A Fuzzy Logic Method for Lightning Prediction Using ... - AMS Journals

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A Fuzzy Logic Method for Lightning Prediction Using Thermodynamic and Kinematic Parameters from Radio Sounding Observations in South Korea BONGJAE KUK Launch Operations Department, Naro Space Center, Korea Aerospace Research Institute, Goheung, South Korea, and Department of Astronomy and Atmospheric Sciences, Kyungpook National University, Daegu, South Korea

HONGIL KIM, JONGSUNG HA, AND HYOKEUN LEE Launch Operations Department, Naro Space Center, Korea Aerospace Research Institute, Goheung, South Korea

GYUWON LEE Department of Astronomy and Atmospheric Sciences, Kyungpook National University, Daegu, South Korea (Manuscript received 11 November 2010, in final form 15 April 2011) ABSTRACT Lightning is one of the most troubling weather phenomena for weather forecasters at space centers. In this study, proximity sounding and lightning data were used to evaluate the utility of thermodynamic and kinematic parameters for forecasting lightning prior to launch operations. Various parameters from 4138 radio sounding observations at five sites and cloud-to-ground (CG) stroke data from the Korea Meteorological Administration’s Lightning Detection Network (KLDN) over South Korea during 2004–09 were used. To support launch operations, forecasts of the total membership function for lightning (TMF) were derived from the combination of membership functions of selected thermodynamic and kinematic parameters with each objective weight using a fuzzy logic algorithm. The forecast skill of TMF was evaluated by computing several skill statistics, which include probability of detection (POD), false alarm rate (FAR), percent correct (PC), critical success index (CSI), and the true skill statistic (TSS). The lightning forecasting method for Gwangju, South Korea (site nearest to the Naro Space Center), was found to have a POD of 0.68, an FAR of 0.45, a PC of 0.76, a CSI of 0.44, and a TSS of 0.47.

1. Introduction Lightning poses a serious threat to space launch vehicles, launch complexes, and ground range tracking systems at space centers. Lightning strikes on launch vehicles can cause electrical and physical damage to the avionics, fuel ignition, and guidance navigation and control systems. To minimize the threat from lightning strikes, the meteorologists dedicated to launch operations have to establish weather launch commit criteria (WLCC) and determine the status of their WLCC at launch time (Durrett 1976; Maier 1999; Roeder et al. 1999). Isabelle (2004) showed that of 162 Ariane-4 and -5 launches at the Centre Spatial Guyanais (CSG) as of 1 March 2004, 13 had to be Corresponding author address: BongJae Kuk, Launch Operation Dept., Korea Aerospace Research Institute, 1 Yenae-ri Bongraemyeon, Goheung-gun, Jeollanam-do 548944, South Korea. E-mail: [email protected] DOI: 10.1175/WAF-D-10-05047.1 Ó 2012 American Meteorological Society

postponed due to weather conditions. Maier (1999) and Roeder et al. (1999) have presented statistical data for launch scrubbing due to WLCC violation and the financial impacts of launch operation scrubbing for the Eastern Range at the National Aeronautics and Space Administration’s (NASA) Cape Canaveral Air Force Station (CCAFS) and the Kennedy Space Center (KSC) in Florida. South Korea’s Naro Space Center is located along the southern coast of the Korean Peninsula. During 2004–08, 2 314 034 cloud-to-ground (CG) strokes and 1 152 475 CG flashes were detected over the Korean Peninsula by the Korea Meteorological Administration’s Lightning Detection Network (KLDN). The CG flash density and flash hours, defined as the number of 15-min interval time blocks in which a flash is observed, multiplied by 0.25 to make a flash hour, around Naro Space Center are relatively high compared to all other regions of the Korean Peninsula. (Kuk et al. 2010).

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FIG. 1. Locations of the five radio sounding sites.

