Rainfall nowcasting models: state of the art and possible future perspectives
Journal
Hydrological Sciences Journal
Start Page
1
End Page
20
ISSN
0262-6667
2150-3435
Date Issued
2025
Author(s)
De Luca, Davide Luciano
Napolitano, Francesco
Kim, Dongkyun
Onof, Christian
Biondi, Daniela
Russo, Fabio
Ridolfi, Elena
Moccia, Benedetta
Marconi, Flavia
Abstract
This paper represents a short review of the most widely adopted types of models for rainfall nowcasting, which is an important component for early warning systems (EWSs). Specifically, the authors focus on: (1) extrapolation techniques from remote sensing observations; (2) numerical weather prediction (NWP) models; (3) stochastic models; (4) deep learning models. Moreover, the possibility of realizing blended systems with two or more different kinds of models is also described, in order to overcome the limitations of using only a single model and to take advantage of pros from the considered model ensemble. However, both single and blended models are affected by uncertainty, a topic characterized by ongoing debates in the scientific community. In this context, evaluation of predictive uncertainty (PU) is also discussed, as it provides an important perspective for an EWS, enabling informed decision making based on the forecasts of one or multiple models.
Subjects
deep learning
NWP models
predictive uncertainty
rainfall nowcasting
stochastic models
SDGs
Publisher
Informa UK Limited
Type
review