气候变化领域集成服务门户(CCPortal): A meteorological analysis interpolation scheme for high spatial-temporal resolution in complex terrain

CCPortal

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.
英文关键词	Clustering; Complex terrain; Hourly analysis; Regression model; Spatial interpolation
语种	英语
scopus关键词	Errors; Interpolation; Logistic regression; Mean square error; Open source software; Open systems; Cross validation errors; High temporal resolution; Interpolation schemes; Leave one out methods; Linear regression models; Meteorological analysis; Meteorological observation; Root mean square errors; Reduction; climate modeling; cluster analysis; numerical model; regional climate; regression analysis; spatial resolution; spatiotemporal analysis; temporal analysis; Catalonia; Spain; Emilia
来源期刊	Atmospheric Research
文献类型	期刊论文
条目标识符	http://gcip.llas.ac.cn/handle/2XKMVOVA/141805
作者单位	Department of Applied Physics - Meteorology, University of Barcelona, Barcelona, Spain; Meteorological Service of Catalonia, Barcelona, Spain
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],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.Atmospheric Research,246.
MLA	Casellas E.,et al."A meteorological analysis interpolation scheme for high spatial-temporal resolution in complex terrain".Atmospheric Research 246(2020).