3. Research Capability
Joint Center for Satellite Data Assimilation (JCSDA)
Scientific projects undertaken at the JCSDA are aligned
with several high priorities. The goals of these priorities and their
impact on data assimilation capability are given below.
Improve Radiative Transfer Models.
Radiative transfer models represent
the glue that connects the satellite observations to the meteorological
variables of the numerical prediction models. Under this priority, JCSDA
will improve the accuracy and capability of fast forward radiative transfer
models, by including additional physical processes (e.g., atmospheric
scattering) and better numerical techniques. JCSDA will also improve
emissivity modeling to allow more satellite data affected by surface
to be properly assimilated.
Prepare for Advanced Instruments.
As shown in Section 2, JCSDA must prepare for many new satellite
sensors to be launched over the next 5 years. JCSDA will develop
software algorithms for calibration, navigation, data selection,
simulating observations, processing and quality control in advance
of launch to reduce elapsed time from launch to operational use.
Advance Techniques for Assimilating Cloud and Precipitation Information.
Satellite observations of clouds and precipitation are not currently
assimilated in NWP models. JCSDA will develop a capability to assimilate
satellite data in cloudy and precipitation regions by improving
radiative transfer models and NWP cloud prediction schemes, thereby
significantly increasing the fraction of satellite data being ingested
into the assimilation systems.
Improve Uses of Satellite Land Products.
Improved land surface products (e.g., green vegetation fraction,
snow cover, snow pack parameters, surface albedo, land, and sea
surface temperature) will make forecasts more accurate and increase
the fraction of satellite data used.
Improve Use of Satellite Data for Ocean Data Assimilation.
Provide assimilated ocean data sets to the community for research
purposes and provide access to and support of (a version of) an
operational ocean data assimilation system.
Assimilate Satellite Derived Atmospheric Chemical Species.
NWP models are being enhanced to model stratospheric processes and
perform air quality forecasting. Satellite observations of aerosols,
ozone and other trace gases will be assimilated.
Implement 4D Variational Data Assimilation (4D Var).
Based on results from several NWP centers around the world,
implementation of 4D Var should significantly improve forecast skill.