Intelligent Information Management, 2014, 6, 38-44 Published Online March 2014 in SciRes. http://www.scirp.org/journal/iim http://dx.doi.org/10.4236/iim.2014.62006 How to cite this paper: Mohanty, P.K., et al. (2014) MissRF: A Visual Basic Application in MS Excel to Find out Missing Rain- fall Data and Related Analysis. Intelligent Information Management, 6, 38-44. http://dx.doi.org/10.4236/iim.2014.62006 MissRF: A Visual Basic Application in MS Excel to Find out Missing Rainfall Data and Related Analysis Pradeep Kumar Mohanty 1 , Dwitikrishna Panigrahi 2 , Milu Acharya 3 1 Department of Water Resources, Government of Odisha, Bhubaneswar, India 2 Central Agricultural University, Gangtok, India 3 Siksha ‘O’ Anusandhan University, Bhubaneswar, India Email: pradeep5552002@yahoo.com , dwiti_2000@yahoo.com , milu_acharya@yahoo.com Received 20 December 2013; revised 19 January 2014; accepted 18 February 2014 Copyright © 2014 by authors and Scientific Research Publishing Inc. This work is licensed under the Creative Commons Attribution International License (CC BY). http://creativecommons.org/licenses/by/4.0/ Abstract Hydrological analyses are often encountered with many missing periods of rainfall while design- ing developmental action plans for inaccessible and disadvantageous area. Visual basic based ap- plication software (to run in MS Excel) was developed to calculate and autofilled the missing rain- fall data using widely followed Normal Ratio Method. The operational details of the software are described in the paper. Keywords MissRF; Normal Ratio Method; Visual Basic; MS Excel 1. Introduction Continuous meteorological data of contiguous stations are extremely important for forecasting and planning de- velopmental activities in agriculture. Meteorological parameters change erratically at short intervals both spa- tially and temporally. It’s a huge task to accumulate data at decentralised locations, particularly for the develop- ing and under developed countries because of inaccessibility and difficult field situations. Rainfalls being one of the most important meteorological parameters, numbers of statistical methods are in use for filling of the miss- ing rainfall data with varied logical and technical considerations. Dynamics of short term rainfall has a signifi- cant role in hydrological planning [1] and the missing periods need therefore be filled up for better analysis, prediction and efficient rainfall-runoff modeling [2]. Simple (simple arithmetic average, normal ratio or NR, and NR weighted with correlations) as well as complex type neural network, and multiple imputation strategy