Data assimilation of ocean surface waves using Sentinel-1 SAR during typhoon Malakas

Meng Sun*, Yongzeng Yang, Xunqiang Yin, Jianting Du

*Corresponding author for this work

Research output: Contribution to journalJournal articleResearchpeer-review

Abstract

In this study, a data assimilation system is constructed in a third generation ocean surface wave model, MASNUM-WAM, to improve wave simulations. The data assimilation system uses Ensemble Adjustment Kalman Filter (EAKF) method, which is based on dynamic sampling. Difference between 24 h-interval wave parameter fields during the period 7-day before and after assimilation time, is used to construct dynamic ensemble, which is an approximation to background error. Eight experiments are carried out during typhoon Malakas to investigate the impact of different assimilating wave parameters to the simulation errors of significant wave height (SWH). Wave spectrum observations from satellite Sentinel-1 SAR are used for data assimilation. SWH, peak wave period, mean wave direction and wave spectrum are adjusted simultaneously when an observation is available. Results show that the data assimilation system improves the simulation of SWH during typhoon Malakas.
Original languageEnglish
JournalInternational Journal of Applied Earth Observation and Geoinformation
Volume70
Pages (from-to)35-42
Number of pages8
ISSN0303-2434
DOIs
Publication statusPublished - 2018

Keywords

  • Wave assimilation
  • Sentinel-1 SAR
  • Typhoon

Cite this

@article{39483c2f81f94d79a9d41ffbe2f2417a,
title = "Data assimilation of ocean surface waves using Sentinel-1 SAR during typhoon Malakas",
abstract = "In this study, a data assimilation system is constructed in a third generation ocean surface wave model, MASNUM-WAM, to improve wave simulations. The data assimilation system uses Ensemble Adjustment Kalman Filter (EAKF) method, which is based on dynamic sampling. Difference between 24 h-interval wave parameter fields during the period 7-day before and after assimilation time, is used to construct dynamic ensemble, which is an approximation to background error. Eight experiments are carried out during typhoon Malakas to investigate the impact of different assimilating wave parameters to the simulation errors of significant wave height (SWH). Wave spectrum observations from satellite Sentinel-1 SAR are used for data assimilation. SWH, peak wave period, mean wave direction and wave spectrum are adjusted simultaneously when an observation is available. Results show that the data assimilation system improves the simulation of SWH during typhoon Malakas.",
keywords = "Wave assimilation, Sentinel-1 SAR, Typhoon",
author = "Meng Sun and Yongzeng Yang and Xunqiang Yin and Jianting Du",
year = "2018",
doi = "10.1016/j.jag.2018.04.004",
language = "English",
volume = "70",
pages = "35--42",
journal = "International Journal of Applied Earth Observation and Geoinformation",
issn = "0303-2434",
publisher = "Elsevier",

}

Data assimilation of ocean surface waves using Sentinel-1 SAR during typhoon Malakas. / Sun, Meng; Yang, Yongzeng; Yin, Xunqiang; Du, Jianting.

In: International Journal of Applied Earth Observation and Geoinformation, Vol. 70, 2018, p. 35-42.

Research output: Contribution to journalJournal articleResearchpeer-review

TY - JOUR

T1 - Data assimilation of ocean surface waves using Sentinel-1 SAR during typhoon Malakas

AU - Sun, Meng

AU - Yang, Yongzeng

AU - Yin, Xunqiang

AU - Du, Jianting

PY - 2018

Y1 - 2018

N2 - In this study, a data assimilation system is constructed in a third generation ocean surface wave model, MASNUM-WAM, to improve wave simulations. The data assimilation system uses Ensemble Adjustment Kalman Filter (EAKF) method, which is based on dynamic sampling. Difference between 24 h-interval wave parameter fields during the period 7-day before and after assimilation time, is used to construct dynamic ensemble, which is an approximation to background error. Eight experiments are carried out during typhoon Malakas to investigate the impact of different assimilating wave parameters to the simulation errors of significant wave height (SWH). Wave spectrum observations from satellite Sentinel-1 SAR are used for data assimilation. SWH, peak wave period, mean wave direction and wave spectrum are adjusted simultaneously when an observation is available. Results show that the data assimilation system improves the simulation of SWH during typhoon Malakas.

AB - In this study, a data assimilation system is constructed in a third generation ocean surface wave model, MASNUM-WAM, to improve wave simulations. The data assimilation system uses Ensemble Adjustment Kalman Filter (EAKF) method, which is based on dynamic sampling. Difference between 24 h-interval wave parameter fields during the period 7-day before and after assimilation time, is used to construct dynamic ensemble, which is an approximation to background error. Eight experiments are carried out during typhoon Malakas to investigate the impact of different assimilating wave parameters to the simulation errors of significant wave height (SWH). Wave spectrum observations from satellite Sentinel-1 SAR are used for data assimilation. SWH, peak wave period, mean wave direction and wave spectrum are adjusted simultaneously when an observation is available. Results show that the data assimilation system improves the simulation of SWH during typhoon Malakas.

KW - Wave assimilation

KW - Sentinel-1 SAR

KW - Typhoon

U2 - 10.1016/j.jag.2018.04.004

DO - 10.1016/j.jag.2018.04.004

M3 - Journal article

VL - 70

SP - 35

EP - 42

JO - International Journal of Applied Earth Observation and Geoinformation

JF - International Journal of Applied Earth Observation and Geoinformation

SN - 0303-2434

ER -