The Spinning Enhanced Visible and Infrared Imagery (SEVIRI) instrument, on board the Meteosat Second Generation (MSG), is a radiometer with eight infrared (IR) spectral bands. Layer Precipitable Water (LPW) from MSG SEVIRI, over the northern hemisphere area covered by MSG (MSG N), has been developed by INM within the EUMETSAT SAFNWC (Satellite Application Facility on support to Nowcasting and Very Short-Range Forecasting) framework. Seven of the SEVIRI IR channels are used to retrieve the LPW (Layer Precipitable Water) using neural networks. The Total Precipitable Water (LPW(TPW)) is one of the LPW parameters. The LPW(TPW) is routinely generated every fifteen minutes at a satellite horizontal resolution of 3 km in nadir on clear air pixels. Total column Integrated Water Vapor data derived from Zenith Total Delay GPS (Global Positioning System) data (GPS_IWV) and surface measurements/NWP estimations, are provided by eleven different ground based GPS data processing centres participating in the TOUGH EU Project in near real time (NRT). A set of LPW(TPW) values co-located with GPS_IWV stations has been added to NRT GPS data that are being routinely introduced in passive mode in the Hirlam 3DVar Assimilation system operational at INM. This paper will show an intercomparison of total column integrated water vapor from the HIRLAM first guess for the 3DVar analysis, the NRT GPS_IWV data and the LPW(TPW) product.
The Spinning Enhanced Visible and Infrared Imager (SEVIRI) instrument, onboard the Meteosat Second Generation (MSG) is a radiometer with 8 infrared (IR) spectral bands. IR retrievals of Layer Precipitable Water (LPW) and Lifted Index (LI) allow to identify potential severe weather when the system is still in a preconvective state. Statistical retrieval is computationally fast and it is a requirement for the SAFNWC PGEs. The study presented here, is part of an attempt to improve the algorithm developed in the SAFNWC framework to calculate Layer Precipitable Water and Stability Analysis Imagery (SAI) from SEVIRI radiances. The first codified algorithms (in the SAFNWC version 0.1 package) are a statistical retrieval where neural networks were trained with the available data (simulated radiances using numerical profiles from 60L-SD and RTTOV-7). These statistical retrievals have been evaluated against co-located products obtained from numerical weather analysis and radiosonde profiles, as well as MODIS products obtained in the areas scanned at the same time. The availability of real SEVIRI radiances allows us to compare real SEVIRI radiances with simulated radiances and to detect systematic bias among both datasets. In this study, first the retrieved LPW and LI will be evaluated, and the error sources will be identified. And later, the method for correcting the detected bias, between real and simulated radiances, will be analysed, and the improvements will be compared to calculated ("clear") values from the nearest (in space and time) ECMWF profiles and similar MODIS products.