Convective cloud identification and classification in daytime satellite imagery using standard deviation limited adaptive clustering

Abstract

This paper describes a statistical clustering approach toward the classification of cloud types within meteorological satellite imagery, specifically, visible and infrared data. The method is based on the Standard Deviation Limited Adaptive Clustering (SDLAC) procedure, which has been used to classify a variety of features within both polar orbiting and geostationary imagery, including land cover, volcanic ash, dust, and clouds of various types. In this study, the focus is on classifying cumulus clouds of various types (e.g., “fair weather, ”towering, and newly glaciated cumulus, in addition to cumulonimbus). The SDLAC algorithm is demonstrated by showing examples using Geostationary Operational Environmental Satellite (GOES) 12, Meteosat Second Generation's (MSG) Spinning Enhanced Visible and Infrared Imager (SEVIRI), and the Moderate Resolution Infrared Spectrometer (MODIS). Results indicate that the method performs well, classifying cumulus similarly between MODIS, SEVIRI, and GOES, despite the obvious channel and resolution differences between these three sensors. The SDLAC methodology has been used in several research activities related to convective weather forecasting, which offers some proof of concept for its value.

The GOES-R Proving Ground: Accelerating User Readiness for the Next-Generation Geostationary Environmental Satellite System

“…The algorithm uses a daytime statistically based convective cloud mask (Berendes et al 2008) and performs multiple spectral differencing of IR fields (Mecikalski and Bedka 2006). SATCAST then quantifies and monitors cumulus cloud objects (Goodman et al 2011) while applying object-based atmospheric motion vectors (AMV) to track cloud objects being monitored for future CI (Zinner et al 2008).…”

A Satellite-Based Summer Convective Cloud Frequency Analysis over the Southeastern United States

“…The method isolates CCs in satellite imagery using a ''convective cloud mask'' (CCM; Berendes et al 2008). In MB06, CCs are tracked within a sequence of three satellite images over 30-min intervals from a geostationary sensor [e.g., the Geostationary Operational Environmental Satellite (GOES)].…”

Detection of convective initiation using Meteosat SEVIRI: implementation in and verification with the tracking and nowcasting algorithm Cb-TRAM

“…IF (7) investigates the time trend of the channel difference in IF (5). IF (6) and IF (8) are used to highlight cloud pixels that are likely to develop into a precipitating cloud (see Table 1, Bedka and Mecikalski, 2005;Mecikalski et al, 2008Mecikalski et al, , 2010Roberts and Rutledge, 2003;Mueller et al, 2003). As in Mecikalski and Bedka (2006), in order to have confidence that CI will occur, 7 out of 8 criteria per pixel have to be met.…”

“…The convective cloud mask (Berendes et al, 2008) splits the satellite scene into four different cloud types: (1) immature cumulus defined as warm clouds (> −20 • C) with pronounced texture (standard deviation of brightness counts); (2) thick stratus or thin cirrus that shows both little texture and warm cloud top temperatures, (3) thick cirrus, i.e. cold clouds (< −20 • C) with little texture; and (4) cumulonimbus (Cb) which typically shows cold cloud top temperatures and high texture in their active centre.…”

“…Therefore motion information on smaller scales is lost. To deal with this issue the following relaxations to the original Velden et al (1997) algorithm were applied in order to yield a denser mesoscale AMV field (Bedka and Mecikalski, 2005): reduction of the NWP firstguess constraint as subgrid motions can not be resolved reliable by the model; changes of feature selection and vector editing schemes so that horizontal resolution of feature box size and vertical resolution are increased. By this relaxation a 20 times greater number of vectors is achieved.…”