ne of the biggest challenges when applying GPGPU frameworks to complex codebases is the necessity for redesigning loops and data access patterns.   Experiences with the physical core of the ASUCA weather prediction model have shown that using pure CUDA Fortran or OpenACC leads to a lengthy manual redesign and large execution time overheads when executing the new code back on the CPU.  The Hybrid Fortran meta programming framework has been designed to (a) automate this process and (b) be able to run the user code in CPU optimized loop structure as well, thus enabling optimal performance both on GPU and CPU. Results when using it for the ASUCA physical core show High GPU performance, CPU performance on par with the original x86-optimized code, and reduced portation overhead.