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Improving estimation of missing values in daily precipitation series by a probability density function‐preserving approach
Author(s) -
Simolo C.,
Brunetti M.,
Maugeri M.,
Nanni T.
Publication year - 2010
Publication title -
international journal of climatology
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 1.58
H-Index - 166
eISSN - 1097-0088
pISSN - 0899-8418
DOI - 10.1002/joc.1992
Subject(s) - precipitation , series (stratigraphy) , regression , statistics , linear regression , probability density function , regression analysis , climatology , estimation , cluster (spacecraft) , missing data , environmental science , mathematics , meteorology , computer science , geography , geology , paleontology , management , economics , programming language
This work presents a novel method for estimating missing values in daily precipitation series. It is aimed at identifying the event time location with good accuracy and reconstructing the correct amount of daily rainfall. In addition, the statistical properties of the time series, i.e. both probability distribution and long‐term statistics, are preserved. The completion method is based on a two‐step algorithm that uses information from a cluster of neighboring stations. First, wet and dry days are tagged, and subsequently, the full precipitation amount for wet‐classified days is estimated by a modified multi‐linear regression approach. This method avoids overestimation of the number of wet days and underestimation of intense precipitation events, which are typical side effects of common regression‐based approaches. Copyright © 2009 Royal Meteorological Society