Filling the gap of wind observations inside tropical cyclones

Tridon, F. ORCID: https://orcid.org/0000-0002-0436-283X, Battaglia, A. ORCID: https://orcid.org/0000-0001-9243-3484, Rizik, A., Scarsi, F. E. and Illingworth, A. ORCID: https://orcid.org/0000-0002-5774-8410 (2023) Filling the gap of wind observations inside tropical cyclones. Earth and Space Science, 10 (11). e2023EA003099. ISSN 2333-5084 doi: 10.1029/2023ea003099

Abstract/Summary

The WIVERN (WInd VElocity Radar Nephoscope) mission, currently under the Phase‐0 of the ESA Earth Explorer program, promises to provide new insight in the coupling between winds and microphysics by globally observing, for the first time, vertical profiles of horizontal winds in cloudy areas. The objective of this work is to explore the potential of the WIVERN conically scanning Doppler 94 GHz radar for filling the wind observation gap inside tropical cyclones (TCs). To this aim, realistic WIVERN notional observations of TCs are produced by combining the CloudSat 94 GHz radar reflectivity observations from 2007 to 2009 with ECMWF co‐located winds. Despite the short wavelength of the radar (3 mm), which causes strong attenuation in presence of large amount of liquid hydrometeors, the system can profile most of the TCs, particularly the cloudy areas above the freezing level and the precipitating stratiform regions. The statistical analysis of the results shows that, (a) because of its lower sensitivity, a nadir pointing WIVERN would detect 75% of the clouds observed by CloudSat (45% of winds with 3 m s−1 accuracy, in comparison to CloudSat sampling of clouds), (b) but thanks to its scanning capability, WIVERN would actually provide 53 times more observations of clouds than CloudSat in TCs (30 times more observations of horizontal winds), (c) this corresponds to about 350 (200) million observations of clouds (accurate winds) every year. Such observations could be used to shed light on the physical processes underpinning the evolution of TCs and in data assimilation in order to improve numerical weather prediction.

Item Type	Article
URI	https://reading-clone.eprints-hosting.org/id/eprint/114685
Identification Number/DOI	10.1029/2023ea003099
Refereed	Yes
Divisions	Science > School of Mathematical, Physical and Computational Sciences > Department of Meteorology
Uncontrolled Keywords	General Earth and Planetary Sciences, Environmental Science (miscellaneous)
Publisher	American Geophysical Union (AGU)
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