On the Harvest of Predictability From Land States in a Global Forecast Model

Dirmeyer, Paul A.; Halder, Subhadeep; Bombardi, Rodrigo

doi:10.1029/2018JD029103

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On the Harvest of Predictability From Land States in a Global Forecast Model

2018
By Dirmeyer, Paul A. ; Halder, Subhadeep ; Bombardi, Rodrigo
Source: Journal of Geophysical Research: Atmospheres, 123(23), 13,111-13,127

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Journal Title:

Journal of Geophysical Research: Atmospheres
Personal Author:

Dirmeyer, Paul A. ; Halder, Subhadeep ; Bombardi, Rodrigo

Dirmeyer, Paul A. ; Halder, Subhadeep ; Bombardi, Rodrigo Less -
NOAA Program & Office:

OAR (Oceanic and Atmospheric Research) ; CPO (Climate Program Office)

OAR (Oceanic and Atmospheric Research) ; CPO (Climate Program Office) Less -
Description:

The performance of the National Weather Service's operational climate forecast model is examined to see how the land surface, namely, moisture in the soil, affects the skill of forecasts. We estimate the potential skill derived from the best possible initialization and prediction of land surface states and how much of that potential skill can be realized by the current version of the forecast model.
Keywords:

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Forecast Skill Predictability Prediction Subseasonal
Source:

Journal of Geophysical Research: Atmospheres, 123(23), 13,111-13,127
DOI:

https://doi.org/10.1029/2018JD029103
Document Type:

Journal Article
Funding:

Grant no. NA16OAR4310095
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Submitted
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urn:sha256:059b0d38c081aacfd8388446cfbc69798be88ece9ae6dea1f9d93022722d03b8
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https://repository.library.noaa.gov/view/noaa/36793/noaa_36793_DS1.pdf
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