scholarly journals Bayesian Time Series Modelling of the Italian Daily Rainfall Data using Mixed Distribution

2019 ◽  
Vol 8 (4) ◽  
pp. 2279-2288
Keyword(s):  
Time Series ◽  
Daily Rainfall ◽  
Rainfall Data ◽  
Seasonal Effects ◽  
Model Parameters ◽  
Daily Rainfall Data ◽  
Mixed Distribution ◽  
Time Series Modelling ◽  
Bayesian Time Series ◽  
The Ideal

A combination of continuous and discrete elements is referred to as a mixed distribution. For example, daily rainfall data consist of zero and positive values. We aim to develop a Bayesian time series model that captures the evolution of the daily rainfall data in Italy, focussing on directly linking the amount and occurrence of rainfall. Two gamma (G1 and G2) distributions with different parameterisations and lognormal distribution were investigated to identify the ideal distribution representing the amount process. Truncated Fourier series was used to incorporate the seasonal effects which captures the variability in daily rainfall amounts throughout the year. A first-order Markov chain was used to model rainfall occurrence conditional on the presence or absence of rainfall on the previous day. We also built a hierarchical prior structure to represent our subjective beliefs and capture the initial uncertainties of the unknown model parameters for both amount and occurrence processes. The daily rainfall data from Urbino rain gauge station in Italy were then used to demonstrate the applicability of our proposed methods. Residual analysis and posterior predictive checking method were utilised to assess the adequacy of model fit. In conclusion, we clearly found that our proposed method satisfactorily and accurately fits the Italian daily rainfall data. The gamma distribution was found to be the ideal probability density function to represent the amount of daily rainfall.

The effect of weather, drainage efficiency and duration of spring cultivations on barley yields in England

1972 ◽  
Vol 7 (2) ◽  
pp. 79-83 ◽  
Author(s):  
L P Smith
Keyword(s):  
Yield Loss ◽  
Daily Rainfall ◽  
Rainfall Data ◽  
Work Schedule ◽  
Average Yield ◽  
Daily Rainfall Data ◽  
Arable Farming ◽  
Work Distribution

Daily rainfall data for twenty years in arable farming areas are analysed with respect to four standards of drainage and for three lengths of schedule of spring work. Distribution and frequency in time of available work days are interpreted in terms of lateness of sowing and of barley yield. Formulae are established to calculate average yield loss in terms of drainage standard and work schedule, enabling estimates to be made of the effect of planned improvements.

scholarly journals Structural break or long memory: an empirical survey on daily rainfall data sets across Malaysia

2013 ◽  
Vol 17 (4) ◽  
pp. 1311-1318 ◽  
Author(s):  
F. Yusof ◽  
I. L. Kane ◽  
Z. Yusop
Keyword(s):  
Long Memory ◽  
Structural Breaks ◽  
Structural Break ◽  
Daily Rainfall ◽  
Least Square ◽  
Rainfall Data ◽  
Data Sets ◽  
Original Series ◽  
Empirical Survey ◽  
Daily Rainfall Data

Abstract. A short memory process that encounters occasional structural breaks in mean can show a slower rate of decay in the autocorrelation function and other properties of fractional integrated I (d) processes. In this paper we employed a procedure for estimating the fractional differencing parameter in semiparametric contexts proposed by Geweke and Porter-Hudak (1983) to analyse nine daily rainfall data sets across Malaysia. The results indicate that all the data sets exhibit long memory. Furthermore, an empirical fluctuation process using the ordinary least square (OLS)-based cumulative sum (CUSUM) test for the break date was applied. Break dates were detected in all data sets. The data sets were partitioned according to their respective break date, and a further test for long memory was applied for all subseries. Results show that all subseries follows the same pattern as the original series. The estimate of the fractional parameters d1 and d2 on the subseries obtained by splitting the original series at the break date confirms that there is a long memory in the data generating process (DGP). Therefore this evidence shows a true long memory not due to structural break.

scholarly journals The Fitting of a Truncated Log?Normal Curve to Daily Rainfall Data

1956 ◽  
Vol 9 (1) ◽  
pp. 151 ◽  
Author(s):  
SC Das
Keyword(s):  
Curve Fitting ◽  
Daily Rainfall ◽  
Rainfall Data ◽  
Normal Curve ◽  
Daily Rainfall Data ◽  
Log Normal

In a previous paper (Das 1955) the author discussed a problem of curve fitting which arose in testing the hypothesis proposed by Bowen (1953) concerning daily rainfall data.

scholarly journals Validation of Daily Rainfall Based on Global Satellite Mapping of Precipitation (GSMAP) Data of Bali and Nusa Tenggara Region

2018 ◽  
Vol 19 (1) ◽  
pp. 12
Author(s):  
Sanjaya Natadiredja ◽  
I Ketut Sukarasa ◽  
Gusti Ngurah Sutapa
Keyword(s):  
Time Series ◽  
Time Series Analysis ◽  
Satellite Data ◽  
Daily Rainfall ◽  
Rainfall Data ◽  
Observation Data ◽  
Similar Data ◽  
Strong Positive Correlation ◽  
Cause Analysis ◽  
Satellite Rainfall

Limitations of observation data cause analysis and prediction of precipitation is difficult. One way to overcome such limitations is the use of satellite data such as GSMaP, but satellite data needs to be validated before use. This study aims to validate GSMaP rainfall data on observation data in Bali and Nusa Tenggara. Through monthly time series analysis, GSMaP rainfall data tend to have smaller value than observation data, but it has similar data pattern in each region with rain pattern that occurs in November to March (NDJFM). While validation between GSMaP satellite rainfall data and observation using Pearson and RMSE correlation and MBE at each location showed strong positive correlation value (> 0.5), correlation value obtained from each location from 0.82 to 0.93 with RMSE value from 2.08 to 5.51 and MBE values ??from 0.23 to 0.89, this indicates that GSMaP satellite data is valid and can be used to fill in empty data especially in 5 observation areas ie Denpasar, Ampenan, Sumbawa Besar, Bima and Kupang.

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