Microphysical Characteristics of Super Typhoon Lekima (2019) and Its Impacts on Polarimetric Radar Remote Sensing of Precipitation
- 1Hangzhou Meteorological Bureau, Hangzhou 310051, China
- 2Zhejiang Institute of Meteorological Sciences, Hangzhou 321000, China
- 3Colorado State University, Fort Collins, CO 80523, USA
- 4National Center for Atmospheric Research, Boulder, CO 80307, USA
- 1Hangzhou Meteorological Bureau, Hangzhou 310051, China
- 2Zhejiang Institute of Meteorological Sciences, Hangzhou 321000, China
- 3Colorado State University, Fort Collins, CO 80523, USA
- 4National Center for Atmospheric Research, Boulder, CO 80307, USA
Abstract. The complex precipitation microphysics associated with super typhoon Lekima (2019) and its potential impacts on the consistency of multi-source datasets and radar quantitative precipitation estimation has been disentangled using a suite of in situ and remote sensing observations around the disastrous waterlogging area near the groove windward slope (GWS) of Yan Dang Mountain and Kuo Cang Mountain, China. The dynamic microphysical processes, in which breakup overwhelms over coalescence as the main outcome of the collision of precipitation particles, noticeably changes the self-consistency between radar reflectivity (ZH), differential reflectivity (ZDR) and the specific differential phase (KDP), and their consistency with theoretical counterparts simulated with surface disdrometer measurements, which are quality-controlled in terms of fall velocity characteristics of different hydrometeors to remove wind effects, hailstones and graupels. The overwhelming breakup accounts for phenomenon that high concentration rather than size contributes more to large ZH of the storm and the large deviation of attenuation-corrected ZDR from its expected values (ẐDR). As a result, although a comparable performance of ZH-based rainfall estimates R() and KDP-based rainfall estimates R(KDP) can be achieved, R(ZH, ZDR) tends to overestimate precipitation. R(ZH, ẐDR) performs the best among all rainfall estimators and it is less sensitive to the potential microphysical processes, owning to the improved self-consistency between radar-measured ZH, ZDR and KDP and their consistency with surface counterparts.
Yabin Gou et al.
Status: closed
-
RC1: 'Comment on acp-2022-495', Anonymous Referee #2, 22 Aug 2022
-
AC2: 'Reply on RC1', Haonan Chen, 17 Nov 2022
The comment was uploaded in the form of a supplement: https://acp.copernicus.org/preprints/acp-2022-495/acp-2022-495-AC2-supplement.pdf
-
AC2: 'Reply on RC1', Haonan Chen, 17 Nov 2022
-
RC2: 'Comment on acp-2022-495', Anonymous Referee #1, 03 Sep 2022
The comment was uploaded in the form of a supplement: https://acp.copernicus.org/preprints/acp-2022-495/acp-2022-495-RC2-supplement.pdf
-
AC1: 'Responses to comments from Reviewer #2', Haonan Chen, 07 Nov 2022
The comment was uploaded in the form of a supplement: https://acp.copernicus.org/preprints/acp-2022-495/acp-2022-495-AC1-supplement.pdf
-
AC1: 'Responses to comments from Reviewer #2', Haonan Chen, 07 Nov 2022
Status: closed
-
RC1: 'Comment on acp-2022-495', Anonymous Referee #2, 22 Aug 2022
-
AC2: 'Reply on RC1', Haonan Chen, 17 Nov 2022
The comment was uploaded in the form of a supplement: https://acp.copernicus.org/preprints/acp-2022-495/acp-2022-495-AC2-supplement.pdf
-
AC2: 'Reply on RC1', Haonan Chen, 17 Nov 2022
-
RC2: 'Comment on acp-2022-495', Anonymous Referee #1, 03 Sep 2022
The comment was uploaded in the form of a supplement: https://acp.copernicus.org/preprints/acp-2022-495/acp-2022-495-RC2-supplement.pdf
-
AC1: 'Responses to comments from Reviewer #2', Haonan Chen, 07 Nov 2022
The comment was uploaded in the form of a supplement: https://acp.copernicus.org/preprints/acp-2022-495/acp-2022-495-AC1-supplement.pdf
-
AC1: 'Responses to comments from Reviewer #2', Haonan Chen, 07 Nov 2022
Yabin Gou et al.
Yabin Gou et al.
Viewed
HTML | XML | Total | BibTeX | EndNote | |
---|---|---|---|---|---|
249 | 74 | 11 | 334 | 1 | 1 |
- HTML: 249
- PDF: 74
- XML: 11
- Total: 334
- BibTeX: 1
- EndNote: 1
Viewed (geographical distribution)
Country | # | Views | % |
---|
Total: | 0 |
HTML: | 0 |
PDF: | 0 |
XML: | 0 |
- 1