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Enhancing the Satisfaction Value of User Group Using Meteorological Forecast Information

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Monday, 3 February 2014
Hall C3 (The Georgia World Congress Center )
In-Gyum Kim, National Institute of Meteorological Research, Seoul, South Korea; and J. Y. kim and B. J. Kim

Handout (395.8 kB)

The providers of meteorological information want to know the level of satisfaction of forecast users with their services. To provide better service, meteorological communities of each nation are administering a survey on satisfaction of forecast users. The KMA (Korea Meteorological Administration) is conducting a research on this survey annually since 2008 as well.

However, including the government, institution, weather company, and etc., most researchers provided forecast users with simple questionnaires using the Likert scale to review their satisfaction, and the respondents had to choose one answer among different options of satisfaction levels. For this reason, the result of this particular survey has low explanation power and is difficult to use in developing a strategy for forecast service, because the answer has unidimensional implication and the space between each option cannot be equidistant. Hence, it is difficult to measure the true attitudes of the respondents. So, we attempted to quantitatively evaluate user satisfaction by modifying the cost-loss model of Value Score concept that many researchers had used to estimate a value of forecast information.

Instead of cost-loss concept, we applied satisfaction-dissatisfaction concept to the 22 contingency table and estimated satisfaction value of 24h precipitation forecasts in Shanghai, China (2003-2005) and Seoul, Korea (2003-2005 and 2010-2012). Shanghai had only deterministic forecast but both deterministic and probabilistic forecast was collected in Seoul. Moreover, not only the individual satisfaction value of forecast but the user group's satisfaction value was evaluated.

As for the results, it is effective to enhance forecast accuracy to improve the satisfaction value of deterministic forecast user group. However, in the case of probabilistic forecast, it is important to know the level of dissatisfaction of user group and distribution of probability threshold of forecast users. Finally, even though accuracy of the forecast is high compared to the other forecast, not all deterministic (probabilistic) forecast produce high satisfaction level to user group. These results can help meteorological communities to search for a solution which can provide better satisfaction value to forecast users.

Keywords: meteorological forecast, value of forecast, 22 contingency table, satisfaction value of group