Development of a Convection-Permitting Air-Sea-Coupled Ensemble Data Assimilation System for Tropical Cyclone Prediction

Xingchao Chen, Fuqing Zhang

Research output: Contribution to journalArticle

Abstract

A regional-scale fully coupled data assimilation (DA) system based on the ensemble Kalman filter is developed for a high-resolution coupled atmosphere-ocean model. Through the flow-dependent covariance both within and across the oceanic and atmospheric domains, the fully coupled DA system is capable of updating both atmospheric and oceanic state variables simultaneously by assimilating either atmospheric and/or oceanic observations. The potential impacts of oceanic observations, including sea-surface temperature, sea-surface height anomaly, and sea-surface current, in addition to the observation of the minimum surface pressure at the storm center (HPI), on tropical cyclone analysis and prediction are examined through observing system simulation experiments of Hurricane Florence (2018). Results show that assimilation of oceanic observations not only resulted in better analysis and forecast of the oceanic variables but also considerably reduced analysis and forecast errors in the atmospheric fields, including the intensity and structure of Florence. Compared to weakly coupled DA in which the analysis update is performed separately for the atmospheric and oceanic domains, fully coupled DA reduces the forecast errors of tropical cyclone track and intensity. Results show promise in potential further improvement in tropical cyclone prediction through assimilation of both atmospheric and oceanic observations using the ensemble-based fully coupled DA system.

Original languageEnglish (US)
Pages (from-to)3474-3496
Number of pages23
JournalJournal of Advances in Modeling Earth Systems
Volume11
Issue number11
DOIs
StatePublished - Nov 1 2019

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tropical cyclone
data assimilation
convection
air
Air
prediction
Hurricanes
Kalman filters
sea surface height
surface pressure
Kalman filter
hurricane
sea surface
sea surface temperature
sea
Convection
anomaly
analysis
atmosphere
ocean

All Science Journal Classification (ASJC) codes

  • Global and Planetary Change
  • Environmental Chemistry
  • Earth and Planetary Sciences(all)

Cite this

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title = "Development of a Convection-Permitting Air-Sea-Coupled Ensemble Data Assimilation System for Tropical Cyclone Prediction",
abstract = "A regional-scale fully coupled data assimilation (DA) system based on the ensemble Kalman filter is developed for a high-resolution coupled atmosphere-ocean model. Through the flow-dependent covariance both within and across the oceanic and atmospheric domains, the fully coupled DA system is capable of updating both atmospheric and oceanic state variables simultaneously by assimilating either atmospheric and/or oceanic observations. The potential impacts of oceanic observations, including sea-surface temperature, sea-surface height anomaly, and sea-surface current, in addition to the observation of the minimum surface pressure at the storm center (HPI), on tropical cyclone analysis and prediction are examined through observing system simulation experiments of Hurricane Florence (2018). Results show that assimilation of oceanic observations not only resulted in better analysis and forecast of the oceanic variables but also considerably reduced analysis and forecast errors in the atmospheric fields, including the intensity and structure of Florence. Compared to weakly coupled DA in which the analysis update is performed separately for the atmospheric and oceanic domains, fully coupled DA reduces the forecast errors of tropical cyclone track and intensity. Results show promise in potential further improvement in tropical cyclone prediction through assimilation of both atmospheric and oceanic observations using the ensemble-based fully coupled DA system.",
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Development of a Convection-Permitting Air-Sea-Coupled Ensemble Data Assimilation System for Tropical Cyclone Prediction. / Chen, Xingchao; Zhang, Fuqing.

In: Journal of Advances in Modeling Earth Systems, Vol. 11, No. 11, 01.11.2019, p. 3474-3496.

Research output: Contribution to journalArticle

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