advanced techniques for microwave data assimilation

1. Issue with Non-ocean surface / land
Over non-ocean surfaces, clear-sky passive microwave radiances are difficult to assimilate due to the lack of knowledge about surface properties which are generally characterized by surface emissivity. 

What is done with surface emissivity in DA?
Current assimilation systems combine emissivity catalogs with land emissivity models to provide a surface emissivity value for each grid point.
Emissivity catalog          \
Land emissivity model   /  ==> surface emissivity value
==> DA processing
==> dTB(difference between simulated and observed radiances)
==> Quality Control to filter out dTB > 10-15K. (filter)
==> less data gets assimilated

Using 1dvar preprocessor
Emissivity catalog                                                         \
1dvar preprocessor  ==> derived surface emissivity   /  ==> surface emissivity value
==> DA processing
==> smaller dTB(difference between simulated and observed radiances)
==> Quality Control to filter out dTB > 10-15K. (filter)
==> more data gets assimilated

2. Issue with clouds and precipitation due to CRTM ability calculating bias over them.
For clear-sky radiance assimilation, it is necessary and critical to remove all observations containing clouds and precipitation since the radiative transfer model (RTM) biases are not well characterized in these conditions (nor are they equivalent to clear-sky radiance bias).
Background           \
clear-sky params    / ==> crtm (bias well calc-ed)
==> simulated radiance (good)

Background               \
cloudy-sky params    / ==> crtm (bias no well calc-ed)
==> simulated radiance (no good)

Using 1dvar preprocesso
1DVAR preprocessor can provide information on cloud liquid water content (CLW) to flag cloudy/rainy radiances for QC
1DVAR
==> derived CLW
==> Quality Control to filter out cloudy/rainy radiances (filter)
==> more / less data gets assimilated depending on CLW.