Thunderstorm forecasting is one of the most difficult research fields due to the small spatial size, the short life span from initialization to decay, and our lack of understanding of the physical mechanisms involved with

lightning production. Therefore, many atmospheric scientists have conducted thunderstorm forecasting using weather radar data, especially dual-polarization moments, numerical weather prediction, and stability indices

TABLE 1. The five radio sounding stations used in this study. Station

Organization

Station ID

Lat (8N)

Lon (8E)

Elev (m)

Sounding time (UTC)

Cheju Baengnyeongdo Pohang Osan Gwangju

KMA KMA KMA ROKAF ROKAF

47185 47102 47138 47122 47158

33.28 37.97 36.03 37.09 35.11

126.16 124.63 129.38 127.02 126.81

73 158 6 52 13

0000, 1200 0000, 1200 0000, 1200 0000, 0600, 1200, 1800 0000, 0600, 1200, 1800

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derived from radio sounding observations (Craven and Brooks 2004; Dixon and Wiener 1993; Mueller et al. 2003; Rasmussen and Blanchard 1998; Saxen 2002; Wilson et al. 1998; Wolf 2006). In particular, weather radar has been highlighted for the identification, nowcasting, and tracking of thunderstorms and severe weather due to the high temporal and spatial resolutions of these data (Han et al. 2009; Johnson et al. 1998; Witt et al. 1998). Radio sounding observations indicate vertical profiles of wind speed and direction, pressure, temperature, and humidity. The stability indices, energy, shear indices, and cloud-base heights calculated from the vertical profile are some of the most important data for lightning forecasting (Doswell 1987; Johns and Doswell 1992; Showalter 1953). In many previous studies, the selection of proper stability indices and the performance of selected indices for thunderstorm forecasting were investigated (Fuelberg and Biggar 1994; Haklander and Van Delden 2003; Huntrieser et al. 1997; Kunz 2007). Chuda and Niino (2005) presented climatological statistics for parameters such as convective available potential energy (CAPE), the Showalter stability index (SSI), convective inhibition (CIN), precipitable water (PW), and the bulk Richardson number, which are used to characterize atmospheric environments for mesoscale convective systems in Japan. They determined that these environmental parameters are storm dependent rather than location or season dependent. For tropical humid regions, Oliveira and Oyama (2009) investigated climatological features for some widely used convective parameters calculated from radio sounding observations at the Alcantara Launch Center, located along the northern coast of northeastern Brazil. The main goals of the present study are to investigate the correlation between stability indices and lightning occurrence, to develop a total membership function for lightning (TMF) calculated from fuzzy logic algorithms, and to evaluate the lightning forecasting performance of TMF using skill scores.

2. Data and methodology a. Radio sounding observation data In this study, thermodynamic and kinematic parameters were determined from radio sounding observation data from five sites during 2004–09. The basic information derived from the soundings is summarized in Fig. 1 and Table 1. Osan and Gwangju, operated by the Republic of Korea Air Force (ROKAF), conduct radio sounding observations four times a day (0000, 0600, 1200, and 1800 UTC). However, the Sokcho, Pohang, and Baengnyeongdo sites, belonging to the Korea Meteorological Administration (KMA), perform upper-air observations twice

a day (0000 and 1200 UTC). To make the times coincide for all sites, only observations at 0000 and 1200 UTC for all sites were analyzed. During the observation period, from 1 January 2004 to 31 December 2009, a total of 4138 soundings were investigated, which accounted for 94.5% of all possible soundings.

b. CG stroke data from KLDN In South Korea, there are three lightning detection networks, operated by KMA, the Korean Electricity and Power Corporation (KEPCO), and the Korea Aerospace Research Institute (KARI). The KLDN and KEPCO Lightning Detection Network (KLDNet) can detect CG discharges over the whole of the South Korean peninsula with 90% detection efficiency (Jeong et al. 2002). The KLDN consists of seven Improved Accuracy from Combined Technology Enhanced Sensitivity Performance (IMPACT ESP) sensors for low-frequency (LF) sensing and 14 Lightning Detection and Ranging (LDAR) sensors for very high-frequency (VHF) sensing. Compared with KLDN and KLDnet, the KARI Total Lightning Detection Network (KARITLDS) can monitor cloud-tocloud (CC) discharges in addition to CG discharges, with a small area of detection around the space center since 2007 (when KARITLDS became dedicated solely to launch operations).

c. Proximity sounding and thermodynamic and kinematic parameters Proximity sounding assumptions consist of two criteria required to correlate thermodynamic and kinematic parameters with lightning occurrence. The spatial criterion is that radio sounding data represent the thermodynamic environment of the atmosphere over the area within a 100-km radius of the radio sounding site (Fig. 1). The purpose of the temporal criterion (lightning occurrence within 6 h from radio sounding observation time) is to ensure that radio sounding observation data properly represent the atmospheric conditions within the area of study near the time of any lightning occurrence. In previous studies, scientists have used various temporal and

TABLE 2. Proximity sounding criteria of previous studies and the present study.

