Estimating trend from seasonal data: is daily, monthly or annual data best?

Withers, Christopher S.; Nadarajah, Saralees

doi:10.1002/env.2356

Cited by 7 publications

(5 citation statements)

References 20 publications

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“…The linear trend of wind speed at the annual and monthly time scales is also addressed. The assessment of annual mean values for the estimation of linear slope for a seasonal series is supported by Withers and Nadarajah (2015), where the authors suggest the use of annual mean values if data with duration equal to or longer than 5 years are available. The linear trend provides a quantification of the tendency of the mean intensity of wind fields in the examined time horizon.…”

Section: Variability Measures and Association Of Wind Speed And Direcmentioning

confidence: 99%

Offshore wind climate analysis and variability in the Mediterranean Sea

Soukissian

Karathanasi

Axaopoulos³

et al. 2017

Intl Journal of Climatology

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ABSTRACT:The long-term offshore wind characteristics in the Mediterranean Sea are assessed using the ERA-Interim dataset for the period 1979-2014. Three main aspects of the wind climate analysis are examined in detail; the spatio-temporal behaviour (including variability characteristics) of wind speed and direction for the annual and monthly time scale; the joint association of wind speed and direction for the annual and monthly time scale, and; the wind speed trends and wind direction changes. From this analysis, the systematic wind flow patterns are identified and the general features of the wind climatology patterns are revealed. The most typical examples of stable and intense regional winds are the Mistral (throughout the year) in the Gulf of Lion and the Etesians (during summer) in the Aegean Sea. High values of mean annual variability of wind speed occur over the western part of the basin (Balearic, Tyrrhenian and Ligurian sub-basins) as well as over the Adriatic and N Aegean Seas, and the E Levantine Basin. Moreover, the assessment of the joint association of wind speed and direction indicates that areas with high values of linear-circular correlation are the ones characterized by intense wind climate. As regards, long-term changes of wind speed and direction, the linear trend for the former and the angular distance for the latter variable are provided. The fastest increasing wind speed trends are observed in the Ionian, the N Tyrrhenian and N Adriatic Seas, the eastern part of the Algerian Basin up to Balearic Isl. and the western part of the S Levantine Basin while the fastest decreasing ones are observed offshore Monaco, the central Aegean and the E Alboran Seas and the N Levantine Basin. The highest values of angular variance and mean angular distances are depicted for the Ligurian, Balearic and Alboran Seas.

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Section: Variability Measures and Association Of Wind Speed And Direcmentioning

confidence: 99%

Offshore wind climate analysis and variability in the Mediterranean Sea

Soukissian

Karathanasi

Axaopoulos³

et al. 2017

Intl Journal of Climatology

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“…Among these we model the annual cyclical patterns of the seasons as

{\beta}_3\kern0.3em \sin \left(2\pi j/365\right)+{\beta}_4\kern0.3em \cos \left(2\pi j/365\right).

Similar sinusoidal models for climate data are also employed in Withers and Nadarajah (2015).…”

Section: Data Analysis For Temperatures In Selected Us Citiesmentioning

confidence: 99%

Regression methods for the appearances of extremes in climate data

Bláha

Kane

et al. 2022

Environmetrics

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For any given city, on any calendar day, there will be record high and low temperatures. Which record occurred earlier? If there is a trend towards warming then, intuitively, there should be a preponderance of record highs occurring more recently than the record lows for each of the 365 calendar days. We are interested in modeling the joint distribution of appearances of the extremes but not these values themselves. We develop a bivariate discrete distribution modeling the joint indices of maximum and minimum in a sequence of independent random variables sampled from different distributions. We assume these distributions share a proportional hazard rate and develop regression methods for these paired values. This approach has reasonable power to detect a small mean change over a decade. Using readily available public data, we examine the daily calendar extreme values of five US cities for the decade 2011–2020. We develop linear regression models for these data, describe models to account for calendar‐date dependence, and use diagnostic measures to detect remarkable observations.

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“…Therefore, the general practice is averaging daily or weekly GNSS time series to monthly to keep consistent with the monthly gravity field [ 8 , 9 , 10 ]. In fact, the reduction of temporal resolution would affect the signal extraction and its accuracy [ 11 ]. To increase the temporal resolution, various groups have developed 10-day and weekly interval gravity models.…”

Section: Introductionmentioning

confidence: 99%

Data Adaptive Analysis on Vertical Surface Deformation Derived from Daily ITSG-Grace2018 Model

2020

Sensors

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With the widely used monthly gravity models, it is hard to determine the sub-monthly variations. Thanks to the high temporal resolution, a daily ITSG-Grace2018 gravity model is employed to derive the vertical deformation of the China region in 1.0° × 1.0° grids. The standard deviations of residuals between the daily and monthly averaged displacement range from 1.0 to 3.5 mm, reaching half of the median residuals, which indicates that a higher temporal resolution gravity model is quite necessary for the analysis of crustal displacement. For the signal analysis, traditional least square (LS) is limited in its analysis of signals with constant amplitude. However, geophysical signals in a geodetic time series usually fluctuate over long periods, and missing data happen. In this study, the data adaptive approach called enhanced harmonic analysis (EHA), which is based on an Independent Point (IP) scheme, is introduced to deal with these issues. To demonstrate the time-varying signals, the relative differences between EHA and LS are calculated. It illustrates that the median percentage of epochs at grids with a relative difference larger than 10% is 69.7% and the proportions for the ranges of 30%, 50%, and 70% are about 30.1%, 18.4%, and 13.0%, respectively. The obvious discrepancy suggests the advantage of EHA over LS in obtaining time-varying signals. Moreover, the spatial distribution of the discrepancy also demonstrates the regional characteristics, suggesting that the assumption of constant amplitude is not appropriate in specific regions. To further validate the effectiveness of EHA, the comprehensive analysis on the different noise types, number of IPs, missing data, and simultaneous signals are carried out. Specifically, EHA can deal with series containing white or color noise, although the stochastic model for the color noise should be modified. The signals are slightly different when selecting different numbers of IPs within a range, which could be accepted during analysis. Without interpolation, EHA performs well even with continuously missing data, which is regarded as its feature. Meanwhile, not only a single signal but also simultaneous signals can be effectively identified by EHA.

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Estimating trend from seasonal data: is daily, monthly or annual data best?

Cited by 7 publications

References 20 publications

Offshore wind climate analysis and variability in the Mediterranean Sea

Offshore wind climate analysis and variability in the Mediterranean Sea

Regression methods for the appearances of extremes in climate data

Data Adaptive Analysis on Vertical Surface Deformation Derived from Daily ITSG-Grace2018 Model

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