Identification

Title

Starnet: A deep learning model for enhancing polarimetric radar Quantitative Precipitation Estimation

Abstract

Accurate and real-time estimation of surface precipitation is crucial for decision-making during severe weather events and for water resource management. Polarimetric weather radar serves as the primary operational tool employed for quantitative precipitation estimation (QPE). However, the conventional parametric radar QPE algorithms overlook the dynamic spatiotemporal characteristics of precipitation. In addition, challenges such as radar beam attenuation and imbalanced distribution of precipitation data further compromise the estimation accuracy. This article develops a 3-D star neural network (StarNet) for polarimetric radar QPEs that integrate physical height prior knowledge and employ a reweighted loss function. To better cope with the dynamic characteristics of precipitation patterns, 3-D convolution is introduced within StarNet to effectively capture the spatiotemporal features between successive radar volume scanning data. In particular, multidimensional polarimetric radar observations are utilized as inputs, and surface gauge measurements are employed as training labels. The feasibility and performance of the StarNet model are demonstrated and quantified using U.S. Weather Surveillance Radar-1988 Doppler (WSR-88D) observations collected near Melbourne, Florida. The experimental results show that the StarNet model enhances the prediction accuracy of moderate to heavy precipitation events and improves the estimation performance over long distances, with a mean absolute error (MAE) of 1.55 mm, a root mean square error (RMSE) of 2.63 mm, a normalized standard error (NSE) of 25%, a correlation coefficient (CC) of 0.92, and a BIAS of 0.94 for hourly rainfall estimates. The results suggest that StarNet is able to effectively map the connection between polarimetric radar observations and surface rainfall.

Resource type

document

Resource locator

Unique resource identifier

code

https://n2t.org/ark:/85065/d76q22g3

codeSpace

Dataset language

eng

Spatial reference system

code identifying the spatial reference system

Classification of spatial data and services

Topic category

geoscientificInformation

Keywords

Keyword set

keyword value

Text

originating controlled vocabulary

title

Resource Type

reference date

date type

publication

effective date

2016-01-01T00:00:00Z

Geographic location

West bounding longitude

East bounding longitude

North bounding latitude

South bounding latitude

Temporal reference

Temporal extent

Begin position

End position

Dataset reference date

date type

publication

effective date

2024-01-01T00:00:00Z

Frequency of update

Quality and validity

Lineage

Conformity

Data format

name of format

version of format

Constraints related to access and use

Constraint set

Use constraints

Limitations on public access

None

Responsible organisations

Responsible party

contact position

OpenSky Support

organisation name

UCAR/NCAR - Library

full postal address

PO Box 3000

Boulder

80307-3000

email address

opensky@ucar.edu

web address

http://opensky.ucar.edu/

name: homepage

responsible party role

pointOfContact

Metadata on metadata

Metadata point of contact