Methods for Estimating Wind Speed Frequency Distributions

Justus, C. G.; Hargraves, W. R.; Mikhail, A. S.; Graber, Denise

doi:10.1175/1520-0450(1978)017<0350:mfewsf>2.0.co;2

Cited by 667 publications

(371 citation statements)

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“…Earlier studies have shown that the Weibull distribution is useful and appropriate for representing the temporal frequency distribution of wind speed, for example for wind energy applications (Justus et al, 1978). Ridley et al (2013) showed that if the sub-grid variance inside a GCM grid box is known, using the Weibull PDF to represent the sub-grid variability of surface wind speed can help improve the accuracy of the emission calculation compared to a simulation that does not account for the SGV.…”

Section: Approximating Sub-grid Wind Variabilitymentioning

confidence: 99%

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Quantifying the impact of sub-grid surface wind variability on sea salt and dust emissions in CAM5

et al. 2016

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Abstract. This paper evaluates the impact of sub-grid variability of surface wind on sea salt and dust emissions in the Community Atmosphere Model version 5 (CAM5). The basic strategy is to calculate emission fluxes multiple times, using different wind speed samples of a Weibull probability distribution derived from model-predicted grid-box mean quantities.In order to derive the Weibull distribution, the sub-grid standard deviation of surface wind speed is estimated by taking into account four mechanisms: turbulence under neutral and stable conditions, dry convective eddies, moist convective eddies over the ocean, and air motions induced by mesoscale systems and fine-scale topography over land. The contributions of turbulence and dry convective eddy are parameterized using schemes from the literature. Wind variabilities caused by moist convective eddies and fine-scale topography are estimated using empirical relationships derived from an operational weather analysis data set at 15 km resolution. The estimated sub-grid standard deviations of surface wind speed agree well with reference results derived from 1 year of global weather analysis at 15 km resolution and from two regional model simulations with 3 km grid spacing.The wind-distribution-based emission calculations are implemented in CAM5. In terms of computational cost, the increase in total simulation time turns out to be less than 3 %. Simulations at 2 • resolution indicate that sub-grid wind variability has relatively small impacts (about 7 % increase) on the global annual mean emission of sea salt aerosols, but considerable influence on the emission of dust. Among the considered mechanisms, dry convective eddies and mesoscale flows associated with topography are major causes of dust emission enhancement. With all the four mechanisms included and without additional adjustment of uncertain parameters in the model, the simulated global and annual mean dust emission increase by about 50 % compared to the default model. By tuning the globally constant dust emission scale factor, the global annual mean dust emission, aerosol optical depth, and top-of-atmosphere radiative fluxes can be adjusted to the level of the default model, but the frequency distribution of dust emission changes, with more contribution from weaker wind events and less contribution from stronger wind events. In Africa and Asia, the overall frequencies of occurrence of dust emissions increase, and the seasonal variations are enhanced, while the geographical patterns of the emission frequency show little change.

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Section: Approximating Sub-grid Wind Variabilitymentioning

confidence: 99%

“…k and c can be derived from the mean (U ) and the standard deviation (σ U ) using method 3 of Justus et al (1978), i.e.,…”

Section: Weibull Distributionmentioning

confidence: 99%

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Quantifying the impact of sub-grid surface wind variability on sea salt and dust emissions in CAM5

et al. 2016

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“…The Weibull distribution is typically applied to the 10-, 30-min or 1-h mean wind speed. Justus et al (1978) summarize several methods to derive A and k. From the relationship between A, k, and the average wind speed u ,…”

Section: Weibull Distribution and Reversal Heightmentioning

confidence: 99%

Weibull Wind-Speed Distribution Parameters Derived from a Combination of Wind-Lidar and Tall-Mast Measurements Over Land, Coastal and Marine Sites

Gryning

Floors

Peña

et al. 2015

Boundary-Layer Meteorol

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Wind-speed observations from tall towers are used in combination with observations up to 600 m in altitude from a Doppler wind lidar to study the long-term conditions over suburban (Hamburg), rural coastal (Høvsøre) and marine (FINO3) sites. The variability in the wind field among the sites is expressed in terms of mean wind speed and Weibull distribution shape-parameter profiles. The consequences of the carrier-to-noise-ratio (CNR) threshold-value choice on the wind-lidar observations are revealed as follows. When the wind-lidar CNR is lower than a prescribed threshold value, the observations are often filtered out as the uncertainty in the wind-speed measurements increases. For a pulsed heterodyne Doppler lidar, use of the traditional -22 dB CNR threshold value at all measuring levels up to 600 m results in a ≈7 % overestimation in the long-term mean wind speed over land, and a ≈12 % overestimation in coastal and marine environments. In addition, the height of the profile maximum of the shape parameter of the Weibull distribution (so-called reversal height) is found to depend on the applied CNR threshold; it is found to be lower at small CNR threshold values. The reversal height is greater in the suburban (high roughness) than in the rural (low roughness) area. In coastal areas the reversal height is lower than that over land and relates to the internal boundary layer that develops downwind from the coastline. Over the sea the shape parameter increases towards the sea surface. A parametrization of the vertical profile of the shape parameter fits well with observations over land, coastal regions and over the sea. An applied model for the dependence of the reversal height on the surface roughness is in good agreement with the observations over land.

