Relative Merit of Model Improvement versus Availability of Retrospective Forecasts: The Case of Climate Forecast System MJO Prediction

Abstract Retrospective forecasts of the new NCEP Climate Forecast System (CFS) have been analyzed out to 45 days from 1999 to 2009 with four members (0000, 0600, 1200, and 1800 UTC) each day. The new version of CFS [CFS, version 2 (CFSv2)] shows significant improvement over the older CFS [CFS, version 1 (CFSv1)] in predicting the Madden–Julian oscillation (MJO), with skill reaching 2–3 weeks in comparison with the CFSv1’s skill of nearly 1 week. Diagnostics of experiments related to the MJO forecast show that the systematic error correction, possible only because of the enormous hindcast dataset and the ensemble aspects of the prediction system (4 times a day), do contribute to improved forecasts. But the main reason is the improvement in the model and initial conditions between 1995 and 2010.

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A Hybrid Global Ocean Data Assimilation System at NCEP

Monthly Weather Review ◽

10.1175/mwr-d-14-00376.1 ◽

2015 ◽

Vol 143 (11) ◽

pp. 4660-4677 ◽

Cited By ~ 35

Author(s):

Stephen G. Penny ◽

David W. Behringer ◽

James A. Carton ◽

Eugenia Kalnay

Keyword(s):

Data Assimilation ◽

Initial Conditions ◽

Global Ocean ◽

Climate Forecast ◽

Forecast System ◽

Climate Forecast System ◽

Ocean Data Assimilation ◽

Data Assimilation System ◽

Ncep Climate Forecast System ◽

Assimilation System

Abstract Seasonal forecasting with a coupled model requires accurate initial conditions for the ocean. A hybrid data assimilation has been implemented within the National Centers for Environmental Prediction (NCEP) Global Ocean Data Assimilation System (GODAS) as a future replacement of the operational three-dimensional variational data assimilation (3DVar) method. This Hybrid-GODAS provides improved representation of model uncertainties by using a combination of dynamic and static background error covariances, and by using an ensemble forced by different realizations of atmospheric surface conditions. An observing system simulation experiment (OSSE) is presented spanning January 1991 to January 1999, with a bias imposed on the surface forcing conditions to emulate an imperfect model. The OSSE compares the 3DVar used by the NCEP Climate Forecast System (CFSv2) with the new hybrid, using simulated in situ ocean observations corresponding to those used for the NCEP Climate Forecast System Reanalysis (CFSR). The Hybrid-GODAS reduces errors for all prognostic model variables over the majority of the experiment duration, both globally and regionally. Compared to an ensemble Kalman filter (EnKF) used alone, the hybrid further reduces errors in the tropical Pacific. The hybrid eliminates growth in biases of temperature and salinity present in the EnKF and 3DVar, respectively. A preliminary reanalysis using real data shows that reductions in errors and biases are qualitatively similar to the results from the OSSE. The Hybrid-GODAS is currently being implemented as the ocean component in a prototype next-generation CFSv3, and will be used in studies by the Climate Prediction Center to evaluate impacts on ENSO prediction.

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Statistical-dynamical prediction of the Madden-Julian oscillation using NCEP Climate Forecast System (CFS)

International Journal of Climatology ◽

10.1002/joc.1845 ◽

2009 ◽

Vol 29 (14) ◽

pp. 2146-2155 ◽

Cited By ~ 10

Author(s):

Kyong-Hwan Seo

Keyword(s):

Climate Forecast ◽

Madden Julian Oscillation ◽

Forecast System ◽

Climate Forecast System ◽

Ncep Climate Forecast System

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An Analysis of the Nonstationarity in the Bias of Sea Surface Temperature Forecasts for the NCEP Climate Forecast System (CFS) Version 2

Monthly Weather Review ◽

10.1175/mwr-d-11-00335.1 ◽

2012 ◽

Vol 140 (9) ◽

pp. 3003-3016 ◽

Cited By ~ 77

Author(s):

A. Kumar ◽

M. Chen ◽

L. Zhang ◽

W. Wang ◽

Y. Xue ◽

...

