- Email: [email protected]
Transition of meteorological variables while downburst occurrence by a high density ground surface observation network
Journal of Wind Engineering & Industrial Aerodynamics 184 (2019) 153–161
Contents lists available at ScienceDirect
Journal of Wind Engineering & Industrial Aerodynamics journal homepage: www.elsevier.com/locate/jweia
Transition of meteorological variables while downburst occurrence by a high density ground surface observation network Hisato Iwashita a, *, Fumiaki Kobayashi b a b
Meisei Electric Co.,Ltd, 2223 Naganumamachi, Isesaki-shi, Gunma, 372-8585, Japan National Defense Academy, 1-10-20 Hashirimizu, Yokosuka-shi, Kanagawa, 239-8686, Japan
A R T I C L E I N F O
A B S T R A C T
Keywords: gust Wind direction rotation Wind speed jump Downburst scale High density Surface observation network
A high density ground surface observation network has been realized by about 150 POTEKAs and has a resolution of approximately 2 km in Gunma and Saitama prefectures in Japan. POTEKA (POint TEnki KAnsoku in Japanese) is a compact weather station which can observe seven meteorological variables such as wind speed, wind direction, temperature, pressure, humidity, sunshine and rain. In this observation network, the verification observation has been performed for about 5 years since 2013, and we have succeeded in observing 11 cases of downbursts and damaging gusts, which included 5 events in the F1 category (Fujita scale). By detailed analysis of the F1 downbursts which were accompanied by substantial damage (particularly the downburst on June 15, 2015), the transition characteristics of meteorological variables such as wind, temperature and pressure have been clarified while downburst occurrence. Moreover, we have been able to estimate the actual scale of a downburst.
1. Introduction In recent years, extreme weather is of great interest to society. In particular, rapid onset weather events such as localized heavy rainfall, torrential rain, downburst, tornado, and so on are of great concern as they threaten human life and property. Comprehension of the reasons for the localized extreme weather is absolutely essential for the aspect of not only meteorology but also disaster prevention. However, these events are said to be meso-γ scale and the size is defined as 2–20 km. Because the installation resolution of the Japan Meteorological Agency's (JMA) Automated Meteorological Data Acquisition system (AMeDAS) is approximately 17 km, it is difficult to examine these events in detail. So, we have realized a high density ground surface observation network with a resolution of approximately 2 km composed of these compact weather stations. The observation network is called the POTEKA NET SYSTEM which has the function of data collection and analysis (Maeda et al., 2014). After Fujita's investigations of downbursts/microbursts (Fujita, 1981), the structures of downbursts have been discussed from Doppler radar observations (e.g., Goff, 1976; Wakimoto, 1982; Wilson et al., 1984; Klingle et al., 1987; Hjelmfelt, 1988; Martner, 1997). As a detection technique of gust fronts (Charba, 1974, Droegemeier and
Wilhelmson, 1987), Bedard et al. (1977) installed a high density ground surface pressure observation network around the airport and showed an effect for the safety of airport operation. However, the surface structures of near downbursts are not well known, because of few observation data. So, the common characteristics about the time transition relation between temperature and pressure at the nearest POTEKA station for the particularly severe three downbursts such as on August 11, 2013, on June 15, 2015 and on July 14, 2016, were reported (Iwashita and Kobayashi, 2017). In the previous paper (Norose et al., 2016), the pressure and temperature fields of downbursts and gust fronts in Gunma prefecture on August 11, 2013, on the basis of simultaneous surface observations of meso-γscale network data were presented. However, the spatial structure and time change of downbursts/microbursts remain largely unknown. The purpose of the current study is to clarify the actual horizontal scale of a downburst of severe F1 downburst on June 15, 2015 which was accompanied by substantial damage, and describe the time transition relation between wind and other variables while downburst occurrence. 2. POTEKA weather station The compact weather station included in a POTEKA weather observation equipment was developed in 2011 according to the concept that it
* Corresponding author. E-mail address: [email protected] (H. Iwashita). https://doi.org/10.1016/j.jweia.2018.10.007 Received 29 June 2018; Received in revised form 29 September 2018; Accepted 10 October 2018 Available online 24 November 2018 0167-6105/© 2018 The Authors. Published by Elsevier Ltd. This is an open access article under the CC BY license (http://creativecommons.org/licenses/by/4.0/).
