State-of-the-art weather forecasting systems depend on a variety of data collected by airborne, orbiting, and ground sensors. Regional CubeSat constellations have the potential to improve hurricane forecasting by collecting sensor data over data-starved oceanic regions. Even in regions where strong terrestrial sensor networks exist, constellation sensor data can help reduce forecasting model errors. To this end, the paper considers the problem of designing a low-Earth orbit CubeSat constellation that meets given resolution requirements over a region of interest. We propose a novel optimization framework that utilizes the concept of satellite coverage maps to determine the number of satellites and constellation pattern. Numerical simulations are presented for asymmetric constellation design that can provide sensor data over important geographical regions within a specified repeated time-window.