Data flow computers promise efficient parallel computation limited in speed only by data dependencies in the calculation being performed. At the Massachusetts Institute of Technology Laboratory for Computer Science, the Computation Structures Group is working to design practical data flow computers that can outperform conventional supercomputers. Since data flow computers differ radically in structure from conventional (sequential) computers, the projection of their performance must be done through analysis of specific computations. The performance improvement that data flow computers offer is shown for a NASA benchmark program that implements a global weather model. We present the structure of the weather code as expressed in VAL, a functional programming language designed by the Computation Structures Group, and develop the corresponding machine-level program structures for efficient execution on a data flow supercomputer. On the basis of this analysis, we specify the capacities of hardware units and the number of each type of unit required to achieve a twenty-fold improvement in performance for the weather simulation application.