Objectives Real-time kinematic (RTK) positioning is a prevalent method for bridge health monitoring due to its high accuracy and automation. As demonstrated in the previous research, a fusion weather estimation (FWE) method is developed to mitigate tropospheric delays in mountainous areas. However, it is observed that the performance of FWE method deteriorates in the presence of regional tropospheric anomaly (RTA) conditions, such as heavy rainfall. In RTA scenarios, significant horizontal variations in tropospheric delay introduce directional residuals even in short-baseline measurements, leading to reduced vertical accuracy and unstable ambiguity resolution. A key objective here is to address this limitation by developing an enhanced RTK algorithm tailored for RTA scenarios to improve the robustness and precision of structural displacement monitoring.

Methods An RTK algorithm is proposed that incorporates relative tropospheric delay gradients derived from high-resolution numerical weather prediction (NWP) data. First, a 3-D, time-varying tropospheric delay field is constructed using NWP and ray tracing to enhance un-difference delay corrections. Second, relative tropospheric gradient parameters are incorporated into the double-difference observation model to explicitly account for horizontal delay non-isotropy. Finally, an extended Kalman filter is developed to jointly estimate coordinates, integer ambiguities and tropospheric parameters, including these gradients, using constraint strategy to balance model stability and sensitivity to atmospheric anomalies.

Results The efficacy of the proposed method is demonstrated through field validation during a heavy rainfall event. Compared with both the conventional GPT3 model and FWE method, the proposed method improves vertical positioning accuracy for BDS by approximately 19.8% and 8.3%, respectively. The accuracy in north direction is also improved by an average of about 8%. Residual analysis reveals a quantifiable mitigation of directional error. The introduced circularity index shows residual point clouds shifting from an elliptical to a near-circular distribution, and the total circularity for the combined BDS+GPS system increases by approximately 14.9%, directly indicating a reduction in azimuthally dominant residuals.

Conclusions The proposed gradient-considered RTK algorithm successfully mitigates the detrimental effects of directional tropospheric residuals during RTA events. The enhancement of both the accuracy (particularly in the vertical component) and the isotropy of residuals significantly improves the robustness and reliability of GNSS-based bridge displacement monitoring under complex meteorological conditions, offering strong potential for engineering applications.