{"refrec":{"BRefID":289102,"RR":"<b>Alvera-Azcárate, A.; Barth, A.; Sirjacobs, D.; Lenartz, F.; Beckers, J.-M.</b> (2011). Data Interpolating Empirical Orthogonal Functions (DINEOF): a tool for geophysical data analyses. <i>Mediterr. Mar. Sci. 12(3)</i>: 5-11. <a href=\"https://dx.doi.org/10.12681/mms.64\" target=\"_blank\">https://dx.doi.org/10.12681/mms.64</a>","BEntID":281139,"PublicFlag":1,"CheckedFlag":0,"wosflag":1,"vabbflag":null,"RefStringPartII":". <i>Mediterr. Mar. Sci. 12(3)</i>: 5-11. <a href=\"https://dx.doi.org/10.12681/mms.64\" target=\"_blank\">https://dx.doi.org/10.12681/mms.64</a>","DocTypID":8,"DocType":"Journal article","MarineFlag":1,"FreshFlag":0,"BrackishFlag":0,"TerrestrialFlag":0,"Authorstring":"Alvera-Azcárate, A.; Barth, A.; Sirjacobs, D.; Lenartz, F.; Beckers, J.-M.","OrigTitleTranslFlag":0,"Authorstringtrunc":"Alvera-Azcárate, A. <i>et al.</i>","Englishabstract":"An overview of the technique called DINEOF (Data Interpolating Empirical Orthogonal Functions) is presented. DINEOF reconstructs missing information in geophysical data sets, such as satellite imagery or time series. A summary of the technique is given, with its main characteristics, recent developments and future research directions. DINEOF has been applied to a large variety of oceanographic variables in various domains of different sizes. This technique can be applied to a single variable (monovariate approach), or to several variables together (multivariate approach), with no complexity increase in the application of the technique. Error fields can be computed to establish the accuracy of the reconstruction. Examples are given to illustrate the capabilities of the technique. DINEOF is freely offered to download, and help is provided to users in the form of a wiki and through a discussion email list. 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