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Usefulness of ensemble forecasts from NCEP Climate Forecast System in sub‐seasonal to intra‐annual forecasting
Author(s) -
Kumar Sanjiv,
Dirmeyer Paul A.,
Kinter J. L.
Publication year - 2014
Publication title -
geophysical research letters
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 2.007
H-Index - 273
eISSN - 1944-8007
pISSN - 0094-8276
DOI - 10.1002/2014gl059586
Subject(s) - climate forecast system , initialization , ensemble forecasting , forecast skill , climatology , forecast verification , consensus forecast , environmental science , standard deviation , null hypothesis , meteorology , metric (unit) , ensemble average , statistics , econometrics , computer science , mathematics , geography , precipitation , geology , economics , operations management , programming language
Typically, sub‐seasonal to intra‐annual climate forecasts are based on ensemble mean (EM) predictions. The EM prediction provides only a part of the information available from the ensemble forecast. Here we test the null hypothesis that the observations are randomly distributed about the EM predictions using a new metric that quantifies the distance between the EM predictions from the National Centers for Environmental Prediction (NCEP) Climate Forecast System version 2 (CFSv2) and the observations represented by CFSv2 Reanalysis. The null hypothesis cannot be rejected in this study. Hence, we argue that the higher order statistics such as ensemble standard deviation are also needed to describe the forecast. We also show that removal of systematic errors that are a function of the forecast initialization month and lead time is a necessary pre‐processing step. Finally, we show that CFSv2 provides useful ensemble climate forecasts from 0 to 9 month lead time in several regions.