scholarly journals Model for Multiple Imputation to Estimate Daily Rainfall Data and Filling of Faults

2017 ◽  
Vol 32 (4) ◽  
pp. 575-583 ◽  
Author(s):  
José Ruy Porto de Carvalho ◽  
José Eduardo Boffinho Almeida Monteiro ◽  
Alan Massaru Nakai ◽  
Eduardo Delgado Assad
Keyword(s):  
Multiple Imputation ◽  
Daily Precipitation ◽  
Daily Rainfall ◽  
Auxiliary Variable ◽  
Rainfall Data ◽  
Imputation Model ◽  
Climatic Zones ◽  
Daily Rainfall Data ◽  
Versatile Technique ◽  
Precipitation Records

Abstract Modeling by multiple enchained imputation is an area of growing importance. However, its models and methods are frequently developed for specific applications. In this study the model for multiple imputation was used to estimate daily rainfall data. Daily precipitation records from several meteorological stations were used, obtained from system AGRITEMPO for two homogenous climatic zones. The precipitation values obtained for two dates (Jan. 20th 2005 and May 2nd 2005) using the multiple imputation model were compared with geo-statistics techniques ordinary Kriging and Co-kriging with the altitude as an auxiliary variable. The multiple imputation model was 16% better for the first zone and over 23% for the second one, compared to the rainfall estimation obtained by geo-statistical techniques. The model proved to be a versatile technique, presenting coherent results with the conditions of different zones and times.

scholarly journals Quantifying the Rain-Shadow Effect: Results from the Peak District, British Isles

2018 ◽  
Vol 99 (4) ◽  
pp. 777-790 ◽  
Author(s):  
Alexander J. Stockham ◽  
David M. Schultz ◽  
Jonathan G. Fairman ◽  
Adam P. Draude
Keyword(s):  
Daily Precipitation ◽  
Daily Rainfall ◽  
Rainfall Data ◽  
Shadow Effect ◽  
Daily Rainfall Data ◽  
Westerly Winds ◽  
Westerly Flow ◽  
Definition Of ◽  
Peak District ◽  
Strict Definition

AbstractAlthough rain shadows (i.e., leeside reductions of precipitation downwind of orography) are commonly described in textbooks, quantitative climatologies of the rain-shadow effect are rare. To test quantitatively a classic rain-shadow locality of the Peak District, United Kingdom, precipitation from 54 observing stations over 30 years (1981–2010) are examined. Under 850-hPa westerlies, annual and daily precipitation amounts are on average higher in Manchester in the west and the Peak District than in Sheffield in the east. More precipitation falls—and falls more frequently—frequently in Manchester than Sheffield on 197 westerly flow days annually. In contrast, more precipitation falls—and falls more frequently—in Sheffield than Manchester on 28 easterly flow days annually. These bulk precipitation statistics support a climatological rain shadow. However, when individual days are investigated, only 17% of westerly flow days occur where daily rainfall data might exhibit the rain-shadow effect (defined here as Manchester with precipitation and Sheffield with no precipitation). In contrast, only 10% of easterly flow days occur where daily rainfall data might exhibit the rain-shadow effect (Sheffield with precipitation and Manchester with no precipitation). Thus, westerly winds are more likely to exhibit a rain-shadow effect than easterly winds. Although the distribution of precipitation observed across the Peak District can sometimes be explained by the rain-shadow effect, the occurrence of the rain-shadow effect by our admittedly strict definition is not as frequent as one might expect to explain the local precipitation climate for which it has sometimes been previously credited. Thus, an attempt to understand the climatological relevance of the rain-shadow effect from one location reveals ambiguity in the definition of a rain shadow and in its interpretation from real rainfall data.

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 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.

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.

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