The role of ocean‐atmosphere interaction in <scp>T</scp>yphoon <scp>S</scp>inlaku (2008) using a regional coupled data assimilation system

Wada, Akiyoshi; Kunii, Masaru

doi:10.1002/2017jc012750

Cited by 13 publications

(13 citation statements)

References 49 publications

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“…

W ((), u) = 3.8 \times 10^{- 6} u^{3.4}

In equation , the whitecap coverage could exceed 100% for wind speeds above 40 m/s, which is consistent with Komori et al (). The coupled model with the parameterization of Bao et al () has been used for numerical simulations regarding TCs, and the results were verified by using best track data (e.g., Wada, , ; Wada & Kunii, ).…”

Section: Methodsmentioning

confidence: 96%

“…It should be noted that the parameterization of Taylor and Yelland () was not verified when surface wind speeds exceed 25 m/s. However, the NHM and its atmosphere‐wave‐ocean coupled model have been used for many TC cases to understand roles of the ocean on TC predictions (e.g., Wada, , ; Wada et al, , , , ; Wada & Kunii, ). The present study uses the NHM and the coupled model to maintain consistency with these previous studies.…”

Section: Methodsmentioning

confidence: 99%

“…Computational and observational errors in the estimation of atmospheric and oceanic fields adversely affect the quantification of air‐sea interactions and result in the uncertainty of numerical TC simulations. Better understanding of the effects of atmospheric and oceanic conditions and air‐sea interfacial processes on TC simulations is needed to improve the accuracy of TC predictions, but to our knowledge there have been only a few related studies with relevance to TC forecasting (Kunii et al, ; Kunii & Miyoshi, ; Torn, ; Wada & Kunii, ).…”

Section: Introductionmentioning

confidence: 99%

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Effect of Air‐Sea Environmental Conditions and Interfacial Processes on Extremely Intense Typhoon Haiyan (2013)

2018

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Typhoon Haiyan (2013) intensified rapidly and reached a central pressure of 895 hPa over the warm northwestern Pacific. To clarify why Haiyan became such an extraordinarily intense tropical cyclone (TC), we investigated the impacts of atmospheric and oceanic conditions and air‐sea interfacial processes on Haiyan's intensity. We performed ensemble experiments with many different initial oceanic conditions and a 7‐km‐mesh atmosphere‐wave‐ocean coupled model. We also performed sensitivity experiments to examine the impacts of sea spray and use of a 2‐km‐mesh horizontal resolution. Also, analogous experiments were performed with Typhoon Mike (1990), which followed a similar track but was less intense than Haiyan. For both TCs, ocean coupling helped alleviate uncertainty in the simulated intensities caused by different preexisting oceanic conditions. Increases in air‐sea latent heat fluxes helped increase the intensity of both TCs although the effect of sea spray depended on whitecap coverage. Preexisting oceanic conditions and air‐sea interfacial processes directly affected the secondary circulation via changes in latent heat fluxes and near‐surface moisture influxes. However, the 2‐km‐mesh resolution improved only Haiyan's simulation; the mean latent heat flux over the strength area was insufficient to simulate the intensity. It was easterly winds and specific humidity at 850 hPa that determined the small size, rapid translation, less sea surface cooling, and small decreases in latent heat fluxes beneath the frictional convergence area of simulated Haiyan. Understanding of the effect of preexisting oceanic conditions and air‐sea interfacial processes on simulated TCs will serve as a guideline for improvement of the atmosphere‐wave‐ocean coupled model.

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“…

W ((), u) = 3.8 \times 10^{- 6} u^{3.4}

Section: Methodsmentioning

confidence: 96%

Section: Methodsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Effect of Air‐Sea Environmental Conditions and Interfacial Processes on Extremely Intense Typhoon Haiyan (2013)

2018

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“…All components are coupled together using the Earth System Modeling Framework (Hill et al, 2004) and the Modeling Analysis and Prediction Layer interface layer (Suarez et al, 2007). Various operational centers are developing Coupled AODAS systems (Brassington et al, 2015;Dee et al, 2014), in which different components (e.g., atmosphere and ocean) of the Earth system are analyzed separately (Laloyaux et al, 2016;Lea et al, 2015) or simultaneously (Sluka et al, 2016;Wada & Kunii, 2017). The GEOS-S2S Coupled AODAS relies on an precomputed near-real-time atmospheric analysis and performs an analysis of the ocean state.…”

