Review of Joint inversion of proxy system models to reconstruct paleoenvironmental time series from heterogeneous data

Abstract. Paleoclimatic and paleoenvironmental reconstructions are fundamentally uncertain because no proxy is a direct record of a single environmental variable of interest; all proxies are indirect and sensitive to multiple forcing factors. One productive approach to reducing proxy uncertainty is the integration of information from multiple proxy systems with complementary, overlapping sensitivity. Mostly, such analyses are conducted in an ad hoc fashion, either through qualitative comparison to assess the similarity of single-proxy reconstructions or through step-wise quantitative interpretations where one proxy is used to constrain a variable relevant to the interpretation of a second proxy. Here we propose the integration of multiple proxies via the joint inversion of proxy system and paleoenvironmental time series models in a Bayesian hierarchical framework. The “Joint Proxy Inversion” (JPI) method provides a statistically robust approach to producing self-consistent interpretations of multi-proxy datasets, allowing full and simultaneous assessment of all proxy and model uncertainties to obtain quantitative estimates of past environmental conditions. Other benefits of the method include the ability to use independent information on climate and environmental systems to inform the interpretation of proxy data, to fully leverage information from unevenly and differently sampled proxy records, and to obtain refined estimates of proxy model parameters that are conditioned on paleo-archive data. Application of JPI to the marine Mg∕Ca and δ18O proxy systems at two distinct timescales demonstrates many of the key properties, benefits, and sensitivities of the method, and it produces new, statistically grounded reconstructions of Neogene ocean temperature and chemistry from previously published data. We suggest that JPI is a universally applicable method that can be implemented using proxy models of wide-ranging complexity to generate more robust, quantitative understanding of past climatic and environmental change.

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Joint inversion of proxy system models to reconstruct paleoenvironmental time series from heterogeneous data

10.5194/cp-2018-178 ◽

2019 ◽

Author(s):

Gabriel J. Bowen ◽

Brenden Fisher-Femal ◽

Gert-Jan Reichart ◽

Appy Sluijs ◽

Caroline H. Lear

Keyword(s):

Time Series ◽

Ad Hoc ◽

Joint Inversion ◽

Environmental Variable ◽

Heterogeneous Data ◽

Published Data ◽

Model Parameters ◽

Qualitative Comparison ◽

Bayesian Hierarchical ◽

Proxy Reconstructions

Abstract. Paleoclimatic and paleoenvironmental reconstructions are fundamentally uncertain because no proxy is a direct record of a single environmental variable of interest; all proxies are indirect and sensitive to multiple forcing factors. One productive approach to reducing proxy uncertainty is the integration of information from multiple proxy systems with complimentary, overlapping sensitivity. Most such analyses are conducted in an ad-hoc fashion, either through qualitative comparison to assess the similarity of single-proxy reconstructions or through step-wise quantitative interpretations where one proxy is used to constrain a variable relevant to the interpretation of a second proxy. Here we propose the integration of multiple proxies via the joint inversion of proxy system and paleoenvironmental time series models in a Bayesian hierarchical framework. The "Joint Proxy Inversion" (JPI) method provides a statistically robust approach to producing self-consistent interpretations of multi-proxy datasets, allowing full and simultaneous assessment of all proxy and model uncertainties to obtain quantitative estimates of past environmental conditions. Other benefits of the method include the ability to use independent information on climate and environmental systems to inform the interpretation of proxy data, to fully leverage information from unevenly- and differently-sampled proxy records, and to obtain refined estimates of proxy model parameters that are conditioned on paleo-archive data. Application of JPI to the marine Mg / Ca and δ18O proxy systems at two distinct timescales demonstrates many of the key properties, benefits, and sensitivities of the method, and produces new, statistically-grounded reconstructions of Neogene ocean temperature and chemistry from previously published data. We suggest that JPI is a universally applicable method that can be implemented using proxy models of wide-ranging complexity to generate more robust, quantitative understanding of past climatic and environmental change.

