Skillful forecasts of weather phenomena in numerical models begin with
the most accurate set of initial conditions achievable from
observational datasets. The process of combining observations with
numerical model predictions is called data assimilation. This chapter
describes the types of observations available for data assimilation in
models that predict the transport, fate, and impacts of smoke pollution.
Observation properties needed for effective data assimilation are
identified based on experiences with a variety of observation types in
data assimilation experiments, compiled from the published literature.
The second half of the chapter surveys the data assimilation
methodologies that have been applied to smoke aerosols, and describes
specific problems associated with the smoke observations that require
innovative techniques in data assimilation. The chapter concludes by
providing an outlook for future research and development in data
assimilation for smoke prediction models. Data assimilation for
prediction of smoke is an emerging area of development that promises to
greatly improve forecast skill as new datasets and techniques are
applied.