New applications of data
assimilation: Observation and model improvements, strongly coupled
ocean-atmosphere data assimilation, and surface flux estimations
Eugenia Kalnay
UMD Noon March 17 in Room 2155
Abstract: The data assimilation cycle used for numerical
weather prediction (NWP) interpolates a short model forecast and the
new observations to create new initial conditions (analysis) for
integrating the model through the next cycle. Thus, improvements in NWP
have been considered to require improvements in the model, observing
systems and in the statistical interpolation scheme. Recently, however,
it has become clear that with advanced methods of data assimilation,
such as 4DVar and EnKF, the analysis cycle can be also applied in new
areas: improving the models, observations, assimilation of new
observing systems, and flux estimations. It is plausible that these may
become the main tools to develop and improve Earth System models,
observation forward models, quality control, and estimation of surface
fluxes.
We will present examples of these new applications:
Strongly coupled data assimilation, optimal estimation of model
parameters or surface fluxes by state augmentation, model improvements
through the use of analysis increments, use of Ensemble Forecast
Sensitivity to Observations (EFSO) to detect flawed observations that
make the 6hr forecast worse (Proactive QC), and its application to
estimate the observations Error Covariance R, and efficient operational
implementation of new observing systems.