A specialized algorithm for quadratic optimization (QO, or, formerly, QP) with disjoint linear constraints is presented. In the considered class of problems, a subset of variables are subject to linear equality constraints, while variables in a different subset are constrained to remain in a convex set. The proposed algorithm exploits the structure by combining steps in the nullspace of the equality constraint's matrix with projections onto the convex set. The algorithm is motivated by application in weather forecasting. Numerical results on a simple model designed for predicting rain show that the algorithm is an improvement on current practice and that it reduces the computational burden compared to a more general interior point QO method. In particular, if constraints are disjoint and the rank of the set of linear equality constraints is small, further reduction in computational costs can be achieved, making it possible to apply this algorithm in high dimensional weather forecasting problems.