Comparison of WRF 3D/4D-VAR Data Assimilations for Producing Sea Surface Wind Reanalysis around Korea

Abstract

Sea surface wind plays a dominant role in oceanic phenomena such as oceanic waves, swells and currents that have an important effect of the climate. However, the lack of reliable historical weather observations and limited data make it difficult to effectively analyze climate variability and change over the ocean. In contrast, numerical simulation data takes advantage to analyze spatiotemporal variation of sea surface wind, but has a fundamental limit to exactly reproduce atmospheric phenomena due to initial uncertainty in the model initialization and the chaotic behavior of nonlinear system governing the atmosphere. To overcome the limitations, data assimilation technique has been adopted to improve the model’s initial conditions and generate the final high-resolution analysis. The reanalysis comprises the combination of state of art models and data assimilation methods with highly quality controlled observations including surface, upper-level sounding and satellite based observations. Since the release of the original National Centers for Environmental Prediction/National Center for Atmospheric Research (NCEP/NCAR) Reanalysis Project (NCEP-1), the NCEP/NCAR, the European Centre for Medium-Range Weather Forecasts (ECMWF), and Japan Meteorological Agency (JMA) and the Central Research Institute of Electric Power Industry (CRIEPI) produce reanalysis products covering 1979 to present. However, there is a limitation iicult to effectively analyze climate variability and change over the ocean. In contrast, numerical simulation data takes advantage to analyze spatiotemporal variation of sea surface wind, but has a fundamental limit to exactly reproduce atmospheric phenomena due to initial uncertainty in the model initialization and the chaotic behavior of nonlinear system governing the atmosphere. To overcome the limitations, data assimilation technique has been adopted to improve the model’s initial conditions and generate the final high-resolution analysis. The reanalysis comprises the combination of state of art models and data assimilation methods with highly quality controlled observations including surface, upper-level sounding and satellite based observations. Since the release of the original National Centers for Environmental Prediction/National Center for Atmospheric Research (NCEP/NCAR) Reanalysis Project (NCEP-1), the NCEP/NCAR, the European Centre for Medium-Range Weather Forecasts (ECMWF), and Japan Meteorological Agency (JMA) and the Central Research Institute of Electric Power Industry (CRIEPI) produce reanalysis products covering 1979 to present. However, there is a limitation i