TY - JOUR
T1 - Dynamical structures of cross-domain forecast error covariance of a simulated tropical cyclone in a convection-permitting coupled atmosphere-Ocean Model
AU - Chen, Xingchao
AU - Nystrom, Robert G.
AU - Davis, Christopher A.
AU - Zarzycki, Colin M.
N1 - Funding Information:
This research is sponsored by ONR Grant N000141812517, a NOAA NGGPS grant through University of Michigan Subcontract 3004628721, and the Office of Science of DOE Biological and Environmental Research as part of the Regional and Global Modeling and Analysis program. Author Nystrom is supported by NASA Grant 17-EARTH17F-184 under the NASA Earth and Space Science Fellowship Program. Author Davis is supported by theNationalCenter for Atmospheric Research, which is a major facility sponsored by the National Science Foundation under Cooperative Agreement 1852977. Review comments by two anonymous reviewers were beneficial and greatly appreciated. The authors acknowledge the Texas Advanced Computing Center (TACC) at The University of Texas at Austin for providing computing and storage resources that have contributed to the research results reported within this paper. The GEFS analysis used in the study can be downloaded from https://www.ncdc.noaa.gov/data-access/modeldata/ model-datasets/global-ensemble-forecast-system-gefs.
Funding Information:
Acknowledgments. This research is sponsored by ONR Grant N000141812517, a NOAA NGGPS grant through University of Michigan Subcontract 3004628721, and the Office of Science of DOE Biological and Environmental Research as part of the Regional and Global Modeling and Analysis program. Author Nystrom is supported by NASA Grant 17-EARTH17F-184 under the NASA Earth and Space Science Fellowship Program. Author Davis is supported by the National Center for Atmospheric Research, which is a major facility sponsored by the National Science Foundation under Cooperative Agreement 1852977. Review comments by two anonymous reviewers were beneficial and greatly appreciated. The authors acknowledge the Texas Advanced Computing Center (TACC) at The University of Texas at Austin for providing computing and storage resources that have contributed to the research results reported within this paper. The GEFS analysis used in the study can be downloaded from https://www.ncdc.noaa.gov/data-access/model-data/model-datasets/global-ensemble-forecast-system-gefs.
Publisher Copyright:
© 2020 American Meteorological Society. All rights reserved.
PY - 2020/12
Y1 - 2020/12
N2 - Understanding the dynamics of the flow-dependent forecast error covariance across the air-sea interface is beneficial toward revealing the potential influences of strongly coupled data assimilation on tropical cyclone (TC) initialization in coupled models, and the fundamental dynamics associated with TC air-sea interactions.A200-member ensemble of convection-permitting forecasts from a coupled atmosphere-ocean regional model is used to investigate the forecast error covariance across the oceanic and atmospheric domains during the rapid intensification of Hurricane Florence (2018). Forecast uncertainties in both atmospheric and oceanic domains, from an Eulerian perspective, increase with forecast lead time, mainly from TC displacement errors. In a storm-relative framework, the ensemble forecast uncertainties in both domains are predominantly caused by differences in the simulated storm intensity and structure. The largest ensemble spread in the atmospheric pressure, temperature, and wind fields can be found within the TC inner-core region. Alternatively, the largest ensemble spread in the upper-ocean currents and temperature fields are located along the cold wake behind the storm. Cross-domain ensemble correlations between simulated atmospheric (oceanic) observations and oceanic (atmospheric) state variables in the storm-relative coordinates are highly anisotropic, variable dependent, and ultimately driven by the dynamics of TC air-sea interactions. Meaningful and dynamically consistent cross-domain ensemble correlations suggest that it is possible to use atmospheric and oceanic observations to simultaneously update state variables associated with the coupled ocean-atmosphere prediction of TCs using strongly coupled data assimilation. Sensitivity experiments demonstrate that at least 60-80 ensemble members are required to represent physically consistent cross-domain correlations and minimize sampling errors.
AB - Understanding the dynamics of the flow-dependent forecast error covariance across the air-sea interface is beneficial toward revealing the potential influences of strongly coupled data assimilation on tropical cyclone (TC) initialization in coupled models, and the fundamental dynamics associated with TC air-sea interactions.A200-member ensemble of convection-permitting forecasts from a coupled atmosphere-ocean regional model is used to investigate the forecast error covariance across the oceanic and atmospheric domains during the rapid intensification of Hurricane Florence (2018). Forecast uncertainties in both atmospheric and oceanic domains, from an Eulerian perspective, increase with forecast lead time, mainly from TC displacement errors. In a storm-relative framework, the ensemble forecast uncertainties in both domains are predominantly caused by differences in the simulated storm intensity and structure. The largest ensemble spread in the atmospheric pressure, temperature, and wind fields can be found within the TC inner-core region. Alternatively, the largest ensemble spread in the upper-ocean currents and temperature fields are located along the cold wake behind the storm. Cross-domain ensemble correlations between simulated atmospheric (oceanic) observations and oceanic (atmospheric) state variables in the storm-relative coordinates are highly anisotropic, variable dependent, and ultimately driven by the dynamics of TC air-sea interactions. Meaningful and dynamically consistent cross-domain ensemble correlations suggest that it is possible to use atmospheric and oceanic observations to simultaneously update state variables associated with the coupled ocean-atmosphere prediction of TCs using strongly coupled data assimilation. Sensitivity experiments demonstrate that at least 60-80 ensemble members are required to represent physically consistent cross-domain correlations and minimize sampling errors.
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U2 - 10.1175/MWR-D-20-0116.1
DO - 10.1175/MWR-D-20-0116.1
M3 - Article
AN - SCOPUS:85099286636
SN - 0027-0644
VL - 149
SP - 41
EP - 63
JO - Monthly Weather Review
JF - Monthly Weather Review
IS - 1
ER -