TY - JOUR
T1 - Tracking mesoscale convective systems that are potential candidates for tropical cyclogenesis
AU - Ocasio, Kelly M.Núñez
AU - Evans, Jenni L.
AU - Young, George S.
N1 - Funding Information:
We acknowledge the funding provided for this research from National Oceanic and Atmospheric Administration Educational Partnership Program under Agreement NA16SEC4810006-NCAS-M, and Penn State's African Research Center and Earth Environmental Systems Institute. Thanks to Penn State Institute of CyberScience for computer sources. Thank you to EUMETSAT for data access. Thanks to Dr. Jose Fuentes and Dr. Holly Hamilton for contributing knowledge on African climate and Dr. Kim Whitehall at Microsoft and Valencia College for guidance on tracking MCSs. We would also like to acknowledge Dr. Alan Brammer, University of Albany for providing AEW-TC tracks. Thank you to Chuck Pavloski for database management. Thank you to Zach Moon for guidance on database processing and analysis. We would also like to acknowledge Dr. Kelly Lombardo for contributing knowledge on MCS propagation.
Funding Information:
Acknowledgments. We acknowledge the funding provided for this research from National Oceanic and Atmospheric Administration Educational Partnership Program under Agreement NA16SEC4810006-NCAS-M, and Penn State’s African Research Center and Earth Environmental Systems Institute. Thanks to Penn State Institute of CyberScience for computer sources. Thank you to EUMETSAT for data access. Thanks to Dr. Jose Fuentes and Dr. Holly Hamilton for contributing knowledge on African climate and Dr. Kim Whitehall at Microsoft and Valencia College for guidance on tracking MCSs. We would also like to acknowledge Dr. Alan Brammer, University of Albany for providing AEW-TC tracks. Thank you to Chuck Pavloski for database management. Thank you to Zach Moon for guidance on database processing and analysis. We would also like to acknowledge Dr. Kelly Lombardo for contributing knowledge on MCS propagation.
PY - 2020/2/1
Y1 - 2020/2/1
N2 - This study introduces the development of the Tracking Algorithm for Mesoscale Convective Systems (TAMS), an algorithm that allows for the identifying, tracking, classifying, and assigning of rainfall to mesoscale convective systems (MCSs). TAMS combines area-overlapping and projected-cloud-edge tracking techniques to maximize the probability of detecting the progression of a convective system through time, accounting for splits and mergers. The combination of projection on area overlapping is equivalent to setting the background flow in which MCSs are moving on. Sensitivity tests show that area-overlapping technique with no projection (thus, no background flow) underestimates the real propagation speed of MCSs over Africa. The MCS life cycles and propagation derived using TAMS are consistent with climatology. The rainfall assignment is also more reliable than with previous methods as it utilizes a combination of regridding through linear interpolation with high temporal and spatial resolution data. This makes possible the identification of extreme rainfall events associated with intense MCSs more effectively. TAMS will be utilized in future work to build an AEW-MCS dataset to study tropical cyclogenesis.
AB - This study introduces the development of the Tracking Algorithm for Mesoscale Convective Systems (TAMS), an algorithm that allows for the identifying, tracking, classifying, and assigning of rainfall to mesoscale convective systems (MCSs). TAMS combines area-overlapping and projected-cloud-edge tracking techniques to maximize the probability of detecting the progression of a convective system through time, accounting for splits and mergers. The combination of projection on area overlapping is equivalent to setting the background flow in which MCSs are moving on. Sensitivity tests show that area-overlapping technique with no projection (thus, no background flow) underestimates the real propagation speed of MCSs over Africa. The MCS life cycles and propagation derived using TAMS are consistent with climatology. The rainfall assignment is also more reliable than with previous methods as it utilizes a combination of regridding through linear interpolation with high temporal and spatial resolution data. This makes possible the identification of extreme rainfall events associated with intense MCSs more effectively. TAMS will be utilized in future work to build an AEW-MCS dataset to study tropical cyclogenesis.
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U2 - 10.1175/MWR-D-19-0070.1
DO - 10.1175/MWR-D-19-0070.1
M3 - Article
AN - SCOPUS:85082885131
VL - 148
SP - 655
EP - 669
JO - Monthly Weather Review
JF - Monthly Weather Review
SN - 0027-0644
IS - 2
ER -