Predicting Fine-Scale Weather and Climate Processes

Creating weather and climate products that convey uncertainty is difficult and it often requires running dozens of weather or climate models with small variations in the initial conditions to understand the predictability of the atmosphere. This process requires very large computing resources. Less expensive methods are highly desired.
Challenge: 

Creating weather and climate products that convey uncertainty is difficult and it often requires running dozens of weather or climate models with small variations in the initial conditions to understand the predictability of the atmosphere. This process requires very large computing resources. Less expensive methods are highly desired.

Solution: 

Analog Ensemble (AnEn).  NCAR has introduced a new approach to generate accurate predictions and reliable uncertainty quantification, the analog ensemble (AnEn).  The AnEn estimates a future observation of the quantity to be predicted with a probability density function (PDF) formed by a set of n past verifying observations corresponding to the n best analogs (past model predictions) to a current deterministic model forecasts.  

Benefits: 

The AnEn outperform a power prediction based on the European Center for Medium range Weather Forecasting (ECMWF) ensemble wind predictions, a leading in operational forecasting at a fraction of the cost. AnEn provides forecasters, decision makers, and emergency managers with accurate information to save lives and property.