H. Iwashita, F. Kobayashi
Journal of Wind Engineering & Industrial Aerodynamics 184 (2019) 153–161
Fig. 1. POTEKA weather station and weather observation equipment.
should be compact, light and capable of being installed anywhere. A POTEKA weather station can observe seven meteorological variables such as wind speed, wind direction, temperature, pressure, humidity, sunshine and rain. By utilizing the spare channels, a POTEKA weather station can observe other variables such as precipitation, lightning, snowfall, water level, soil index, PM 2.5 and so on. Moreover, wind speed, temperature, pressure, humidity and precipitation passed the JMA's variable verification test. About wind observation, a POTEKA weather station uses an ultrasonic sensor which has the accuracy within 5% of the actual wind speed value. The height of observation position is approximately 1.5 m from the installation surface such as ground surface, a building roof and so on. The ultrasonic sensor has the sampling frequency of 50 Hz and the weather station records the value every 1 s 1-second value is averaged from the raw data of the nearest 3 s. Moreover, the weather station can calculate the average wind (direction/speed) value and the peak wind (direction/ speed) value and transmit these data to a leased cloud server every 1 min. The average value is calculated from 600-s values for 10 min. The peak value is the maximum among 60-s values for 1 min. An area of 25 m2 (5 m multiplied by 5 m) and an external electric power supply are required for JMA's AMeDAS, whereas a POTEKA weather station can be installed anywhere such as at school, on a building's roof or on electricity pole because of its compact size and solar panel. The installation is straightforward and typically takes less than 1 h. Photographs of the station and equipment are shown in Fig. 1. Several examples are shown in Fig. 2. 3. High density ground surface observation network and POTEKA NET SYSTEM POTEKA weather stations, which have been recording observations since 2013, have been installed at about 500 locations in Japan as of May
Fig. 3. High density ground surface observation network in Gunma and Saitama (POTEKA network) and Image of POTEKA network area size.
2018. The number of installations is now increasing steadily. A high density ground surface observation network with a resolution of approximately 4 km has been realized in many prefectures in Japan. In
Fig. 2. Several examples of installation locations. 154
H. Iwashita, F. Kobayashi
Journal of Wind Engineering & Industrial Aerodynamics 184 (2019) 153–161
2018. The number of installations is now increasing steadily. A high density ground surface observation network with a resolution of approximately 4 km has been realized in many prefectures in Japan. In particular, a very high density ground surface observation network (hereinafter, the POTEKA network) has been realized, consisting of about 150 POTEKAs, with a resolution of approximately 2 km over a wide range of about 30 km in the north-south direction and about 60 km in the eastwest direction in Gunma and Saitama prefectures. The plains of Gunma and Saitama prefectures have a climatological feature that a cumulonimbus is generated over the surrounding mountains and proceeds over the plains accompanied by the growth in the summer. Moreover, the developing cumulonimbus causes severe gusts such as downburst and gust fronts frequently. The plains of Gunma and Saitama are characterized by extreme weather. The POTEKA network is shown in Fig. 3. Also, the image for the area size and the resolution of the POTEKA network is shown in Fig. 3. The area size of Japan country (380,000 km2) is approximately the one twenty-fifth of the area size of United States (9,500,000 km2). The area size of Kanto region (32,000 km2) is approximately the one eleventh of the area size of Japan country (380,000 km2). The POTEKA network size (1800 km2) is approximately the one eighteenth of the area size of Kanto region (32,000 km2). To summarize, the
Fig. 4. POTEKA net system.
