Open AccessThree‐dimensional spatial interpolation of surface meteorological observations from high‐resolution local networks
Author(s) -
Uboldi Francesco,
Lussana Cristian,
Salvati Marta
Publication year - 2008
Publication title -
meteorological applications
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.672
H-Index - 59
eISSN - 1469-8080
pISSN - 1350-4827
DOI - 10.1002/met.76
Subject(s) - multivariate interpolation , orographic lift , interpolation (computer graphics) , covariance , computer science , meteorology , environmental science , data assimilation , remote sensing , field (mathematics) , scale (ratio) , algorithm , statistics , mathematics , geography , cartography , precipitation , computer vision , pure mathematics , bilinear interpolation , animation , computer graphics (images)
An objective analysis technique is applied to a local, high‐resolution meteorological observation network in the presence of complex topography. The choice of optimal interpolation (OI) makes it possible to implement a standard spatial interpolation algorithm efficiently. At the same time OI constitutes a basis to develop, in perspective, a full multivariate data assimilation scheme. In the absence of a background model field, a simple and effective de‐trending procedure is implemented. Three‐dimensional correlation functions are used to account for the orographic distribution of observing stations. Minimum‐scale correlation parameters are estimated by means of the integral data influence (IDI) field. Hourly analysis fields of temperature and relative humidity are routinely produced at the Regional Weather Service of Lombardia. The analysis maps show significant informational content even in the presence of strong gradients and infrequent meteorological situations. Quantitative evaluation of the analysis fields is performed by systematically computing their cross validation (CV) scores and by estimating the analysis bias. Further developments concern the implementation of an automatic quality control procedure and the improvement of error covariance estimation. Copyright © 2008 Royal Meteorological Society