Lab Home | Phone | Search
Center for Nonlinear Studies  Center for Nonlinear Studies
 Home 
 People 
 Current 
 Affiliates 
 Visitors 
 Students 
 Research 
 ICAM-LANL 
 Publications 
 Conferences 
 Workshops 
 Sponsorship 
 Talks 
 Colloquia 
 Colloquia Archive 
 Seminars 
 Postdoc Seminars Archive 
 Quantum Lunch 
 CMS Colloquia 
 Q-Mat Seminars 
 Q-Mat Seminars Archive 
 Archive 
 Kac Lectures 
 Dist. Quant. Lecture 
 Ulam Scholar 
 Colloquia 
 
 Jobs 
 Students 
 Summer Research 
 Visitors 
 Description 
 Past Visitors 
 Services 
 General 
 
 History of CNLS 
 
 Maps, Directions 
 CNLS Office 
 T-Division 
 LANL 
 
Wednesday, July 08, 2015
2:30 PM - 3:30 PM
CNLS Conference Room (TA-3, Bldg 1690)

Seminar

The Square-Root Information Increment Ensemble Kalman Filter and Applications to Upper Atmosphere Data Assimilation

Mark L. Psiaki

A new type of Ensemble Kalman Filter (EnKF) is developed, one that stores and updates its state information in an efficient square-root information filter form. It addresses two shortcomings of conventional EnKFs, the lack of a process noise model and the overly optimistic approximation of the estimation error statistics. The new filter uses an assumed a priori covariance approximation that is full-rank but sparse, possibly with a dense low-rank increment. This matrix can be used to develop a nominal square-root information equation for the system state and uncertainty. The measurements are used to develop an additional low-rank set of square-root information equations. Special algorithms provide forecasts and analyses of these increments at a computational cost comparable to that of existing EnKFs. Process noise effects are implicit in the a priori covariance time history, thereby obviating the need for an ad hoc inflation operation. The use of an a priori full-rank covariance allows the analysis operations to improve the state estimate without the need for an ad hoc localization adjustment. This new filter exhibits superior performance to a typical covariance square-root EnKF when operating on truth-model simulation data from a model of a 200-state spring-mass-damper network.

This new EnKF is being developed to solve data assimilation problems in the upper atmosphere. One target application is the assimilation of data into the Thermosphere-Ionosphere-Electrodynamics General Circulation Model (TIEGCM). An existing EnKF assimilates in situ neutral-density and electron-density data that are measured by satellites. The new EnKF is being considered to enable augmentation of this filter to assimilate GPS slant TEC from satellite-based radio occultations. This data type presents a challenge for traditional EnKFs because of its non-local nature. Another target application is to develop a real-time International Reference Ionosphere model that assimilates GPS slant TEC data from radio occultations and from a network of ground receivers along with data from a network of ground-based ionosondes. The challenges and opportunities of these two proposed applications will be discussed along with the potential for the new EnKF to meet the challenges.

Host: Humberto C. Godinez