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Record W7005307856

Previsão sub-sazonal com o modelo global atmosférico do CPTEC/INPE: configuração, avaliação e investigação das fontes de previsibilidade para a América do Sul

2021· dissertation· pt· W7005307856 on OpenAlexaboutno aff

Bibliographic record

VenueBiblioteca Digital da Memória Científica do INPE (National Institute for Space Research) · 2021
Typedissertation
Languagept
FieldMedicine
TopicBiological and pharmacological studies of plants
Canadian institutionsnot available
Fundersnot available
KeywordsClimate changeGlobal warmingAir temperatureScale (ratio)Trend analysis
DOInot available

Abstract

fetched live from OpenAlex

Este estudo teve como objetivo determinar a habilidade preditiva do modelo atmosférico global do CPTEC (BAM-1.2) na escala sub-sazonal e investigar as principais fontes de previsibilidade associadas sobre a América do Sul, durante 12 verões estendidos (novembro março, 1999/2000 2010/2011). Sete configurações do BAM-1.2 foram avaliadas em termos de resolução vertical, parametrizações de convecção profunda e camada limite planetária, e inicializações da componente atmosférica (condição inicial) e umidade do solo. Através da avaliação dessas sete configurações foi possível determinar a configuração mais adequada do BAM-1.2 para as previsões das anomalias semanais de precipitação e temperatura do ar a 2 metros de altura (T2M) e evolução diária da Oscilação de Madden e Julian (OMJ). Com a configuração do BAM-1.2 determinada, a habilidade preditiva do modelo do CPTEC, a partir de um conjunto de previsões retrospectivas, composto por 11 membros, foi avaliada e comparada com a habilidade preditiva dos modelos do Japan Meteorological Agency (JMA), Environmental and Climate Change Canada (ECCC), European Centre for Medium-range Weather Forecasts (ECMWF) e Australian Bureau of Meteorology (BoM), os quais participam do projeto internacional Sub-seasoanl-to-Seasonal (S2S) do programa mundial de pesquisa em tempo e clima da Organização Meteorológica Mundial (OMM). Por último, os modos de variabilidade espaço-temporal das anomalias semanais de precipitação foram determinados. Tais modos foram associados a fenômenos atmosféricos/oceânicos para definir as principais fontes de previsibilidade na escala sub-sazonal sobre a América do Sul durante o verão estendido. Os resultados mostraram que o BAM-1.2 se mostrou competitivo em relação aos modelos do projeto S2S avaliados. Entretanto, os superiores índices de destreza alcançados pelo modelo do ECMWF indicam que a habilidade preditiva das previsões sub-sazonais do CPTEC pode ser aprimoradas possivelmente com o incremento na resolução espacial do BAM- 1.2, acoplamento com o componente oceânico e melhorias nos esquemas de parametrizações físicas e de geração dos membros para inicialização do modelo. Com relação às principais fontes de previsibilidade sobre a América do Sul, o padrão de dipolo meridional sobre o leste da América do Sul foi encontrado como sendo o modo de variabilidade espaço-temporal dominante. Foi identificado que esse padrão é fortemente influenciado por oscilações dentro das escalas de variabilidade intrassazonal e interanual. Esta constatação indica que vários fenômenos influenciam a formação do padrão de dipolo e, dessa forma, atuam como fontes de previsibilidade sobre a América do Sul [e.g., teleconexões trópico-extratrópico, El-Niño Oscilação Sul (ENOS) e OMJ]. Além disso, o segundo modo de variabilidade espaço-temporal também mostrou relevante contribuição para a habilidade preditiva sobre a região equatorial da América do Sul. Esse padrão apresenta uma forte relação com a atividade convectiva sobre a região equatorial do Oceano Pacífico Oeste, influenciado principalmente pelo ENOS. ABSTRACT: The aim of this work was to study the predictive ability of the Brazilian Global Atmospheric Model version 1.2 (BAM-1.2) at sub-seasonal time-scale and the associated sources of predictability over South America during the 12 extended austral summers (November March, 1999/2000 2010/2011). Seven BAM-1.2 configurations were tested in terms of vertical resolution, deep convection and boundary layer parameterizations, as well as atmospheric component and soil moisture initializations, in order to identify the configuration with best performance when predicting weekly precipitation anomalies, weekly mean 2-meter temperature (T2M) and the Madden and Julian Oscillation (MJO) daily evolution. With BAM-1.2 configuration determined, an inter-comparison performance assessment of 11 member ensemble hindcasts produced with BAM-1.2 against four Sub-seasonal to Seasonal (S2S) prediction project models from Japan Meteorological Agency (JMA), Environmental and Climate Change Canada (ECCC), European Centre for Mediumrange Weather Forecasts (ECMWF) and Australian Bureau of Meteorology (BoM) was performed. Lastly, the main spatio-temporal variability modes of weekly precipitation anomalies were determined. These modes were associated with atmospheric/oceanic phenomena to define the main sources of predictability at subseasonal time-scale over South America during the extended summer. The performed inter comparison revealed that for prediction of precipitation anomalies and MJO BAM-1.2 showed competitive performance compared to the investigated S2S models, but with respect to ECMWF there is scope for improvements, possibly by a combination of including coupling to an interactive ocean and improving resolution, physical parameterization schemes and the ensemble generation approach for initialization. Regarding the main sources of predictability over South America, the South American seesaw pattern has been found to be the dominant mode of variability. This pattern is strongly influenced by oscillations within the intraseasonal and interannual variability time scales. This suggests that several phenomena can influence the formation of this pattern, acting as sources of predictability [e.g., tropical-extratropical interaction, El-Niño South Oscillation (ENSO), and MJO]. Besides, the second mode of variability contributes to the predictive ability over the equatorial South American region. This mode has a strong relationship with the convective activity over the Equatorial West Pacific Ocean, influenced mainly by ENSO.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame machine prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.001
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Simulation or modeling · Consensus signal: Simulation or modeling
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.039
Threshold uncertainty score0.077

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.001
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0000.001
Bibliometrics0.0000.001
Science and technology studies0.0000.000
Scholarly communication0.0010.001
Open science0.0010.001
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0020.000

Machine scores (provisional)

The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.

Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.

Opus teacher head0.158
GPT teacher head0.428
Teacher spread0.270 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designSimulation or modeling
Domainnot available
GenreEmpirical

How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".

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Citations0
Published2021
Admission routes1
Has abstractyes

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