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A Satellite Method to Identify Structural Properties of Mesoscale Convective Systems Based on the Maximum Spatial Correlation Tracking Technique (MASCOTTE)

Leila M. V. CarvalhoDepartment of Atmospheric Sciences, Institute of Astronomy and Geophysics, University of São Paulo, São Paulo, Brazil

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Charles JonesInstitute for Computational Earth System Science, University of California, Santa Barbara, Santa Barbara, California

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Abstract

A simple, fully automated, and efficient method to determine the structural properties and evolution (tracking) of cloud shields of convective systems (CS) is described. The method, which is based on the maximum spatial correlation tracking technique (MASCOTTE), is a new alternative to the existent techniques available for studies that monitor the evolution of CS using satellite images. MASCOTTE provides as CS structural properties the following parameters: mean and variance of brightness temperature, horizontal area, perimeter, minimum brightness temperature, fractional convective area, center of gravity, and fragmentation. The fragmentation parameter has the potential to monitor the evolution of the CS. A new way of estimating the orientation and eccentricity of CS is proposed and is based on the empirical orthogonal function analysis of CS pixel coordinates. The method includes an accurate detection of splitting and merging of convective systems, which is a critical step in the automated satellite CS life cycle determination. Based on the magnitudes of the spatial correlation between consecutive satellite images and the changes in horizontal areas of CS, MASCOTTE provides a simple and skillful technique to track the evolution of CS life cycles. The MASCOTTE methodology is applied to infrared satellite images during seven consecutive days of the Wet-Season Atmospheric Mesoscale Campaign of the Large-Scale Biosphere–Atmosphere Experiment and ground validation experiment of the Tropical Rainfall Measuring Mission in the Brazilian state of Rondônia in the Amazon basin. The results indicate that MASCOTTE is a valuable approach to understanding the variability of CS.

* Current affiliation: Institute for Computational Earth System Science, University of California, Santa Barbara, Santa Barbara, California.

Corresponding author address: Dr. Leila M. V. Carvalho, Institute of Astronomy and Geophysics, University of São Paulo, R. do Matão 1226, 05508-900 São Paulo, Brazil. leila@model.iag.usp.br

Abstract

A simple, fully automated, and efficient method to determine the structural properties and evolution (tracking) of cloud shields of convective systems (CS) is described. The method, which is based on the maximum spatial correlation tracking technique (MASCOTTE), is a new alternative to the existent techniques available for studies that monitor the evolution of CS using satellite images. MASCOTTE provides as CS structural properties the following parameters: mean and variance of brightness temperature, horizontal area, perimeter, minimum brightness temperature, fractional convective area, center of gravity, and fragmentation. The fragmentation parameter has the potential to monitor the evolution of the CS. A new way of estimating the orientation and eccentricity of CS is proposed and is based on the empirical orthogonal function analysis of CS pixel coordinates. The method includes an accurate detection of splitting and merging of convective systems, which is a critical step in the automated satellite CS life cycle determination. Based on the magnitudes of the spatial correlation between consecutive satellite images and the changes in horizontal areas of CS, MASCOTTE provides a simple and skillful technique to track the evolution of CS life cycles. The MASCOTTE methodology is applied to infrared satellite images during seven consecutive days of the Wet-Season Atmospheric Mesoscale Campaign of the Large-Scale Biosphere–Atmosphere Experiment and ground validation experiment of the Tropical Rainfall Measuring Mission in the Brazilian state of Rondônia in the Amazon basin. The results indicate that MASCOTTE is a valuable approach to understanding the variability of CS.

* Current affiliation: Institute for Computational Earth System Science, University of California, Santa Barbara, Santa Barbara, California.

Corresponding author address: Dr. Leila M. V. Carvalho, Institute of Astronomy and Geophysics, University of São Paulo, R. do Matão 1226, 05508-900 São Paulo, Brazil. leila@model.iag.usp.br

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