This paper describes a novel approach in developing a model for forecasting of global insolation on a horizontal plane. In the proposed forecasting model

constraints

such as latitude and whole precipitable water content in vertical column of that location

are used. These parameters can be easily measurable with a global positioning system (GPS). The earlier model was developed by using the above datasets generated from different locations in India. The model has been verified by calculating theoretical global insolation for different sites covering east

west

north

south and the central region with the measured values from the same locations. The model has also been validated on a region

from which data was not used during the development of the model. In the model

clearness index coefficients <tex xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">$(K_{\mathrm{T}})$ are updated using the ensemble Kalman filter (EnKF) algorithm. The forecasting efficacies using the <tex xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">$K_{\mathrm{T}}$ model and EnKF algorithm have also been verified by comparing two popular algorithms

namely the recursive least square (RLS) and Kalman filter (KF) algorithms. The minimum mean absolute percentage error (MAPE)

mean square error (MSE) and correlation coefficient (R) value obtained in global solar insolation estimations using EnKF in one of the locations are 2.4%

0.0285 and 0.9866 respectively.