This article describes a new algorithm to retrieve the atmospheric water vapour total column (TCWV) using the Thermal InfraRed (TIR) measurements from the two TIR channels of the Along Track Scanning Radiometer (ATSR) instrument series, on board the European Space Agency's ERS-1, ERS-2 and ENVISAT satellites spanning 1991 to 2012. The use of the dual view capability of the ATSR-type instruments allows for the accurate and precise, day-time and night-time retrievals of cloud free TCWV over oceans. The retrieval scheme uses the instrument physical characteristics, in combination with advanced radiative transfer models and a sea surface spectral emissivity database. The retrievals therefore do not require algorithm tuning or adjustments to independent water vapour datasets.Inter-comparisons with the Remote Sensing Systems Special Sensor Microwave Imager (RSS SSM/I) and ECMWF ERA-Interim total water vapour column products have been carried out for a set of test cases and are discussed in detail. The initial results show that the ATSR TCWV data, aggregated at the SSMM/I or ECMWF spatial resolution, have no median bias with respect to the collocated datasets, and have an estimated precision of about 4% for the ATSR-2 and AATSR instruments and up to 15% for the ATSR-1 instrument. The TCWV precision for the 1×1km2resolution is of the order of 12% for ATSR-2 and AATSR, while is about 30% for ATSR-1.This new physical algorithm can be readily extended to dual view radiometers similar to ATSR such as the upcoming Sea and Land Surface Temperature Radiometer (SLSTR) instrument on the European Copernicus Sentinel-3 satellite series.
All Science Journal Classification (ASJC) codes
- Soil Science
- Computers in Earth Sciences
Casadio, S., Castelli, E., Papandrea, E., Dinelli, B. M., Pisacane, G., Burini, A., & Bojkov, B. R. (2016). Total column water vapour from along track scanning radiometer series using thermal infrared dual view ocean cloud free measurements: The Advanced Infra-Red WAter Vapour Estimator (AIRWAVE) algorithm. Remote Sensing of Environment, 172, 1 - 14. https://doi.org/10.1016/j.rse.2015.10.037