Abstract
In this work, we used the MICE (Multivariate Imputation by Chained Equations) technique to impute missing daily data from six meteorological variables (precipitation, temperature, relative humidity, atmospheric pressure, wind speed and insolation) from 96 stations located in the northeast region of Brazil (NEB) for the period from 1961 to 2014. We then applied tests with a quality control system (QCS) developed for the detection, correction and possible replacement of suspicious data. Both the applied gap filling technique and the QCS showed that it was possible to solve two of the biggest problems found in time series of daily data measured in meteorological stations: the generation of plausible values for each variable of interest, in order to remedy the absence of observations, and how to detect and allow proper correction of suspicious values arising from observations.
Highlights
Observational meteorological data are basic elements for climatological analyses [1].In Brazil, despite the inherent relevance of such observations, the amount of data of this kind has been suffering a significant reduction over the years [2], with several manual weather stations (MWS) being permanently closed, becoming inoperative or functioning precariously, compromising the quality and continuity of the meteorological records.This situation poses a hindrance to a more detailed, observation-based climate analysis, forcing the use of Reanalyses products, which constitute a synthetic database reconstructed by calibrating a regional climate model to observed historical conditions, in a grid format, with statistical properties, such as means and variances, that are very similar to those of the observations [3]
The aim of this study was to present the results of the application of a quality control system (QCS) and a gap filling technique to time series of meteorological variables collected by manual weather stations in the northeast region of Brazil (NEB) in the period of 1961–2014, corresponding to 54 years of daily data, which were assessed here based on this timescale, as well as on 10-day and monthly averages or accumulated values
The filling is processed on the daily scale, 5 years of observed data are randomly chosen and have their data removed, and Multivariate Imputation by Chained Equations (MICE) is applied again, imputing values that are later compared with real observations; this is the validation process
Summary
Observational meteorological data are basic elements for climatological analyses [1].In Brazil, despite the inherent relevance of such observations, the amount of data of this kind has been suffering a significant reduction over the years [2], with several manual weather stations (MWS) being permanently closed, becoming inoperative or functioning precariously, compromising the quality and continuity of the meteorological records.This situation poses a hindrance to a more detailed, observation-based climate analysis, forcing the use of Reanalyses products, which constitute a synthetic database reconstructed by calibrating a regional climate model to observed historical conditions, in a grid format, with statistical properties, such as means and variances, that are very similar to those of the observations [3]. In Brazil, despite the inherent relevance of such observations, the amount of data of this kind has been suffering a significant reduction over the years [2], with several manual weather stations (MWS) being permanently closed, becoming inoperative or functioning precariously, compromising the quality and continuity of the meteorological records. Useful for analysing long-term climate trends and studying future climate change scenarios [4], they are not reliable in terms of extreme events [5] To overcome such an issue, especially in the case of precipitation data, several methods have been proposed to create gridded products that provide a standardization of the properties of the variable across a spatial field, surmounting problems related to sparse and non-uniform rainfall coverage. Some of those methods are based on exploring the surface observations to the maximum [6,7,8,9]; others are based on combining observed rainfall data with estimates from remote sensing [10,11,12,13,14,15,16,17]
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