Reference

Spatial criteria (km)

Temporal criteria (h)

Craven and Brooks (2004) Doswell and Evans (2003) Rasmussen and Blanchard (1998) This study

185 167 400 100

23 to 13 22 to 12 23 to 16 26 to 16

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TABLE 3. Summary of thermodynamic and kinematic parameters used in this study. Indices

Acronym

Unit

Showalter stability index

SSI

8C

SSI 5 T500 2 Tparcel1

Lifted index

LI

8C

LI 5 T500 2 Tparcel2

Precipitable water Total totals index

PW TTI

mm 8C

K index

KI

8C

Severe weather threat

SWEAT

—

Definition

Description

— TTI 5 T850 2 T500 1 Td850 2 T500 KI 5 T850 2 T500 1 Td850 2 T700 2 Td700

SWEAT 5 12Td850 1 20(TTI 2 49) 1 2WS850 1 WS500 1 SHEAR

spatial criteria for proximity soundings to relate thermodynamic and kinematic parameters to lightning occurrence. The criteria used in previous studies and the present study are summarized in Table 2. Thermodynamic and kinematic parameters and significant cloud-height thresholds have been developed to help meteorologists quickly assess the atmospheric stability. Also, researchers have tried to develop new thermodynamic and kinematic parameters adapted to the local environment, which include tomography (Jacovides

T500: temperature (8C) at 500 hPa Tparcel1: temperature (8C) at 500 hPa of a parcel lifted dry adiabatically from 850 hPa 0 , SSI: stable 24 , SSI # 0: marginal instability 27 , SSI # 24: significant instability SSI # 27: extreme instability Tparcel2: temperature (8C) at 500 hPa of lifted parcel with average temperature, pressure, and dewpoint of the layer 500 hPa above surface 0 , LI: stable 24 , LI # 0: marginal instability 27 , LI # 24: significant instability LI # 27: extreme instability Precipitable water amounts for the entire sounding T850: temperature (8C) at 850 hPa Td850: dewpoint (8C) at 850 hPa T700: temperature (8C) at 700 hPa Td700: dewpoint (8C) at 700 hPa 15 # KI # 25: insignificant convective potential 26 # KI # 39: moderate convective potential 40 # KI: significant convective potential WS850: wind speed (kt, where 1 kt 5 0.514 m s21) at 850 hPa WS500: wind speed (kt) at 500 hPa SHEAR 5 125[sin(WD500 2 WD850) 1 0.2] WD500: wind direction (8) at 500 hPa WD850: wind direction (8) at 850 hPa 150 # SWEAT # 300: slight chance of severe weather 301 # SWEAT # 400: severe weather possible 401 # SWEAT: tornadoes possible

and Yonetani 1990; Haklander and Van Delden 2003; Huntrieser et al. 1997). In this study, SSI, the lifted index (LI), PW, the total totals index (TTI), the K index (KI), and the severe weather threat (SWEAT) were investigated. Definitions, units, and descriptions of these parameters are summarized in Table 3.

d. TMF from fuzzy logic algorithm Fuzzy logic algorithms have been used in various academic and industrial fields including meteorology.

TABLE 4. A 2 3 2 contingency table for dichotomous categorical forecasts. Observation

Forecast

Yes No Subtotal

Yes

No

Subtotal

A (hit) C (miss) OY 5 A 1 C (observation yes)

B (false alarm) D (correct rejection) ON 5 B 1 D (observation no)

FY 5 A 1 B (forecast yes) FN 5 C 1 D (forecast no) T5A1B1C1D (total)

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KUK ET AL. TABLE 5. Definitions and equations for dichotomous forecast verification.

