Data quality control for St. Petersburg flood warning system

This paper focuses on techniques for dealing with imperfect data in a frame of early warning system (EWS). Despite the fact that data may be technically damaged by presenting noise, outliers or missing values, met-ocean simulation systems have to deal with them to provide data transaction between models, real time data assimilation, calibration, etc. In this context data quality-control becomes one of the most important parts of EWS. St. Petersburg FWS was considered as an example of EWS. Quality control in St. Petersburg FWS contains blocks of technical control, human mistakes control, statistical control of simulated fields, statistical control and restoration of measurements and control using alternative models. Domain specific quality control was presented as two types of procedures based on theoretically proved methods were applied. The first procedure is based on probabilistic model of dynamical system, where processes are spatially interrelated and could be implemented in a form of multivariate regression (MRM). The second procedure is based on principal component analysis extended for taking into account temporal relations in data set (ePCA).