A novel technique for nowcasting extreme rainfall events using early microphysical signatures of cloud development

Sinan Nizar, Jobin Thomas, Jainet Pj and Sudheer Kp

PLOS Climate, 2025, vol. 4, issue 5, 1-20

Abstract: Accurate forecasting of extreme rainfall events (EREs) at a regional scale with higher lead times is challenging due to the uncertainties in weather model predictions. This study introduces a novel technique to nowcast heavy- and extreme-rainfall events by analyzing early microphysical signatures in mesoscale convective clouds. The method primarily utilizes the cloud top temperature (T) - cloud effective radius (re) profiles derived using remote sensing. We estimate the probability of the occurrence of heavy- and extreme-rainfall events using a logistic regression model with attributes extracted from the T-re profile and cloud droplet size distribution. Our analysis indicates that the T-re profiles for normal-, heavy-, and extreme-rainfall events exhibit distinct microphysical characteristics, with a prominent diffusional zone during EREs. Applying this model to nowcast recent EREs in the southern Western Ghats (Kerala, India) demonstrates an overall skill score of 93% and a lead time of at least six hours, underscoring the effectiveness of the approach for nowcasting EREs at a regional scale.

Date: 2025
References: Add references at CitEc
Citations:

Downloads: (external link)
https://journals.plos.org/climate/article?id=10.1371/journal.pclm.0000497 (text/html)
https://journals.plos.org/climate/article/file?id= ... 00497&type=printable (application/pdf)

Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:plo:pclm00:0000497

DOI: 10.1371/journal.pclm.0000497

Access Statistics for this article

More articles in PLOS Climate from Public Library of Science
Bibliographic data for series maintained by climate ().