Gap Filling and Quality Control Applied to Meteorological Variables Measured in the Northeast Region of Brazil

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...

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Bibliographic Details
Published in:Atmosphere 2021-10, Vol.12 (10), p.1278
Main Authors: Costa, Rafaela Lisboa, Barros Gomes, Heliofábio, Cavalcante Pinto, David Duarte, da Rocha Júnior, Rodrigo Lins, dos Santos Silva, Fabrício Daniel, Barros Gomes, Helber, Lemos da Silva, Maria Cristina, Luís Herdies, Dirceu
Format: Article
Language:English
Subjects:
Atmospheric pressure
climate analysis
Climate change
Control systems
data quality control
Datasets
Estimates
missing values
Precipitation
Quality control
Rain
Relative humidity
Remote sensing
Software
Time series
Trends
Variables
Vegetation
verification
Weather stations
Wind speed
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Summary: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.
ISSN:2073-4433
2073-4433
DOI:10.3390/atmos12101278