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A new energy-balance approach to linear filtering for estimating effective radiative forcing from temperature time series

Advances in Statistical Climatology Meteorology and Oceanography ◽

10.5194/ascmo-6-91-2020 ◽

2020 ◽

Vol 6 (2) ◽

pp. 91-102

Author(s):

Donald P. Cummins ◽

David B. Stephenson ◽

Peter A. Stott

Keyword(s):

Time Series ◽

Energy Balance ◽

Surface Temperature ◽

Radiative Forcing ◽

Earth System ◽

Linear Filtering ◽

Past Climate ◽

System Models ◽

Effective Radiative Forcing ◽

Earth System Models

Abstract. Reliable estimates of historical effective radiative forcing (ERF) are important for understanding the causes of past climate change and for constraining predictions of future warming. This study proposes a new linear-filtering method for estimating historical radiative forcing from time series of global mean surface temperature (GMST), using energy-balance models (EBMs) fitted to GMST from CO2-quadrupling general circulation model (GCM) experiments. We show that the response of any k-box EBM can be represented as an ARMA(k, k−1) (autoregressive moving-average) filter. We show how, by inverting an EBM's ARMA filter representation, time series of surface temperature may be converted into radiative forcing. The method is illustrated using three-box EBM fits to two recent Earth system models from CMIP5 and CMIP6 (Coupled Model Intercomparison Project). A comparison with published results obtained using the established ERF_trans method, a purely GCM-based approach, shows that our new method gives an ERF time series that closely matches the GCM-based series (correlation of 0.83). Time series of estimated historical ERF are obtained by applying the method to a dataset of historical temperature observations. The results show that there is clear evidence of a significant increase over the historical period with an estimated forcing in 2018 of 1.45±0.504 W m−2 when derived using the two Earth system models. This method could be used in the future to attribute past climate changes to anthropogenic and natural factors and to help constrain estimates of climate sensitivity.

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A Deep Learning and GIS Approach for the Optimal Positioning of Wave Energy Converters

Energies ◽

10.3390/en14206773 ◽

2021 ◽

Vol 14 (20) ◽

pp. 6773

Author(s):

Georgios Batsis ◽

Panagiotis Partsinevelos ◽

Georgios Stavrakakis

Keyword(s):

Neural Network ◽

Land Use ◽

Time Series ◽

Wave Energy ◽

Renewable Energy Sources ◽

Heterogeneous Data ◽

Electricity Production ◽

Final Decision ◽

Wave Energy Converters ◽

Energy Converters

Renewable Energy Sources provide a viable solution to the problem of ever-increasing climate change. For this reason, several countries focus on electricity production using alternative sources. In this paper, the optimal positioning of the installation of wave energy converters is examined taking into account geospatial and technical limitations. Geospatial constraints depend on Land Use classes and seagrass of the coastal areas, while technical limitations include meteorological conditions and the morphology of the seabed. Suitable installation areas are selected after the exclusion of points that do not meet the aforementioned restrictions. We implemented a Deep Neural Network that operates based on heterogeneous data fusion, in this case satellite images and time series of meteorological data. This fact implies the definition of a two-branches architecture. The branch that is trained with image data provides for the localization of dynamic geospatial classes in the potential installation area, whereas the second one is responsible for the classification of the region according to the potential wave energy using wave height and period time series. In making the final decision on the suitability of the potential area, a large number of static land use data play an important role. These data are combined with neural network predictions for the optimizing positioning of the Wave Energy Converters. For the sake of completeness and flexibility, a Multi-Task Neural Network is developed. This model, in addition to predicting the suitability of an area depending on seagrass patterns and wave energy, also predicts land use classes through Multi-Label classification process. The proposed methodology is applied in the marine area of the city of Sines, Portugal. The first neural network achieves 98.7% Binary Classification accuracy, while the Multi-Task Neural Network 97.5% in the same metric and 93.5% in the F1 score of the Multi-Label classification output.

