A New Method to Estimate the Systematical Biases of Expendable Bathythermograph
Abstract A new technique to estimate three major biases of XBT probes (improper fall rate, start-up transient, and pure temperature error) has been developed. Different from the well-known and standard “temperature error free” differential method, the new method analyses temperature profiles instead of vertical gradient temperature profiles. Consequently, it seems to be more noise resistant because it uses the integral property over the entire vertical profile instead of gradients. Its validity and robustness have been checked in two ways. In the first case, the new integral technique and the standard differential method have been applied to a set of simulated XBT profiles having a known fall-rate equation to which various combinations of pure temperature errors, random errors, and spikes have been added for the sake of this simulation. Results indicated that the single pure temperature error has little impact on the fall-rate coefficients for both methods, whereas with the added random error and spikes the simulation leads to better results with the new integral technique than with the standard differential method. In the second case, two sets of profiles from actual XBT versus CTD comparisons, collected near Barbados in 1990 and in the western Mediterranean (2003–04 and 2008–09), have been used. The individual fall-rate coefficients and start-up transient for each XBT profile, along with the overall pure temperature correction, have been calculated for the XBT profiles. To standardize procedures and to improve the terms of comparison, the individual start-up transient estimated by the integral method was also assigned and included in calculations with the differential method. The new integral method significantly reduces both the temperature difference between XBT and CTD profiles and the standard deviation. Finally, the validity of the mean fall-rate coefficients and the mean start-up transient, respectively, for DB and T7 probes as precalculated equations was verified. In this case, the temperature difference is reduced to less than 0.1°C for both datasets, and it randomly distributes around the null value. In addition, the standard deviation on depth values is largely reduced, and the maximum depth error computed with the datasets near Barbados is within 1.1% of its real value. Results also indicate that the integral method has a good performance mainly when applied to profiles in regions with either a very large temperature gradient, at the thermocline or a very small one, toward the bottom.