Multispectral satellite data application to hazardous convection monitoring

Abstract

Satellite remote sensing technology development induced new capabilities of investigating various atmospheric phenomena and processes. Among the most dangerous and causing the greatest losses are phenomena related with strong convection, e.g. Cumulonimbus development with thunderstorms, hail, downpour, strong turbulence and icing zones, downbursts, squalls or wind shear. Although phenomena related with convection are sometimes limited in space and change rapidly, they develop harsh weather conditions and often endanger life by instantaneous floods, fires of premises and infrastructure caused by lightning, damages to forests and crops due to hurricane winds or hail. Convective clouds and phenomena sometimes cover areas too small to be observed at meteorological stations and reported in standard weather charts due to large distances between the stations. Satellite data provide valuable information for detection of such phenomena. Images acquired by the SEVIRI radiometer from a geostationary orbit and by the MODIS spectroradiometer from a polar orbiting satellite are the main source of data for this research. VIIRS data are also used in the experiment. The sensor, installed on the Suomi NPP, is a new generation of imaging instruments for satellites replacing the NOAA series. The comprehensive analysis concerned interpretation of single channel satellite images and multispectral ones composed of differential images and color compositions of appropriately selected sets of spectral channels. The analyses were also complemented with information derived from dispersion diagrams which enables to assess the internal structure of the clouds, phase of water and cloud particles size, and hence to assess the prevailing processes in the cloud for identifying its development. The developing techniques of combining satellite images compositions with emission, absorption and scattering characteristics of various grounds, aerosols and clouds for identifying objects provide a new quality for hazardous convection monitoring. The methods described in the paper may be successfully used in hazardous weather phenomena warning systems.