A combined global total column water vapour data record from microwave and near-infrared imager observations: new developments and results from validation

Water vapour is the single most important natural greenhouse gas in the atmosphere, thereby constraining the Earth’s energy balance, directly and indirectly through the water vapour feedback mechanism. In addition, water vapour is a key component of the water cycle. There is consequently the need to consolidate our knowledge of natural variability and past changes in water vapour and to establish climate data records (CDRs) of both total column and vertically resolved water vapour for use in climate research. This is the objective of the ESA Water Vapour Climate Change Initiative (WV_cci).Within WV_cci a global total column water vapour (TCWV) data record was generated by combining microwave-based TCWV observations over the ice-free ocean with near-infrared imager-based TCWV over land, coastal ocean and sea-ice. The data record relies on microwave imager observations, partly based on a fundamental climate data record from EUMETSAT CM SAF and on near-infrared observations from MERIS, MODIS and OLCI. The microwave and near-infrared data streams are processed independently and combined in a postprocessing, retaining the individual TCWV values and their uncertainties. The precursor version of the data record is freely available to the public via 10.5676/EUM_SAF_CM/COMBI/V001. A new version, which relies on more sensors and features improved stability and uncertainty characterisation, will be released in the coming months. In addition, a high-resolution regional product was generated covering three regions in the sub-tropics and tropics with a spatial resolution of 0.01°.This presentation will briefly introduce WV_cci and new developments and improvements related to the data record generation. The TCWV over land is reliably possible in clear-sky conditions only. Thus, after aggregation and comparison to all-sky data a clear-sky bias is present. Results from the clear-sky bias analysis will be shown as well. A focus will be on results from intercomparisons and validation, including results from uncertainty validation through comparisons against radiosonde and GNSS observations.