SART: A New Association Rule Method for Mining Sequential Patterns in Time Series of Climate Data

The usability of multispectral satellite data for detecting and monitoring supraglacial meltwater ponds has been demonstrated for western Greenland. For a multitemporal analysis of large regions or entire Greenland, largely automated processing routines are required. Here, we present a sequence of algorithms that allow for an automated Sentinel-2 data search, download, processing, and generation of a consistent and dense melt pond area time-series based on open-source software. We test our approach for a ~82,000 km2 area at the 79°N Glacier (Nioghalvfjerdsbrae) in northeast Greenland, covering the years 2016, 2017, 2018 and 2019. Our lake detection is based on the ratio of the blue and red visible bands using a minimum threshold. To remove false classification caused by the similar spectra of shadow and water on ice, we implement a shadow model to mask out topographically induced artifacts. We identified 880 individual lakes, traceable over 479 time-steps throughout 2016–2019, with an average size of 64,212 m2. Of the four years, 2019 had the most extensive lake area coverage with a maximum of 333 km2 and a maximum individual lake size of 30 km2. With 1.5 days average observation interval, our time-series allows for a comparison with climate data of daily resolution, enabling a better understanding of short-term climate-glacier feedbacks.

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Onboard Radio Frequency Interference as the Origin of Inter-Satellite Biases for Microwave Humidity Sounders

Remote Sensing ◽

10.3390/rs11070866 ◽

2019 ◽

Vol 11 (7) ◽

pp. 866 ◽

Cited By ~ 2

Author(s):

Imke Hans ◽

Martin Burgdorf ◽

Stefan A. Buehler

Keyword(s):

Time Series ◽

Radio Frequency ◽

Time Dependent ◽

Radio Frequency Interference ◽

Climate Variables ◽

Compelling Evidence ◽

Climate Data ◽

The Earth ◽

Correction Scheme

Understanding the causes of inter-satellite biases in climate data records from observations of the Earth is crucial for constructing a consistent time series of the essential climate variables. In this article, we analyse the strong scan- and time-dependent biases observed for the microwave humidity sounders on board the NOAA-16 and NOAA-19 satellites. We find compelling evidence that radio frequency interference (RFI) is the cause of the biases. We also devise a correction scheme for the raw count signals for the instruments to mitigate the effect of RFI. Our results show that the RFI-corrected, recalibrated data exhibit distinctly reduced biases and provide consistent time series.

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Predicting trend in futures prices time series using a new association rules algorithm

2009 International Conference on Management Science and Engineering ◽

10.1109/icmse.2009.5317911 ◽

2009 ◽

Cited By ~ 2

Author(s):

Li-ping Qin ◽

Mei Bai

Keyword(s):

Time Series ◽

Association Rules ◽

Futures Prices ◽

New Association

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Looking for Transaction Data Pattern Using Apriori Algorithm with Association Rule Method

IOP Conference Series Materials Science and Engineering ◽

10.1088/1757-899x/662/2/022078 ◽

2019 ◽

Vol 662 ◽

pp. 022078

Author(s):

Y Sutisnawati ◽

M Reski

Keyword(s):

Association Rule ◽

Apriori Algorithm ◽

Transaction Data ◽

Rule Method

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Time series analysis of Holocene climate data

Philosophical Transactions of the Royal Society of London. Series A, Mathematical and Physical Sciences ◽

10.1098/rsta.1990.0041 ◽

1990 ◽

Vol 330 (1615) ◽

pp. 601-616 ◽

Cited By ~ 144

Keyword(s):

Time Series ◽

Time Series Analysis ◽

Time Domain ◽

Small Sample ◽

Climate Data ◽

Holocene Climate ◽

Series Analysis ◽

Window Method ◽

Band Limited ◽

Periodic Components

Holocene climate records are imperfect proxies for processes containing complicated mixtures of periodic and random signals. I summarize time series analysis methods for such data with emphasis on the multiple-data-window technique. This method differs from conventional approaches to time series analysis in that a set of data tapers is applied to the data in the time domain before Fourier transforming. The tapers, or data windows, are discrete prolate spheroidal sequences characterized as being the most nearly band-limited functions possible among functions defined on a finite time domain. The multiple-window method is a small-sample theory and essentially an inverse method applied to the finite Fourier transform. For climate data it has the major advantage of providing a narrowband F -test for the presence and significance of periodic components and of being able to separate them from the non-deterministic part of the process. Confidence intervals for the estimated quantities are found by jackknifing across windows. Applied to 14 C records, this method confirms the presence of the ‘Suess wiggles’ and give an estimated period of 208.2 years. Analysis of the thickness variations of bristlecone pine growth rings shows a general absence of direct periodic components but a variation in the structure of the time series with a 2360-year period.

