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(Referência obtida automaticamente do Web of Science, por meio da informação sobre o financiamento pela FAPESP e o número do processo correspondente, incluída na publicação pelos autores.)

Modeling monthly mean air temperature for Brazil

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Autor(es):
Alvares, Clayton Alcarde [1, 2] ; Stape, Jose Luiz [3, 4] ; Sentelhas, Paulo Cesar [5] ; de Moraes Goncalves, Jose Leonardo [6]
Número total de Autores: 4
Afiliação do(s) autor(es):
[1] Forestry Sci & Res Inst IPEF, BR-13415000 Sao Paulo - Brazil
[2] FPC, BR-13415000 Sao Paulo - Brazil
[3] N Carolina State Univ, Dept Forestry & Environm Resources, Raleigh, NC 27695 - USA
[4] FPC, Raleigh, NC 27695 - USA
[5] Univ Sao Paulo, Coll Agr Luiz de Queiroz, Dept Biosyst Engn, BR-13418900 Sao Paulo - Brazil
[6] Univ Sao Paulo, Coll Agr Luiz de Queiroz, Dept Forestry Sci, BR-13418900 Sao Paulo - Brazil
Número total de Afiliações: 6
Tipo de documento: Artigo Científico
Fonte: THEORETICAL AND APPLIED CLIMATOLOGY; v. 113, n. 3-4, p. 407-427, AUG 2013.
Citações Web of Science: 36
Resumo

Air temperature is one of the main weather variables influencing agriculture around the world. Its availability, however, is a concern, mainly in Brazil where the weather stations are more concentrated on the coastal regions of the country. Therefore, the present study had as an objective to develop models for estimating monthly and annual mean air temperature for the Brazilian territory using multiple regression and geographic information system techniques. Temperature data from 2,400 stations distributed across the Brazilian territory were used, 1,800 to develop the equations and 600 for validating them, as well as their geographical coordinates and altitude as independent variables for the models. A total of 39 models were developed, relating the dependent variables maximum, mean, and minimum air temperatures (monthly and annual) to the independent variables latitude, longitude, altitude, and their combinations. All regression models were statistically significant (alpha a parts per thousand currency signaEuro parts per thousand 0.01). The monthly and annual temperature models presented determination coefficients between 0.54 and 0.96. We obtained an overall spatial correlation higher than 0.9 between the models proposed and the 16 major models already published for some Brazilian regions, considering a total of 3.67 x 10(8) pixels evaluated. Our national temperature models are recommended to predict air temperature in all Brazilian territories. (AU)

Processo FAPESP: 08/05744-0 - Elaboração, calibração e validação de um modelo edafoclimatológico de produtividade florestal integrado com sistemas de informações geográficas
Beneficiário:Clayton Alcarde Alvares
Linha de fomento: Bolsas no Brasil - Doutorado Direto