Acronym

Description

Equation

Range

POD FAR PC CSI TSS

Probability of detection False alarm rate Percent correct Critical success index True skill statistics

POD 5 (A)/(A 1 C) FAR 5 (B)/(A 1 B) PC 5 (A 1 D)/(A 1 B 1 C 1 D) CSI 5 (A)/(A 1 B 1 C) TSS 5 (AD 2 BC)/(A 1 C)(B 1 D)

0 # POD #1 0 # FAR #1 0 # PC #1 0 # CSI #1 21 # TSS #1

In previous studies, particularly in the field of weather radar, fuzzy logic algorithms have been introduced as one of the methods of identifying and separating precipitating echoes from nonprecipitating echoes, such as ground echoes and anomalous propagation (Cho et al. 2006; Gourley et al. 2007). Mueller et al. (2003) and showed that fuzzy logic can be used for nowcasting in various ways. In addition, Hansen (2007) and Leyton and Fritsch (2003) used fuzzy logic for forecasting ceiling and visibility. TMF for lightning forecasting can be derived from a combination of normalized membership functions of the selected parameters with proper weights. Of the six parameters derived from radio sounding data, at least three with their minimum overlapping areas between the lightning and nonlightning normalized probability distributions were employed to calculate TMF using a fuzzy logic algorithm. The membership function represents the degree of membership depending on the values of the variables. The proper weights of each parameter can be calculated from the overlapping areas between the normalized frequencies for lightning occurrence and nonoccurrence for selected parameters. This approach compensates for the weaknesses of each selected parameter within the context of a fuzzy logic algorithm. TMF for lightning forecasting can be calculated with a membership function (MFi) and weights (Wi) (see Table 8):

TMF 5

å å

i5SelectedParameters

Wi MFi

i5SelectedParameters

, Wi

(yes or no) events (Jacovides and Yonetani 1990; Haklander and Van Delden 2003; Wilks 2006). In this paper, we have followed the common definition of skill scores and the elements of the contingency table, as shown in Tables 4 and 5. The four variables, A, B, C, and D, in the contingency table denote hit, false alarm, miss, and correct rejection, respectively. For quantitative evaluation of the TMF algorithm, traditional skill scores were used: probability of detection (POD), false alarm rate (FAR), critical success index (CSI), true skill statistic (TSS), and percent correct (PC) (see Table 5). In this paper, POD, FAR, and PC are computed where TSS is maximized.

3. Results and discussion a. CG stroke occurrence and thermodynamic and kinematic parameters 1) CG STROKE OCCURRENCE The total number of CG strokes within 100 km from each radio sounding site is summarized in Table 6. The Cheju site, located at the most southern margin, had 110 545 CG strokes, which is one-half to one-third of the total number of CG strokes that other sites received. Decreased detection efficiency here is thought to be due to the lack of lightning detection sensors and by the blocking of electromagnetic wave propagation by Hanla Mountain (at 1950 m above sea level) (Kuk et al. 2011). A total of 321 242 CG strokes occurred within 100 km of the Gwangju site during 2004–09. In a previous study performed by Kuk et al. (2010), it was found that the

where TMF is the total membership function for lightning forecasting (0 # range # 1) and Wi and MFi denote the weighting and membership functions of the ith variable, respectively.

TABLE 6. Number of CG strokes located within 100-km radius from each radio sounding site during 2004–09.

e. Performance evaluation of TMF lightning forecasting using skill scores


Skill scores calculated from a 2 3 2 contingency table have been used for evaluating forecasts of dichotomous

Station

Total No. of CG strokes 110 222 214 257 321

545 824 475 496 242

Max No. of CG strokes per proximity sounding 13 62 13 18 35

403 840 160 540 293

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FIG. 2. Time series of the total number of CG strokes within a 100-km radius of the (a) Gwangju, (b) Baengnyeongdo, (c) Cheju, (d) Osan, and (e) Pohang sites.

southwestern inland region and the southern maritime region of Korea have high CG flash densities. The main cause is thought to be due to the fact that weather systems usually develop and move from southwest to northeast in the summer season or from northwest to southeast in the winter season. When weather systems pass from maritime to inland, the possibility of lightning occurrence increases due to orographic forcing as well as increasing instability resulting

from ground heat flux. The Baengnyeongdo site recorded 62 840 CG strokes during one sounding period (12 h), the highest number of CG strokes among all sites. Figure 2 shows the time series of the total number of CG strokes for five radio sounding sites. CG stroke frequency was highest during June–August, which is consistent with the results of previous studies of lightning characteristics for the Korean Peninsula (Kuk et al.

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FIG. 3. Time series of the thermodynamic and kinematic parameters at the Gwangju site for lightning (red dots) and nonlightning (black dots) cases.

