A Metrological Approach to Producing Harmonised Fundamental Climate Data Records from Long-Term Sensor Series Data

Meaningful study and quantification of climatic trends requires decades of observational data. Within satellite remote sensing this motivates the generation fundamental climate data records (FCDRs) - datasets containing the combined observations from a series of missions of a given sensor, to span the required duration for study. Ensuring the radiometric stability of such series is vital for such applications. To this end a consistent in-flight retrospective recalibration of all the sensors in such a series is required, called harmonisation. Presented here is a methodology for achieving this by analysing match-ups between sensors in the series and applicable sensors with modern, well-calibrated reference sensors, fitting new calibration parameters for each sensor in the series. Such a problem is not tractable in the most optimal, metrologically rigorous manner by existing solvers, due to the possibly complex error and geophysical correlation structures and high data volume (potentially >10 ⁸ match-ups). Discussed are a palette of novel optimisation algorithms developed to overcome this, which investigate alternative approaches to handling the full problem. Preliminary results are shown for one these approaches, Fast EIV (Errors in Variables), for the recalibration of the AVHRR sensor series.