All Time Past Year Past 30 Days
Abstract Views 0 0 0
Full Text Views 173 19 2
PDF Downloads 62 16 4

An Examination of Four-Dimensional Data-Assimilation Techniques for Numerical Weather Prediction

Dewey E. Harms
Search for other papers by Dewey E. Harms in
Current site
Google Scholar
PubMed
Close
,
Sethu Raman
Search for other papers by Sethu Raman in
Current site
Google Scholar
PubMed
Close
, and
Rangarao V. Madala
Search for other papers by Rangarao V. Madala in
Current site
Google Scholar
PubMed
Close
Full access

Four-dimensional data-assimilation methods, along with the most commonly used objective analysis and initialization techniques, are examined from a historical perspective. Operational techniques, including intermittent data assimilation and Newtonian nudging, and next-generation methods (Kalman–Bucy filtering and the adjoint method) are briefly described. Several methods are compared, with primary emphasis being placed on recent papers dealing with the operational assimilation techniques. Ongoing and future research is outlined, and some important implications of this research are discussed.

*Department of Marine, Earth and Atmospheric Sciences, North Carolina State University, Raleigh, NC 27695-8208

+Naval Research Laboratory, Washington, D.C. 20375

Four-dimensional data-assimilation methods, along with the most commonly used objective analysis and initialization techniques, are examined from a historical perspective. Operational techniques, including intermittent data assimilation and Newtonian nudging, and next-generation methods (Kalman–Bucy filtering and the adjoint method) are briefly described. Several methods are compared, with primary emphasis being placed on recent papers dealing with the operational assimilation techniques. Ongoing and future research is outlined, and some important implications of this research are discussed.

*Department of Marine, Earth and Atmospheric Sciences, North Carolina State University, Raleigh, NC 27695-8208

+Naval Research Laboratory, Washington, D.C. 20375

Save