1. Xin Kong, Lizzie Noyes, Gary Corlett, John Remedios, Simon Good and David Llewellyn-Jones Earth Observation Science, Space Research Centre, University of Leicester, LE1 7RH, UK The Advanced Along-Track Scanning Radiometer (AATSR) instrument is one of ten instruments on board ESA’s Envisat Satellite. The AATSR is primarily designed to measure global Sea Surface Temperature (SST). However, a Land Surface Temperature (LST) product has been available since March 2004 due to the increasing needs for climate research. These data can significantly improve our ability to monitor regional and global surface temperature changes in a consistent manner and provide input data of use to land surface and climate models. The LST product has good potential for synergistic applications for Global Monitoring for Environment and Security (GMES) projects. Introduction Product Algorithm Validation Results Recommendations Acknowledgements Regression-based split-window algorithm (nadir-only): where T 11 and T 12 are the nadir 11 and 12  m channel brightness temperatures (BT); a, b, c are retrieval coefficients that depend on global auxiliary data (some of which are seasonally dependent), which account for the effects of the atmosphere and emissivity. The auxiliary data are: - Surface/vegetation type (i); - Vegetation fraction (f) – seasonally dependent; - Precipitable water (pw) – seasonally dependent; - Satellite zenith view angle (n(  )); Atmospheric effects BT measured by the sensor Surface emissivity The initial validation results demonstrate that the AATSR LST retrievals have the potential to meet the target accuracy of the product in most cases under cloud-free conditions. The AATSR LST retrievals show consistency with both in-situ land surface temperature data (Figure 2) and the surface temperature simulations from a model, UKMO Nimrod [2] nowcasting system (Figure 3). The bias in Figure 3 in autumn likely to be caused, at least in part, by the model errors (Nimrod overestimates LST) [3]. However, some limitations of the retrieval algorithm for the current product have also been observed. For example, results in Figure 2 shows that an additional cloud screening is needed to achieve better accuracy. Also, there is a seasonal bias (warmer in summer and colder in winter) which indicates the accuracy and spatial resolution of auxiliary data used in the algorithm needs to be improved. Efforts to improve these limitations are currently underway. From the initial validation results, our recommendations for improvements to the current AATSR LST product are: Improvement of the current auxiliary biome map Improvement of the estimation of fractional vegetation Removal of the seasonal bias in the current retrieval coefficients Implementation of separate retrieval coefficients for day and night Improvement of cloud detection algorithm over land The improved AATSR LST product will be able to serve GMES service element projects either directly or more efficiently through improvement of the accuracy of the SEVIRI LST product. This work was carried out under European Space Agency (ESA) contract 19054/05/NL/FF.The in situ radiometric data was supplied by the Atmospheric Radiation Measurement (ARM) Program. The Nimrod LST data are provided by the UK Meteorological Office (UKMO). AATSR LST Product AATSR Instrument - Imaging radiometer - Seven spectral channels - LST retrieved using nadir 11µm and 12µm channels - On board calibration (<0.1K radiometric accuracy) Land Surface Temperature (LST) This product provides global observations of LST, derived from split-window radiances, at 1 km spatial resolution with a target accuracy of 2.5 K during the day, and 1 K at night. Fig1. LST/SST image of the UK and N. France AATSR Overpass: 16 July 2005 (night) [1] Prata, A. J., 2000, Land Surface Temperature Measurement from Space: AATSR Algorithm Theoretical Basis Document, ESA/CSIRO Publication [2]Golding, B.W., 1998. Nimrod: a system for generating automated very short range forecasts. Meteorology Applications, 5:1-16. Radiative Transfer equation [1] Cloud Contamination Cloud screening by eye and 3σ filtering of differences Fig 3. Comparison between cloud-free AATSR and UKMO Nimrod LST data for (a) Day time and (b) Night time data over a site in East Anglia, UK. The dashed line show the target accuracy of the AATSR LST product in each case N = 42; R 2 = 0.91; Bias = -1.97K; Stdev = 3.75KN = 33; R 2 = 0.98; Bias = - 0.20K; Stdev = 0.95K Operational cloud clearingAdditional cloud clearing Fig 2. Validation using in-situ LST data measured by a pyrgeometer (ARM) at Oklahoma, USA Day: N = 33; Bias = 0.3 K; StDev = 3.5 K Night: N = 28 ; Bias = 0.9 K; StDev = 1.5 K [3] Moberg, A. and Jones, P.D., 2004. Regional climate model simulations of daily maximum and minimum near-surface temperatures across Europe compared with observed station data 1961- 1990. Climate Dynamics 23, 695-715. Envisat AATSR Global Land Surface Temperature Product Space Research Centre