2010). However, the number of lightning strokes at the Pohang site during 2009 increased compared to other years (Fig. 2). Further investigation of local environment changes around the Pohang site should be conducted to identify the causes of this increase.

2) THERMODYNAMIC AND KINEMATIC PARAMETERS

As shown in Fig. 3, the seasonal variation of six thermodynamic and kinematic parameters (SSI, LI, SWEAT,

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TABLE 7. Statistics for six parameters at all sites. The mean, median, and standard deviation are calculated for all cases, the nonlightning cases, and the lightning cases at each site separately. All cases Site Gwangju

Baengnyeongdo

Cheju

Osan

Pohang

Nonlightning cases

Lightning cases

Parameter (unit)

Mean

Median

Std dev

Mean

Median

Std dev

Mean

Median

Std dev

SSI (8C) LI (8C) KI (8C) TTI (8C) SWEAT (2) PW (mm) SSI (8C) LI (8C) KI (8C) TTI (8C) SWEAT (2) PWC (mm) SSI (8C) LI (8C) KI (8C) TTI (8C) SWEAT (2) PW (mm) SSI (8C) LI (8C) KI (8C) TTI (8C) SWEAT (2) PW (mm) SSI (8C) LI (8C) KI (8C) TTI (8C) SWEAT (2) PW (mm)

10.16 10.36 0.64 31.81 132.68 21.91 10.23 10.35 23.14 31.98 114.26 16.18 10.60 9.61 22.22 29.63 138.10 22.45 9.86 10.07 0.48 32.86 127.52 19.61 9.73 10.17 20.91 32.78 127.53 20.26

10.04 10.12 0.90 34.50 11.99 15.64 10.14 10.04 20.30 34.80 96.99 10.97 10.95 9.82 20.10 33.00 114.79 15.56 9.70 9.52 1.90 35.40 104.02 13.92 9.70 9.66 1.10 35.30 106.99 14.35

6.78 8.37 23.34 12.21 81.73 17.06 6.09 7.09 25.52 13.06 71.50 14.28 6.78 7.52 27.62 13.48 84.45 17.30 6.41 8.13 23.21 12.12 79.25 16.00 6.03 7.13 24.78 11.56 72.57 16.52

11.93 12.59 25.46 29.45 114.28 16.78 11.34 11.61 27.62 30.29 1002.49 13.28 12.44 11.52 29.21 26.92 119.19 17.19 11.04 11.53 23.62 31.18 112.77 16.47 10.73 11.18 24.38 31.20 116.52 17.74

12.1 13 25.70 31.80 100.02 11.52 11.41 11.68 25.90 33.20 90.02 8.79 12.83 12.01 28.70 29.40 100.99 11.74 11.12 11.47 22.80 33.50 97.01 11.38 10.63 10.69 22.70 33.70 99.39 12.80

6.25 7.80 21.99 12.43 65.51 13.61 5.70 6.76 24.60 13.14 60.31 12.01 6.23 7.00 26.52 13.78 70.89 14.01 6.02 7.76 22.14 12.10 65.26 13.65 5.67 6.86 24.21 11.62 64.32 14.46

5.48 4.45 16.70 38.01 181.18 35.41 4.34 3.72 20.52 40.95 176.46 31.49 5.91 4.76 15.51 36.51 186.15 35.80 3.99 2.83 20.83 41.20 200.72 35.16 6.23 6.63 11.24 38.31 166.13 29.08

4.29 2.97 22.60 40.10 176.39 35.36 3.56 2.79 24.70 41.90 174.00 30.43 4.39 3.74 23.20 39.30 182.60 36.79 2.86 1.9 25.90 42.60 199.79 36.30 4.98 5.19 18.10 40.90 156.61 26.69

5.82 6.79 18.70 9.01 98.69 17.85 4.43 4.77 15.07 7.95 91.24 15.55 5.80 6.52 21.83 9.74 96.29 17.67 4.86 5.72 16.84 8.07 99.45 17.64 5.96 6.90 22.86 9.48 85.56 19.91

