Abstract's details
Empirical, Nonparametric Estimation of the Sea State Bias using the Interpolation Method
Event: 2022 Ocean Surface Topography Science Team Meeting
Session: Instrument Processing: Propagation, Wind Speed and Sea State Bias
Presentation type: Poster
An alternative approach to empirical, non-parametric sea state bias (SSB) modeling was developed with intention of providing a simple, transparent and efficient means to derive both a raw and smoothed SSB solution. This alternative approach, referred to as the interpolation method, maintains the flexibility to generate 2-D or 3-D models using either direct or difference measurements of the sea level anomaly uncorrected for SSB (uSLA). The interpolation method was further used to introduce a combined, or joint (JNT), SSB solution using a weighted combination of collinear and crossover uSLA measurements. The raw and smoothed SSB solutions derived using the interpolation method are obtained over three steps, with a supplemental fourth step that consists of estimating a model-dependent dual-frequency ionosphere calibration bias to correct for a relative range+SSB error.
We have generated SSB models for Topex/Poseidon and Jason-1 through -3 using the interpolation method. By applying the same SSB modeling method that includes a dual-frequency ionosphere calibration bias to four sequential satellites, we explore the potential benefits to the long-term sea level time series.
We have generated SSB models for Topex/Poseidon and Jason-1 through -3 using the interpolation method. By applying the same SSB modeling method that includes a dual-frequency ionosphere calibration bias to four sequential satellites, we explore the potential benefits to the long-term sea level time series.
Contribution: IPC2022-Empirical__Nonparametric_Estimation_of_the_Sea_State_Bias_using_the_Interpolation_Method.pdf (pdf, 628 ko)
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