Section: Description Of the Coupled Model Data Assimilation System mentioning

confidence: 99%

GEOS‐S2S Version 2: The GMAO High‐Resolution Coupled Model and Assimilation System for Seasonal Prediction

et al. 2020

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The Global Modeling and Assimilation Office (GMAO) has recently released a new version of the Goddard Earth Observing System (GEOS) Subseasonal to Seasonal prediction (S2S) system, GEOS‐S2S‐2, that represents a substantial improvement in performance and infrastructure over the previous system. The system is described here in detail, and results are presented from forecasts, climate equillibrium simulations, and data assimilation experiments. The climate or equillibrium state of the atmosphere and ocean showed a substantial reduction in bias relative to GEOS‐S2S‐1. The GEOS‐S2S‐2 coupled reanalysis also showed substantial improvements, attributed to the assimilation of along‐track absolute dynamic topography. The forecast skill on subseasonal scales showed a much improved prediction of the Madden‐Julian Oscillation in GEOS‐S2S‐2, and on a seasonal scale the tropical Pacific forecasts show substantial improvement in the east and comparable skill to GEOS‐S2S‐1 in the central Pacific. GEOS‐S2S‐2 anomaly correlations of both land surface temperature and precipitation were comparable to GEOS‐S2S‐1 and showed substantially reduced root‐mean‐square error of surface temperature. The remaining issues described here are being addressed in the development of GEOS‐S2S Version 3, and with that system GMAO will continue its tradition of maintaining a state‐of‐the‐art seasonal prediction system for use in evaluating the impact on seasonal and decadal forecasts of assimilating newly available satellite observations, as well as evaluating additional sources of predictability in the Earth system through the expanded coupling of the Earth system model and assimilation components.

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“…SST is an example of essential variables derived from remote sensing data [2][3][4][5][6], which play a critical role in climate models as well as numerical weather forecasts. SST field time series are for instance among the key satellite-derived data assimilated in ocean-atmosphere models [7,8] and hurricane dynamics [9]. Spaceborne sensors provide invaluable data to reconstruct satellite-derived high-resolution SST fields (typically, up to a few kilometers) on a global scale.…”

Section: Introductionmentioning

confidence: 99%

Spatio-Temporal Interpolation of Cloudy SST Fields Using Conditional Analog Data Assimilation

et al. 2018

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The ever increasing geophysical data streams pouring from earth observation satellite missions and numerical simulations along with the development of dedicated big data infrastructure advocate for truly exploiting the potential of these datasets, through novel data-driven strategies, to deliver enhanced satellite-derived gapfilled geophysical products from partial satellite observations. We here demonstrate the relevance of the analog data assimilation (AnDA) for an application to the reconstruction of cloud-free level-4 gridded Sea Surface Temperature (SST). We propose novel AnDA models which exploit auxiliary variables such as sea surface currents and significantly reduce the computational complexity of AnDA. Numerical experiments benchmark the proposed models with respect to state-of-the-art interpolation techniques such as optimal interpolation and EOF-based schemes. We report relative improvement up to 40%/50% in terms of RMSE and also show a good parallelization performance, which supports the feasibility of an upscaling on a global scale.filled and the fine-scale variability of the SST fields. Physically-driven data assimilation models [20] may outperform OI if relevant dynamical priors can be defined [21]. The trade-off to be considered between the complexity and genericity of this physical prior remains however complex, especially when considering the assimilation of a single sea surface tracer as SST.Besides model-driven schemes, the ever increasing availability of satellite-derived data and of simulation data from high-resolution ocean models has paved the way for the development of data-driven methods. EOF-based models were among the early and perhaps most popular data-driven methods applied to the reconstruction of SST fields from cloudy SST data [14,15,22] as well of other sea surface tracers such as ocean colour [22]. EOF-based approaches are particularly appealing for ocean remote sensing as they relate to a model of the covariance structure of the considered fields and may adapt to any type of geometry of missing data and interpolation grid. Their use is also motivated by their ability to decompose the spatiotemporal variability of the sea surface fields according to different modes, which may be interpreted geophysically. A renewed interest can also be noticed for analog schemes and applications to forecasting and assimilation issues [23,24]. Analog schemes, proposed a long time ago in geoscience [25], rely on the idea that the dynamics of a given system may repeat to some extent. Given a set of previously observed or analysed data, one may retrieve examples similar to a current state in this set, such that the future of this current state may be forecasted from the known evolution of these similar situations. The lack of large-scale dataset along with the computational complexity of analog methods has long limited their applicability. In this context, we recently introduced the analog data assimilation (AnDA) and demonstrated its relevance for the reconstruction of complex dynamical systems fo...

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The role of ocean‐atmosphere interaction in Typhoon Sinlaku (2008) using a regional coupled data assimilation system

Cited by 13 publications

References 49 publications

Effect of Air‐Sea Environmental Conditions and Interfacial Processes on Extremely Intense Typhoon Haiyan (2013)

Effect of Air‐Sea Environmental Conditions and Interfacial Processes on Extremely Intense Typhoon Haiyan (2013)

GEOS‐S2S Version 2: The GMAO High‐Resolution Coupled Model and Assimilation System for Seasonal Prediction

Spatio-Temporal Interpolation of Cloudy SST Fields Using Conditional Analog Data Assimilation

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