## 1 Introduction  
Land surface temperature (LST) is an important climate state variable. Precise estimates of the radiative surface skin temperature are essential to compute the surface radiative and sensible heat balance [1]. Moreover, LST is a key variable for a wide range of applications related to land surface processes, such as drought [2] and evaporation monitoring [3]. Satellite-based LSTs are important for the evaluation of surface-emitting temperatures in climate models at various time scales [1]. Ideally, they can also be assimilated into land surface models [4; 5; 6] to improve numerical weather and climate model predictions. This wide range of applications makes a long-term homogeneous LST climate data record (CDR) highly desirable [7]. Large-scale LST can only be measured by satellite instruments [8] and is best represented by measurements of geostationary satellite sensors, as it is subject to strong diurnal variation [9; 10]. Geostationary LST climate data records (CDRs) are available from the International Satellite Cloud Climatology Project (ISCCP) [11] and the Pathfinder Atmospheres-Extended dataset (PATMOS-x) [8]. The global ISCCP LST CDR has several limitations, as the primary goal of the ISCCP analysis was the retrieval of cloud properties and not LST. The strongest limitation is the very coarse 30 km spatial resolution and, to a lesser extent, the 3-h temporal resolution [11]. Moreover, the original ISCCP retrieval assumes that all surfaces behave like a black body with unit emissivity, which can lead to significant LST retrieval errors, particularly in dry regions (e.g. [12]). The PATMOS-x geostationary LST CDR is only available for the Geostationary Operational Environmental Satellite (GOES) field of view (North and South America). Starting in 1983, the European's Organization for the Exploitation of Meteorological Satellites (EUMETSAT) Meteosat First (MFG) and Second Generation (MSG) satellites have provided the unique opportunity to compile a 30+ year LST CDR with a 30-min temporal and 5-km spatial resolution over Africa and Europe. Since the Meteosat Visible and Infra-Red Imager (MVIRI) on-board Meteosat 2-7 is equipped with a single thermal infrared channel, single-channel LST retrieval models can ensure consistency across all Meteosat satellites. A consistent approach maximizes long-term and inter-satellite consistency [8]. Most state-of-the-art satellite-based LST retrieval models, such as the Meteosat LST model from the Satellite Application Facility on Land Surface Analysis (LSA SAF), employ the generalized split-window model (GSW) [13; 14; 15], where atmospheric absorption is estimated through a two-channel regression of top-of-atmosphere (TOA) brightness temperatures. This atmospheric correction is less dependent on atmospheric ancillary data than single-channel LST models, which depend completely on ancillary data from numerical weather prediction (NWP) models to estimate the atmospheric state. They range from statistical mono-window models (SMW), which use the observed 11 \(\upmu\)m radiance, the total column water vapor (TCWV) from NWP models and _a priori_ fitted LST model parameters [16, 17] to physical mono-window models (PMW), which are based on radiative transfer modelling [8, 18, 19, 9]. PMWs require significantly more processing time than SMWs, as PMW algorithms run radiative transfer models for each satellite acquisition, while SMW algorithms estimate the correction term using a pre-computed statistical relationship. Reported accuracies are 1-2 K for GSW [12, 20, 21], 2.5 K or less for PMW [8, 19] and 2-4 K for SMW [22]. Those performance metrics from the literature cannot be compared, since they refer to different satellite sensors with distinct viewing geometries, with variations in instrument calibration and different validation data for a physical parameter (LST), which is highly variable in time and space [8, 10, 23]. In order to investigate the achievable accuracy of Meteosat single-channel LST models, the models have to be exercised in a comparable setting.