Sensitivity of Typhoon Forecast to Prescribed Sea Surface Temperature Data

Abstract

This study investigates the impact of the sea surface temperature (SST) on the forecast of two typhoons, which consecutively hit South Korea in 2020. SST data were obtained from the Daily Optimum Interpolation Sea Surface Temperature (OISST) version 2 and HYbrid Coordinate Ocean Model/Navy Coupled Ocean Data Assimilation (HYCOM/NCODA; GLBy0.08/expt_93.0). When verified using in situ observational data, the OISST data did not accurately estimate the changes in SST during each typhoon’s landfall period compared to the HYCOM data since it has a relatively low temporal resolution. To investigate the impact of these two SST data on typhoon forecasts, we conducted sensitivity experiments using the Weather Research and Forecasting (WRF) model. The results showed that simulated typhoon intensities were significantly improved in the simulations with HYCOM data (HY runs), while typhoon track forecast performances were similar in both runs. In addition, the forecast performances of the maximum wind speed at 10 m during the typhoon landfall period were improved in the HY runs. Therefore, this study showed that the overall typhoon intensity and forecast performances during the landfall period could be improved when the higher temporal-resolution SST data were prescribed in the model boundary conditions for a better representation of typhoon-induced SST changes.

Python Programming for Degree Heating Weeks Estimation using Sea Surface Temperature Data from The Google Earth Engine Dataset as Coral Bleaching Analysis Tools

“…This NOAA OISST was proposed by DeCarlo [35] because of its high temporal resolution and one of the most appropriate daily SST datasets to detect anomalies in the SST. However, this data may be less accurate for calculating DHW with small areas due to its low resolution compared to HyCom [36]. Thus, if the calculation area is small and the calculation time is before 2023, HyCom is preferable.…”