The Analysis of Meteorological Time Series for Use in Forecasting

Craddock, J. M.

doi:10.2307/2987390

Cited by 12 publications

(8 citation statements)

References 8 publications

(13 reference statements)

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“…Secondly, the restricted duration of the GRACE dataset should be acknowledged, particularly with regard to the annual time series. In mitigation, statistical significance appears to be robust when tested using the methods described by Zwiers and von Storch (Zwiers and von Storch, 1995), and the use of PCPA and PCPA datasets is designed to minimise the effect of seasonal climate and short-term trends in GWS (Craddock, 1965). Thirdly, the use of Eq.…”

Section: Resultsmentioning

confidence: 99%

“…In using cumulative rainfall anomalies, this study invokes the concept of system memory (Weber and Stewart, 2004). Several studies have considered the question of hydraulic or hydrologic memory, as it impacts both soil moisture, including land-atmosphere dynamics (Castro et al, 2009;Lo and S. Opie et al: Climate-groundwater dynamics inferred from GRACE and the role of hydraulic memory Famiglietti, 2010;Wu et al, 2002), and groundwater (Currell et al, 2016;Cuthbert et al, 2019b;Güntner et al, 2007;Rodell and Famiglietti, 2001). Central to the definition of this "memory" is that it represents the time taken for a system to re-equilibrate following a change in boundary conditions (Downing et al, 1974).…”

Section: Hydraulic Memorymentioning

confidence: 99%

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Climate–groundwater dynamics inferred from GRACE and the role of hydraulic memory

et al. 2020

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Abstract. Groundwater is the largest store of freshwater on Earth after the cryosphere and provides a substantial proportion of the water used for domestic, irrigation and industrial purposes. Knowledge of this essential resource remains incomplete, in part, because of observational challenges of scale and accessibility. Here we examine a 14-year period (2002–2016) of Gravity Recovery and Climate Experiment (GRACE) observations to investigate climate–groundwater dynamics of 14 tropical and sub-tropical aquifers selected from WHYMAP's (Worldwide Hydrogeological Mapping and Assessment Programme) 37 large aquifer systems of the world. GRACE-derived changes in groundwater storage resolved using GRACE Jet Propulsion Laboratory (JPL) mascons and the Community Land Model's land surface model are related to precipitation time series and regional-scale hydrogeology. We show that aquifers in dryland environments exhibit long-term hydraulic memory through a strong correlation between groundwater storage changes and annual precipitation anomalies integrated over the time series; aquifers in humid environments show short-term memory through strong correlation with monthly precipitation. This classification is consistent with estimates of groundwater response times calculated from the hydrogeological properties of each system, with long (short) hydraulic memory associated with slow (rapid) response times. The results suggest that groundwater systems in dryland environments may be less sensitive to seasonal climate variability but vulnerable to long-term trends from which they will be slow to recover. In contrast, aquifers in humid regions may be more sensitive to climate disturbances such as drought related to the El Niño–Southern Oscillation but may also be relatively quick to recover. Exceptions to this general pattern are traced to human interventions through groundwater abstraction. Hydraulic memory is an important factor in the management of groundwater resources, particularly under climate change.

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Section: Resultsmentioning

confidence: 99%

Section: Hydraulic Memorymentioning

confidence: 99%

Climate–groundwater dynamics inferred from GRACE and the role of hydraulic memory

et al. 2020

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show abstract

“…Secondly, the restricted duration of the GRACE dataset should be acknowledged, particularly with regard to the annual time series. In mitigation, statistical significance appears to be robust when tested (Zwiers and von Storch, 1995) and the use of PCPA 400 and ∫PCPA datasets is designed to minimise the effect of seasonal climate and short-term trends in ∆GWS (Craddock, 1965). Thirdly, the use of Eq.…”

Section: Log (Grt) (Grt In Yrs)mentioning

confidence: 99%

Climate-groundwater dynamics inferred from GRACE and the role of hydraulic memory

Opie¹,

Taylor²,

Brierley³

et al. 2020

Preprint

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Abstract. Groundwater is the largest store of freshwater on Earth after the cryosphere and provides a substantial proportion of the water used for domestic, irrigation and industrial purposes. Knowledge of this essential resource remains incomplete, in part, because of observational challenges of scale and accessibility. Here we examine a 14-year period (2002–2016) of GRACE observations to investigate climate-groundwater dynamics of 14 tropical and sub-tropical aquifers selected from WHYMAP's 37 large aquifer systems of the world. GRACE-derived changes in groundwater storage resolved using GRACE JPL Mascons and the CLM Land Surface Model are related to precipitation time series and regional-scale hydrogeology. We show that aquifers in dryland environments exhibit long-term hydraulic memory through a strong correlation between groundwater storage changes and annual precipitation anomalies integrated over the time series; aquifers in humid environments show short-term memory through strong correlation with monthly precipitation. This classification is consistent with estimates of Groundwater Response Times calculated from the hydrogeological properties of each system, with long (short) hydraulic memory associated with slow (rapid) response times. The results suggest that groundwater systems in dryland environments may be less sensitive to seasonal climate variability but vulnerable to long-term trends from which they will be slow to recover. In contrast, aquifers in humid regions may be more sensitive to seasonal climate disturbances such as ENSO-related drought but may also be relatively quick to recover. Exceptions to this general pattern are traced to human interventions through groundwater abstraction. Hydraulic memory is an important factor in the management of groundwater resources, particularly under climate change.

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“…There has been a lot of research in time-series data representation [8][9][10][11][12]. For example, the main goal of time-series analysis in econometrics, geophysics, meteorology, quantitative finance, seismology, and statistics is prediction or forecasting [13][14][15][16][17][18][19][20]. On the other hand, it is used for signal detection and estimation in communication engineering, control engineering, and signal processing [21][22][23][24][25][26][27][28].…”

Section: Introductionmentioning

confidence: 99%

Data Modeling With Polynomial Representations and Autoregressive Time-Series Representations, and Their Connections

Nandi

2020

IEEE Access

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Two of the data modelling techniques-polynomial representation and time-series representation-are explored in this paper to establish their connections and differences. All theoretical studies are based on uniformly sampled data in the absence of noise. This paper proves that all data from an underlying polynomial model of finite degree q can be represented perfectly by an autoregressive time-series model of order and a possible constant term µ as in equation (2). Furthermore, all polynomials of degree q are shown to give rise to the same set of time-series coefficients of specific forms with the only possible difference being in the constant term µ. It is also demonstrated that time-series with either non-integer coefficients or integer coefficients not of the aforementioned specific forms represent polynomials of infinite degree. Six numerical explorations, with both generated data and real data, including the UK data and US data on the current Covid-19 incidence, are presented to support the theoretical findings. It is shown that all polynomials of degree q can be represented by an all-pole filter with q repeated roots (or poles) at = +1. Theoretically, all noise-free data representable by a finite order all-pole filter, whether they come from finite degree or infinite degree polynomials, can be described exactly by a finite order AR time-series; if the values of polynomial coefficients are not of special interest in any data modelling, one may use time-series representations for data modelling.

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The Analysis of Meteorological Time Series for Use in Forecasting

Cited by 12 publications

References 8 publications

Climate–groundwater dynamics inferred from GRACE and the role of hydraulic memory

Climate–groundwater dynamics inferred from GRACE and the role of hydraulic memory

Climate-groundwater dynamics inferred from GRACE and the role of hydraulic memory

Data Modeling With Polynomial Representations and Autoregressive Time-Series Representations, and Their Connections

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