Limitations of NWP weather forecasts
As of today, there are many limitations in the GCM based method of weather forecasting, firstly, the GCM produced charts represent the atmosphere in general only at levels well above the land surface, due to inadequate representation of land surface processes and topography in the model. In addition, errors in the model forecasts are known to be due to 1) inadequate representations of the initial conditions of the atmosphere, 2) inadequate finite resolution of the model - presents difficulty in the representation of orography, giving rise to differences in station levels, 3) Inadequate parameterizations of the boundary layer and other physical processes, and 4) incorrect representation of ground conditions. These lead to deficiencies in the forecast fields, with the surface forecast being particularly erroneous. Owing to the inherent shortcomings of the NWP model forecasts, human interpretations of the meteorological data generated by the models become a necessity. There have been tremendous improvements in the dynamical and statistical models used in NWP in the last few decades, but still, it will be awhile before totally automated forecasts based on unmodified model output, will be of skill comparable with those produced by human forecasters who modify such output based on their broad meteorological knowledge and forecasting confidence (Doswell et al., 1995). Sousounis et al. (1999) also opines that despite improvements in GCMs, Statistical models, forecast decision making trees, and forecast rules of thumb, etc. in automated weather forecasting, human synoptic interpretation of meteorological information for a particular situation can yield superior results. It was also, reported that the value added by humans over model forecast quality can be significant at times, especially when the forecast involves convective situations or shallow cold air outbreaks, which operational models still do not handle well (Cortinas and Stensrud, 1995). As such, a man machine mix approach is currently advocated for preparation of weather forecasts from GCM runs, especially in the medium range.

Keeping the above limitations of NWP based medium range weather forecasts in view, this paper focuses at improving the human component of the man-machine mix philosophy for improving forecast skill by making use of new technological tools like Geographical Information System (GIS) software for plotting, analyses and visualization of observed meteorological parameters, superior to the conventional techniques otherwise followed for the purpose. The GIS software - ArcView has been made use of for developing the application tool for the purpose, and demonstrated how this tool can help the human forecaster in his efforts at producing a better forecast from the model output products.

Materials and Methods for the Study
The GIS used for the application development is the ArcView GIS of ESRI with extensions of Spatial analyst, and Image analyst.

The weather data utilized is the T80 model analysis (initial conditions) for the weather parameters viz. Wind Speed, Wind Direction, temperature and Geopotential height at vertical levels of the atmosphere at 850 hPa and 500 hPa at the T80 model grid points (approximately 150 km apart) over the globe. The 5-day weather forecasts for the same parameters based of the above initial conditions (analysis) were made use of as weather forecasts.

Discussion
The synoptic weather chart is the main tool of the forecaster. A forecaster need analyze, and interpret numerous weather charts of the past period, before he gets a grip of the current weather situation, in order to evolve likely changes in the weather systems as time advances. Weather analysis is the process of drawing isobars, isohyets, isotachs, etc. and locating pressure systems, fronts, etc. on a base map of an area on which the weather observations from a wide area are plotted, following meteorological conventions. The forecast in general is generated from the observations. Fig. 1. provide the locations of the synoptic weather observation stations over the globe.


Figure 1: Locations of global synoptic weather data observing stations (source: ECMWF, U.K.)