particular, a very high density ground surface observation network (hereinafter, the POTEKA network) has been realized, consisting of about 150 POTEKAs, with a resolution of approximately 2 km over a wide range of about 30 km in the north-south direction and about 60 km in the eastwest direction in Gunma and Saitama prefectures. POTEKA weather stations, which have been recording observations since 2013, have been installed at about 500 locations in Japan as of May
Fig. 5. Weather charts of June 15, 2015. 155
H. Iwashita, F. Kobayashi
Journal of Wind Engineering & Industrial Aerodynamics 184 (2019) 153–161
POTEKA network size (1800 km2) is approximately the one five thousandth of the area size of United States (9,500,000 km2). We could have installed many stations of about 150 POTEKAs in this size area and have realized the resolution of approximately 2 km. As soon as a POTEKA weather observation equipment is installed and turned on, observations can begin due to the electric power storage of the solar panel and 3G module built into the electric power supply box. The observation data are transmitted at 1-min intervals. ALL POTEKA observation data are collected in a leased cloud server automatically and in real time. If the electric storage of the solar panel is fully charged, a POTEKA weather station can operate for several days. Therefore, the observation, transmission and collection of data are rarely interrupted. Moreover, a POTEKA weather station includes a data recovery function that is called past compensation. Even if the real time transmission or collection is interrupted, if the observations are not interrupted, late transmission or collection will be automatically performed after reconnecting. The POTEKA NET SYSTEM is resilient against external disturbances and missing observations are rare. The data collected in a leased cloud server can be utilized from “POTEKA NET” website. POTEKA NET has several output formats for the data such as maps, contours, graphs and tables. The POTEKA NET SYSTEM is shown in Fig. 4.
4.1. Time transition relation characteristics between wind and other variables The POTEKA network was able to record observations for the severe downburst. The observation data for the nearest POTEKA station from the most damaged point is shown in Fig. 7. The purple solid line indicates peak wind velocity for each 1 min. We found that peak wind speed increases and decreases complicatedly while downburst occurrence. Fig. 7 shows the observation data at a distance of about 1 km from the most damaged point. A first steep jump of peak wind speed of 9.2 m/s (from 3.4 to 12.6 m/s) over 2 min (from 15:55 to 15:57 JST) was observed. This time was 8 min before (15:57 JST) the time that the damage was reported to have occurred (16:05 JST) by JMA. Around the same time, a small pressure jump of 1 hPa (from 1005.2 to 1006.2 hPa) over 9 min (from 15:47 to 15:56 JST) and a steep temperature drop of 3.9 C (from 26.7 to 22.8 C) over 1 min (from 15:57 to 15:58 JST) were observed. We estimated that these transitions occurred owing to the gust front passing. Also, a second steep jump of peak wind speed of 7.7 m/s (from 10.8 to 18.5 m/s) over 2 min (from 15:59 to 16:01 JST) was observed 4 min before the damage occurrence time (16:05 JST). Around the same time, a steep pressure jump of 4.4 hPa (from 1004.7 to 1009.1 hPa) over 4 min (from 15:59 to 16:03 JST) was observed. We estimated that these transitions occurred owing to the downdraft from downburst itself. A steep pressure jump of 3–4 hPa accompanied with a second steep jump of peak wind speed was observed only the limited area near to the most damaged point. On the other side, many points except for the limited area observed only a small pressure jump of approximately 1 hPa. Generally, the downburst area of increasing in pressure is more localized compared with the widespread area of previous pressure jump associated with gust front (e.g., Wilson et al., 1984; Hjelmfelt, 1988; Norose et al., 2016). Therefore, we estimated that a steep pressure jump had been caused by the downdraft from downburst itself. Moreover, in contrast to the temperature, which monotonically decreased, pressure and peak wind speed increased and decreased complicatedly. We have already reported the transition characteristics of pressure and temperature just before downburst occurrence on not only June 15, 2015 but also August 11, 2013 and July 14, 2016 (Iwashita and Kobayashi, 2017). In this section, we have found the following characteristics for peak wind speed.
4. Downburst observation on June 15, 2015 As discussed in No.3 chapter, the plains of Gunma and Saitama prefectures experience severe gusts such as downbursts and gust fronts in the summer. The POTEKA network has succeeded in observing 11 cases of downbursts and damaging gusts, which included five in the F1 category (Fujita scale) over about 5 years since 2013. In this chapter, we introduce the result of observation and detailed analysis for particular severe downburst on June 15, 2015 that was in the F1 category and was accompanied by substantial damage. Because the cold air of less than 9 C was covered at high altitudes of 500 hPa and the highest temperature approached to 32 C on the ground surface in many areas, the lapse rate of approximately 8 ( C/km) was generated and the atmosphere was being unstable over eastern Japan throughout the day of June 15, 2015. The ground surface weather charts of 9 JST and 21 JST for this day are shown in Fig. 5–1 and Fig. 5–2 respectively. The 500 hPa upper air weather charts of 9 JST and 21 JST for this day are shown in Fig. 5–3 and Fig. 5–4 respectively. In Gunma and Saitama prefectures, a cumulonimbus was developing over the mountains in the northwest. In the afternoon, the cumulonimbus that was located to the northwest of the POTEKA network produced an F1 downburst resulting in damage in Maebashi and Isesaki cities while advancing to the southeast.
