A Markov chain model for daily rainfall occurrence at Tel Aviv

Gabriel, K. Ruben; Neumann, J.

doi:10.1002/qj.49708837511

Cited by 482 publications

(246 citation statements)

References 7 publications

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“…Rainfall occurrence is represented in two ways: either as a Markov process [Gabriel and Neumann, 1962], the assumption being that the rainfall state on the next day is related to the state of rainfall on a finite number of previous days; or as an alternating renewal process for dry and wet sequences [Buishand, 1978], the approach being to stochastically generate the dry and wet spell lengths, either unconditionally, or conditional to appropriately selected predictors. Rainfall amount is generated once a day has been specified as wet, the amount being generated either unconditionally or conditional to appropriately specified variables.…”

Section: Modeling Of Daily Rainfallmentioning

confidence: 99%

A nonparametric model for stochastic generation of daily rainfall occurrence

Harrold

Sharma

Sheather

2003

Water Resources Research

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[1] This paper presents a nonparametric model for generating single-site daily rainfall occurrence. The model is formulated to reproduce longer-term variability and lowfrequency features such as drought and sustained wet periods, while still reproducing characteristics at daily time scales. Parsimony is achieved within a Markovian framework by using ''aggregate'' predictor variables that describe how wet it has been over a period of time. The use of a moving window to give a seasonally representative sample at any given time of year ensures an accurate representation of the seasonal variations present in the rainfall time series. Actual simulation proceeds by resampling from the historical record of rainfall occurrence, conditional to the current values of the associated predictors. The model is applied using historical daily rainfall occurrence from Sydney, Australia. We find that the use of multiple predictors specified at a daily level and at seasonal, annual, and multiyear aggregation levels leads to sequences that more closely reproduce the longer-term variability present in the historic records, compared to sequences produced by models incorporating only one or two predictors. INDEX TERMS:1854 Hydrology: Precipitation (3354); 1857 Hydrology: Reservoirs (surface); 1869 Hydrology: Stochastic processes; KEYWORDS: rainfall, stochastic, low frequency, nonparametric Citation: Harrold, T. I., A. Sharma, and S. J. Sheather, A nonparametric model for stochastic generation of daily rainfall occurrence,

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Section: Modeling Of Daily Rainfallmentioning

confidence: 99%

A nonparametric model for stochastic generation of daily rainfall occurrence

Harrold

Sharma

Sheather

2003

Water Resources Research

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“…Many authors have used Markov chains to model the daily occurrence of precipitation. Gabriel and Neumann (1962) analyzed the occurrence of rain at Tel Aviv, Israel, by fitting a two sate, first-order Markov chain. The two states corresponded to 'rain' and 'no rain'.…”

Section: Introductionmentioning

confidence: 99%

Markov chain modeling of daily rainfall in Lay Gaint Woreda, South Gonder Zone, Ethiopia

Getachew

Teshome

2018

J.Degrade.Min.Land Manage.

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Abstract:An understanding and knowledge of seasonal rainfall distribution, e.g. length of the growing season and rainfall extremes, is very important in agro-based economies like Ethiopia, where 95% of the farmers depend on rainfed agricultural production. The distribution pattern of rainfall rather than the total amount of rainfall within the entire period of time is more important for studying the pattern of rainfall occurrence. Zero, first and second order Markov chain was used to describe the characteristics of rainfall occurrences in this woreda. The states considered were; dry (d) and rainy (r).The overall chance of rain and the fitted curve tells us that the chance of getting rain in the main rainy season is quadruple as compared to the small rainy season. The first order Markov chain model indicates that the probability of getting rain in the small rainy season is significantly dependent on whether the earlier date was dry or wet while the second order Marko chain indicates that during the main rainy season the dependence of the probability of rain on the previous two dates' conditions is less as compared with the small rainy season. Rainfall amounts are very variable and are usually modeled by a gamma distribution.Therefore, the pattern of rainfall is somewhat unimodial having only one extreme value in August. Onset, cessation and length of growing season of rainfall for the main rainy season show medium variation compared to the small rainy season.