KI, TTI, and PW as defined in Table 3) for the Gwangju site were investigated using scatterplots with discrimination between lightning cases (red dots) and nonlightning cases (black dots). It is found that a considerable proportion of the lightning strikes occur when the values of SSI, LI, and KI are categorized in the stable category (Haby 2011). For example, 22% of the time LI was categorized as stable (greater than 0), lightning occurred. This means that the thresholds of each parameter of lightning occurrence should be adjusted to the site environment, and that the development of proper parameters is necessary for the successful forecasting of lightning. A discussion on the probability distribution of the thermodynamic and kinematic parameters for lightning and nonlightning cases takes place in the next section. The means, medians, and standard deviations for the six thermodynamic and kinematic parameters are summarized in Table 7 for nonlightning and lightning cases, as well as all of the cases together. Standard deviations of SSI, LI, KI, and TTI of all sites except Pohang showed smaller values in lightning cases than in nonlightning cases. However, SWEAT and PW of all sites showed

higher variations (bigger standard deviation) for lightning cases as compared to nonlightning cases. It appears that the altitude of the water relative to the freezing level is more important to lightning occurrence than is the overall PW value. A feature of the Pohang site is that the standard deviations of SSI and LI, in addition to SWEAT and PW, are smaller in nonlightning cases than in lightning cases.

b. Relative and normalized frequencies, membership function, and TMF The probability distributions of six parameters for the Gwangju site are shown in Figs. 4 and 5 for the relative and normalized frequencies, respectively. The size of the overlapping area between the lightning and nonlightning cases is most important. The smaller the size of the overlapping area, the more efficient the parameter is for forecasting. The sizes of the overlapping areas for each parameter were calculated and are summarized in Table 8. The three or four parameters having the smallest overlapping areas were selected for inclusion in the TMF. For example, KI, PW, and SSI were used to make the TMF for the Cheju site (see Table 8). The membership function was

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FIG. 4. Probability distributions (relative frequency) of six parameters at the Gwangju site.

calculated from the normalized frequencies; results are shown in Fig. 5. Also in Fig. 5, smaller values of SSI and LI result in membership function values closer to one, suggesting that smaller values of SSI and LI lead to a

larger probability of lightning occurrence. From the membership function in Fig. 5, the 0.5 membership function values for KI, LI, and PW at Gwangju are 88C, 88C, and 20 mm, respectively.

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FIG. 5. Probability distributions (normalized frequency) and membership function of SSI, LI, and PW at the Gwangju site.

c. Performance evaluation of TMF lightning forecasting To evaluation forecast performance, the common evaluation method using the 2 3 2 contingency table for dichotomous categorical forecasts and evaluation measures,

including POD, FAR, CSI, PC, and TSS was used, as shown in Tables 4 and 5. The skill scores of all sites, both for lightning and nonlightning cases, as a function of TMF values are presented in Fig. 6 and Table 9. The highest POD (0.82 where TSS reached a maximum) among all sites is recorded at Baengnyeongdo. The Pohang site

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FIG. 6. Skill scores (POD, FAR, CSI, TSS, and PC) as a function of TMF values for the (a) Gwangju, (b) Baengnyeongdo, (c) Cheju, (d) Osan, and (e) Pohang sites.

shows the lowest POD (0.59). The Gwangju site (the nearest site to the Naro Space Center) shows a POD of 0.68 at 0.55 TMF. The TMF for lightning prediction at the Gwangu site is useful for meteorologists at Naro

Space Center from the viewpoint of its application to launch operations. Contrasting with other sites, this fuzzy logic method performs relatively poorly for Pohang. It is thought that the poor performance at

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TABLE 8. Sizes of overlapping areas between the lightning and nonlightning cases. Station

SSI

LI

SWEAT

KI

TTI

PWC


0.57* 0.49* 0.68* 0.51* 0.58

0.58 0.49* 0.72* 0.53* 0.56*

0.75 0.81 0.89 0.84 0.79

0.47* 0.47* 0.72* 0.50* 0.57*

0.65 0.57 0.69* 0.57 0.66

0.49* 0.49* 0.75 0.55 0.54*

* Parameters selected to be included in TMF calculation.

Pohang is due to the local weather characteristics of the Pohang site, for example, weather system passage patterns. Another possible reason for the poor performance at the Pohang site is that radio sounding data cannot adequately represent the local weather environment. Since the Pohang site is on the extreme eastern side of the Korean Peninsula, the prevailing westerly winds take the radiosonde’s trajectory over the maritime conditions immediately after it is released.