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“…Besides the use of the Weibull distribution in engineering applications, such as random failures, fatigue and reliability of materials, it is also applied in several fields of climatology, such as wind regimes (Justus et al, 1978;Tackle & Brown, 1978;Tuller & Brett, 1985;Bauer, 1996), thunderstorms (Schütte et al, 1987) and rainfall statistics (Swift & Schreuder, 1981;Wilks, 1989;Groisman et al, 1999;Stephenson et al, 1999;Burgueño et al, 2004). In particular, the Weibull distribution has also been applied to model the time intervals between successive events in the same storm cluster, while the lognormal distribution is applied to fit the elapsed time between successive storm clusters (Cowpertwait, 2001).…”

Section: Introductionmentioning

confidence: 99%

Return period maps of dry spells for Catalonia (northeastern Spain) based on the Weibull distribution / Périodes de retour des périodes sèches en Catalogne (nord-est de l'Espagne) à partir de la distribution de Weibull

Lana

Martínez

Burgueño

et al. 2008

Hydrological Sciences Journal

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A detailed description of the spatial distribution of dry spell lengths in Catalonia (northeastern Spain) for the years 1950-2000 is obtained from a statistical analysis. The database is derived from the daily records of 75 raingauges, and a dry spell is defined as a set of consecutive days with daily rainfall amounts below thresholds of 0.1, 1.0, 5.0 and 10.0 mm/d. The Weibull model fits the distribution of dry spell lengths well, whatever the raingauge and threshold. The Weibull parameters estimated by L-moments and the L-skewness-kurtosis diagrams allow quantification of the goodness of fit between the model and the empirical distribution. Dry spell lengths for return periods of 2, 5, 10, 25 and 50 years indicate the areas where drought phenomena might be more severe, as well as how often they might occur. A regional homogeneity method is used to test whether a single set of Weibull parameters could describe the series of dry spells for the whole region.Key words dry spells; spatial patterns; L-moments; Weibull distribution; return periods; regional homogeneity; Catalonia (northeastern Spain) Périodes de retour des périodes sèches en Catalogne (nord-est de l'Espagne) à partir de la distribution de Weibull Résumé Une analyse statistique des longitudes des périodes sèches en Catalogne (nord-est de l'Espagne) a permis la description détaillée de leur distribution spatiale. Les résultats ont été obtenus à partir des données journalières de 75 pluviomètres pour la période 1950-2000. La période sèche est définie comme la succession des jours consécutifs avec quantités de pluie au dessous de 0.1, 1.0, 5.0 et 10.0 mm/jour. La répartition observée des longueurs des périodes sèches est bien modelée par la distribution de Weibull pour l'ensemble des pluviomètres et pour les différents seuils. Les paramètres de cette distribution ont été estimés à partir des moments L et, avec l'aide des diagrammes dissymétrie-curtosie-L, ils ont permis la quantification du degré de bonté de l'ajustement des répartitions observées à la distribution de Weibull. La grande variabilité spatiale du régime des pluies journalières en Catalogne est montrée à travers des longueurs des périodes sèches avec périodes de retour de 2, 5, 10, 25 et 50 années. Les résultats signalent où la sécheresse peut arriver à être plus rigoureuse et avec quelle fréquence celle-ci devient. Une méthode pour étudier l'homogénéité régionale est appliquée afin de savoir si un ensemble unique de paramètres de Weibull peut décrire tous les périodes sèches observées dans la région.Mots clefs periode sèche; distribution spatiale; moments L; distribution de Weibull; periodes de retour; homogeneite régionale; La Catalogne (nord-est de l'Espagne)

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Methods for Estimating Wind Speed Frequency Distributions

Cited by 667 publications

References 0 publications

Quantifying the impact of sub-grid surface wind variability on sea salt and dust emissions in CAM5

Quantifying the impact of sub-grid surface wind variability on sea salt and dust emissions in CAM5

Weibull Wind-Speed Distribution Parameters Derived from a Combination of Wind-Lidar and Tall-Mast Measurements Over Land, Coastal and Marine Sites

Return period maps of dry spells for Catalonia (northeastern Spain) based on the Weibull distribution / Périodes de retour des périodes sèches en Catalogne (nord-est de l'Espagne) à partir de la distribution de Weibull

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