During the past year, the University of North Dakota (UND), under the auspices of work funded by the U.S. Army's High Performance Computing Research Center, has developed a Meso-gamma/Micro-alpha Analysis and Assimilation Package (MAAP) suitable for use at 1-10 km scales. The UND MAAP system is a hybrid of existing analysis and data assimilation technologies and currently includes: (1) a heavily-modified version of the Local Analysis and Prediction System (LAPS) analysis and data assimilation system developed originally at NOAA's Global Systems Division (formerly the Forecast Systems Laboratory); 2) version 2.1.2 of the Advanced Research Weather Research and Forecasting Model (WRF-ARW); and 3) a modified stand-alone version of the NOAH land surface model driven by the LAPS component that has the capability to ingest retrievals of skin temperature from infrared satellite platforms and which provides lower boundary fields into the WRF component of the cycling system. Within this framework, other recent improvements to the package include incorporation of both level-2 and level-3 radar data and TAMDAR soundings in the LAPS component, parallelization of the LAPS Kalman filter routines and ability of the entire system to operate in a cycled mode.

In this paper we will present a brief overview of the UND MAAP system as well as results and preliminary validation of the system for short-term (6-12 hr) cycled mode forecasts and analyses at the meso-gamma scale (~ 4 km grids). We anticipate presenting results over the Florida peninsula and adjacent regions for forecasts during the lifetime of Tropical Storm Alberto (June 12-14 2006). Potentially, results from other cases as well as other analysis/forecast resolutions on the meso-gamma to lower meso-beta scales may also be presented or summarized. The validation discussion will highlight strengths and weaknesses of the current system as well as areas of further development to address any areas for which improvements are desirable.