There is an ever-growing need for understanding the climate through modeling which will aid in future forecasting of climate for compounded events that has impact on life and environment on earth. 

Many of these climate models are general circulation models (GCMs) that are domain and theoretical based physical models using energy and mass balances with their complexities for weather and climate modeling and forecasting. It is difficult with such approach for forecasting seasonal to sub-seasonal and upto decades the weather and climate forecasts for global as well as for a specific location with high accuracy.

geopredict believes that a data driven climate modeling and forecasting approach is one of the best ways for such seasonal to sub-seasonal and decadal forecasting and through its technology it would also be complimentary for checking the domain-based modeling and forecasting systems.

Mathematical modeling is at the core of many decision support systems. Like many real-world problems the earth climate system is ill-defined and can be characterized by: 

• insufficient a priori information about the system for adequately describing the inherent system relationships, 
• possessing a large number of variables, many of which are unknown and/or cannot be measured, 
• noisy data available in very small to very large data sets, 
• vague and fuzzy objects whose variables has to be described adequately.

For ill-defined systems the classical hard approach that is based on the assumption that the world can be understood objectively and that knowledge about the world can be validated through empirical means needs to be replaced by a soft systems paradigm which can better describe vagueness and imprecision. 

Climate systems and in general any system can be modeled either by deductive logical-mathematical methods (hard or theory-driven approach) or by inductive modeling methods (soft systems or data-driven approach). Deductive methods have been used to advantages in the cases of well-understood problems and which obey well-known principles. The spectacular results in aerospace are prime examples of this approach. Here, the theory of the object being modeled is well known and it obeys the known physical laws. However, they are unduly restrictive to one end because of insufficient a priori knowledge, complexity and the uncertainty of the objects, as well as the exploding time and computing demands.

In contrast, inductive methods are used when data-driven approaches is the only alternative. These models are derived from the real physical data and they represent the relationships implicit within the system without prior knowledge of the physical processes or mechanisms involved. 

With these advanced possibilities with inductive methods, geopredict has developed an AI System, called CLIMFOR system, that is entirely based on SELF-ORGANIZING, HIGH-DIMENSIONAL KNOWLEDGE EXTRACTION TECHNOLOGIES to predict atmospheric parameters over different time horizons, that is used for Climate modeling and forecasting at different spatial temporal levels from a specific regional level to higher global levels.