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“…Usualmente un modelo de Markov se refiere a un proceso estocástico discreto en el que la probabilidad de que ocurra un evento depende de los eventos anteriores (Gabriel and Neumann, 1962). Esto significa que dado un estado x t para el tiempo t, se mantiene inalterada la probabilidad condicional de un estado futuro en el tiempo t + t, aunque se introduzca información de tiempos anteriores a t. La distribución de estados futuros x t+ t usualmente se denomina como probabilidades de transición (Wilks, 2002).…”

Section: Introduccionunclassified

Variabilidad de Baja Frecuencia de la Persistencia de la Temperatura en el Sudeste de Sudamérica

Naumann

Vargas

2017

Rev. bras. meteorol.

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Recebido em 21 de Abril de 2015 -Aceito em 3 de Junho de 2016 ResumenSe analizan las estructuras de las autocovarianzas y autocorrelaciones de las temperaturas máximas y mínimas en estaciones de referencia en el sudeste de Sudamérica. A través de estas propiedades se observa que existen oscilaciones de períodos entre 18 y 25 años en las series bajo estudio, aunque estas periodicidades presentan cambios con el tiempo, especialmente entre los años . La excepción es Río Gallegos donde la periodicidad de 18 años se observa durante todo el registro. La persistencia definida mediante el primer coeficiente de autocorrelación es función de la cantidad de términos de autocovarianza negativas. No obstante, durante algunos años es dependiente de la magnitud de los términos positivos dados por las irrupciones cálidas o frías intensas. Entre los años 1950 y 1970 se ha observado un aumento de la variabilidad con una alta alternancia entre períodos de mayor y menor persistencia. Estos cambios analizados en la persistencia sugieren una variación en la frecuencia de distintos tipos de circulación que tienen un impacto directo en la estructura térmica regional. Así mismo, variaciones de este tipo pueden tener serios impactos socio-económicos ya que estos afectarían directamente la frecuencia y duración de los eventos extremos. Esto tiene importancia también en el diseño de métodos de control y consistencia ya que en ellos es posible considerar como outlier o información errónea los cambios producidos por las irrupciones de escala sinóptica mencionadas anteriormente. Palabras-clave: variabilidad climática, memoria, temperatura diaria, análisis wavelet. Low-Frequency Variability of Surface Air Temperature Persistence in Southern South America AbstractEight reference stations in south-eastern South America are analysed to derive the auto-covariance and auto-correlations of maximum and minimum temperature. Through the analysis are observed oscillations of periods between 18 and 25 years in the series studied. These periodicities show changes over time, especially in the 1950-1970 period. The exception is Río Gallegos where a periodicity of 18 years is observed throughout the entire record. Persistence is defined as the first correlation coefficient and is a function of the number of negative terms in the auto-covariance. However for some years it is mainly driven by the magnitude of the positive terms given by extreme warm or cold outbreaks. Between 1950 and 1970 it was observed an increased variability in the analysed properties. These changes suggest a variation in the frequency of different circulation patterns having a direct impact on the regional thermal structure. Likewise, this kind of variations can have serious socio-economic impacts as these directly affect the frequency and duration of extreme events. This is relevant in the design of consistency and quality control methods to detect outliers or systematic errors.

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A Markov chain model for daily rainfall occurrence at Tel Aviv

Cited by 482 publications

References 7 publications

A nonparametric model for stochastic generation of daily rainfall occurrence

A nonparametric model for stochastic generation of daily rainfall occurrence

Markov chain modeling of daily rainfall in Lay Gaint Woreda, South Gonder Zone, Ethiopia

Variabilidad de Baja Frecuencia de la Persistencia de la Temperatura en el Sudeste de Sudamérica

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