TY - JOUR
T1 - Quadratic polynomial regression using serial observation processing
T2 - Implementation within DART
AU - Hodyss, Daniel
AU - Anderson, Jeffrey L.
AU - Collins, Nancy
AU - Campbell, William F.
AU - Reinecke, Patrick A.
N1 - Publisher Copyright:
© 2017 American Meteorological Society.
PY - 2017/11/1
Y1 - 2017/11/1
N2 - It is well known that the ensemble-based variants of the Kalman filter may be thought of as producing a state estimate that is consistent with linear regression. Here, it is shown how quadratic polynomial regression can be performed within a serial data assimilation framework. The addition of quadratic polynomial regression to the Data Assimilation Research Testbed (DART) is also discussed and its performance is illustrated using a hierarchy of models from simple scalar systems to a GCM.
AB - It is well known that the ensemble-based variants of the Kalman filter may be thought of as producing a state estimate that is consistent with linear regression. Here, it is shown how quadratic polynomial regression can be performed within a serial data assimilation framework. The addition of quadratic polynomial regression to the Data Assimilation Research Testbed (DART) is also discussed and its performance is illustrated using a hierarchy of models from simple scalar systems to a GCM.
KW - Bayesian methods
KW - Kalman filters
KW - Regression analysis
KW - Statistical techniques
UR - https://www.scopus.com/pages/publications/85034642746
U2 - 10.1175/MWR-D-17-0089.1
DO - 10.1175/MWR-D-17-0089.1
M3 - Article
AN - SCOPUS:85034642746
SN - 0027-0644
VL - 145
SP - 4467
EP - 4479
JO - Monthly Weather Review
JF - Monthly Weather Review
IS - 11
ER -