Multicore processors are becoming dominant in the high performance computing (HPC) area, so multithread programming with OpenMP is becoming a key to good performance on such processors, though debugging problems remain. In particular, it is difficult to detect data races among threads with nondeterministic results, thus calling for tools to detect data races. Because HPC programs tend to run for long periods, detection tools that do not need to run the target programs are strongly preferred. We developed a static program analysis tool to detect data races in OpenMP loops in FORTRAN programs. Programmers can quickly use the tool at compile time without executing the target program. Because static analysis tools tend to report many false positives, we counted the false positives in some large applications to assess the utility and limits of static analysis tools. We have devised a new approach to detect data races. Our approach combines existing program analysis methods with a new analysis. We experimented with NAS parallel benchmarks and two real applications, GTC for plasma physics and GFMC for nuclear physics. Our new analysis method also reduces number of reported candidates from totally 97 to 33 in these applications. We found 13 previously unknown bugs out of 33 candidates reported by our prototype. Our analysis is fast enough for practical use, since the analysis time for the NAS parallel benchmark was shorter than the compilation time (18.5 seconds compared to 33.0 seconds).