A Progress Report on the Development of the High-Resolution Rapid Refresh Ensemble

Kalina, Evan A.; Jankov, Isidora; Alcott, Trevor; Olson, Joseph; Beck, Jeffrey; Berner, Judith; Dowell, David; Alexander, Curtis

doi:10.1175/WAF-D-20-0098.1

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A Progress Report on the Development of the High-Resolution Rapid Refresh Ensemble

2021
By Kalina, Evan A. ; Jankov, Isidora ; Alcott, Trevor ; ...
Source: Weather and Forecasting, 36(3), 791-804

[PDF-2.82 MB]

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Journal Title:

Weather and Forecasting
Personal Author:

Kalina, Evan A. ; Jankov, Isidora ; Alcott, Trevor ; Olson, Joseph ; Beck, Jeffrey ; ... More +

Kalina, Evan A. ; Jankov, Isidora ; Alcott, Trevor ; Olson, Joseph ; Beck, Jeffrey ; Berner, Judith ; Dowell, David ; Alexander, Curtis Less -
NOAA Program & Office:

OAR (Oceanic and Atmospheric Research) ; GSL (Global Systems Laboratory) ; CIRES (Cooperative Institute for Research in Environmental Sciences)

OAR (Oceanic and Atmospheric Research) ; GSL (Global Systems Laboratory) ; CIRES (Cooperative Institute for Research in Environmental Sciences) Less -
Description:

The High-Resolution Rapid Refresh Ensemble (HRRRE) is a 36-member ensemble analysis system with 9 forecast members that utilizes the Advanced Research version of the Weather Research and Forecasting (ARW-WRF) dynamic core and the physics suite from the operational Rapid Refresh/High-Resolution Rapid Refresh deterministic modeling system. A goal of HRRRE development is a system with sufficient spread among members, comparable in magnitude to the random error in the ensemble mean, to represent the range of possible future atmospheric states. HRRRE member diversity has traditionally been obtained by perturbing the initial and lateral boundary conditions of each member, but recent development has focused on implementing stochastic approaches in HRRRE to generate additional spread. These techniques were tested in retrospective experiments and in the May 2019 Hazardous Weather Testbed Spring Experiment (HWT-SE). Results show a 6%-25% increase in the ensemble spread in 2-m temperature, 2-m mixing ratio, and 10-m wind speed when stochastic parameter perturbations are used in HRRRE (HRRRE-SPP). Case studies from HWT-SE demonstrate that HRRRE-SPP performed similar to or better than the operational High-Resolution Ensemble Forecast system, version 2 (HREFv2), and the nonstochastic HRRRE. However, subjective evaluations provided by HWT-SE forecasters indicated that overall, HRRRE-SPP predicted lower probabilities of severe weather (using updraft helicity as a proxy) compared to HREFv2. A statistical analysis of the performance of HRRRE-SPP and HREFv2 from the 2019 summer convective season supports this claim, but also demonstrates that the two systems have similar reliability for prediction of severe weather using updraft helicity.
Keywords:

[+]

Ensembles Forecast Verification/Skill Interpolation Mesoscale Meteorology & Atmospheric Sciences Model Uncertainties Numerical Weather Parameterization Parametrization Predictability Prediction Prediction/Forecasting Severe Storms System Verification Weather
Source:

Weather and Forecasting, 36(3), 791-804
DOI:

https://doi.org/10.1175/WAF-D-20-0098.1
Document Type:

Journal Article
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CC BY
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Submitted
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urn:sha256:2735c6027a8a043a01102d1a0da5af6fc02d308d9e525dc13e5a6ae12dd8c1d2
Download URL:

https://repository.library.noaa.gov/view/noaa/30977/noaa_30977_DS1.pdf
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A Progress Report on the Development of the High-Resolution Rapid Refresh Ensemble

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