Trends in the predictive performance of raw ensemble weather forecasts

Hemri, S.; Scheuerer, M.; Pappenberger, F.; Bogner, K.; Haiden, T.

doi:10.1002/2014GL062472

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Trends in the predictive performance of raw ensemble weather forecasts

2015
By Hemri, S. ; Scheuerer, M. ; Pappenberger, F. ; ...
Source: Geophys. Res. Lett., 41(24), 9197-9205.

[PDF-592.91 KB]

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Details:

Journal Title:

Geophysical Research Letters
Personal Author:

Hemri, S. ; Scheuerer, M. ; Pappenberger, F. ; Bogner, K. ; Haiden, T.

Hemri, S. ; Scheuerer, M. ; Pappenberger, F. ; Bogner, K. ; Haiden, T. Less -
NOAA Program & Office:

OAR (Oceanic and Atmospheric Research) ; ESRL (Earth System Research Laboratory) ; PSL (Physical Sciences Laboratory)

OAR (Oceanic and Atmospheric Research) ; ESRL (Earth System Research Laboratory) ; PSL (Physical Sciences Laboratory) Less -
Description:

This study applies statistical postprocessing to ensemble forecasts of near-surface temperature, 24 h precipitation totals, and near-surface wind speed from the global model of the European Centre for Medium-Range Weather Forecasts (ECMWF). The main objective is to evaluate the evolution of the difference in skill between the raw ensemble and the postprocessed forecasts. Reliability and sharpness, and hence skill, of the former is expected to improve over time. Thus, the gain by postprocessing is expected to decrease. Based on ECMWF forecasts from January 2002 to March 2014 and corresponding observations from globally distributed stations, we generate postprocessed forecasts by ensemble model output statistics (EMOS) for each station and variable. Given the higher average skill of the postprocessed forecasts, we analyze the evolution of the difference in skill between raw ensemble and EMOS. This skill gap remains almost constant over time indicating that postprocessing will keep adding skill in the foreseeable future.
Source:

Geophys. Res. Lett., 41(24), 9197-9205.
DOI:

https://doi.org/10.1002/2014GL062472
Document Type:

Journal Article
Rights Information:

CC BY-NC-ND
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Submitted
Main Document Checksum:

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urn:sha256:2a0ff1a253196aabfd0c518dfcdb5800387261f2cfd109ad264c47f1db7f4756
Download URL:

https://repository.library.noaa.gov/view/noaa/14547/noaa_14547_DS1.pdf
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abstract

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