The Advanced Infra-Red WAter Vapour Estimator (AIRWAVE) version 2: Algorithm evolution, dataset description and performance improvements

Total column water vapour (TCWV) is a key atmospheric variable which is generally evaluated on global scales through the use of satellite data. Recently a new algorithm, called AIRWAVE (Advanced Infra-Red WAter Vapour Estimator), has been developed for the retrieval of the TCWV from the Along-Track Scanning Radiometer (ATSR) instrument series. The AIRWAVE algorithm retrieves TCWV by exploiting the dual view of the ATSR instruments using the infrared channels at 10.8 and 12μm and nadir and forward observation geometries. The algorithm was used to produce a TCWV database over sea from the whole ATSR mission. When compared to independent TCWV products, the AIRWAVE version 1 (AIRWAVEv1) database shows very good agreement with an overall bias of 3% all over the ATSR missions. A large contribution to this bias comes from the polar and the coastal regions, where AIRWAVE underestimates the TCWV amount. In this paper we describe an updated version of the algorithm, specifically developed to reduce the bias in these regions. The AIRWAVE version 2 (AIRWAVEv2) accounts for the atmospheric variability at different latitudes and the associated seasonality. In addition, the dependency of the retrieval parameters on satellite across-track viewing angles is now explicitly handled. With the new algorithm we produced a second version of the AIRWAVE dataset. As for AIRWAVEv1, the quality of the AIRWAVEv2 dataset is assessed through the comparison with the Special Sensor Microwave/Imager (SSM/I) and with the Analyzed RadioSounding Archive (ARSA) TCWV data. Results show significant improvements in both biases (from 0.72 to 0.02kg m-2) and standard deviations (from 5.75 to 4.69kgm-2), especially in polar and coastal regions. A qualitative and quantitative estimate of the main error sources affecting the AIRWAVEv2 TCWV dataset is also given. The new dataset has also been used to estimate the water vapour climatology from the 1991-2012 time series.