scholarly journals A Markov chain model for daily rainfall occurrences  at east Thanjavur district

MAUSAM ◽  
2022 ◽  
Vol 46 (4) ◽  
pp. 383-388
Author(s):  
M. THIYAGARAJAN ◽  
RAMA DOSS ◽  
RAMA RAJ
Keyword(s):  
Markov Chain ◽  
Poisson Process ◽  
State Estimation ◽  
Statistical Inference ◽  
Markov Chain Model ◽  
Daily Rainfall ◽  
Chain Model ◽  
New Approach ◽  
State 1 ◽  
Rainfall Occurrences

 The occurrences and non-occurrences of the rainfall can be described by a two-state Markov chain. A dry date is denoted by state 0 and wet date is denoted by state 1. We have taken the sample which follows a Poisson process with known parameter. Using this Poisson sample we have given a new approach to affect statistical inference for the law of the Markov chain and state estimation concerning un-observed past values or not yet observed future values. The paper aims at comparing the earlier fit of the data with the new approach.      

scholarly journals Fitting of a Markov chain model for daily rainfall data at Calcutta

MAUSAM ◽  
2021 ◽  
Vol 22 (1) ◽  
pp. 67-74
Author(s):  
A. N. BASU
Keyword(s):  
Markov Chain ◽  
Geometric Distribution ◽  
Markov Chain Model ◽  
Probability Model ◽  
Daily Rainfall ◽  
Rainfall Data ◽  
Actual Data ◽  
Chain Model ◽  
Daily Rainfall Data ◽  
Rainy Days

A Markov chain probability model has been fitted to the daily rainfall data recorded at Calcutta. The 'wet spell' and 'weather cycles' are found to obey geometric distribution, The distribution of the number of rainy days per week has been calculated and compared with the actual data.

A Markov Chain Model of daily rainfall

1976 ◽  
Vol 12 (3) ◽  
pp. 443-449 ◽  
Author(s):  
C. T. Haan ◽  
D. M. Allen ◽  
J. O. Street
Keyword(s):  
Markov Chain ◽  
Markov Chain Model ◽  
Daily Rainfall ◽  
Chain Model

scholarly journals Markov Chain Decomposition of Monthly Rainfall into Daily Rainfall: Evaluation of Climate Change Impact

2016 ◽  
Vol 2016 ◽  
pp. 1-10 ◽  
Author(s):  
Chulsang Yoo ◽  
Jinwook Lee ◽  
Yonghun Ro
Keyword(s):  
Climate Change ◽  
Markov Chain ◽  
Rainfall Intensity ◽  
Transition Probabilities ◽  
Markov Chain Model ◽  
Daily Rainfall ◽  
Monthly Rainfall ◽  
Chain Model ◽  
Climate Change Scenarios ◽  
The Mean

This study evaluates the effect of climate change on daily rainfall, especially on the mean number of wet days and the mean rainfall intensity. Assuming that the mechanism of daily rainfall occurrences follows the first-order Markov chain model, the possible changes in the transition probabilities are estimated by considering the climate change scenarios. Also, the change of the stationary probabilities of wet and dry day occurrences and finally the change in the number of wet days are derived for the comparison of current (1x CO2) and 2x CO2conditions. As a result of this study, the increase or decrease in the mean number of wet days was found to be not enough to explain all of the change in monthly rainfall amounts, so rainfall intensity should also be modified. The application to the Seoul weather station in Korea shows that about 30% of the total change in monthly rainfall amount can be explained by the change in the number of wet days and the remaining 70% by the change in the rainfall intensity. That is, as an effect of climate change, the increase in the rainfall intensity could be more significant than the increase in the wet days and, thus, the risk of flood will be much highly increased.

scholarly journals Aplicação do Modelo Estocástico Cadeia de Markov a Dados Diários de Precipitação dos Estados da Bahia e Sergipe (Application of Stochastic Markov Chain Model to Data Daily Rainfall of the States of Bahia and Sergipe)

2012 ◽  
Vol 5 (3) ◽  
pp. 509 ◽  
Author(s):  
Winicius Santos Araújo ◽  
Francisco De Assis Salviano de Sousa ◽  
José Ivaldo Barbosa de Brito ◽  
Lourivaldo Mota Lima
Keyword(s):  
Spatial Distribution ◽  
Markov Chain ◽  
Markov Chain Model ◽  
Winter Season ◽  
Coastal Region ◽  
Daily Rainfall ◽  
Summer Season ◽  
High Availability ◽  
Chain Model ◽  
Stochastic Markov Chain

O objetivo desta pesquisa foi fornecer uma distribuição espacial e computar as probabilidades incondicionais e condicionais de primeira ordem das precipitações dos Estados da Bahia e Sergipe. Para tanto, foram utilizados dados diários pluviais referentes a um período de 47 anos (1960-2006) de 75 postos e/ou estações meteorológicas fornecidos pela antiga rede de postos da SUDENE através do DCA. Os resultados mostram que a zona oeste da área pesquisada é a mais favorecida com a precipitação na estação verão, ocorrendo o oposto disto na estação inverno; o leste é o mais beneficiado no inverno, o sul na primavera, e o norte no outono. Foi obtido que a probabilidade incondicional, P(C), na região costeira, é influenciada pela alta disponibilidade de umidade do Atlântico e pela geração de sistemas que provocam precipitação devido ao contraste de temperatura da superfície oceano-continente, particularmente durante os meses de outono e primavera. No verão o efeito oceânico não é percebido devido à alta disponibilidade de umidade sobre a área pesquisada e a alta persistência observada da precipitação diária está associada com os núcleos de máximas precipitações que se destacam na estação verão, enquanto que no inverno a baixa persistência foi predominante.Palavras - chave: distribuição espacial, cadeia de markov, precipitação diária. Application of Stochastic Markov Chain Model to Data Daily Rainfall of the States of Bahia and Sergipe ABSTRACTThe objective of this research was to provide a spatial distribution and compute the probabilities conditional and unconditional first order of precipitation of the States of Bahia and Sergipe. It had been used daily rainfall data relating to a period of 47 years (1960-2006) of 75 stations and/or meteorological stations provided by the former station network SUDENE by DCA. The results show that the area west of the area searched is more favored with rainfall in the summer season, the opposite occurring in this winter season, the east is the most improved in the winter, spring in the south, and north in the autumn. It was obtained that the unconditional probability, P(C), the coastal region, is influenced by the high availability of moisture from the Atlantic and the generation of systems that cause precipitation due to the contrast of surface temperature of ocean-continent, particularly during the autumn months and Spring. In summer the ocean effect is not perceived due to the high availability of moisture over the area surveyed and observed high persistence of daily rainfall is associated with the nuclei of maximum precipitation that stand out in the summer season, while in winter the low persistence prevailed.Keywords: spatial distribution, markov chain, daily precipitation.

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