Bias Corrections in Subseasonal to Interannual Predictions
30 September - 2 October 2014
Archive of presentations
Dynamical models used in climate prediction systems are not perfect, and the resulting forecast has biases in the mean state and in the space-time statistics of the variability. In forecast mode, initial model states will drift toward the model climate as the forecast progresses, and this drift confounds extracting the climate signal that is being predicted. For this reason, short-term climate predictions are usually “bias corrected.” The bias correction is particularly important for the effective use of global forecasts in forcing application models and regional models.
Corrections of mean bias generally rely on a set of hindcasts or retrospective forecasts to define the model climate, which is then subtracted from the forecast to define a predicted anomaly. This approach assumes that the bulk of the bias can be removed linearly and that the model climate is effectively stationary or without trends. However, modern prediction systems include evolving external forcing (e.g., aerosols, greenhouse gases) and it is also likely that the bias depends on the evolving forcing. How to quantify and remove biases in non-stationary climate remains an open question.
As noted above, the space-time statistics of any prediction system also have significant biases. There have been numerous attempts to correct these biases – typical examples include spatial pattern correction techniques or variance modification. All of these techniques have strengths and weaknesses, but universally rely on the assumption of a stationary climate. Again, it is unclear how to apply these approaches in a non-stationary climate.
There are also bias correction techniques that are applied to the prediction system as the forecast evolves. Flux corrections and anomaly coupling are two well-known examples, but some aspects of stochastic physics approaches can also be though of as bias corrections as the forecast evolves.
The main goals of this Virtual Workshop are to review current practices and challenges in bias correcting sub-seasonal to interannual predictions and to foster new strategies particularly for non-stationary prediction systems. Submission of abstracts that address following two themes are invited as part of this virtual workshop:
The outcome of the workshop will be:
The scientific organizing committee for the workshop is the NOAA Climate Program Office Climate Prediction Task Force (CPTF).
Format of the Virtual Workshop:
The virtual workshop will be for three days with 1-two or three hour session per-day. Each session will be organized around a specific theme with one invited speaker (30 minutes) and then three contributed talks (20 minutes each) followed by a discussion session (30 minutes). It is anticipated that there will be about 30 participants at the workshop. The virtual workshop communications will be run using Adobe Connect.
Abstract submission was accepted from 7 July 2014 until 25 August 2014 and decisions regarding presentation at the workshop will be made by the workshop organizers by 15 September 2014.
Americans’ health, security and economic wellbeing are tied to climate and weather. Every day, we see communities grappling with environmental challenges due to unusual or extreme events related to climate and weather. In 2011, the United States experienced a record high number (14) of climate- and weather-related disasters where overall costs reached or exceeded $1 billion. Combined, these events claimed 670 lives, caused more than 6,000 injuries, and cost $55 billion in damages. Businesses, policy leaders, resource managers and citizens are increasingly asking for information to help them address such challenges.
Oceanic and Atmospheric Research (OAR)
National Oceanic and Atmospheric Administration (NOAA)
Department of Commerce
Climate Program Office
1315 East-West Hwy, Suite 1100 Silver Spring, MD 20910
Copyright 2017 by NOAA
NOAA Privacy Statement|
Web Accessibility Statement|
Disclaimer for External Links|
U.S. Department of Commerce|