Prediction of Frost Occurrences Using Statistical Modeling Approaches

Hindawi Publishing Corporation Advances in Meteorology Volume 2016, Article ID 2075186, 9 pages http://dx.doi.org/10.1155/2016/2075186 Research Article Prediction of Frost Occurrences Using Statistical Modeling Approaches Hyojin Lee,1 Jong A. Chun,1 Hyun-Hee Han,2 and Sung Kim3 1 APEC Climate Center, Climate Research Department, 12 Centum 7-ro, Haeundae-gu, Busan 48058, Republic of Korea Department of Horticultural Crop Research, National Institute of Horticultural and Herbal Science, 100 Nongsaengmyeong-ro, Iseo-myeon, Wanju-gun, Jeollabuk-do 55365, Republic of Korea 3 Republic of Korea Air Force Weather Wing, Gyeryong-si, Chungcheongnam-do 32809, Republic of Korea 2 Correspondence should be addressed to Jong A. Chun; Received 5 February 2016; Revised 24 March 2016; Accepted 29 March 2016 Academic Editor: Herminia Garcı́a Mozo Copyright © 2016 Hyojin Lee et al. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. We developed the frost prediction models in spring in Korea using logistic regression and decision tree techniques. Hit Rate (HR), Probability of Detection (POD), and False Alarm Rate (FAR) from both models were calculated and compared. Threshold values for the logistic regression models were selected to maximize HR and POD and minimize FAR for each station, and the split for the decision tree models was stopped when change in entropy was relatively small. Average HR values were 0.92 and 0.91 for logistic regression and decision tree techniques, respectively, average POD values were 0.78 and 0.80 for logistic regression and decision tree techniques, respectively, and average FAR values were 0.22 and 0.28 for logistic regression and decision tree techniques, respectively. The average numbers of selected explanatory variables were 5.7 and 2.3 for logistic regression and decision tree techniques, respectively. Fewer explanatory variables can be more appropriate for operational activities to provide a timely warning for the prevention of the frost damages to agricultural crops. We concluded that the decision tree model can be more useful for the timely warning system. It is recommended that the models should be improved to reflect local topological features. 1. Introduction It is widely known that many perennial crops such as fruit tree in South Korea are prone to be damaged from late-spring frost events. Dehydration resulting from the extracellular ice formation leads to permanent tissue damage of crops through a freeze event from a frost event [1, 2]. Frost events can be divided into two categories: radiation frosts and advective frosts [3]. The former tends to occur at the meteorological characteristics of clear skies, no wind, and a low dewpoint temperature. The latter typically occurs under the meteorological conditions of cloudy skies, moderate to strong winds, no temperature inversion, and low humidity. There have been many studies on meteorological conditions at frost events [4, 5]. Kwon et al. [4] analyzed the following eight meteorological variables at each station in South Korea when frost events occurred from 1973 to 2007: minimum temperature (denoted as TMIN), grass minimum temperature (denoted as GMINT), dewpoint temperature (denoted as Dewpoint), and wind speed (denoted as Wind) on frost occurrence days, mean relative humidity (denoted as RHmean ), minimum relative humidity (denoted as RHmin ), and cloud amount (denoted as Cloud) on one day before the frost occurrence days, and difference between maximum temperature on one day before the frost occurrence days and minimum temperature on the frost occurrence days (denoted as 𝑇diff ). These meteorological variables have been used to estimate the frost probability. For example, Floor [6] used wind speed, total cloud amount, minimum temperature, and grass minimum temperature for the estimation of frost events at Eelde (Netherlands). Frost warning systems have been developed based on meteorological variables. Three levels of frost warnings are currently issued by The National Weather Service (NWS) based on air temperatures and wind speeds [7]. The criteria for the three levels (frost warning, frost/free warning, and 2 2. Materials and Methods 2.1. Study Site and Data Collection. For this study, six stations (Chuncheon: 101, Suwon: 119, Seosan: 129, Cheongju: 131, Gwangju: 156, and Jinju: 192) were selected as described in Figure 1. The numbers after each station are the station numbers named by the Korea Meteorological Administration (KMA). Based on a study by Kwon et al. [4], the eight meteorological variables were selected for this study. The frost occurrences and eight meteorological variables from the years 1973 to 2014 were collected from the KMA. For the developed system for frost predictions or warnings, two [7] to seven [8] meteorological variables were used. For this study, spring seasons defined as March to May were focused, especially since late frost in spring seasons frequently damaged overall crop growth seriously. The meteorological characteristics and their statistics at the frost occurrence days at the six stations from 1973 to 2014 are summarized in Table 1. The Seosan station with 909 days found that the most frost events occurred, while the smallest was observed at the Gwangju station with 510 days. Frost in spring occurred when minimum temperature is approximately −2∘ C at most of stations except for Gwangju station (−0.2∘ C). Frost events at Chuncheon, Suwon, Seosan, and Cheongju stations were observed when the range of grass minimum temperature was between −6.1 and −7.4∘ C, while frost events at Gwangju and Jinju stations occurred when the range of grass minimum temperature was between −4.1 and −4.9∘ C. For wind speed, the range when frost occurred was 1.6 to 2.3 m s−1 . 39 38 Latitude (∘ N) frost warning) are 0∘ C and 16 km h−1 , for air temperature and wind speed, respectively. Chevalier et al. [3] developed a web-based fuzzy expert system for frost event warnings based on predicted air and dewpoint temperatures and observed current wind speeds. An expert system was used to predict the frost occurrence on roads and bridges [8]. The prediction systems used observed maximum and minimum temperatures from the previous day and estimates of air temperature, dewpoint temperature, cloud cover, precipitation, and average wind speed. Frost occurrences can be described as a binary variable since we can divide days into two: days when frost occurs and when frost does not occur. The logistic regression and decision tree techniques can be used to predict frost occurrences. Those techniques have been used for various fields including agronomy [9, 10], meteorology [11], and medicine [12]. However, to the best of our knowledge, few studies have been conducted on frost prediction using those techniques. These techniques are m (...truncated)