Capabilities of the Johnson SB distribution in estimating rain variables

D’Adderio, Leo Pio; Cugerone, Katia; Porcú, Federico; Michele, Carlo De; Tokay, Ali

doi:10.1016/j.advwatres.2016.09.017

Cited by 10 publications

(4 citation statements)

References 29 publications

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“…The great flexibility and versatility of this distribution, together with its boundedness (which matches the physical limitations of the analysed aerosol particles) make the Johnson SB a good candidate for this purpose. The use of this distribution has also been inspired by (Cugerone and De Michele, 2015;D'Adderio et al, 2016); and (Cugerone and De Michele, 2017), where the authors have recently demonstrated the accuracy of this probability function in modelling the number size distribution of drops at the ground, a particular case of PNSD. Furthermore, the outcomes of this study are in accordance with the works of (Yu and Standish, 1990) and (Liu and Liu, 1994), in which JSB was firstly proposed for this aim.…”

Section: Introductionmentioning

confidence: 99%

On the functional form of particle number size distributions: influence of particle source and meteorological variables

et al. 2018

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Abstract. Particle number size distributions (PNSDs) have been collected periodically in the urban area of Milan, Italy, during 2011 and 2012 in winter and summer months. Moreover, comparable PNSD measurements were carried out in the rural mountain site of Oga-San Colombano (2250 m a.s.l.), Italy, during February 2005 and August 2011. The aerosol data have been measured through the use of optical particle counters in the size range 0.3-25 µm, with a time resolution of 1 min. The comparison of the PNSDs collected in the two sites has been done in terms of total number concentration, showing higher numbers in Milan (often exceeding 10 3 cm −3 in winter season) compared to Oga-San Colombano (not greater than 2×10 2 cm −3 ), as expected. The skewness-kurtosis plane has been used in order to provide a synoptic view, and select the best distribution family describing the empirical PNSD pattern. The four-parameter Johnson system-bounded distribution (called Johnson SB or JSB) has been tested for this aim, due to its great flexibility and ability to assume different shapes. The PNSD pattern has been found to be generally invariant under site and season changes. Nevertheless, several PNSDs belonging to the Milan winter season (generally more than 30 %) clearly deviate from the standard empirical pattern. The seasonal increase in the concentration of primary aerosols due to combustion processes in winter and the influence of weather variables throughout the year, such as precipitation and wind speed, could be considered plausible explanations of PNSD dynamics.

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Section: Introductionmentioning

confidence: 99%

On the functional form of particle number size distributions: influence of particle source and meteorological variables

et al. 2018

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“…4) is, under its generalized form, a fourparameter distribution, for which the γ and δ (δ > 0) are the shape parameters, the parameter a = −1 is the position, and the parameter b = 2 is the scale. This distribution has been used before in meteorology (Cugerone and Michele, 2015;Wakazuki, 2013), in forestry (Rennolls and Wang, 2005), and in hydrology (D'Adderio et al, 2016).…”

Section: Distribution Selection and Fitmentioning

confidence: 99%

IcePAC – a probabilistic tool to study sea ice spatio-temporal dynamics: application to the Hudson Bay area

2019

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Abstract. A reliable knowledge and assessment of the sea ice conditions and their evolution in time is a priority for numerous decision makers in the domains of coastal and offshore management and engineering as well as in commercial navigation. As of today, countless research projects aimed at both modelling and mapping past, actual and future sea ice conditions were completed using sea ice numerical models, statistical models, educated guesses or remote sensing imagery. From this research, reliable information helping to understand sea ice evolution in space and in time is available to stakeholders. However, no research has, until present, assessed the evolution of sea ice cover with a frequency modelling approach, by identifying the underlying theoretical distribution describing the sea ice behaviour at a given point in space and time. This project suggests the development of a probabilistic tool, named IcePAC, based on frequency modelling of historical 1978–2015 passive microwave sea ice concentrations maps from the EUMETSAT OSI-409 product, to study the sea ice spatio-temporal behaviour in the waters of the Hudson Bay system in northeast Canada. Grid-cell-scale models are based on the generalized beta distribution and generated at a weekly temporal resolution. Results showed coherence with the Canadian Ice Service 1981–2010 Sea Ice Climatic Atlas average freeze-up and melt-out dates for numerous coastal communities in the study area and showed that it is possible to evaluate a range of plausible events, such as the shortest and longest probable ice-free season duration, for any given location in the simulation domain. Results obtained in this project pave the way towards various analyses on sea ice concentration spatio-temporal distribution patterns that would gain in terms of information content and value by relying on the kind of probabilistic information and simulation data available from the IcePAC tool.

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“…before in meteorology (Cugerone and Michele, 2015;Wakazuki, 2013), in forestry (Rennolls and Wang, 2005) and in hydrology (D'Adderio et al, 2016).…”

Section: Distribution Selection and Fitmentioning

confidence: 99%

IcePAC – a Probabilistic Tool to Study Sea Ice Spatiotemporal Dynamic: Application to the Hudson Bay area, Northeastern Canada

Gignac

Bernier

Chokmani

2018

Preprint

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Abstract. A reliable knowledge and assessment of the sea ice conditions and their evolution in time is a priority for numerous decision makers in the domains of coastal and offshore management and engineering as well as in commercial navigation. As of today, countless research projects aimed at both modelling and mapping past, actual and future sea ice conditions were realized using sea ice numerical models, statistical models, educated guesses or remote sensing imagery. From these research, reliable information helping to understand sea ice evolution in space and in time are available to stakeholders. However, no research has, as of today, assessed the evolution of the sea ice cover with a frequency modelling approach, by identifying the underlying theoretical distribution describing the sea ice behaviour at a given point in space and time. This project suggests the development of a probabilistic tool, named IcePAC, based on frequency modelling of historical 1978–2015 passive microwave sea ice concentrations maps from EUMETSAT OSI-409 product, to study the sea ice spatiotemporal behaviour in the waters of the Hudson Bay System in Northeastern Canada. Pixel scale models are based on the generalized Beta distribution and generated at a weekly temporal resolution. Results showed coherent with the Canadian Ice Service 1980–2010 Sea Ice Climatic Atlas average freeze-up and meltdown dates for numerous coastal communities in the study area and showed that it is possible to evaluate a range of plausible events, such as the shortest and longest probable ice free season duration, for any given location on the simulation domain. This innovative research opens a path towards various analyses on sea ice dynamics that would gain in informative content and value by relying on the kind of probabilistic information and simulation data available from the IcePAC tool.

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Capabilities of the Johnson SB distribution in estimating rain variables

Cited by 10 publications

References 29 publications

On the functional form of particle number size distributions: influence of particle source and meteorological variables

On the functional form of particle number size distributions: influence of particle source and meteorological variables

IcePAC – a probabilistic tool to study sea ice spatio-temporal dynamics: application to the Hudson Bay area

IcePAC – a Probabilistic Tool to Study Sea Ice Spatiotemporal Dynamic: Application to the Hudson Bay area, Northeastern Canada

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