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A Long Short Term Memory with Peephole Connections and Generative Adversarial Network Based Collaborative Methodology to Identify Outliers in ECG Dataset

Journal of Computational and Theoretical Nanoscience ◽

10.1166/jctn.2020.9273 ◽

2020 ◽

Vol 17 (8) ◽

pp. 3798-3803

Author(s):

M. D. Anto Praveena ◽

B. Bharathi

Keyword(s):

Time Series ◽

Big Data ◽

Data Analytics ◽

Time Series Data ◽

Short Term Memory ◽

Big Data Analytics ◽

Data Preprocessing ◽

Heterogeneous Data ◽

Series Data ◽

Outlier Identification

Big Data analytics has become an upward field, and it plays a pivotal role in Healthcare and research practices. Big data analytics in healthcare cover vast numbers of dynamic heterogeneous data integration and analysis. Medical records of patients include several data including medical conditions, medications and test findings. One of the major challenges of analytics and prediction in healthcare is data preprocessing. In data preprocessing the outlier identification and correction is the important challenge. Outliers are exciting values that deviates from other values of the attribute; they may simply experimental errors or novelty. Outlier identification is the method of identifying data objects with somewhat different behaviors than expectations. Detecting outliers in time series data is different from normal data. Time series data are the data that are in a series of certain time periods. This kind of data are identified and cleared to bring the quality dataset. In this proposed work a hybrid outlier detection algorithm extended LSTM-GAN is helped to recognize the outliers in time series data. The outcome of the proposed extended algorithm attained better enactment in the time series analysis on ECG dataset processing compared with traditional methodologies.

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A Review on Time Series Aggregation Methods for Energy System Models

Energies ◽

10.3390/en13030641 ◽

2020 ◽

Vol 13 (3) ◽

pp. 641 ◽

Cited By ~ 5

Author(s):

Maximilian Hoffmann ◽

Leander Kotzur ◽

Detlef Stolten ◽

Martin Robinius

Keyword(s):

Time Series ◽

Input Data ◽

Renewable Energy Sources ◽

Energy Systems ◽

Energy System ◽

Machine Learning Techniques ◽

Future Research ◽

System Models ◽

Number Of Publications ◽

The Impact

Due to the high degree of intermittency of renewable energy sources (RES) and the growing interdependences amongst formerly separated energy pathways, the modeling of adequate energy systems is crucial to evaluate existing energy systems and to forecast viable future ones. However, this corresponds to the rising complexity of energy system models (ESMs) and often results in computationally intractable programs. To overcome this problem, time series aggregation (TSA) is frequently used to reduce ESM complexity. As these methods aim at the reduction of input data and preserving the main information about the time series, but are not based on mathematically equivalent transformations, the performance of each method depends on the justifiability of its assumptions. This review systematically categorizes the TSA methods applied in 130 different publications to highlight the underlying assumptions and to evaluate the impact of these on the respective case studies. Moreover, the review analyzes current trends in TSA and formulates subjects for future research. This analysis reveals that the future of TSA is clearly feature-based including clustering and other machine learning techniques which are capable of dealing with the growing amount of input data for ESMs. Further, a growing number of publications focus on bounding the TSA induced error of the ESM optimization result. Thus, this study can be used as both an introduction to the topic and for revealing remaining research gaps.

