Adaptive sampling with the ensemble transform Kalman filter. Part II: Field program implementation

Abstract

The practical application of the ensemble transform Kalman filter (ET KF), used in recent Winter Storm Reconnaissance (WSR) programs by the National Centers for Environmental Prediction (NCEP), is described. The ET KF assesses the value of targeted observations taken at future times in improving forecasts for preselected critical events. It is based on a serial assimilation framework that makes it an order of magnitude faster than its predecessor, the ensemble transform technique. The speed of the ET KF enabled several different forecast scenarios to be assessed for targeting during recent WSR programs. Each potential observational network is broken down into idealized routine and adaptive c..