| 27 Apr 2026
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.
The author has evaluated an existing method, Šoljan et al. (2024), for the retrival of cloud top height (CTH) using the 11 micro meter InfraRed channel, and used the retrieved CTH information for correcting the viewing parallax of Deep Convective Clouds (DCCs). The topic is very relevant due to the fact that parallax correction becomes more and more important with the ever-increasing resolution of satellite imagery. However, the study either lacks new information or it has not been clearly stated.
In particular:
(1) It is not clear if CLAAS-3 is a reliable reference against which the CTH retrievals with Solijan et al. 2024 can be compared. Especially because CLAAS-3 retrieves CTH using a Neural network based algorithm and does not represent ground truth. Why not use ground-based ceilometer measurements as reference ?
(2) Why is ICAO standard atmosphere, which is a static profile under an idealized condition, used for converting cloud top pressure to height ? Could it be more accurate or more closer to reality when profiles from NWP or Reanalysis are used ?
(3) There is a lack of reliable ground truth for assessing the accuracy of the parallax correction using the different CTH sources. Would it make sense to look at some surface parameters such as precipitation, direct solar irradiance, lightning, etc. that are directly impacted by the location of the DCC ?
(4) Section 5.1. Lines 350-355. The mean displacement vector length for parallax may not be very informative. As the author himself noted, parallax displacement is heavily dependent on the viewing satellite zenith angle (SZA). Therefore, it could be more intuitive to have the mean displacement vector and standard deviation for different SZA ranges.