・ A first steep jump of peak wind speed is observed about 10 min before the damage time and it is estimated to be caused by the gust front passing. ・ A second steep jump of peak wind speed is observed about 5 min before the damage time and it is estimated to be caused by the downdraft from downburst itself.
Fig. 6. Damage situation on the most damaged point (Cursors indicate the direction of damaging such as falling and blowing affected from the north wind).
Fig. 7. Observation data from the POTEKA station nearest the most damaged point on Jun 15, 2015. 156
H. Iwashita, F. Kobayashi
Journal of Wind Engineering & Industrial Aerodynamics 184 (2019) 153–161
Fig. 8. Ground surface transition for wind direction/speed while downburst occurrence on June 15, 2015.
・ In contrast to temperature, which monotonically decreases, peak wind speed increases and decreases complicatedly.
length of about 18 km. In the red area, the points which can observe wind (direction/speed) are only three points of ①, ② and ③ in Figs. 8–1. Also, a back ground color indicates temperature. A purple broken arrow in Figs. 8–6 has been estimated to be the advancing path of the cumulonimbus center with tracing the temperature drop points (Iwashita and Kobayashi, 2017). Whereas the eminent wind from the east and the southeast was passing all over the POTEKA network, we found that sudden rotation of average wind direction and steep jump of average wind speed was observed about 16:05 JST. The 16:05 was the time that the cumulonimbus which was seemed to have caused downburst was passing over. Fig. 9 is the only northwest area of the POTEKA network at 16:05 JST. Same as Fig. 8, X is the most damaged point and the red realm indicates the approximately damaged area. The observation points of ①, ② and ③ correspond with the three points on Figs. 8–1. ① is about 2 km of the northwest from X, ② is about 2 km of the west from X and ③ is about 1 km of the west from X. The ③ point is the nearest of most damaged point where the observation data is shown in Fig. 7. Our estimated advancing line of the cumulonimbus center indicates by the blue broken arrow. The cumulonimbus advancing speed is estimated to be approximately 3 m/s while downburst occurrence. The observation data for average wind (direction/speed) and peak wind (direction/speed) on the points of ①, ② and ③ are shown in Fig. 10(1–3). As shown in Fig. 9, we estimated that the cumulonimbus center which was seemed to have caused downburst was passing on the
4.2. Ground surface transition for wind direction/speed We have already reported that the advancing direction of the cumulonimbi which caused downbursts on not only June 15, 2015 but also August 11, 2013 and July 14, 2016 had clarified with tracing the steep temperature drop points or the steep pressure jump points. About the downburst on June 15, 2015, we have found that the cumulonimbus which was generated on the northwest of the POTEKA network was advancing to the southeast and the actual damage occurred in the process of advancing (Iwashita and Kobayashi, 2017). In this section, we introduce the localized transition for wind direction/speed on ground surface while downburst occurrence. Wind (direction/speed) observation variable was added in a new type of POTEKA weather station and was performed in about 90 observation points among the 150 POTEKAs in the POTEKA network in Gunma and Saitama prefectures after 2015. The time transition of ground surface for average wind direction/speed while downburst occurrence on June 15, 2015 is shown in Fig. 8–1 to 8-6. Arrows on the contour maps indicate average wind direction/speed for 10 min. The stronger red indicates the faster speed. An X mark is the most damaged point which has been shown in Fig. 6. The red realm surrounding the X indicates the approximately damaged area reported by JMA and has the width of about 8 km and the 157
H. Iwashita, F. Kobayashi
Journal of Wind Engineering & Industrial Aerodynamics 184 (2019) 153–161
cumulonimbus approaching. Moreover, after this rotation, the points of ② and ③ observed that the north wind direction was keeping for about 15 min (from 15:55 to 16:10 JST). On the other hand, the point of ① observed that after the north wind direction was keeping for about 5 min (from 15:55 to 16:00 JST), the sudden rotation from the north to the south occurred and the south wind direction was keeping for about 10 min (from 16:00 to 16:10 JST). In other words, for about 10 min near to the actual damage time (16:05 JST), the point of ① observed the south wind direction and the points of ② and ③ observed the north wind direction. As discussed, we have reported that the cumulonimbus center was advancing between on the south side of the point of ① and on the north side of the points of ② and ③. Therefore, we estimated that three observation points observed the divergent wind from the downburst cumulonimbus center. About wind speed, more than 15 m/s of peak wind speed and more than 5 m/s of average wind speed were observed on the observation points of ①, ② and ③ which was included in the approximately damaged area. Also, we considered that the cumulonimbus advancing speed was little influenced to wind speed observation because its speed was approximately 3.8 m/s. In this section, to summarize the above, we have found the following characteristics for the surface transition for wind direction/speed while downburst occurrence.
