Evaluation of wind forecasts over Svalbard using the high-resolution Polar WRF with 3DVAR

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Abstract

In this study, the performance of wind forecasts over Svalbard, located between the Arctic Ocean and the Norwegian Sea, was evaluated using the Polar Weather Research and Forecasting (PWRF) model and three-dimensional variational data assimilation (DA) system. The forecasts of the analysis–forecast cycling experiment using the PWRF 3DVAR were compared with those of the cold start experiment using reanalysis as the initial condition. Three strong wind cases that occurred during January and February 2011–2012 were selected, where polar lows were generated on the east coast of Greenland and generated a wind speed above 20 m s−1 in Svalbard. The wind speed forecasts for both cycling and cold start experiments were similar to the highest 10-minute average wind speed for the last 1 hour (HAW). The average root mean square error (RMSE) of the forecasts in the cold start experiment from HAW was 3.78 m s−1 for three cases and was greater than that in the cycling experiment. The forecast performance in the cycling experiment was comparable to, or even better than, that in the cold start experiment, which implies that the cycling system with DA is more useful than the cold start system in forecasting polar weather to support research activities.

Original languageEnglish
Pages (from-to)471-489
Number of pages19
JournalArctic, Antarctic, and Alpine Research
Volume51
Issue number1
DOIs
Publication statusPublished - 2019 Jan 1

Bibliographical note

Funding Information:
This work was supported by the Korea Polar Research Institute (KOPRI, PN19081) and a National Research Foundation of Korea (NRF) grant funded by the South Korean government (Ministry of Science and ICT, Grant 2017R1E1A1A03070968). The simulations were mostly carried out by utilizing the supercomputer system supported by the National Center for Meteorological Supercomputer of Korea Meteorological Administration (KMA). The authors appreciate the reviewers? valuable comments. The authors also appreciate Dr. Keith Hines from Ohio State University for providing the Polar WRF model and the Norwegian Meteorological Institute for providing observation data through the eKlima site.

All Science Journal Classification (ASJC) codes

  • Global and Planetary Change
  • Ecology, Evolution, Behavior and Systematics
  • Earth-Surface Processes

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