High-level programming languages that allow to express numerical methods and generate efficient parallel implementations are of key importance for the productivity of domain-scientists. The diversity and complexity of hardware architectures is imposing a huge challenge for large and complex models that must be ported and maintained for multiple architectures combining various parallel programming models. Several domain-specific languages (DSLs) have been developed to address the portability problem, but they usually impose a parallel model for specific numerical methods and support optimizations for limited scope operators. Dawn provides a high-level concise language for expressing numerical finite difference/volume methods using a sequential and descriptive language. The sequential statements are transformed into an efficient target-dependent parallel implementation by the Dawn compiler toolchain. We demonstrate our approach on the dynamical solver of the COSMO model, achieving performance improvements and code size reduction of up to 2x and 5x, respectively.