Evidence map›Paper›PMID 33469714›Full record

ArticleEnvironmental monitoring and assessment2021

Statistical modeling of sea ice concentration in the northwest region of the Antarctic Peninsula.

Fernando Luis Hillebrand, Ulisses Franz Bremer, Marcos Wellausen Dias de Freitas, Juliana Costi, Cláudio Wilson Mendes Júnior, Jorge Arigony-Neto, Jefferson Cardia Simões, Cristiano Niederauer da Rosa, Janisson Batista de Jesus

Abstract read
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In one paragraph

Article in Environmental monitoring and assessment, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.

0numbers the graph read from it
0cells of the map it votes in
0citing papers in PubMed
–field-weighted citation impact
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

0 citing papers in PubMed.

No citing paper in PubMed yet.

4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

9 authors.

Fernando Luis HillebrandPrograma de Pós-Graduação em Sensoriamento Remoto, Universidade Federal do Rio Grande do Sul, Porto Alegre, Brazil. fernando.hillebrand@rolante.ifrs.edu.br.ORCID http://orcid.org/0000-0002-0182-8526
Ulisses Franz BremerPrograma de Pós-Graduação em Sensoriamento Remoto, Universidade Federal do Rio Grande do Sul, Porto Alegre, Brazil.ORCID https://orcid.org/0000-0003-0519-5807
Marcos Wellausen Dias de FreitasPrograma de Pós-Graduação em Sensoriamento Remoto, Universidade Federal do Rio Grande do Sul, Porto Alegre, Brazil.ORCID https://orcid.org/0000-0001-9879-2584
Juliana CostiPrograma de Pós-Graduação em Modelagem Computacional, Universidade Federal do Rio Grande, Rio Grande, Brazil.ORCID https://orcid.org/0000-0001-6220-2343
Cláudio Wilson Mendes JúniorPrograma de Pós-Graduação em Sensoriamento Remoto, Universidade Federal do Rio Grande do Sul, Porto Alegre, Brazil.ORCID https://orcid.org/0000-0003-1745-348X
Jorge Arigony-NetoInstituto de Oceanografia, Universidade Federal do Rio Grande, Rio Grande, Brazil.ORCID https://orcid.org/0000-0003-4848-2064
Jefferson Cardia SimõesCentro Polar e Climático, Universidade Federal do Rio Grande do Sul, Porto Alegre, Brazil.ORCID https://orcid.org/0000-0001-5555-3401
Cristiano Niederauer da RosaPrograma de Pós-Graduação em Sensoriamento Remoto, Universidade Federal do Rio Grande do Sul, Porto Alegre, Brazil.ORCID https://orcid.org/0000-0003-3693-4764
Janisson Batista de JesusPrograma de Pós-Graduação em Sensoriamento Remoto, Universidade Federal do Rio Grande do Sul, Porto Alegre, Brazil.ORCID https://orcid.org/0000-0001-8372-5557

Funding

Fundação de Amparo à Pesquisa do Estado do Rio Grande do Sul 17/25510000518-0Instituto Nacional de Ciência e Tecnologia da Criosfera 465680/2014-3
6 · The paper itself

Abstract

Sea ice is one of the main components of the cryosphere that modifies the exchange of heat and moisture between the ocean and atmosphere, regulating the global climate. In this sense, it is important to identify the concentration of sea ice in different regions of Antarctica in order to measure the impact of environmental changes on the region's ecosystem. The objective of this study was to evaluate the performance of the multiple linear regression and Box-Jenkins methods for predicting the concentration of sea ice along the northwest coast of the Antarctic Peninsula. Sea ice concentration data from May to November for the period 1979-2018 were extracted from passive remote sensors including a scanning multichannel microwave radiometer, special sensor microwave imager, and special sensor microwave imager/sounder. Meteorological variables from the atmospheric reanalysis model ERA5 of the European Center for Medium-Range Weather Forecasts were used as predictor variables, and the leave-one-out cross-validation technique was used to calibrate and validate the models. It was found that both statistical models have similar performance when analyzing residual analysis results, root mean square error of cross-validation, and final accuracy and residual standard deviation, these responses being related to the regionalization of the study area and to the Box-Jenkins presents strong, homogeneous, and stable correlations in the time series modeled for each pixel.

Indexed as

EcosystemIce CoverAntarctic RegionsEnvironmental MonitoringModels, StatisticalBox–JenkinsERA5Multiple linear regressionPassive remote sensorsTime series

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Registered trials

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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.