Update of the corrective model for Jason-1 DORIS data in relation to the South Atlantic Anomaly and a corrective model for SPOT-5
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文摘
After recalling the principle of the Jason-1 data corrective model in relation to the South Atlantic Anomaly (SAA) developed by Lemoine and Capdeville (2006), we present a model update which takes into account the orbit changes and the recent DORIS data. We propose also here a method to the International DORIS Service (IDS) Analysis Centers (ACs) in their contribution to the ITRF2014 for adding DORIS Jason-1 data into their solutions. When the Jason-1 satellite is added to the multi-satellite solution (orbit of inclination of 66° complements the polar-orbiting satellites), the stability of the geocenter Z-translation is improved (standard deviation of 11.5 mm against 16.5 mm).In a second part we take advantage of a high-energy particles dosimeter (CARMEN) on-board Jason-2 to improve the corrective model of Jason-1. We completed a correlation study showing that the CARMEN >87 MeV integrated proton flux map averaged over the period 2009–2011 is the energy band of the CARMEN maps which are the most coherent with the one obtained from Jason-1 DORIS measurements. The model based on the Jason-1 map and the one based on the CARMEN map are then compared in terms of orbit determination and station position estimation.We derive and validate a SPOT-5 data corrective model. We determine the SAA grid at the altitude of SPOT-5 from the frequency time derivative of the on-board frequency offsets and estimated the model parameters. We demonstrate the impact of the SPOT-5 data corrective model on the Precise Orbit Determination and the station position estimation from the weekly solutions, based on two individual Analysis Centers solutions, GOP (Geodetic Observatory Pecny) and GRG (Groupe de Recherche de Géodésie Spatiale). The SPOT-5 data corrective model significantly improves the Precise Orbit Determination (reduction of 1.4% in 2013 of RMS of the fit, reduction of 25% in normal direction of arc overlap RMS) and the overall statistics of the station position estimation (reduction of 2% of repeatability RMS, reduction of 3–7% of geocenter variations). Moreover, the application of the data corrective model strongly reduces the individual station bias in the North component for all the SAA-affected stations and the height bias for the most affected SAA stations. The East bias is, however, not reduced by this data corrective model.

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