Abstract

The data assimilation method to improve the sea fog forecast over the Yellow Sea is usually three-dimensional variational assimilation (3DVAR), whereas ensemble Kalman filter (EnKF) has not yet been applied to this weather phenomenon. In this paper, two sea fog cases over the Yellow sea, one spread widely and the other spread narrowly along the coastal area, are studied in detail by a series of numerical experiments with 3DVAR and EnKF based on the Grid-point Statistical Interpolation (GSI) system and the Weather Research and Forecasting (WRF) model. The results show that the assimilation effect of EnKF outperforms that of 3DVAR: for the widespread-fog case, the probability of detection and equitable threat scores of the forecasted sea fog area are improved respectively by ~57.9% and ~55.5%; the sea fog formation of the other case completely mis-forecasted by 3DVAR was produced successfully by EnKF. These improvements of EnKF relative to 3DVAR benefit from its flow-dependent background error covariances, resulting in more realistic depiction of sea surface wind for the widespread-fog case and better moisture distribution for the other case in the initial conditions. More importantly, the correlation between temperature and humidity in the background error covariances of EnKF plays a vital role in the response of moisture to the assimilation of temperature, which leads to a great improvement in the initial moisture conditions for sea fog forecast. Extra sensitivity experiments of EnKF indicate that the forecast result is sensitive to ensemble inflation and localization factors, in particular, highly sensitive to the latter.

Highlights

  • Sea fog usually refers to the fog that occurs over the ocean or a coastal region [1,2]

  • The correlation between temperature and humidity in the background error covariances of ensemble Kalman filter (EnKF) plays a vital role in the response of moisture to the assimilation of temperature, which leads to a great improvement in the initial moisture conditions for sea fog forecast

  • The sea fog simulation result can be improved to some extent by assimilating many types of observations using the three-dimensional variational data assimilation (3DVAR) method based on the Weather Research and Forecasting (WRF) model

Read more

Summary

Introduction

Sea fog usually refers to the fog that occurs over the ocean or a coastal region [1,2]. The sea fog simulation result can be improved to some extent by assimilating many types of observations using the three-dimensional variational data assimilation (3DVAR) method based on the Weather Research and Forecasting (WRF) model. These assimilated observations include routine measurements along the coast of the Yellow Sea [15], sea surface wind data aboard QuikSCAT (Quick Scatterometer) [16], satellite radiance [17], Doppler radar radial velocity [18], as well as temperature and moisture profiles derived from satellites [19]. Due to a lack of routine observations over sea, a new method was proposed by Wang et al [5] to assimilate

Objectives
Methods
Results
Conclusion
Full Text
Published version (Free)

Talk to us

Join us for a 30 min session where you can share your feedback and ask us any queries you have

Schedule a call