Keyword(s):

Surface Temperature ◽

Sea Surface Temperature ◽

Long Range ◽

Initial Conditions ◽

Sea Surface ◽

Climate Forecast ◽

Prediction System ◽

Forecast System ◽

Climate Forecast System ◽

Forecast Bias

Abstract For long-range predictions (e.g., seasonal), it is a common practice for retrospective forecasts (also referred to as the hindcasts) to accompany real-time predictions. The necessity for the hindcasts stems from the fact that real-time predictions need to be calibrated in an attempt to remove the influence of model biases on the predicted anomalies. A fundamental assumption behind forecast calibration is the long-term stationarity of forecast bias that is derived based on hindcasts. Hindcasts require specification of initial conditions for various components of the prediction system (e.g., ocean, atmosphere) that are generally taken from a long reanalysis. Trends and discontinuities in the reanalysis that are either real or spurious can arise due to several reasons, for example, the changing observing system. If changes in initial conditions were to persist during the forecast, there is a potential for forecast bias to depend over the period it is computed, making calibration even more of a challenging task. In this study such a case is discussed for the recently implemented seasonal prediction system at the National Centers for Environmental Prediction (NCEP), the Climate Forecast System version 2 (CFS.v2). Based on the analysis of the CFS.v2 for 1981–2009, it is demonstrated that the characteristics of the forecast bias for sea surface temperature (SST) in the equatorial Pacific had a dramatic change around 1999. Furthermore, change in the SST forecast bias, and its relationship to changes in the ocean reanalysis from which the ocean initial conditions for hindcasts are taken is described. Implications for seasonal and other long-range predictions are discussed.

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The NCEP Climate Forecast System Version 2

Journal of Climate ◽

10.1175/jcli-d-12-00823.1 ◽

2014 ◽

Vol 27 (6) ◽

pp. 2185-2208 ◽

Cited By ~ 1314

Author(s):

Suranjana Saha ◽

Shrinivas Moorthi ◽

Xingren Wu ◽

Jiande Wang ◽

Sudhir Nadiga ◽

...

Keyword(s):

Energy Use ◽

Initial Conditions ◽

Forecast Model ◽

Seasonal Prediction ◽

The United States ◽

Climate Forecast ◽

Seasonal Forecasts ◽

Forecast System ◽

Climate Forecast System ◽

Ncep Climate Forecast System

Abstract The second version of the NCEP Climate Forecast System (CFSv2) was made operational at NCEP in March 2011. This version has upgrades to nearly all aspects of the data assimilation and forecast model components of the system. A coupled reanalysis was made over a 32-yr period (1979–2010), which provided the initial conditions to carry out a comprehensive reforecast over 29 years (1982–2010). This was done to obtain consistent and stable calibrations, as well as skill estimates for the operational subseasonal and seasonal predictions at NCEP with CFSv2. The operational implementation of the full system ensures a continuity of the climate record and provides a valuable up-to-date dataset to study many aspects of predictability on the seasonal and subseasonal scales. Evaluation of the reforecasts show that the CFSv2 increases the length of skillful MJO forecasts from 6 to 17 days (dramatically improving subseasonal forecasts), nearly doubles the skill of seasonal forecasts of 2-m temperatures over the United States, and significantly improves global SST forecasts over its predecessor. The CFSv2 not only provides greatly improved guidance at these time scales but also creates many more products for subseasonal and seasonal forecasting with an extensive set of retrospective forecasts for users to calibrate their forecast products. These retrospective and real-time operational forecasts will be used by a wide community of users in their decision making processes in areas such as water management for rivers and agriculture, transportation, energy use by utilities, wind and other sustainable energy, and seasonal prediction of the hurricane season.

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