Conversation with Cees Goos

Goos, Cees

doi:10.4324/9781315083063-4

Cited by 1 publication

(1 citation statement)

References 0 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…The initial and lateral boundary conditions for the WRF simulation later were derived from the NCEP Final Analysis (FNL; 1 • × 1 • , 6-hourly; [46]), and the sea surface temperature (SST) data were extracted from daily NEAR-GOOS (North-East Asian Regional Global Ocean Observing System) dataset [47]. Observations for data assimilation include radiosonde and surface measurements, satellite-retrieved sea surface winds, and radiation brightness temperatures from AMSU-A/B (Advanced Microwave Sounding Unit A/B), HIRS-3/4 (High Resolution Infrared Radiation Sounder 3/4) and MHS (Microwave Humidity Sounder) carried by satellites [48].…”

Section: Datamentioning

confidence: 99%

A Comparison between 3DVAR and EnKF for Data Assimilation Effects on the Yellow Sea Fog Forecast

Gao

Yang

2018

Atmosphere

View full text Add to dashboard Cite

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.

show abstract

Section: Datamentioning

confidence: 99%