A Method for Constructing an Empirical Model of Short-Term Offshore Ocean Tide Loading Displacement Based on PPP

Abstract

The ocean tide loading (OTL) can result in displacements of centimeters or even decimeters at nearshore stations. Global ocean tide models exhibit errors in nearshore regions, which limit the accuracy of maintaining the coordinates of these stations. GNSS positioning can obtain tidal load displacements in nearshore areas, but it often requires long-term observation data and cannot provide timely correction models for newly established reference stations. This paper proposes a method for an empirical correction model of short-term OTL displacements using GNSS observations, where the kinematic coordinate sequences are first obtained by multi-GNSS precise point positioning with ambiguity resolution (PPP-AR), and then the OTL corrections are obtained by window-sliding forecast based on random forest modeling. Through experiments conducted in the Hong Kong region, the empirical model with a window of 15 days is established by the proposed method. After applying the empirical model, root mean square errors of the residuals are reduced by 1.5 (30.6%), 3.7 (53.6%), and 3.7 mm (37.8%) in the East, North, and Up (ENU) components, respectively. When using the global ocean tide model FES2014, the RMSE values are reduced by 1.2 (24.5%), 0.3 (4.3%), and 3.7 mm (37.8%) in the ENU components, respectively. The empirical model shows better effects for the OTL displacement compared to FES2014, especially in the N component, with an improvement ratio of about 49.3%.

Keywords

• global navigation satellite system
• ocean tide loading
• PPP-AR
• random forest modeling