Session 3: Atmospheric retrieval over land and sea ice: assimilation experiment

Session 3: Atmospheric retrieval over land and sea ice: assimilation
Session Chair: Fatima Karbou
18. Impact of Microwave Land Emissivity Information on Satellite
Data Assimilation
Yong Chen, and Fuzhong Weng
Affiliation: CIRA, JCSDA
A robust schedule for assimilating satellite microwave sounding data at
surface-sensitive channels in numerical weather prediction (NWP) models is very
important since the measurements contain rich information about lower-level
temperature, water vapor, cloud liquid water, and precipitable water. Recently, two
global land emissivity datasets named TELSEM and CNRM Microwave Atlas have
been produced directly from satellite observations using Special Sensor
Microwave/Imager (SSM/I) and Advanced Microwave Sounding Units (AMSU).
These emissivity datasets are included in the Radiative Transfer for the Television
and infrared Observation satellite operational Vertical Sounder (RTTOV)-10. An
interface software is also developed to link the RTTOV-10 emissivity data base with
the Community Radiative Transfer Model (CRTM). The land emissivity can be
calculated before calling CRTM main functions (such as Forward, and K-Matrix
models), and passed to CRTM through Options structure instead of using the internal
default physical emissivity models. In CRTM, the physical model can simulate land
emissivity and its variation in time and space through a set of surface parameters
such as soil type, roughness and moisture content. The model requires accurate input
parameters for emissivity calculations. On a global scale, these input parameters are
still poorly described although some improvements have been recently made. These
are two physical models available in CRTM: (1) NESDIS default model, in which
volumetric scattering was calculated using a two-stream radiative transfer
approximation; Soil moisture content, vegetation fraction, soil temperature, and skin
temperature are input parameters. (2) NESDIS_new model, in which additional input
parameters are added, such as leaf area index, vegetation type, and soil type. In this
study, we have evaluated impacts of these microwave land emissivity model and data
sets on assimilation of AMSU data. Results show that more observation data are
assimilated for datasets than physical models for AMSUA channels 1, 2, and 3. The
impact on forecast scores positive in the tropics within 4 day forecast range for
CNRM but neutral for both southern and northern hemispheres.