Spatial Aligned Mean: A Method to Improve Consensus Forecasts of Precipitation from Convection-Allowing Model Ensembles

Lee, ChangJae; Brewster, Keith A.; Snook, Nathan; Spencer, Phillip; Park, Jun

doi:10.1175/WAF-D-23-0229.1

i

Spatial Aligned Mean: A Method to Improve Consensus Forecasts of Precipitation from Convection-Allowing Model Ensembles

2025
By Lee, ChangJae ; Brewster, Keith A. ; Snook, Nathan ; ...

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Journal Title:

Weather and Forecasting
Personal Author:

Lee, ChangJae ; Brewster, Keith A. ; Snook, Nathan ; Spencer, Phillip ; Park, Jun
NOAA Program & Office:

OAR (Oceanic and Atmospheric Research) ; WPO (Weather Program Office)
Description:

Ensembles of convection-allowing model (CAM) forecasts are increasingly being used in operational numerical weather forecasting. Several approaches have been devised to find consensus among ensemble forecast fields, including the arithmetic ensemble mean and, more recently, the patchwise localized probability-matched (LPM) mean. However, differences in spatial distribution and intensity of precipitation features among ensemble members make it difficult to construct an ensemble mean product that characterizes the consensus while preserving precipitation structures forecasted by the individual ensemble members. To overcome this problem, this study aims to develop and test a method for improving ensemble consensus precipitation forecasts by directly considering the spatial offsets among ensemble members. This study uses a multiscale spatial alignment technique to align the precipitation features of each ensemble member to a common location, and the spatial aligned mean (SAM) is obtained by averaging the realigned members. It is shown that implementing SAM and subsequently applying the LPM technique to the average of all aligned members (SAM–LPM) can significantly improve the warm season precipitation forecast scores using common metrics such as equitable threat score (ETS). Also, improvement in the structure of features of heavy rainfall is shown from summer 2023 flash-flooding cases. Thus, SAM and SAM–LPM can be excellent candidate methods for calculating an ensemble consensus and providing ensemble consensus guidance to forecasters.
Keywords:

Numerical weather forecasting Ensembles Forecast Verification Mesoscale Forecasting Numerical Weather Prediction Operational Forecasting Postprocessing
Source:

Wea. Forecasting, 39, 1545–1558
DOI:

https://doi.org/10.1175/WAF-D-23-0229.1
Format:

PDF
Document Type:

Journal Article
Funding:

Grant no. NA19OAR4590141 ; Grant no. NA22OAR4590522
License:

https://creativecommons.org/licenses/by/4.0/
Rights Information:

CC BY
Compliance:

Submitted
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https://repository.library.noaa.gov/view/noaa/71975/noaa_71975_DS1.pdf
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