The Relationship Between Reflectivity and Rainfall Rate From Rain Size Distributions Observed in Hurricanes.
In: Geophysical Research Letters, Jg. 49 (2022-12-16), Heft 23, S. 1-8
Online
academicJournal
Zugriff:
Raindrop size distributions collected by the DROPLET MEASUREMENT TECHNOLogies Precipitation imaging probe from 17 flights through 6 hurricanes during National Oceanic and Atmospheric Administration's hurricane field program in 2020 are used to study reflectivity (Z) and rainfall rate (RR) (R) relationship (i.e., Z‐R relationship). The results show that the Z‐R distribution is highly scattered and the scatter increases with RR and reflectivity up to 48 dBZ or 25 mm hr−1, after which it decreases rapidly. The range of the estimated RR from a power‐law Z‐R relationship can be as large as 50 mm hr−1 at reflectivity of 40 dBZ. The result from random forest regression model demonstrates that including the information of mass‐weighted‐diameter (Dm) along with radar reflectivity improves the estimated RR significantly. Plain Language Summary: One of the main applications of radar measurements is to estimate rainfall rate (RR) based on its power‐law relationships with radar reflectivity. Different relationships have been obtained for different weather scenarios. Hurricanes, in comparison with other severe weather systems, have a much longer life cycle and travel across much greater zonal and meridional extent. Therefore, in order to accurately estimate the RR for a specific time at a specific location in a hurricane, the power‐law relationship with constant coefficients needs to be improved. The uncertainties of the estimated RR using power‐law relationship are related to the raindrop sizes. The usage of a machine learning model (random forest regression) demonstrates that the uncertainties in the estimated rainfall are reduced significantly when both radar reflectivity and the drop sizes are taken into account. Key Points: The scatter in reflectivity (Z) ‐rainfall rate (R) distribution increases up to 48 dBZ or 25 mm hr−1, after which it decreases rapidlyThere are significant uncertainties associated with using a power law Z‐R relationship to estimate rainfall rate from radar reflectivityThe result from a machine learning model shows that rainfall estimation is improved if drop size information and reflectivity are both used [ABSTRACT FROM AUTHOR]
Copyright of Geophysical Research Letters is the property of Wiley-Blackwell and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)
Titel: |
The Relationship Between Reflectivity and Rainfall Rate From Rain Size Distributions Observed in Hurricanes.
|
---|---|
Autor/in / Beteiligte Person: | Leighton, Hua ; Black, Robert ; Zhang, Xuejin ; Marks, Frank D. |
Link: | |
Zeitschrift: | Geophysical Research Letters, Jg. 49 (2022-12-16), Heft 23, S. 1-8 |
Veröffentlichung: | 2022 |
Medientyp: | academicJournal |
ISSN: | 0094-8276 (print) |
DOI: | 10.1029/2022GL099332 |
Schlagwort: |
|
Sonstiges: |
|