Strongly coupled assimilation of a hypothetical ocean current observing network within a regional ocean-atmosphere coupled model: an OSSE case study of typhoon hato
Publication available at
Author(s)
Phillipson, L
Li, Y
Toumi, R
Type
Journal Article
Abstract
The forecast of tropical cyclone (TC) intensity is a significant challenge. In this study, we showcase the impact of strongly coupled data assimilation with hypothetical ocean currents on analyses and forecasts of Typhoon Hato (2017). Several observation simulation system experiments (OSSE) were undertaken with a regional coupled ocean–atmosphere model. We assimilated combinations of (or individually) a hypothetical coastal current HF radar network, a dense array of drifter floats, and minimum sea level pressure. During the assimilation, instant updates of many important atmospheric variables (winds and pressure) are achieved from the assimilation of ocean current observations using the cross-domain error covariance, significantly improving the track and intensity analysis of Typhoon Hato. Relative to a control experiment (with no assimilation), the error of minimum pressure decreased by up to 13 hPa (4 hPa/57% on average). The maximum wind speed error decreased by up to 18 kt (5 kt/41% on average) (1 kt ≈ 0.5 m s−1). By contrast, weakly coupled implementations cannot match these reductions (10% on average). Although traditional atmospheric observations were not assimilated, such improvements indicate that there is considerable potential in assimilating ocean currents from coastal HF radar and surface drifters within a strongly coupled framework for intense landfalling TCs.
Date Issued
2021-05-01
Online Publication Date
2021-10-30T23:02:27Z
Date Acceptance
2021-05-01
ISSN
0027-0644
Publisher
American Meteorological Society
Start Page
1317
End Page
1336
Journal / Book Title
Monthly Weather Review
Volume
149
Issue
5
Copyright Statement
© 2021 American Meteorological Society. For information regarding reuse of this content and general copyright information, consult the AMS Copyright Policy (www.ametsoc.org/PUBSReuseLicenses).
Sponsor
Met Office
Identifier
https://journals.ametsoc.org/view/journals/mwre/149/5/MWR-D-20-0108.1.xml
http://gateway.webofknowledge.com/gateway/Gateway.cgi?GWVersion=2&SrcApp=PARTNER_APP&SrcAuth=LinksAMR&KeyUT=WOS:000698521400008&DestLinkType=FullRecord&DestApp=ALL_WOS&UsrCustomerID=1ba7043ffcc86c417c072aa74d649202
Grant Number
P106409
Subjects
Science & Technology
Physical Sciences
Meteorology & Atmospheric Sciences
Atmosphere-ocean interaction
Tropical cyclones
Numerical weather prediction
forecasting
Coupled models
Data assimilation
Regional models
ENSEMBLE DATA ASSIMILATION
ADAPTIVE COVARIANCE INFLATION
LAGRANGIAN DATA ASSIMILATION
TROPICAL CYCLONE
KALMAN FILTER
VARIATIONAL ASSIMILATION
NUMERICAL-SIMULATION
VELOCITY OBSERVATIONS
SURFACE OBSERVATIONS
RADAR OBSERVATIONS
Science & Technology
Physical Sciences
Meteorology & Atmospheric Sciences
Atmosphere-ocean interaction
Tropical cyclones
Numerical weather prediction
forecasting
Coupled models
Data assimilation
Regional models
ENSEMBLE DATA ASSIMILATION
ADAPTIVE COVARIANCE INFLATION
LAGRANGIAN DATA ASSIMILATION
TROPICAL CYCLONE
KALMAN FILTER
VARIATIONAL ASSIMILATION
NUMERICAL-SIMULATION
VELOCITY OBSERVATIONS
SURFACE OBSERVATIONS
RADAR OBSERVATIONS
0102 Applied Mathematics
0401 Atmospheric Sciences
Meteorology & Atmospheric Sciences
Publication Status
Published
Date Publish Online
2021-05-01