Evaluation of a moist-adiabat cloud-top height retrieval for parallax correction of deep convective clouds across Meteosat generations

Abstract. Accurate estimation of deep convective cloud (DCC) top height is essential for reliable parallax correction of geostationary satellite imagery and for constructing homogeneous long-term climatologies from multi-generation meteorological satellite records (e.g. Meteosat MVIRI, SEVIRI, and FCI). This study validates the cloud-top height (CTH) retrieval method of Šoljan et al. (2024), which estimates cloud-top pressure in deep moist convection from satellite-derived 11 µm brightness temperature using a fast polynomial approximation of the moist adiabat. The primary objective is to assess whether this method enables physically consistent parallax correction across more than 40 years of Meteosat data, including early missions lacking multispectral capability.

Validation was conducted in two configurations reflecting polar-orbiting and geostationary viewing geometries. First, approximately 1.7 million DCC collocations from 2007 were used to compare CTH estimates with MODIS retrievals and lidar–radar profiles from CloudSat and CALIPSO. Second, a SEVIRI time series from 2004 to 2024 was evaluated against the operational CLAAS-3 CTH product. Overall, the method underestimated CTH by 2.7 km (18 %) relative to lidar–radar data and by 1.1 km (10 %) relative to CLAAS, with mean absolute errors of 1.5–2.8 km and correlations up to 0.9. Following a simple regression-based bias adjustment, normalized errors decreased to below 7 % and absolute errors fell below 1.2 km.

The method was subsequently applied to SEVIRI observations to assess its suitability for parallax correction over the European domain. Despite a ~10 % underestimation of CTH, the impact on parallax correction was minimal: 84 % of pixel geolocations coincided with those derived from CLAAS-based corrections, increasing to 97 % when CLAAS retrieval uncertainty was accounted for. Differences in DCC frequency remained below 7 % on a monthly scale and below 5 % seasonally. These results demonstrate that the method provides operationally sufficient accuracy for parallax correction of DCCs and supports the development of a homogeneous DCC climatology across all generations of Meteosat satellites and, potentially, other geostationary platforms.