4. Conclusions In this study, 4138 radio sounding observations and the corresponding nearby CG lightning stroke data were investigated. Radio sounding data meeting particular temporal and spatial criteria with respect to CG stroke data were identified. Six thermodynamic and kinematic parameters were derived from this radio sounding data. The six parameters showed obvious seasonal trends in atmospheric stability and the possibility of being useful as a predictor for lightning forecasting. Three or four parameters that had small overlapping areas between the lightning and nonlightning cases were employed to calculate TMF, with objective weights derived. For Gwangju these parameters include LI, KI, and PW. Based on the skill scoring method, the performance of the TMF is evaluated. A POD of 0.68, an FAR of 0.45, a PC of 0.76, and a TMF of 0.55 were calculated for the local area of the Gwangju site. Gwangju parameters were emphasized in the conclusion since the Naro Space Center is closest to Gwangju. The results may be helpful to weather forecasters who must predict lightning. It is proposed that the lightning forecasting method based on the fuzzy logic approach can be applied to lightning forecasting using radio observations after calculation and adjustment of the weights for each parameter. Further study is needed to investigate the reasons for the poor performance of the fuzzy logic approach at the Pohang site. To verify the performance of the fuzzy logic approach, a comparative study between individual parameters and TMF is planned.

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TABLE 9. Skill scores of the TMF where TSS reached its maximum. Skill score Cheju Baengnyeongdo Pohang Osan Gwangju TMF POD FAR PC CSI TSS

0.49 0.74 0.48 0.74 0.44 0.47

0.50 0.82 0.62 0.76 0.35 0.56

0.50 0.59 0.61 0.70 0.31 0.33

0.51 0.77 0.60 0.77 0.36 0.54

0.55 0.68 0.45 0.76 0.44 0.47

Acknowledgments. This study was financially supported by the Space Center Development Project(II) of Ministry of Education, Science and Technology (MEST). REFERENCES Cho, Y. H., G. W. Lee, K.-E. Kim, and I. Zawadzki, 2006: Identification and removal of ground echoes and anomalous propagation using the characteristics of radar echoes. J. Atmos. Oceanic Technol., 23, 1206–1222. Chuda, T., and H. Niino, 2005: Climatology of environmental parameters for mesoscale convections in Japan. J. Meteor. Soc. Japan, 83, 391–408. Craven, J. P., and H. E. Brooks, 2004: Baseline climatology of sounding derived parameters associated with deep moist convection. Natl. Wea. Dig., 28, 13–24. Dixon, M. J., and G. Wiener, 1993: TITAN: Thunderstorm Identification, Tracking, Analysis, and Nowcasting—A radar-based methodology. J. Atmos. Oceanic Technol., 10, 785–797. Doswell, C. A., III, 1987: The distinction between large-scale and mesoscale contribution to severe convection: A case study example. Wea. Forecasting, 2, 3–16. ——, and J. S. Evans, 2003: Proximity sounding analysis for derechos and supercells: An assessment of similarities and difference. Atmos. Res., 67–68, 117–133. Durrett, W. R., 1976: Lightning—Apollo to shuttle....Case histories and spacecraft protection. Technology for the New Horizon: Proc. 13th Space Congress, Cocoa Beach, FL, Canaveral Council of Technical Societies, 4-27–4-32. Fuelberg, H. E., and D. G. Biggar, 1994: The preconvective environment of summer thunderstorms over the Florida panhandle. Wea. Forecasting, 9, 316–326. Gourley, J. J., P. Tabary, and J. Parent du Chatelet, 2007: A fuzzy logic algorithm for the separation of precipitating from nonprecipitating echoes using polarimetric radar observations. J. Atmos. Oceanic Technol., 24, 1439–1451. Haby, J., cited 2011: Weather prediction education and resources. [Available online at http://www.theweatherprediction.com/ thermo/parameters/.] Haklander, A. J., and A. Van Delden, 2003: Thunderstorm predictors and their forecast skill for the Netherlands. Atmos. Res., 67–68, 273–299. Han, L., S. Fu, L. Zhao, Y. Zheng, H. Wang, and Y. Lin, 2009: 3D convective storm identification, tracking, and forecasting—An enhanced TITAN algorithm. J. Atmos. Oceanic Technol., 26, 719–732. Hansen, B., 2007: A fuzzy logic–based analog forecasting system for ceiling and visibility. Wea. Forecasting, 22, 1319–1330.

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