Identification

Title

Calibration of machine learning-based probabilistic hail predictions for operational forecasting

Abstract

In this study, we use machine learning (ML) to improve hail prediction by postprocessing numerical weather prediction (NWP) data from the new High-Resolution Ensemble Forecast system, version 2 (HREFv2). Multiple operational models and ensembles currently predict hail, however ML models are more computationally efficient and do not require the physical assumptions associated with explicit predictions. Calibrating the ML-based predictions toward familiar forecaster output allows for a combination of higher skill associated with ML models and increased forecaster trust in the output. The observational dataset used to train and verify the random forest model is the Maximum Estimated Size of Hail (MESH), a Multi-Radar Multi-Sensor (MRMS) product. To build trust in the predictions, the ML-based hail predictions are calibrated using isotonic regression. The target datasets for isotonic regression include the local storm reports and Storm Prediction Center (SPC) practically perfect data. Verification of the ML predictions indicates that the probability magnitudes output from the calibrated models closely resemble the day-1 SPC outlook and practically perfect data. The ML model calibrated toward the local storm reports exhibited better or similar skill to the uncalibrated predictions, while decreasing model bias. Increases in reliability and skill after calibration may increase forecaster trust in the automated hail predictions.

Resource type

document

Resource locator

Unique resource identifier

code

http://n2t.net/ark:/85065/d7ft8q79

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

2020-02-01T00:00:00Z

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