The economic value of weather forecasts for decision‐making problems in the profit/loss situation

Lee, Ki-Kwang; Lee, Joong-Woo

doi:10.1002/met.44

Cited by 20 publications

(14 citation statements)

References 19 publications

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“…Consequently, it is necessary to normalize the difference by the relative increase in expected satisfaction level to that of a perfect forecast. The concept of a normalized scale was established as a value score in previous studies (Wilks, ; Mylne, ; Lee and Lee, ). The relative increased satisfaction level, i.e., value score (VS) of forecasts can be defined as shown by Equation .…”

Section: Satisfaction Value Scores Of Individuals and Groups Using Prmentioning

confidence: 99%

“…The technical understanding of probabilistic precipitation forecasts is not an essential factor in the use of forecasts (Morss et al , ). Nevertheless, many researchers have strived to improve decision‐makers' confidence in weather forecasts through value evaluation of probabilistic forecast (Thompson and Brier, ; Murphy, ; Epstein and Murphy, ; Wilks, ; Mylne, ; Lee and Lee, ).…”

Section: Introductionmentioning

confidence: 99%

“…This research proved that the forecast value could be affected by the degree of satisfaction instead of costs, losses and profits at the aspect of customer relationship management (CRM). Lee and Lee () presented a continuous cost‐profit model to be applied to specific business decision‐making situations. Therefore, with the concept of Lee and Han (), the user's satisfaction for a weather service can be verified instead of the forecast value in terms of disaster prevention and loss reductions.…”

Section: Introductionmentioning

confidence: 99%

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The collective value of weather probabilistic forecasts according to public threshold distribution patterns

Kim

et al. 2013

Met. Apps

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ABSTRACT:The Korea Meteorological Administration has limited understanding of how users interpret probabilistic forecast information and use it in their decision-making processes. Thus, a survey was conducted among users to find out at which probability threshold for adverse weather they would react and take protective action to minimize the losses. According to previous studies, probability threshold depends on the type of users but they are in general higher than a 50% forecasts chance of adverse weather, even though research has shown that the forecast value is maximized when actions are taken for probability of adverse weather lower than 50%. A collective value score model, defined as a weighted sum of user's satisfaction and threshold distribution in a group of users, is introduced as a measure of total satisfaction of a user group. The collective value score model is applied to a set of precipitation probabilistic forecasts of Seoul, South Korea during 2002-2011. The results show that the collective value score can be improved if the range of users' probability thresholds is widened rather than improving the forecast accuracy.

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Section: Satisfaction Value Scores Of Individuals and Groups Using Prmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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The collective value of weather probabilistic forecasts according to public threshold distribution patterns

Kim

et al. 2013

Met. Apps

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“…This is especially evident for precipitation forecasts as some users need quantitative precipitation data [5] while others are interested in the rainfall threshold and spatial scale [6]. Special procedures should be used to provide profit-oriented enterprises with the necessary information to make proper decisions [7,8]. On the other hand, economic benefits from improved forecast accuracy should be estimated to justify the investments into the weather service [9].…”

Section: Introductionmentioning

confidence: 97%

Statistics of different public forecast products of temperature and precipitation in Estonia

Keevallik¹,

Spirina²,

Sula³

et al. 2014

Proc. Estonian Acad. Sci.

