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Metadados

1. Identificação
Tipo de ReferênciaArtigo em Revista Científica (Journal Article)
Sitemtc-m21d.sid.inpe.br
Código do Detentorisadg {BR SPINPE} ibi 8JMKD3MGPCW/3DT298S
Identificador8JMKD3MGP3W34T/47S48FE
Repositóriosid.inpe.br/mtc-m21d/2022/10.24.13.50
Repositório de Metadadossid.inpe.br/mtc-m21d/2022/10.24.13.50.18
Última Atualização dos Metadados2023:01.03.16.46.21 (UTC) administrator
DOI10.1175/MWR-D-20-0379.1
ISSN0027-0644
Chave de CitaçãoZhangLWGMCBYLMSCZSWRPFSBHS:2022:EvSuCo
TítuloEvaluation of Surface Conditions from Operational Forecasts Using In Situ Saildrone Observations in the Pacific Arctic
Ano2022
Data de Acesso18 maio 2024
Tipo de Trabalhojournal article
Tipo SecundárioPRE PI
2. Contextualização
Autor 1 Zhang, Chidong
 2 Levine, Aaron F.
 3 Wang, Muyin
 4 Gentemann, Chelle
 5 Mordy, Calvin W.
 6 Cokelet, Edward D.
 7 Browe, Philip A.
 8 Yang, Qiong
 9 Lawrence-Slavas, Noah
10 Meinig, Christian
11 Smith, Gregory
12 Chiodi, Andy
13 Zhang, Dongxiao
14 Stabeno, Phyllis
15 Wang, Wanqiu
16 Ren, Hong-Li
17 Peterson, K. Andrew
18 Figueroa, Silvio Nilo
19 Steele, Michael
20 Barton, Neil P.
21 Huang, Andrew
22 Shin, Hyun Cheol
Grupo 1
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18 DIMNT-CGCT-INPE-MCTI-GOV-BR
Afiliação 1 NOAA/Pacific Marine Environmental Laboratory
 2 University of Washington
 3 NOAA/Pacific Marine Environmental Laboratory
 4 Farallon Institute
 5 NOAA/Pacific Marine Environmental Laboratory
 6 NOAA/Pacific Marine Environmental Laboratory
 7 European Centre for Medium-Range Weather Forecasts
 8 NOAA/Pacific Marine Environmental Laboratory
 9 NOAA/Pacific Marine Environmental Laboratory
10 NOAA/Pacific Marine Environmental Laboratory
11 Environment and Climate Change Canada
12 NOAA/Pacific Marine Environmental Laboratory
13 NOAA/Pacific Marine Environmental Laboratory
14 NOAA/Pacific Marine Environmental Laboratory
15 NOAA/National Centers for Environmental Prediction
16 Chinese Academy of Meteorological Sciences
17 Environment and Climate Change Canada
18 Instituto Nacional de Pesquisas Espaciais (INPE)
19 University of Washington
20 Naval Research Laboratory
21 Science Applications International Corporation
22 Korea Meteorological Administration
Endereço de e-Mail do Autor 1
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18 nilo.figueroa@inpe.br
RevistaMonthly Weather Review
Volume150
Número6
Páginas1437-1455
Nota SecundáriaA1_GEOCIÊNCIAS A2_INTERDISCIPLINAR B2_ASTRONOMIA_/_FÍSICA
Histórico (UTC)2022-10-24 13:50:18 :: simone -> administrator ::
2022-10-24 13:50:19 :: administrator -> simone :: 2022
2022-10-24 13:53:05 :: simone -> administrator :: 2022
2023-01-03 16:46:21 :: administrator -> simone :: 2022
3. Conteúdo e estrutura
É a matriz ou uma cópia?é a matriz
Estágio do Conteúdoconcluido
Transferível1
Tipo do ConteúdoExternal Contribution
Tipo de Versãopublisher
Palavras-ChaveArctic
Atmosphere-ocean interaction
Forecast verification/skill
ResumoObservations from uncrewed surface vehicles (saildrones) in the Bering, Chukchi, and Beaufort Seas during June-September 2019 were used to evaluate initial conditions and forecasts with lead times up to 10 days produced by eight operational numerical weather prediction centers. Prediction error behaviors in pressure and wind are found to be different from those in temperature and humidity. For example, errors in surface pressure were small in short-range (,6 days) forecasts, but they grew rapidly with increasing lead time beyond 6 days. Non-weighted multimodel means outperformed all individual models approaching a 10-day forecast lead time. In contrast, errors in surface air temperature and relative humidity could be large in initial conditions and remained large through 10-day forecasts without much growth, and non-weighted multimodel means did not outperform all individual models. These results following the tracks of the mobile platforms are consistent with those at a fixed location. Large errors in initial condition of sea surface temperature (SST) resulted in part from the unusual Arctic surface warming in 2019 not captured by data assimilation systems used for model initialization. These errors in SST led to large initial and prediction errors in surface air temperature. Our results suggest that improving predictions of surface conditions over the Arctic Ocean requires enhanced in situ observations and better data assimilation capability for more accurate initial conditions as well as better model physics. Numerical predictions of Arctic atmospheric conditions may continue to suffer from large errors if they do not fully capture the large SST anomalies related to Arctic warming.
ÁreaMET
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4. Condições de acesso e uso
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Política de Arquivamentodenypublisher6 allowfinaldraft
Permissão de Leituradeny from all and allow from 150.163
Permissão de Atualizaçãonão transferida
5. Fontes relacionadas
Unidades Imediatamente Superiores8JMKD3MGPCW/46KUATE
DivulgaçãoWEBSCI; PORTALCAPES; SCOPUS.
Acervo Hospedeirourlib.net/www/2021/06.04.03.40
6. Notas
Campos Vaziosalternatejournal archivist callnumber copyholder copyright creatorhistory descriptionlevel e-mailaddress format isbn label lineage mark mirrorrepository month nextedition notes numberoffiles orcid parameterlist parentrepositories previousedition previouslowerunit progress project resumeid rightsholder schedulinginformation secondarydate secondarykey session shorttitle size sponsor subject targetfile tertiarymark tertiarytype url
7. Controle da descrição
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