Twelve years (1991-2003) of ERS-1 and ERS-2 altimetry data have been reprocessed within the European Space Agency (ESA) reprocessing altimeter products for ERS (REAPER) project using an updated, modern set of algorithms and auxiliary models. The reprocessed data set (identified as RP01) has been cross-calibrated against the reprocessed ENVISAT V2.1 data. The format of this reprocessed data set is network common data form (version 3). The new data set shows a clear improvement in data quality beyond that of previous releases. The product validation shows reduction of the mean standard deviation of the sea-surface height differences from 8.1 (previously available product) to 6.7 cm (RP01). This paper presents the details of how the reprocessing was conducted and shows selected results from the validation and quality-assurance processes. The major improvements of the REAPER RP01 data set with respect to the previous ESA ERS radar altimetry (RA) products are due to the use of four ENVISAT RA-2 retrackers, RA calibration improvements, new reprocessed precise orbit solutions, ECMWF ERA-interim model for meteorological corrections, new ionospheric corrections, and new sea state. The intent of this paper is to aid the reader in understanding the benefits of the new data set for their particular use-case.@en

Gas path analysis (GPA) plays a major role in turbofan condition monitoring. GPA uses thermodynamic engine models to identify deterioration of individual turbofan components. The accuracy of GPA results depends on the quality of the engine models. Turbofan performance is influenced by secondary performance parameters, which include variable geometry, active clearance control, bleeds flows and power off-take. Hence, if not accounted for in engine models, it decreases GPA accuracy. A method is proposed to increase the accuracy of engine models by accounting for secondary performance parameters. Combining an evolutionary algorithm with on-wing engine data has resulted in a novel approach to determine relationships between secondary performance parameter settings and turbofan component performance deviation. Results show that accounting for these relationships in the engine models increases the model accuracy. Consequently, an accuracy increase of the GPA results is achieved. The method is verified with simulated data and validated with GEnx-1B on-wing engine data.