Fig. 9. Downburst cumulonimbus center advancing line and positioning relation around the most damaged point.
south side of observation point of ①. On the other hand, we estimated that the cumulonimbus center was passing on the north side of observation points of ② and ③. About wind direction, all observation points of ①, ② and ③ observed rotation of both average and peak from approximately the east-southeast to the north about 10 min before the actual damage time (16:05 JST). We estimated that these observation results of rotation were caused by the
・ A dominant wind direction (of the east-southeast) suddenly changes (to the north) about 10 min before the damage time owing to the downburst center approaching.
Fig. 10. Wind direction/speed data around the most damaged point while downburst occurrence on June 15, 2015. 158
H. Iwashita, F. Kobayashi
Journal of Wind Engineering & Industrial Aerodynamics 184 (2019) 153–161
・ A divergent wind from the downburst center have been observed for about 10 min near to the actual damage time. ・ More than 15 m/s of peak wind speed and more than 5 m/s of average wind speed are observed around the approximately damaged area.
observed on the all observation points of from A1 to A5. Whereas, neither a steep pressure jump of about 3–4 hPa nor a steep temperature drop of 2 C or less per minute was observed on the all observation points of from B1 to B5. We have estimated that the steep temperature drop and the steep pressure jump were generated because of downburst itself (Iwashita and Kobayashi, 2017). Therefore, we found the following characteristics by the detailed analysis.
4.3. Estimated result for the actual scale of a downburst As discussed in No.4.2 section, we have estimated the advancing direction of a downburst by the POTEKA network. As shown in Fig. 10, in the case of the downburst on June 15, 2015, we found that the realm of extreme variable transitions such as the temperature drop and pressure jump was advancing from the northwest to the southeast. Fig. 11 is the same map data as Fig. 10 and indicates the temperature observation data on the northwest area of the POTEKA network at the actual damage time (16:05 JST) on the F1 downburst on June 15, 2015. The most damaged point is marked with an X and the red realm indicates the approximately damaged area by the downburst. Moreover, we estimated that the blue arrow on the contour was the advancing line of the cumulonimbus center. In Fig. 11, the points marked with from A1 to A5 exist within 5 km from the center advancing line of the cumulonimbus center. Whereas, the other points marked with from B1 to B5 exist out of 5 km from the center advancing line. We plotted the graphs of temperature/pressure transition for all points of from A1 to A5 and from B1 to B5 on Fig. 11. These graphs have the same scales for both perpendicular and horizontal axes. Blue lines indicate time transition of temperature and red lines indicates time transition of pressure on these graphs. We confirmed that both a steep pressure jump of about 3–4 hPa and a steep temperature drop of 2 C or less per minute were certainly
・ A steep temperature drop point corresponds with a steep pressure jump point. ・ A steep temperature drop/pressure jump point exist within approximately 5 km from the advancing line of the downburst center. ・ A steep temperature drop/pressure jump point don't exist out of approximately 5 km from the advancing line of the downburst center. According to these characteristics, it was considered that the downdraft from the downburst itself was expanding in a radius of at least 5 km. Therefore, the downburst scale could be estimated to be approximately a radius of 5 km. Fig. 12 has the same background map of the northwest area of the POTEKA network as Fig. 11. This map indicates the pressure contour line described by hand at the just damaged time (16:05 JST). The localized high pressure area of more than 1007 hPa was appeared and has the scale of approximately 5 km. We have considered that our estimation for a downburst scale was almost correct. Downburst observation result is very rare and valuable especially on the ground surface. We strongly hope that these results will be effectively
Fig. 11. Temperature/Pressure transition data and positioning relation for each observation point while downburst occurrence on June 15, 2015. 159
H. Iwashita, F. Kobayashi
Journal of Wind Engineering & Industrial Aerodynamics 184 (2019) 153–161
Fig. 12. Pressure contour at the actual damage time (16:05 JST).
