EDITORIAL

Science of preparedness

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Science  15 Sep 2017:
Vol. 357, Issue 6356, pp. 1073
DOI: 10.1126/science.aap9025

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  • Historical information is available additionally for disaster mitigation.
    • Shino Iwami, Project Researcher, University of Jyväskylä

    Jeremy Berg is worrying about the damage of the hurricane catastrophe and claimed the importance of disaster prediction in the Editorial “Science of preparedness” (1). Several typhoons come to Japan every year, and Japanese people must prepare various natural disasters such as earthquakes, eruptions of volcanoes, tsunami, heavy snow, and others. Thus, they rely on the Japan Meteorological Agency's forecast, and the government prepares daily necessities for evacuees in schools and public facilities.
    In addition, ancestor’s messages about disasters remain in the modern. They are geographical names (2) and shrine’s locations (3). For example, an inland that is named with a kanji of island may be landfilled and weak against disasters (2). It is considered logically that current shrines remain after shrines in dangerous locations are washed away by disasters. A shrine with Piloti also remains because water flowed under the floor (3). The historical information will be useful to know safe or dangerous places after people watch weather forecasts.
    However, despite the message from the ancestors, humans have shifted their living bases to dangerous lands seeking the convenience. Furthermore, due to the control of administrators such as mergers of municipalities, it has been eliminated the ancestor's messages to change the location names.
    In Europe, there are disaster memories such as a broken structure related to 1755 Lisbon earthquake in the church of Ou...

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    Competing Interests: None declared.
  • Machine learning should be used for improving weather forecast.

    Jeremy Berg wrote an article entitled " Science of preparedness" published in Science (1). It is well known that better models should be used for weather forecasting. Fig.1 illustrates the comparison of weather forecasting errors between eta (U.S. North American Model), ecmwf (European Centre for Medium Range Weather Forecasting), cmcglb (Canadian GEM GDPS), jma (Japan Meteorological Agency) (2). The U.S. North American Model has been the worst in the existing models. Better models use integrated forecast system like ecmwf. This eta fact (the worst model) has been caused not only by Science but also by the operated science policy. The Japan Meteorological Agency' model (jma) has been the second worst in the existing models because of the similar science policy problem. In order to further improve the weather forecasting models, machine learning should be used instead of using man-made weather forecast models.

    References:
    1. Jeremy Berg, Science of preparedness, Science 15 Sep 2017: Vol. 357, Issue 6356, pp. 1073
    2. Weather forecast errors comparison (24 hours, 120 hours)
    https://weather.gc.ca/verification/monthly_ts_e.html

    Competing Interests: None declared.
  • RE: Simple preparedness
    • Silvio Pitlik, Physician, Visiting physician, Weizmann Institute of Science
    • Other Contributors:
      • Nina Pitlik, Pharmacologist, The Academic College of Tel Aviv-Yaffo

    In hurricane prone areas, houses should include a robust steel or concrete skeleton and should stand several feet above maximal expected water levels during flooding. Following these simple rules may free us from complex and cumbersome computational calculations and difficult to take decisions by national and local authorities.

    Competing Interests: None declared.

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