A comparison of methods used in estimating missing rainfall data

Abstract

Precipitation or rainfall (in tropics) is an important climatic parameter and the studies on rainfall are commonly hampered due to lack of continuous data. To fill the gaps (missing observations) in data, several interpolation techniques are currently used. However, the lack of knowledge on the suitability of these methods for Sri Lanka is a practical problem. In view of this problem, this study is aimed at comparing a few selected methods used for the estimation of missing rainfall data with a new method introduced by the authors to determine their suitability in Sri Lankan context. The methods studied were Arithmetic Mean (Local Mean) method, Normal Ratio method and Inverse Distance method. The new method introduced by the authors is named as Aerial Precipitation Ratio method. In this approach, rain gauging stations where complete monthly rainfall data sets are available were selected in such a way that the selected stations represent each of the seven major Agro-ecological zones of Sri Lanka. This selection procedure of stations makes it possible to generalize the results to the entire country. The period of data ranged from 15 years in the case of mid country intermediate zone to 28 years Up country intermediate zone and Mid country wet zone. Subsequently, monthly rainfall data of each station were estimated using the data of surrounding stations based on the above selected methods so that actual data and the estimated data can be compared. Each estimated series was compared with the actual data series using different statistical comparison techniques. These comparisons include Descriptive Statistics of Error, Root Mean Square Error, Mean Absolute Percentage of Error and Correlation Coefficient. Results of the study show that the Inverse Distance method is the most suitable method for all three Low-country zones (wet, intermediate, and dry).

A Comparison of Methods of Estimating Missing Daily Rainfall Data

“…Deterministic approaches are more suitable because of their robustness, ease of implementation and computational efficiency [1,2]. They are mathematical models that always produce the same output from a given initial condition and they neither contemplate on the existence of randomness nor do they attribute the results to a probability of occurrence.…”

Normal Ratio in Multiple Imputation Based on Bootstrapped Sample for Rainfall Data With Mis Singness

“…normal ratio, inverse distance weighting, multiple linear regression, and kriging. However, normal ratio (NR) has appeared to be the most commonly used method in estimating missing rainfall values as stated in literature [2]- [4] due to its simplicity and efficiency.…”

Evaluation of an inverse distance weighting method for patching daily and dekadal rainfall over the Free State Province, South Africa

“…In utilizing the closest station method, the nearest weather station with data corresponding to the period of concern is identified and missing values are either replaced directly by the value at the neighbour station or adjusted by a factor from the ratio of long-term means between the two stations (Xia et al, 2001). In simple arithmetic averaging, the missing data are obtained by arithmetically averaging data of the 2 to 5 closest weather stations around a station (Tang et al, 1996;Xia et al, 1999;De Silva et al, 2007). Inverse distance weighting utilizes the distances from the target station of 2 to 5 neighbour stations, giving more weight to data from the nearest weather station (Tang et al, 1996;Xia et al, 1999;De Silva et al, 2007;Chen and Liu, 2012).…”

“…In simple arithmetic averaging, the missing data are obtained by arithmetically averaging data of the 2 to 5 closest weather stations around a station (Tang et al, 1996;Xia et al, 1999;De Silva et al, 2007). Inverse distance weighting utilizes the distances from the target station of 2 to 5 neighbour stations, giving more weight to data from the nearest weather station (Tang et al, 1996;Xia et al, 1999;De Silva et al, 2007;Chen and Liu, 2012). The multiple regression model employs step-wise regression to determine the coefficients for all the significant neighbour stations (Makhuvha et al, 1997;Xia et al, 1999), while the normal ratio method utilizes the correlation between the neighbour and target 467 station as well as the number of paired datasets as the weight in estimating values at the target station (Tang et al, 1996;De Silva et al, 2007).…”

“…The method was chosen based on recommendations by Chen and Liu (2012) in a study carried out in Taiwan which yielded a high correlation between measured and estimated values. De Silva et al (2007) compared the normal ratio, arithmetic averaging and inverse distance methods and concluded that the IDW method performed better than the other two methods in estimating rainfall in all the climate zones of Sri Lanka. Studies by Tang et al (1996) also revealed that the IDW method has the potential of estimating missing rainfall values with minimum inaccuracy.…”