RESUME
/ ABSTRACT
Land surface models (LSM's) are
necessary to provide the lower boundary conditions for weather and
climate numerical models. To do this, LSM's typically model the heat
and moisture processes from 2 meters deep into the soil to the plant
canopy. Of fundamental importance in these models is the soil moisture
for it is known that large errors for this variable can negatively
affect the LSM and impact a numerical weather prediction model coupled
to it. The initialization of soil moisture in these models has been
regarded as a challenge for more than a decade, given, among other
issues, the unavailability of reliable soil moisture observations on a
continental scale.
The above problem often leads to the need for using other observations
to infer the necessary corrections to the soil moisture state. My past
work at NCEP involved the improvement of several aspects ofthe NCEP
Noah land surface model and its numerical properties (reliability,
efficiency, updates and differentiability) and the inference of soil
moisture from skin temperature via variational methods in a single
point off-line mode.
My current work with CaLDAS (Canadian Land Data Assimilation System)
involves the use of screen level air temperature and humidity to infer
soil moisture states on a continental scale.