Abstract
A new algorithm for the retrieval of day-time total column water vapour (TCWV) from measurements of a MSG-SEVIRI (Meteosat Second Generation Spinning Enhanced Visible and Infrared Imager) instrument is presented. The retrieval is based on a forward operator, at the core of which lies Radiative Transfer for TIROS Operational Vertical Sounder (RTTOV). This forward model relates TCWV and surface temperature to brightness temperatures in the split window at 11 and 12µm with the use of a first guess for temperature and humidity profiles from the ERA5 reanalysis. The forward model is then embedded in a full Optimal Estimation (OE) method, which yields pixel by pixel uncertainty estimates and performance indicators. The algorithm is applicable to any instrument which features the split window configuration, given a first guess for atmospheric conditions (i.e., from NWP) and an estimate of surface emissivity at 11 µm. The algorithm was developed within the framework of RealPEP (Near-Realtime Quantitative Precipitation Estimation and Prediction) in which the advancement of the estimation and nowcasting of extreme precipitation and flooding in Germany are studied. Thus, processing and validation has been limited to the German domain. Three independent ground-based TCWV observation data sets were used as reference, i.e., AERONET (Aerosol Robotic Network), GNSS Germany (Global Navigation Satellite System) and measurements from two MWR (Microwave Radiometer) sites. The validation concludes with good agreement, with absolute biases between 0.11 and 2.85 kg/m2, root mean square deviations (rmsds) between 1.63 and 3.24 kg/m2 and Pearson correlation coefficients ranging from 0.96 to 0.98. The retrievals uncertainty estimates were evaluated against AERONET. The comparison suggests that, in sum, uncertainties are estimated well, while still some error sources seem to be over- and underestimated. In limited case studies it could be shown that SEVIRI TCWV is capable to both display large scale variabilities in water vapour fields and reproduce the daily course of water vapour exposed by ground-based observations.
Highlights
Water vapour is one of the most abundant trace gases in the atmosphere
Three independent ground-based total column water vapour (TCWV) observation data sets were used as reference, i.e., AERONET (Aerosol Robotic Network), GNSS Germany (Global Navigation Satellite System) and measurements from two microwave radiometers (MWR) (Microwave Radiometer) sites
The algorithm presented in this paper exploits differential water vapour absorption in the split window to improve TCWV retrievals using geostationary satellite observations
Summary
Water vapour is one of the most abundant trace gases in the atmosphere. The evaporation of water and the transport of water vapour in the atmosphere play an integral role in the global hydrological cycle [1]. The Global Climate Observing System (GCOS) declared total column water vapour (TCWV) as a critical variable for the characterisation of the climate system and its changes [10]. TCWV describes the mass of integrated atmospheric humidity, if the total column of it would condense and precipitate over a unit cross-section and is measured in [kg/m2], [mm] or [cm]. The standard long name for TCWV as defined by the climate and forecast (CF) Metadata Conventions is atmosphere water vapour content [11]. We will exclusively use TCWV and the unit [kg/m2]
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