Optimal estimation retrieval framework for daytime clear-sky total column water vapour from MTG-FCI near-infrared measurements

Abstract

A retrieval of total column water vapour (TCWV) from the new daytime, clear-sky near-infrared (NIR) measurements of the Flexible Combined Imager (FCI) onboard the geostationary satellite Meteosat Third Generation Imager (MTG-I, Meteosat-12) is presented. The retrieval algorithm is based on the differential absorption technique, relating TCWV amounts to the radiance ratio of a non-absorbing band at 0.865 µm and a nearby water vapour (WV) absorbing band at 0.914 µm. The sensitivity of the band ratio to WV amount increases towards the surface which means that the whole atmospheric column down to the boundary-layer moisture variability can be observed well.

The retrieval framework is based on an optimal estimation (OE) method, providing pixel-based uncertainty estimates. It builds on well-established algorithms for other passive imagers with similar spectral band settings. Transferring knowledge gained in their development onto FCI required new approaches. The absence of additional, adjacent window bands to estimate the surface reflectance within FCI's absorbing channel is mitigated using a principal component regression (PCR) from the bands at 0.51, 0.64, 0.865, 1.61, and 2.25 µm.

We utilize synergistic observations from Sentinel-3 Ocean and Land Colour Instrument (OLCI) and Sea and Land Surface Temperature Radiometer (SLSTR) to generate “FCI-like” measurements. OLCI bands were complemented with SLSTR bands, enabling evaluation of the retrieval's robustness and global performance of the PCR. Furthermore, this enabled algorithm testing under realistic conditions using well-characterized data, at a time when a long-term, fully calibrated FCI Level 1c dataset was not available. We built a forward model for two FCI equivalent OLCI bands at 0.865 and 0.9 µm. A long-term validation of OLCI against a single atmospheric radiation measurement (ARM) reference site without the PCR resulted in a bias of 1.85 kg m−2, centred root-mean-square deviation (cRMSD) of 1.26 kg m−2, and a Pearson correlation coefficient (r) of 0.995.

A first verification of the OLCI/SLSTR “FCI-like” TCWV against well-established ground-based TCWV products concludes with a wet bias between 0.33–2.84 kg m−2, a cRMSD between 1.46–2.21 kg m−2, and r between 0.98–0.99. In this set of comparisons, only land pixels were considered. Furthermore, a dataset of FCI Level 1c observations with a preliminary calibration was processed. The TCWV processed for these FCI measurements aligns well with reanalysis TCWV and collocated OLCI/SLSTR TCWV but shows a dry bias. A more rigorous validation and assessment will be done once a longer record of FCI data is available.

TCWV observations derived from geostationary satellite measurements enhance monitoring of WV distributions and associated meteorological phenomena from synoptic scales down to local scales. Such observations are of special interest for the advancement of nowcasting techniques and numerical weather prediction (NWP) accuracy as well as process-studies.