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Modeling monthly mean air temperature for Brazil 总被引：1，自引：1，他引：0
Clayton Alcarde Alvares José Luiz Stape Paulo Cesar Sentelhas José Leonardo de Moraes Gonçalves 《Theoretical and Applied Climatology》2013,113(3-4):407-427
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 (α?≤?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?×?108?pixels evaluated. Our national temperature models are recommended to predict air temperature in all Brazilian territories. 相似文献
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Monteiro Galvão Ícaro Silva Pereira Gislaine Sentelhas Paulo Cesar 《Theoretical and Applied Climatology》2022,149(1-2):557-567
Theoretical and Applied Climatology - Air temperature and relative humidity are the main drivers of many fungal diseases, such as moniliasis (Moniliophthora roreri), which affects cocoa production... 相似文献
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Summary Leaf wetness duration (LWD) is related to plant disease occurrence and is therefore a key parameter in agrometeorology. As
LWD is seldom measured at standard weather stations, it must be estimated in order to ensure the effectiveness of warning
systems and the scheduling of chemical disease control. Among the models used to estimate LWD, those that use physical principles
of dew formation and dew and/or rain evaporation have shown good portability and sufficiently accurate results for operational
use. However, the requirement of net radiation (Rn) is a disadvantage foroperational physical models, since this variable
is usually not measured over crops or even at standard weather stations. With the objective of proposing a solution for this
problem, this study has evaluated the ability of four models to estimate hourly Rn and their impact on LWD estimates using
a Penman-Monteith approach. A field experiment was carried out in Elora, Ontario, Canada, with measurements of LWD, Rn and
other meteorological variables over mowed turfgrass for a 58 day period during the growing season of 2003. Four models for
estimating hourly Rn based on different combinations of incoming solar radiation (Rg), airtemperature (T), relative humidity
(RH), cloud cover (CC) and cloud height (CH), were evaluated. Measured and estimated hourly Rn values were applied in a Penman-Monteith
model to estimate LWD. Correlating measured and estimated Rn, we observed that all models performed well in terms of estimating
hourly Rn. However, when cloud data were used the models overestimated positive Rn and underestimated negative Rn. When only
Rg and T were used to estimate hourly Rn, the model underestimated positive Rn and no tendency was observed for negative Rn.
The best performance was obtained with Model I, which presented, in general, the smallest mean absolute error (MAE) and the
highest C-index. When measured LWD was compared to the Penman-Monteith LWD, calculated with measured and estimated Rn, few
differences were observed. Both precision and accuracy were high, with the slopes of the relationships ranging from 0.96 to
1.02 and R2 from 0.85 to 0.92, resulting in C-indices between 0.87 and 0.93. The LWD mean absolute errors associated with Rn estimates
were between 1.0 and 1.5 h, which is sufficient for use in plant disease management schemes.
Authors’ addresses: Paulo C. Sentelhas, Agrometeorology Group, Department of Exact Sciences, ESALQ, University of S?o Paulo,
P.O. Box 9, 13418-900, Piracicaba, SP, Brazil; Terry J. Gillespie, Agrometeorology Group, Department of Land Resource Science,
Ontario Agricultural College, University of Guelph, NIG-2W1, Guelph, ON, Canada. 相似文献
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Clayton?Alcarde?AlvaresEmail author Paulo?César?Sentelhas José?Luiz?Stape 《Theoretical and Applied Climatology》2018,134(1-2):177-191
Although Brazil is predominantly a tropical country, frosts are observed with relative high frequency in the Center-Southern states of the country, affecting mainly agriculture, forestry, and human activities. Therefore, information about the frost climatology is of high importance for planning of these activities. Based on that, the aims of the present study were to develop monthly meteorological (F MET) and agronomic (F AGR) frost day models, based on minimum shelter air temperature (T MN), in order to characterize the temporal and spatial frost days variability in Center-Southern Brazil. Daily minimum air temperature data from 244 weather stations distributed across the study area were used, being 195 for developing the models and 49 for validating them. Multivariate regression models were obtained to estimate the monthly T MN, once the frost day models were based on this variable. All T MN regression models were statistically significant (p < 0.001), presenting adjusted R 2 between 0.69 and 0.90. Center-Southern Brazil is mainly hit by frosts from mid-fall (April) to mid-spring (October). The period from November to March is considered as frost-free, being very rare a frost day within that period. Monthly F MET and F AGR presented significant sigmoidal relationships with T MN (p < 0.0001), with adjusted R 2 above of 0.82. The residuals of the frost day models were random, which means that the sigmoidal models performed quite well for interpreting the frost day variability throughout the study area. The highlands of Santa Catarina, Rio Grande do Sul, São Paulo, and Minas Gerais had in average more than 25 and 13 frosts per year, respectively, for F MET and F AGR. The F MET and F AGR maps developed in this study for Center-Southern Brazil is a useful tool for farmers, foresters, and researchers, since they contribute to reduce frost spatial and temporal uncertainty, helping in planning project for strategic purposes. Furthermore, the monthly F MET and F AGR maps for this Brazilian region are the first zoning of these variables for the country. 相似文献
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