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

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

doi:10.1029/2018JD029103

i

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

Supporting Files

2018
By Dirmeyer, Paul A. ; Halder, Subhadeep ; Bombardi, Rodrigo

Details

Journal Title:

Journal of Geophysical Research: Atmospheres
Personal Author:

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

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

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
Rights Information:

Other
Compliance:

Submitted
Main Document Checksum:

urn:sha256:059b0d38c081aacfd8388446cfbc69798be88ece9ae6dea1f9d93022722d03b8
Download URL:

https://repository.library.noaa.gov/view/noaa/36793/noaa_36793_DS1.pdf
File Type:

[PDF - 5.70 MB ]

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