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@ARTICLE{Rttgers:910523,
      author       = {Rüttgers, Mario and Soohwan, Jeon and Sangseung, Lee and
                      Donghyun, You},
      title        = {{P}rediction of {T}yphoon {T}rack and {I}ntensity {U}sing a
                      {G}enerative {A}dversarial {N}etwork {W}ith {O}bservational
                      and {M}eteorological {D}ata},
      journal      = {IEEE access},
      volume       = {10},
      issn         = {2169-3536},
      address      = {New York, NY},
      publisher    = {IEEE},
      reportid     = {FZJ-2022-03906},
      pages        = {48434-48446},
      year         = {2022},
      abstract     = {To save lives and reduce damage from the destructive
                      impacts of a typhoon, an accurate and fast forecast method
                      is highly demanded. Particularly, predictions for short lead
                      times, known as nowcasting, rely on fast forecasts allowing
                      immediate emergency plannings in the affected areas. In this
                      paper, we propose a generative adversarial network that
                      operates on a single graphics processing unit, to predict
                      both the track and intensity of typhoons for short lead
                      times within fractions of a second. To investigate the
                      effects of meteorological variables on typhoon forecasts, we
                      conducted a parameter study for 6-h track predictions. The
                      results of the study indicate that learning velocity,
                      temperature, pressure, and humidity along with satellite
                      images have positive effects on prediction accuracy. To
                      address the limited access to observational data and
                      facilitate predictions for 12-h intervals, we replaced
                      satellite images with reanalysis data of the total cloud
                      cover and vorticity fields. This replacement led to an
                      increase in data from 76 to 757 typhoons, and it reduced the
                      error of the 6-h track forecasts by $23.5\%.$ The best
                      combination of the parameter study yields track predictions
                      in intervals of 6 and 12 h with the corresponding averaged
                      absolute errors of 44.5 and 68.7 km. Typhoon intensities are
                      predicted by extracting information from generated velocity
                      fields with averaged hit rates of $87.3\%$ and $83.2\%$ for
                      6- and 12-h interval forecasts, respectively. For typhoons
                      after 1994, tracks and intensities for 12-h intervals are
                      compared to forecasts from the Joint Typhoon Warning Center
                      and Regional Specialized Meteorological Center Tokyo.},
      cin          = {JSC},
      ddc          = {621.3},
      cid          = {I:(DE-Juel1)JSC-20090406},
      pnm          = {5111 - Domain-Specific Simulation $\&$ Data Life Cycle Labs
                      (SDLs) and Research Groups (POF4-511) / HDS LEE - Helmholtz
                      School for Data Science in Life, Earth and Energy (HDS LEE)
                      (HDS-LEE-20190612)},
      pid          = {G:(DE-HGF)POF4-5111 / G:(DE-Juel1)HDS-LEE-20190612},
      typ          = {PUB:(DE-HGF)16},
      UT           = {WOS:000793810600001},
      doi          = {10.1109/ACCESS.2022.3172301},
      url          = {https://juser.fz-juelich.de/record/910523},
}