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Day (0600-1800 UTC) maximum and night (1800-0600 UTC) minimum temperature forecasts as well as prediction of the occurrence of precipitation are evaluated for different sites in Estonia: southern coast of the Gulf of Finland (Tallinn), West-Estonian archipelago (Kuressaare), and inland Estonia (Tartu). The forecasts are collected from Estonian weather service. Several traditional verification methods are used, first of all reliability (root mean square error (RMSE)) and validity (mean error (ME)). Detailed analysis is carried out by means of the contingency tables that enable the user to calculate percent correct, percent underestimated, and percent overestimated. The contingency tables enable the user to calculate conditional probabilities of the realizations of certain forecasts. The paper is user-oriented and does not analyse the forecast technique. On the other hand, attention is drawn to the subjectivity of such evaluation, as the results may depend on the forecast presentation style and/or on the choice of the features of the meteorological parameter under consideration. For the current case study (the coldest hour during night and the warmest hour during day chosen to validate the temperature forecast, the temperature validation bin size 3 degrees, precipitation forecast validated in three categories based on the 12 h precipitation sums) one may say that the RMSE of the shortterm prediction of night minimum and day maximum temperature is 1.5 °C…3.1 °C. It was also noticed that Estonian weather service predicts lower night minimum temperature than it follows in reality. The skill of the temperature forecast is estimated by comparison of its RMSE with that of the persistence forecast (next night/day will be similar to the previous one). The RMSE of the 1st day/night forecast is by 1.3 °C…1.4 °C less for Tallinn and Tartu and 0.3 °C…0.8 °C for Kuressaare than that of the persistence forecast. For the precipitation forecast, percent correct is 60…70, the probability that dry weather forecast is followed by no precipitation is 70%…80%. At the end of the paper the long-term forecasts of two international weather portals www.gismeteo.ru (Russia) and www.weather.com (USA) are briefly analysed.

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“…It is well-known that forecasting whether a rainfall event of concern will occur in a deterministic or probabilistic way is quite useful for the enterprise, government, and agricultural decision-makers (Katz and Murphy, 1997;Lee and Lee, 2007). As for the traditional Probabilistic Quantitative Precipitation Forecast (PQPF), many works (e.g., Eckel and Walters, 1998;Buizza et al, 1999;Hamill et al, 2008) have shown its limitation for 6-15 day precipitation events since the corresponding skill generally decreases significantly after 6 days; this is because it lacks the sharpness to discriminate which events occurred and which events did not in this period.…”

Section: Introductionmentioning

confidence: 99%

Using a deterministic time-lagged ensemble forecast with a probabilistic threshold for improving 6–15day summer precipitation prediction in China

Wei

Wang

et al. 2015

Atmospheric Research

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A Deterministic Time-lagged Ensemble Forecast using a Probabilistic Threshold (DEFPT) method is suggested for improving summer 6-15 day categorical precipitation prediction in China from the Beijing Climate Center Atmospheric General Circulation Model version 2.1 (BCC_AGCM2.1). It is based on a time-lagged ensemble system that consists of 13 ensemble members separated sequentially at 6 hour intervals lagging the last three days. The DEFPT is not intended to predict the probability of rainfall, but rather to forecast rainfall (yes/no) occurrence for different categories of precipitation at any model grid box. A given categorical precipitation is forecasted to occur at one gridbox only when the ensemble probability for that categorical precipitation exceeds a certain threshold. This method is useful for providing an estimate of whether precipitation events will occur to decision-makers based on probabilistic forecasts during days 6-15. A large number of hindcast experiments for 1996-2005 summers reveal that this threshold can be best (and empirically) set as 5/13 and 4/13 respectively for the 6-15 day prediction of 1+ mm (i.e., above 1 mm per day) and 5+ mm rainfall events, using the Relative Operating Characteristic (ROC) curve, the Equitable Threat Score (ETS), the Hanssen and Kuipers (HK) score, and frequency bias (BIA) to achieve best prediction performance. With this set of thresholds, the DEFPT shows skill improvement over the corresponding single deterministic forecast using one initial value and the Time-Lagged Average Forecast (LAF) ensemble method. Similar improvements by the DEFPT are also found for the prediction of several other categories of precipitation between 1+ mm and 10+ mm per day. Application of DEFPT to larger ensemble size and BCC_AGCM version 2.2 with a higher horizontal resolution also demonstrates the effectiveness of the DEFPT for 6-15 day categorical precipitation forecasts.

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The economic value of weather forecasts for decision‐making problems in the profit/loss situation

Cited by 20 publications

References 19 publications

The collective value of weather probabilistic forecasts according to public threshold distribution patterns

The collective value of weather probabilistic forecasts according to public threshold distribution patterns

Statistics of different public forecast products of temperature and precipitation in Estonia

Using a deterministic time-lagged ensemble forecast with a probabilistic threshold for improving 6–15day summer precipitation prediction in China

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