Evaluation of the Global and Regional Assimilation and Prediction System for Predicting Sea Fog over the South China Sea

Abstract

In the South China Sea, sea fog brings severe disasters every year, but forecasters have yet to implement an effective sea-fog forecast. To address this issue, we test a liquid-water-content-only (LWC-only) operational sea-fog prediction method based on a regional mesoscale numerical model with a horizontal resolution of about 3 km, the Global and Regional Assimilation and Prediction System (GRAPES), hereafter GRAPES-3km. GRAPES-3km models the LWC over the sea, from which we infer the visibility that is then used to identify fog. We test the GRAPES-3km here against measurements in 2016 and 2017 from coastal-station observations, as well as from buoy data, data from the Integrated Observation Platform for Marine Meteorology, and retrieved fog and cloud patterns from Himawari-8 satellite data. For two cases that we examine in detail, the forecast region of sea fog overlaps well with the multi-observational data within 72 h. Considering forecasting for 0–24 h, GRAPES-3km has a 2-year-average equitable threat score (ETS) of 0.20 and a Heidke skill score (HSS) of 0.335, which is about 5.6% (ETS) and 6.4% (HSS) better than our previous method (GRAPES-MOS). Moreover, the stations near the particularly foggy region around the Leizhou Peninsula have relatively high forecast scores compared to other sea areas. Overall, the results show that GRAPES-3km can roughly predict the formation, evolution, and dissipation of sea fog on the southern China coast.