Splitting of the middle layer of LPW SAFNWC/MSG satellite product in order to improve the monitoring of pre-convective environments

Abstract

Seven of the infrared channels from the Spinning Enhanced Visible and Infrared Imagery (SEVIRI) instrument, on board the Meteosat Second Generation (MSG), are used to retrieve Layer Precipitable Water (LPW) and Stability Analysis Imagery (SAI) in the SAFNWC framework. Both products are retrieved using a statistical retrieval based on neural networks; they are routinely generated every fifteen minutes at a satellite horizontal resolution of 3 km in NADIR only in cloud-free areas. Many factors are involved in the development of severe weather and these parameters are only some of the indicators. However, due to the high resolution of these products, the use of them in conjunction with satellite and radar images can help to identify mesoscale features related to convection. The MSG moisture and parcel instability time trend fields are especially useful during the period previous to convection. Once the outbreak of convection occurs, the products calculated in the clear air pixels surrounding the convective system can give us hints to anticipate its evolution. SAFNWC LPW and SAI were analyzed for a severe weather event during August 2004. A thunderstorm over Teruel (Spain) produced intense precipitation and hail; a tornado developed while this thunderstorm was moving towards SE. The pre-convective parcel potential buoyancy and moisture SAFNWC products changed in a way that was consistent with the observed intense convective activity. In previous studies, the atmospheric moisture in medium levels, which has been proven to be relevant in some cases, was represented by only one level parameter (ML: middle layer LPW). However, it was observed that this layer is too thick to do an adequate analysis of moisture available for convection. Hence, an improvement on the LPW algorithm has been carried out by splitting the middle layer into two new sub-layers (approximately separated at 700 hPa) and training two new neural networks. The impact of monitoring moisture in the new sub-layers separately in this severe weather event has been tested, and the improvements achieved have been evaluated.