The Cloud Layer Experiment (CLEX) is an on-going research project funded by the Department of Defense Center for Geosciences/ Atmospheric Research whose goals are to better understand the microphysical and dynamical properties of non-frontal, non-orographic, mid-level, mixed phase clouds (i.e. altocumulus and altostratus) so that we may improve forecasting of this cloud niche. Mid-level clouds of the type examined here are typically thin (< 1km in vertical extent) and mixed phase with weak vertical velocities (< 1 m/s). They have been shown to cover ~ 22% of the earth's surface at any given time. Their role in climate is poorly understood at present given the dependence of their radiative properties on microphysical structure, however, it has been established that they interfere with military operations and pose a significant aircraft icing risk due to the presence of supercooled liquid droplets.

Previous CLEX field campaigns have shown an inability of many common operational forecast models to predict the formation of these clouds. During the most recent field campaign (CLEX-9) in the autumn of 2001, six clouds were observed by aircraft over the western Great Plains- none of which were predicted by any of the operational forecast models used by members of the forecasting team. Recent work with the Regional Atmospheric Modeling System (RAMS) has suggested that the inability of RAMS to forecast these clouds is due, in part, to a lack of moisture in the mid- and upper troposphere, possibly due to a known dry bias in radiosonde humidity sensors. In this work, mid- and upper tropospheric humidity information is gathered from GOES infrared sensors (both Imager and Sounder) and assimilated into RAMS using the Regional Atmospheric Modeling Data Assimilation System (RAMDAS) developed at CSU/CIRA to improve the initial humidity field and allow the model to predict the formation of the clouds that were observed during CLEX-9.

Results presented demonstrate the impact of assimilating GOES infrared radiances on the initialization and forecast of several cases of mid-level, mixed phase clouds over the western Great Plains of the United States using a mesoscale four dimensional variational (4DVAR) data assimilation scheme.