Toward 1-km ensemble forecasts over large domains

Craig S. Schwartz; Glen S. Romine; Kathryn R. Fossell; Ryan A. Sobash; Morris L. Weisman

doi:10.1175/MWR-D-16-0410.1

Toward 1-km ensemble forecasts over large domains

Craig S. Schwartz, Glen S. Romine, Kathryn R. Fossell, Ryan A. Sobash, Morris L. Weisman

National Center for Atmospheric Research

Research output: Contribution to journal › Article › peer-review

53 Scopus citations

Abstract

Precipitation forecasts from convection-allowing ensembles with 3- and 1-km horizontal grid spacing were evaluated between 15 May and 15 June 2013 over central and eastern portions of the United States. Probabilistic forecasts produced from 10- and 30-member, 3-km ensembles were consistently better than forecasts from individual 1-km ensemble members. However, 10-member, 1-km probabilistic forecasts usually were best, especially over the first 12 h and at rainfall rates ≥ 5.0 mm h^-1 at later times. Further object-based investigation revealed that better 1-km forecasts at heavier rainfall rates were associated with more accurate placement of mesoscale convective systems compared to 3-km forecasts. The collective results indicate promise for 1-km ensembles once computational resources can support their operational implementation.

Original language	English
Pages (from-to)	2943-2969
Number of pages	27
Journal	Monthly Weather Review
Volume	145
Issue number	8
DOIs	https://doi.org/10.1175/MWR-D-16-0410.1
State	Published - Aug 1 2017

Keywords

Ensembles
Forecast verification/skill
Model evaluation/performance
Numerical weather prediction/forecasting

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10.1175/MWR-D-16-0410.1

Cite this

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title = "Toward 1-km ensemble forecasts over large domains",

abstract = "Precipitation forecasts from convection-allowing ensembles with 3- and 1-km horizontal grid spacing were evaluated between 15 May and 15 June 2013 over central and eastern portions of the United States. Probabilistic forecasts produced from 10- and 30-member, 3-km ensembles were consistently better than forecasts from individual 1-km ensemble members. However, 10-member, 1-km probabilistic forecasts usually were best, especially over the first 12 h and at rainfall rates ≥ 5.0 mm h-1 at later times. Further object-based investigation revealed that better 1-km forecasts at heavier rainfall rates were associated with more accurate placement of mesoscale convective systems compared to 3-km forecasts. The collective results indicate promise for 1-km ensembles once computational resources can support their operational implementation.",

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author = "Schwartz, \{Craig S.\} and Romine, \{Glen S.\} and Fossell, \{Kathryn R.\} and Sobash, \{Ryan A.\} and Weisman, \{Morris L.\}",

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doi = "10.1175/MWR-D-16-0410.1",

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T1 - Toward 1-km ensemble forecasts over large domains

AU - Schwartz, Craig S.

AU - Romine, Glen S.

AU - Fossell, Kathryn R.

AU - Sobash, Ryan A.

AU - Weisman, Morris L.

PY - 2017/8/1

Y1 - 2017/8/1

N2 - Precipitation forecasts from convection-allowing ensembles with 3- and 1-km horizontal grid spacing were evaluated between 15 May and 15 June 2013 over central and eastern portions of the United States. Probabilistic forecasts produced from 10- and 30-member, 3-km ensembles were consistently better than forecasts from individual 1-km ensemble members. However, 10-member, 1-km probabilistic forecasts usually were best, especially over the first 12 h and at rainfall rates ≥ 5.0 mm h-1 at later times. Further object-based investigation revealed that better 1-km forecasts at heavier rainfall rates were associated with more accurate placement of mesoscale convective systems compared to 3-km forecasts. The collective results indicate promise for 1-km ensembles once computational resources can support their operational implementation.

AB - Precipitation forecasts from convection-allowing ensembles with 3- and 1-km horizontal grid spacing were evaluated between 15 May and 15 June 2013 over central and eastern portions of the United States. Probabilistic forecasts produced from 10- and 30-member, 3-km ensembles were consistently better than forecasts from individual 1-km ensemble members. However, 10-member, 1-km probabilistic forecasts usually were best, especially over the first 12 h and at rainfall rates ≥ 5.0 mm h-1 at later times. Further object-based investigation revealed that better 1-km forecasts at heavier rainfall rates were associated with more accurate placement of mesoscale convective systems compared to 3-km forecasts. The collective results indicate promise for 1-km ensembles once computational resources can support their operational implementation.

KW - Ensembles

KW - Forecast verification/skill

KW - Model evaluation/performance

KW - Numerical weather prediction/forecasting

UR - https://www.scopus.com/pages/publications/85026311582

U2 - 10.1175/MWR-D-16-0410.1

DO - 10.1175/MWR-D-16-0410.1

M3 - Article

AN - SCOPUS:85026311582

SN - 0027-0644

VL - 145

SP - 2943

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JO - Monthly Weather Review

JF - Monthly Weather Review

IS - 8

ER -

Toward 1-km ensemble forecasts over large domains

Abstract

Keywords

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