Determining Tropical Cyclone Surface Wind Speed Structure and Intensity with the CYGNSS Satellite Constellation
The Cyclone Global Navigation Satellite System (CYGNSS) consists of a constellation of eight microsatellites that provide observations of surface wind speed in all precipitating conditions. A method for estimating tropical cyclone (TC) metrics—maximum surface wind speed V MAX, radius of maximum surf...
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Published in | Journal of applied meteorology and climatology Vol. 56; no. 7; pp. 1847 - 1865 |
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Main Authors | , |
Format | Journal Article |
Language | English |
Published |
American Meteorological Society
01.07.2017
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Online Access | Get full text |
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Summary: | The Cyclone Global Navigation Satellite System (CYGNSS) consists of a constellation of eight microsatellites that provide observations of surface wind speed in all precipitating conditions. A method for estimating tropical cyclone (TC) metrics—maximum surface wind speed V
MAX, radius of maximum surface wind speed R
MAX, and wind radii (R
64, R
50, and R
34)—from CYGNSS observations is developed and tested using simulated CYGNSS observations with realistic measurement errors. Using two inputs, 1) CYGNSS observations and 2) the storm center location, estimates of TC metrics are possible through the use of a parametric wind model algorithm that effectively interpolates between the available observations as a constraint on the assumed wind speed distribution. This methodology has a promising performance as evaluated from the simulations presented. In particular, after quality-control filters based on sampling properties are applied to the population of test cases, the standard deviation of retrieval error for V
MAX is 4.3ms−1 (where 1ms−1 = 1.94 kt), for R
MAX is 17.4 km, for R
64 is 16.8 km, for R
50 is 21.6 km, and for R
34 is 41.3 km (where 1 km = 0.54 nmi). These TC data products will be available for the 2017 Atlantic Ocean hurricane season using on-orbit CYGNSS observations, but near-real-time operations are the subject of future work. Future work will also include calibration and validation of the algorithm once real CYGNSS data are available. |
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ISSN: | 1558-8424 1558-8432 |
DOI: | 10.1175/JAMC-D-16-0375.1 |