Identification

Title

Just what is “good”? Musings on hail forecast verification through evaluation of FV3-hailcast hail forecasts

Abstract

Hail forecasts produced by the CAM-HAILCAST pseudo-Lagrangian hail size forecasting model were evaluated during the 2019, 2020, and 2021 NOAA Hazardous Weather Testbed (HWT) Spring Forecasting Experiments (SFEs). As part of this evaluation, HWT SFE participants were polled about their definition of a "good" hail forecast. Participants were presented with two different verification methods conducted over three different spa-tiotemporal scales, and were then asked to subjectively evaluate the hail forecast as well as the different verification methods themselves. Results recommended use of multiple verification methods tailored to the type of forecast ex-pected by the end-user interpreting and applying the forecast. The hail forecasts evaluated during this period in-cluded an implementation of CAM-HAILCAST in the Limited Area Model of the Unified Forecast System with the Finite Volume 3 (FV3) dynamical core. Evaluation of FV3-HAILCAST over both 1-and 24-h periods found contin-ued improvement from 2019 to 2021. The improvement was largely a result of wide intervariability among FV3 en-semble members with different microphysics parameterizations in 2019 lessening significantly during 2020 and 2021. Overprediction throughout the diurnal cycle also lessened by 2021. A combination of both upscaling neighborhood verification and an object-based technique that only retained matched convective objects was necessary to under-stand the improvement, agreeing with the HWT SFE participants' recommendations for multiple verification methods. SIGNIFICANCE STATEMENT: "Good" forecasts of hail can be determined in multiple ways and must depend on both the performance of the guidance and the perspective of the end-user. This work looks at different verification strategies to capture the performance of the CAM-HAILCAST hail forecasting model across three years of the Spring Forecasting Experiment (SFE) in different parent models. Verification strategies were informed by SFE participant in-put via a survey. Skill variability among models decreased in SFE 2021 relative to prior SFEs. The FV3 model in 2021, compared to 2019, provided improved forecasts of both convective distribution and 38-mm (1.5 in.) hail size, as well as less overforecasting of convection from 1900 to 2300 UTC.

Resource type

document

Resource locator

Unique resource identifier

code

http://n2t.net/ark:/85065/d74x5cqj

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

2023-02-01T00:00:00Z

Frequency of update

Quality and validity

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Conformity

Data format

name of format

version of format

Constraints related to access and use

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Use constraints

Copyright 2023 American Meteorological Society (AMS).

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

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 date

2023-08-18T18:42:00.056295

Metadata language

eng; USA