Tracking and short-term forecasting of mesoscale convective cloud clusters over southeast Brazil using satellite infrared imagery

Abstract

This paper presents the tracking and short-term forecasting of mesoscale convective cloud clusters (CCs) that occurred over southeast Brazil and the adjacent Atlantic Ocean during 2009–17. These events produce intense rainfall and severe storms that impact agriculture, defence, hydroelectricity and offshore oil production. To identify, track and forecast CCs, the Geostationary Operational Environmental Satellite infrared imagery and the Forecasting and Tracking the Evolution of Cloud Clusters method are used. The forecast performance is investigated by applying statistical analyses between the observed and forecasted CCs’ physical properties. A total of 7139 mesoscale convective CCs were identified, tracked and selected for the short-term forecasting at their maturation phases. The CC tracking showed a high frequency of CCs over the Atlantic Ocean and mainly over continental and coastal southeast Brazil during the wet season. This indicates an important role played by the cold fronts and convective diurnal forcing on the organisation of convective cloudiness over that region. The majority of the CCs reached their maturation phases within the first 2 h of life cycle, which occurred mostly between the late afternoon and evening. The CCs had short lifetimes and were predominantly in meso-β scales, followed by meso-α convective CCs. The CCs showed cloud-top temperatures typical of clouds with strong vertical development and potential to produce rainfall. The short-term forecasting of CCs at their maturation phases revealed different behaviours of the statistical indices with forecast range. For the 30–60-min timeframe, the forecasts performed relatively well. For longer forecast lead times (90–120 min), the forecasts overestimated the occurrences, intensities and growth of the CCs and forecasted the CCs to be further north and east of their actual observed locations. Overall, our results may contribute to improving the forecast quality of these intense weather events.