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| DOI | 10.1016/j.atmosres.2020.105103 |
| A meteorological analysis interpolation scheme for high spatial-temporal resolution in complex terrain | |
| Casellas E.; Bech J.; Veciana R.; Miró J.R.; Sairouni A.; Pineda N. | |
| 发表日期 | 2020 |
| ISSN | 0169-8095 |
| 卷号 | 246 |
| 英文摘要 | An adaptive high-temporal resolution interpolation scheme for meteorological observations is presented. It stems from a combination of linear regression, anomaly correction and clustering. A number of approaches to tackle this problem for monthly and daily data have been proposed in the past, but interpolation studies at sub-daily temporal scales are much more limited. Hourly and sub-hourly observational datasets use to present high variability that may be related to different weather conditions. In the proposed methodology, rather than considering the whole data set to perform the interpolation, data are divided in different clusters of variable size, separating regions with potential dissimilar behaviour. A linear regression model is calculated for each cluster and compared against a global model obtained considering all the observations. Only those clusters whose regression model yields a reduction of errors compared to the global model are selected. The adaptive condition lays on that several numbers of clusters are tested and the one that performs the best, in terms of Root Mean Square Error, is selected every time an interpolation is conducted. The methodology presented provides gridded analysis fields of hourly and sub-hourly intervals at 250 m of horizontal resolution. It was originally developed for a complex terrain region (Catalonia, NE Spain), and it was also demonstrated in the German Land of Baden-Württemberg and in the Italian region of Emilia-Romagna. Results show a reduction of cross-validation errors using the leave-one-out method for air temperature and dew point temperature fields and a proper representation of complex orography features. The scheme presented is implemented in Python as pyMICA and it is available as open-source software. © 2020 Elsevier B.V. |
| 关键词 | ErrorsInterpolationLogistic regressionMean square errorOpen source softwareOpen systemsCross validation errorsHigh temporal resolutionInterpolation schemesLeave one out methodsLinear regression modelsMeteorological analysisMeteorological observationRoot mean square errorsReductionclimate modelingcluster analysisnumerical modelregional climateregression analysisspatial resolutionspatiotemporal analysistemporal analysisCataloniaSpainEmilia |
| 语种 | 英语 |
| 来源机构 | Atmospheric Research |
| 文献类型 | 期刊论文 |
| 条目标识符 | http://gcip.llas.ac.cn/handle/2XKMVOVA/132354 |
| 推荐引用方式 GB/T 7714 | Casellas E.,Bech J.,Veciana R.,et al. A meteorological analysis interpolation scheme for high spatial-temporal resolution in complex terrain[J]. Atmospheric Research,2020,246. |
| APA | Casellas E.,Bech J.,Veciana R.,Miró J.R.,Sairouni A.,&Pineda N..(2020).A meteorological analysis interpolation scheme for high spatial-temporal resolution in complex terrain.,246. |
| MLA | Casellas E.,et al."A meteorological analysis interpolation scheme for high spatial-temporal resolution in complex terrain".246(2020). |
| 条目包含的文件 | 条目无相关文件。 | |||||
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