Assimilating cloud water path as a function of WRF cloud microphysics option

Assimilating retrieved cloud properties from satellite data into convective-scale models has to date received limited attention in the research community despite its potential to provide a wide array of information to a model analysis. Products currently available include cloud water path (CWP), which represents the amount of cloud water and cloud ice present in an integrated column and cloud top (base) pressures (CTP, CBP), which represent the top and bottom pressure levels of a cloud layer respectively. Retrieval algorithms have been developed to derive these products from operational GOES-13 Imager data with a spatial resolution of 4 km at 15 minute intervals. These data are assimilated into a convective scale (3 km) resolution WRF model using an Ensemble Kalman Filter (EnKF) approach through the Data Assimilation Research Testbed (DART) software. The new CWP forward operator combines the satellite cloud height information with the cloud property variables generated by WRF to determine where in the atmospheric column and how much to adjust the CWP to match the observation. Testing of this forward operator with a case study occurring on 10 May 2010 in Oklahoma and Kansas is conducted using several experiments comparing the effects of using different cloud microphysical schemes within WRF. A total of two experiments are conducted for each microphysical scheme assimilating either conventional observations only (CONV) or conventional along with GOES CWP observations (PATH). For all microphysical schemes, assimilating CWP observations improved the representation of cloud microphysical properties and downward shortwave flux within the model analysis after a 3-hr assimilation period. Comparison of the results between different microphysical schemes shows that each has characteristic errors and biases, which are discussed.