includes a new rainfall type that better represents rainfall events with a relatively uniform temporal pattern. The developed rainfall generator was used to reproduce rainfall series for the Yilan River Basin in Taiwan. The statistical indices of the generated rainfall series were close to those of the observed rainfall series. The results obtained for rainfall type classification indicated the necessity and suitability of the proposed new rainfall type. Overall, the developed stochastic rainfall generator can suitably reproduce continuous rainfall time series with a resolution of 10 min.