One of the most intriguing physical feature of the West African monsoon (WAM) is its rainfall diurnal cycle, characterized by a “phase-locked” occurrence of rainfall episodes. If such phenomenon is clearly detected from satellite observations, it is not well represented by global models. This is a great limitation since understanding the organization of convective activity represents a powerful tool for a full understanding of WAM physical core, and thus the chance of expanding our level of knowledge of monsoon dynamics.

In order to provide a dynamical description of monsoon mechanisms during the warm rainy season a regional reanalysis strategy has been developed based on Regional Atmospheric Modeling System (RAMS), forced by NCEP/NCAR Reanalysis. The RAMS better physical description, with respect to the NCEP/NCAR dataset, along with its higher spatial resolution (50km), provide a coherent and reliable atmospheric dataset especially for what concerns surface – atmosphere interaction governing monsoon dynamics. In this framework, two-years of seasonal simulations, from April to October 2004-2005, have been performed.

The method consistency has been evaluated through classical statistical skill scores analysis, with respect to the rainfall daily estimates provided by CMORPH/NOAA datasets for a whole season along with the NCEP/NCAR Reanalysis global atmospheric dataset. The observed diurnal cycle, computed from the estimated rainfall, coming from a satellite multispectral technique based on MSG observations, has been compared with the vertical velocity, at 300hPa, coming from regional reanalysis dataset based on RAMS model. A good accordance is revealed by the regional dataset both for spatial and temporal distribution along the season.

The special model set – up proposed for this regional reanalysis approach is able to reproduce most of the diurnal cycle observed variability revealing the chance to describe WAM fine scale dynamical features for further modeling experiments e.g. sensitivity long term simulations for climate change impacts.