Forecasting a Large Number of Tropical Cyclone Intensities around Japan Using a High-Resolution Atmosphere–Ocean Coupled Model

Ito, Kosuke; Kuroda, Tohru; Saitō, Kazuo; Wada, Akiyoshi

doi:10.1175/waf-d-14-00034.1

Cited by 50 publications

(33 citation statements)

References 34 publications

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“…For simplicity, the forecast time of x hour is henceforth described as FTx. A tracking algorithm for the simulated TC is the same as in Ito et al (2015), except that the TC center is defined by the method of Braun (2002). Although we focused on the TC position and intensity, the analysis increment and the precipitation forecast skill are described in the supplemental materials.…”

Section: Experimental Setup and Validationmentioning

confidence: 99%

Analysis and Forecast Using Dropsonde Data from the Inner-Core Region of Tropical Cyclone Lan (2017) Obtained during the First Aircraft Missions of T-PARCII

Ito

Yamada

Yamaguchi³

et al. 2018

SOLA

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The inner core of Tropical Cyclone Lan was observed on 21− 22 October 2017 by GPS dropsondes during the first aircraft missions of the Tropical Cyclones-Pacific Asian Research Campaign for the Improvement of Intensity Estimations/Forecasts (T-PARCII). To evaluate the impact of dropsondes on forecast skill, 12 36-h forecasts were conducted using a Japan Meteorological Agency non-hydrostatic model (JMA-NHM) with a JMA-NHM-based mesoscale four-dimensional data assimilation (DA) system. Track forecast skill improved over all forecast times with the assimilation of the dropsonde data. The improvement rate was 8−16% for 27−36-h forecasts. Minimum sea level pressure (Pmin) forecasts were generally degenerated (improved) for relatively short-term (long-term) forecasts by adding the dropsonde data, and maximum wind speed (Vmax) forecasts were degenerated. Some of the changes in the track and Vmax forecasts were statistically significant at the 95% confidence level. It is notable that the dropsonde-derived estimate of Pmin was closer to the realtime analysis by the Regional Specialized Meteorological Center (RSMC) Tokyo than the RSMC Tokyo best track analysis. The degeneration in intensity forecast skill due to uncertainties in the best track data is discussed.

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Section: Experimental Setup and Validationmentioning

confidence: 99%

Analysis and Forecast Using Dropsonde Data from the Inner-Core Region of Tropical Cyclone Lan (2017) Obtained during the First Aircraft Missions of T-PARCII

Ito

Yamada

Yamaguchi³

et al. 2018

SOLA

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“…As a sub subject of one of the five strategic fields of SPIRE, "Ultra-high Precision Meso-Scale Weather Prediction" has been conducted ) with three subthemes: 1) the development of cloud-resolving fourdimensional data assimilation systems, 2) the development and validation of a cloud-resolving ensemble analysis and forecast system, and 3) basic research using very high-resolution atmospheric models. Several of the published results (e.g., Kobayashi et al 2013;Kunii 2014a, b;Chen et al 2015a, b;Duc et al 2015;Ito, K. et al 2015;Seko et al 2015;Ito and Niino 2016) are on the project website (https://www. jamstec.go.jp/hpci-sp/en/strategy/mswp.html).…”

Section: Introductionmentioning

confidence: 99%

Ultra-High-Resolution Numerical Weather Prediction with a Large Domain Using the K Computer: A Case Study of the Izu Oshima Heavy Rainfall Event on October 15-16, 2013

Oizumi

Saitō

Ito³

et al. 2018

Journal of the Meteorological Society of Japan

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An intense rainband associated with Typhoon 1326 (Wipha) induced a fatal debris flow on Izu Oshima, Japan, on October 15 -16, 2013. This rainband formed along a local front between the southeasterly humid warm air around the typhoon and the northeasterly cold air from the Kanto Plain. In this paper, the Japan Meteorological Agency Nonhydrostatic Model was optimized for the "K computer", and ultra-high-resolution (500 -250 m grid spacing) numerical simulations of the rainband with a large domain were conducted.Two of main factors that affect a numerical weather prediction (NWP) model, (1) grid spacing and (2) planetary boundary layer (PBL) schemes [Mellor-Yamada-Nakanishi-Niino (MYNN) and Deardorff (DD)], were investigated. Experiments with DD (Exps_DD: grid spacings of 2 km, 500 m, and 250 m) showed better reproducibility of the rainband position than experiments with MYNN (Exps_MYNN: grid spacings of 5 km, 2 km, and 500 m). Exps_DD simulated distinct convective-scale up/downdraft pairs on the southeast/northwest sides of the front, whereas those of Exps_MYNN were not clear. Exps_DD yielded stronger cold pools near the surface than did Exps_MYNN. These differences in the boundary layer structures likely had a large impact on the position of the front and the associated rainband. Exps_DD with the 500-m grid spacing showed the best precipitation performance according to the Fractions Skill Score.To check other factors which influence precipitation forecast, model domain sizes, lateral boundary conditions in nesting simulations, and terrain representations were investigated. In the small domain experiments, the rainCorresponding author: Tsutao Oizumi, 3173-25 Showamachi,

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“…Moreover, a 20-km mesh atmospheric model is currently used in JMA as a base model. The use of a high-resolution atmosphere-ocean coupled model can enhance accuracy as exemplified by Ito et al (2015) for TC intensity forecasts around Japan.…”

Section: Discussionmentioning

confidence: 99%

Errors in Tropical Cyclone Intensity Forecast by RSMC Tokyo and Statistical Correction Using Environmental Parameters

Ito

2016

SOLA

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Tropical cyclone (TC) intensity forecasts issued by the Regional Specialized Meteorological Centre (RSMC) Tokyo -Typhoon Center are systematically compiled to analyze the long-term behavior of errors and to explore the potential for improvement in the forecast accuracy using a statistical correction approach. In this study, a dataset is constructed from annual statistics and every single forecast listed on annual reports on the activities of the RSMC Tokyo. This study found that (1) the accuracy of annual mean forecast has not improved over 26 years and that (2) forecast errors tend to be larger in the rapidly developing TCs. Further analysis reveals that recent forecast output (2008−2014) contains biases associated with the magnitude of the vertical shear of horizontal wind, convective available potential energy, upper ocean temperature, maximum potential intensity (MPI) and ocean coupling potential intensity (OC_PI). To evaluate the adverse effect of such biases in the current forecast system, a simple statistical correction is applied. It improved TC intensity forecast by 7.8−16.9% when an OC_PI is employed.(Citation: Ito, K., 2016: Errors in tropical cyclone intensity forecast by RSMC Tokyo and statistical correction using environmental parameters. SOLA, 12, 247−252,

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Forecasting a Large Number of Tropical Cyclone Intensities around Japan Using a High-Resolution Atmosphere–Ocean Coupled Model

Cited by 50 publications

References 34 publications

Analysis and Forecast Using Dropsonde Data from the Inner-Core Region of Tropical Cyclone Lan (2017) Obtained during the First Aircraft Missions of T-PARCII

Analysis and Forecast Using Dropsonde Data from the Inner-Core Region of Tropical Cyclone Lan (2017) Obtained during the First Aircraft Missions of T-PARCII

Ultra-High-Resolution Numerical Weather Prediction with a Large Domain Using the K Computer: A Case Study of the Izu Oshima Heavy Rainfall Event on October 15-16, 2013

Errors in Tropical Cyclone Intensity Forecast by RSMC Tokyo and Statistical Correction Using Environmental Parameters

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