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A New Association Rule Mining Algorithm Based on Compression Matrix

Lecture Notes in Electrical Engineering - Computer Engineering and Networking ◽

10.1007/978-3-319-01766-2_33 ◽

2013 ◽

pp. 281-289

Author(s):

Sihui Shu

Keyword(s):

Association Rule ◽

Association Rule Mining ◽

Rule Mining ◽

Mining Algorithm ◽

New Association

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Distinguishing Trends and Shifts from Memory in Climate Data

Journal of Climate ◽

10.1175/jcli-d-17-0863.1 ◽

2018 ◽

Vol 31 (23) ◽

pp. 9519-9543 ◽

Cited By ~ 8

Author(s):

Claudie Beaulieu ◽

Rebecca Killick

Keyword(s):

Climate Change ◽

Time Series ◽

Information Criterion ◽

Steady Increase ◽

Climate Data ◽

Surface Warming ◽

Abrupt Changes ◽

Short Memory Process ◽

The 1960S ◽

And Shifts

The detection of climate change and its attribution to the corresponding underlying processes is challenging because signals such as trends and shifts are superposed on variability arising from the memory within the climate system. Statistical methods used to characterize change in time series must be flexible enough to distinguish these components. Here we propose an approach tailored to distinguish these different modes of change by fitting a series of models and selecting the most suitable one according to an information criterion. The models involve combinations of a constant mean or a trend superposed to a background of white noise with or without autocorrelation to characterize the memory, and are able to detect multiple changepoints in each model configuration. Through a simulation study on synthetic time series, the approach is shown to be effective in distinguishing abrupt changes from trends and memory by identifying the true number and timing of abrupt changes when they are present. Furthermore, the proposed method is better performing than two commonly used approaches for the detection of abrupt changes in climate time series. Using this approach, the so-called hiatus in recent global mean surface warming fails to be detected as a shift in the rate of temperature rise but is instead consistent with steady increase since the 1960s/1970s. Our method also supports the hypothesis that the Pacific decadal oscillation behaves as a short-memory process rather than forced mean shifts as previously suggested. These examples demonstrate the usefulness of the proposed approach for change detection and for avoiding the most pervasive types of mistake in the detection of climate change.

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A copula-based assessment of Bartlett–Lewis type of rainfall models for preserving drought statistics

Hydrology and Earth System Sciences Discussions ◽

10.5194/hessd-10-7469-2013 ◽

2013 ◽

Vol 10 (6) ◽

pp. 7469-7516 ◽

Cited By ~ 2

Author(s):

M. T. Pham ◽

W. J. Vanhaute ◽

S. Vandenberghe ◽

B. De Baets ◽

N. E. C. Verhoest

Keyword(s):

Time Series ◽

Important Variable ◽

Drought Severity ◽

Test Case ◽

Rainfall Time Series ◽

Climate Data ◽

Drought Duration ◽

Environmental Consequences ◽

Stochastic Point Process ◽

The Impact

Abstract. Of all natural disasters, the economic and environmental consequences of droughts are among the highest because of their longevity and widespread spatial extent. Because of their extreme behaviour, studying droughts generally requires long time series of historical climate data. Rainfall is a very important variable for calculating drought statistics, for quantifying historical droughts or for assessing the impact on other hydrological (e.g. water stage in rivers) or agricultural (e.g. irrigation requirements) variables. Unfortunately, time series of historical observations are often too short for such assessments. To circumvent this, one may rely on the synthetic rainfall time series from stochastic point process rainfall models, such as Bartlett–Lewis models. The present study investigates whether drought statistics are preserved when simulating rainfall with Bartlett–Lewis models. Therefore, a 105 yr 10 min rainfall time series obtained at Uccle, Belgium is used as test case. First, drought events were identified on the basis of the Effective Drought Index (EDI), and each event was characterized by two variables, i.e. drought duration (D) and drought severity (S). As both parameters are interdependent, a multivariate distribution function, which makes use of a copula, was fitted. Based on the copula, four types of drought return periods are calculated for observed as well as simulated droughts and are used to evaluate the ability of the rainfall models to simulate drought events with the appropriate characteristics. Overall, all Bartlett–Lewis type of models studied fail in preserving extreme drought statistics, which is attributed to the model structure and to the model stationarity caused by maintaining the same parameter set during the whole simulation period.

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