## 4 Conclusions  
Long-term LST climate data records with a high temporal and spatial resolution are useful for climate monitoring and climate applications [7]. This requirement can be met by extending LST data records from geostationary satellites into the past. Since heritage sensors provide only one thermal infrared band, multi-channel LST retrieval approaches cannot be used. This study thus evaluated the performance of single-channel retrieval models developed for the geostationary Meteosat satellite against _in situ_ LST and the GSW-based LSA SAF dataset. The key question that we investigated is to what extent a single-channel LST model can achieve the accuracy of a two channel GSW model. This comprehensive validation study, which included more than 60,000 _in situ_ LST measurements for very different atmospheric conditions, demonstrates that Meteosat-based single-channel LSTs agree with those from GSW to within 0.1-0.5 K and are within or very close to the 2 K target accuracy of the LSA SAF Meteosat LST data, except for very moist atmospheres (TCWV \(>\) 45 mm), but with the added benefit that they can be applied across satellite generations. TCWVs above 45 mm primarily occur in tropical and subtropical regions, which are regularly cloud covered and correspond to less than 5% of the MSG disk. We can hence expect the overall majority of MSG single-channel LSTs to meet the 2-3 K \"currently achievable performance\" defined by the Global Climate Observing System (GCOS) [7]. However, this study also reveals a significant negative bias (-4.3 K) for the PMW for very moist atmospheres (TCWV \(>\) 45 mm) at Dahra station, Senegal. We found some indications that cloud contamination and/or inaccurate NWP input contributes to this strong negative bias. This issue needs to be investigated before generating a CDR. This study also demonstrates that it is possible to characterize retrieval uncertainties for Meteosat single-channel LST, except for very moist atmospheres, which will simplify, for instance, the assimilation of those data into land surface models. RMSDs estimated from a theoretical, radiative transfer-based sensitivity study matched RMSDs from the ground-based comparison within 0.3-1 K for TCWVs \(\leq 45\) mm. For very moist atmospheres with TCWV \(>45\) mm, we observe a distinct higher RMSD (\(>\)3 K) compared to the theoretical uncertainties. We found indications that this is partly due to cloud contamination, which is not accounted for in the theoretical error analysis. Moreover, the adapted approach to characterize NWP uncertainties by simply replacing NWP profiles might not be realistic for topic conditions. More advanced TCWV error characterizations, such as, e.g., an error characterization based on the NWP background error covariance matrix, as proposed by [PERSON] and [PERSON] [41], should be tested. The authors propose to put a \"low quality\" flag on Meteosat single-channel LST retrievals for TCWV \(>45\) mm and to inform users that associated LST uncertainties might not be realistic for very moist atmospheres. Additional LST validation stations in very moist climate zones will be highly valuable to find realistic LST model uncertainty for those conditions. The results of this study suggest that computationally more expensive PMWs do not necessarily outperform SMWs. We observed a distinct higher accuracy (\(\Delta\)RMSD \(>0.2\) K) for the PMW compared to SMW only at one at of four validation stations. Possible improvements of the current PMW and SMW model should be addressed in future studies. The presented single-channel models might be improved by including an orographic correction for the atmospheric profiles and by an improved cloud screening in tropical regions. The results presented here are strictly only valid for MSG, since the MFG thermal sensor has a slightly different spectral response function, a lower digital quantification and a less accurate absolute radiometric accuracy. Accordingly, the LST retrieval errors may be greater than the errors presented in this study.