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Global cyclone detection and tracking (GLYDER) for climate variability studies: A multisensor data processing approach
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| We will implement an automated data processing and pattern recognition system, termed GLYDER (GLobal cYclone DEtection and tRacking), that classifies and tracks cyclones in multi-sensor satellite data sets. This system will be used to quantify the spatial and temporal variability of historical cyclones and their tracks globally and thereby supports a key goal of NASA's Earth Science Enterprise (ESE) to quantify the Earths natural variability and understand how it is changing. Cyclone detection with high accuracy using only single data modalities is an unsolvable problem, largely due to variations in the types of cyclones, and the presence of other weather patterns that appear similar within the sensor measurements. Incorporating multiple sensor types together provides several cues that can better characterize cyclones.
The joint temporal-spatial structure of cyclones has not been exploited in previous efforts and we believe that using such constraints will significantly improve detection accuracy over NCEP analysis, while reducing false alarms. In GLYDER, we will demonstrate the first multimodal cyclone detection scheme from multiple remote sensors with varying spatio-temporal resolution. We will develop and test a software system that will co-register multiple remote sensory data (GOES, QuickScat, AVHRR, SSMI and AMSR-E) that are at different spatial and temporal resolutions and detect and track cyclones from co-registered multiple remote sensors using advanced proven pattern recognition and tracking capabilities. Our goal in GLYDER is to co-register & detect cyclones at a spatial resolution of 10 kms and a temporal resolution of 3 hrs. We will use advanced multiscale wavelet techniques to co-register disparate multiple satellite datasources at different temporal and spatial resolutions. We will then use proven pattern recognition and data mining Maximum Discriminant Function Classifiers (MDF) combined with robust feature tracking algorithms on the remote multisensor co-registered data to detect cyclones and then track them temporally as they evolve over time. Based on previous performance of our detection and tracking algorithms on other datasets, we estimate that GLYDER will enable at least a 50% improvement in automated cyclone detection/classification from remote sensors with a 4X reduction in false alarms compared to current NCEP-based single sensor cyclone detection. We will initially train, test and validate our algorithms on regions that have multiple in-situ measurements and also experience frequent and intense storms and hurricanes (tropical eastern Atlantic and northeast pacific). Finally we will test and demonstrate our system with data from the Southern Oceans.
The GLYDER system will empower scientists with the capability to detect global cyclones in long time series of data. GLYDER also has the potential for use in an operational environment for early detection of tropical storms. Another arena in which GLYDER can play an important role is that of knowledge search and discovery. Current NASA's GCMD satellite data search support only simple searches for satellite measurements in time & space only. To facilitate advanced content-based searches, the NASA PO.DAAC would use GLYDER to mine its data holdings for the global cyclones and then tag the metadata of each granule with properties related to the cyclone for use in systems such as ECHO.
As part of the NASA AISR program, GLYDER will be utilized by:
1) JPL climate scientists to study and quantify the spatial and temporal variability of cyclones and their tracks (a critical need that has been identifies by the Intergovernmental Panel on Climate Change);
2) NASA's Physical Oceanography Distributed Active Archive Center (PO.DAAC) to tag metadata with information pertaining to cyclones and enable content-based searching. |
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