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Meteorological data analysis capabilities in the “VEGA constellation” information systems family

E3S Web of Conferences ◽

10.1051/e3sconf/202014901003 ◽

2020 ◽

Vol 149 ◽

pp. 01003

Author(s):

Ivan Uvarov ◽

Viktor Marchenkov ◽

Vladimir Tolpin

Keyword(s):

Time Series ◽

Information Systems ◽

Data Analysis ◽

Meteorological Data ◽

Vegetation Indices ◽

Heterogeneous Data ◽

Satellite Monitoring ◽

Joint Analysis ◽

Automatic Acquisition ◽

Monitoring Information

Satellite monitoring information systems require joint analysis of heterogeneous data, including meteorological data. To solve these problems, IKI RAS developed a technology for automatic acquisition, storage, presentation and analysis of meteorological data. The archive of meteorological data in IKI RAS is maintained within the Center for Collective Use “IKI-Monitoring“. The data is being continuously updated from a number of sources, primarily with the data obtained by the NCEP model. The data is available for visualization on the map in various ways. Plotting provides time series analysis in comparison to other variables such as vegetation indices. The developed technologies have been applied in development of a number of specialized information systems of satellite monitoring of natural and anthropogenic objects.

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K-MDTSC: K-Multi-Dimensional Time-Series Clustering Algorithm

Electronics ◽

10.3390/electronics10101166 ◽

2021 ◽

Vol 10 (10) ◽

pp. 1166

Author(s):

Danilo Giordano ◽

Marco Mellia ◽

Tania Cerquitelli

Keyword(s):

Time Series ◽

Clustering Algorithm ◽

Welding Process ◽

Heterogeneous Data ◽

Current Data ◽

Industrial Plant ◽

Process Data ◽

Case Scenario ◽

Time Series Clustering ◽

Synthetic Datasets

The increasing capability to collect data gives us the possibility to collect a massive amount of heterogeneous data. Among the heterogeneous data available, time-series represents a mother lode of information yet to be fully explored. Current data mining techniques have several shortcomings while analyzing time-series, especially when more than one time-series, i.e., multi-dimensional time-series, should be analyzed together to extract knowledge from the data. In this context, we present K-MDTSC (K-Multi-Dimensional Time-Series Clustering), a novel clustering algorithm specifically designed to deal with multi-dimensional time-series. Firstly, we demonstrate K-MDTSC capability to group multi-dimensional time-series using synthetic datasets. We compare K-MDTSC results with k-Shape, a state-of-art time-series clustering algorithm based on K-means. Our results show both K-MDTSC and k-Shape create good clustering results. However, K-MDTSC outperforms k-Shape when complicating the synthetic dataset. Secondly, we apply K-MDTSC in a real case scenario where we are asked to replace a scheduled maintenance with a predictive approach. To this end, we create a generalized pipeline to process data from a real industrial plant welding process. We apply K-MDTSC to create clusters of weldings based on their welding shape. Our results show that K-MDTSC identifies different welding profiles, but that the aging of the electrode does not negatively impact the welding process.

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Reducing energy time series for energy system models via self-organizing maps

it - Information Technology ◽

10.1515/itit-2019-0025 ◽

2019 ◽

Vol 61 (2-3) ◽

pp. 125-133

Author(s):

Hasan Ümitcan Yilmaz ◽

Edouard Fouché ◽

Thomas Dengiz ◽

Lucas Krauß ◽

Dogan Keles ◽

...

Keyword(s):

Time Series ◽

Renewable Energy Sources ◽

Energy System ◽

Model Complexity ◽

Electricity Production ◽

Self Organizing Maps ◽

System Models ◽

Execution Times ◽

New Research ◽

Self Organizing

Abstract The recent development of renewable energy sources (RES) challenges energy systems and opens many new research questions. Energy System Models (ESM) are important tools to study these problems. However, including RES into ESM strongly increases the model complexity, because one needs to model the fluctuant, weather-dependent electricity production from RES with a high level of granularity. This leads to long execution times. To deal with this issue, our objective is to reduce the input time series of ESM without losing their energy-related key characteristics, such as weather-dependent fluctuations in production or peak demands. This task is challenging, because of the variety and high-dimensionality of the data. We describe a carefully engineered data-processing pipeline to reduce energy time series. We use Self-Organizing Maps, a specific kind of neural network, to select “representative days”. We show that our approach outperforms the existing ones with respect to the quality of ESM results, and leads to a significant reduction of ESM execution times.

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