Cb-TRAM: Tracking and monitoring severe convection from onset over rapid development to mature phase using multi-channel Meteosat-8 SEVIRI data

Abstract

Cb-TRAM is a new fully automated tracking and nowcasting algorithm. Intense convective cells are detected, tracked and discriminated with respect to onset, rapid development, and mature phase. The detection is based on Meteosat-8 SEVIRI (Spinning Enhanced Visible and Infra-Red Imager) data from the broad band high resolution visible, infra-red 6.2 mm (water vapour), and the infra-red 10.8 mm channels. In addition, tropopause temperature data from ECMWF operational model analyses is utilised as an adaptive detection criterion. The tracking is based on geographical overlap between current detections and first guess patterns of cells predicted from preceeding time steps. The first guess patterns as well as short range forecast extrapolations are obtained with the aid of a new image matching algorithm providing complete fields of approximate differential cloud motion. Based on these motion vector fields interpolation and extrapolation of satellite data are obtained which allow to generate synthetic intermediate data fields between two known fields as well as nowcasts of motion and development of detected areas. Examples of the application of Cb-TRAM and a comparison to precipitation radar and lightning data as independent data sources demonstrate the capabilities of the new technique.

Vertical distribution of the particle phase in tropical deep convective clouds as derived from cloud-side reflected solar radiation measurements

“…From the ensemble of retrieved effective droplet sizes, a vertical profile of cloud phase can be estimated because of the relationship between cloud phase and vertical profile of the cloud particle size (Rosenfeld and Feingold, 2003;Yuan et al, 2010;Martins et al, 2011). However, the retrieval of the effective droplet size relies on 1-D radiative transfer simulations, which incorporate retrieval uncertainties due to plane-parallel cloud assumptions and neglecting the net horizontal radiative transport between the satellite pixels (Zinner et al, 2006). Consequently, a decrease in pixel size causes an increase in the independent pixel bias, because the smaller the pixel, the more important is the net horizontal photon transport, particularly for the wavelengths in the visible spectral range, which are used for the retrieval of the effective droplet radius.…”

Analysis and Application of the Relationship between Cumulonimbus (Cb) Cloud Features and Precipitation Based on FY-2C Image

“…Temporal characteristics, such as cloud dynamic features, do not appear to have been adequately analyzed. In addition, except for the Cb-TRAM (Cumulonim Bus Tracking And Monitoring) [15], there is no study that has been performed to extract the Cb patch. Thus, no studies analyze the statistical relationship between precipitation and the features of the Cb patch, not to mention its dynamic characteristics.…”

Comparative verification of different nowcasting systems to support optimisation of thunderstorm warnings

“…Most of them are based on either radar reflectivity measurements, like KONRAD (KONvektionsentwicklung in RADarprodukten, convection evolution in radar products, Lang 2001), CellMOS (Cell Model Output Statistics, Hoffmann, 2008) or on satellite measurements like RDT (rapid developing thunderstorm, Morel et al, 2000) and Cb-TRAM (Cumulonimbus TRAcking and Monitoring, Zinner et al, 2008). Some algorihtms rely on the combination of various observational input data (e.g.…”