・ A dominant wind direction (of the east-southeast) suddenly changes (to the north) about 10 min before the damage time owing to the downburst center approaching. ・ A divergent wind from the downburst center have been observed for about 10 min near to the actual damage time. ・ More than 15 m/s of peak wind speed and more than 5 m/s of average wind speed are observed around the approximately damaged area.
utilized in order to investigate the real structure of downburst. In the future, with understanding and utilizing the transition characteristics of meteorological variables just before and during downburst occurrence, it will be able to predict the occurrence time and place of a downburst in advance. Moreover, the prediction technology will be directly linked to protect human life and fortunes from a downburst. If we have a high density observation network, wherever it is, we have the probability that prediction technology for downburst will be realized. 5. Conclusion
Moreover, with detailed analyzing the observation points of a steep high pressure jump and a steep temperature drop, it was considered that the downdraft from the downburst itself was expanding in a radius of at least 5 km. Therefore, the actual scale of a downburst on the plains of Gunma and Saitama prefectures could be estimated to be approximately a radius of 5 km. In the future, with utilizing these characteristics, we may be able to predict a downburst occurrence in advance and estimate a damaged area. Moreover, we may be able to analyze the advancing line of a downburst center and the surface structures of near a downburst more accurately.
A high density ground surface observation network which has a resolution of approximately 2 km in Gunma and Saitama prefectures in Japan succeeded in observing the F1 downburst on June 15, 2015 which was accompanied by substantial damage. By the detailed analysis of the severe F1 downburst, the several transition characteristics of meteorological variables have been clarified while downburst occurrence. About the time transition relation between wind and other variables, the following characteristics have been clarified. ・ A first steep jump of peak wind speed is observed about 10 min before the damage time and it is estimated to be caused by the gust front passing. ・ A second steep jump of peak wind speed is observed about 5 min before the damage time and it is estimated to be caused by the downdraft from downburst itself. ・ In contrast to temperature, which monotonically decreases, peak wind speed increases and decreases complicatedly.
References Bedard, A.J., Hooke, W.H., Beran, D.W., 1977. The dulles airport pressure jump detector array for gust front detection. Bull. Am. Meteorol. Soc. 58, 920–926. Charba, J., 1974. Application of gravity current model to analysis of squall-line gust front. Mon. Weather Rev. 102, 140–156. Droegemeier, K.K., Wilhelmson, R.B., 1987. Numerical simulation of thunderstorm outflow dynamics. PartⅠ: outflow sensitivity experiments and turbulence dynamics. J. Atmos. Sci. 44, 1180–1210. Fujita, T.T., 1981. Tornadoes and downbursts in the context of generalized planetary scales. J. Atmos. Sci. 38, 1511–1534. Goff, R.C., 1976. Vertical structure of thunderstorm outflows. Mon. Weather Rev. 104, 1429–1440. Hjelmfelt, M.R., 1988. Structure and life cycle of microburst outflows observed in Colorado. J. Appl. Meteorol. 27, 900–927.
About the surface transition for wind direction/speed, the following characteristics have been clarified.
160
H. Iwashita, F. Kobayashi
Journal of Wind Engineering & Industrial Aerodynamics 184 (2019) 153–161 Norose, K., Kobayashi, F., Kure, H., Yada, T., Iwasaki, H., 2016. Observation of downburst event in Gunma prefecture on August 11, 2013 using a surface dense observation network. J. Atmos. Electr. 35, 31–41. Wakimoto, R.M., 1982. The life cycle of thunderstorm gust fronts as viewed with Doppler radar and rawinsonde data. Mon. Weather Rev. 110, 1060–1082. Wilson, J.W., Roberts, R.D., Kessinger, C., McCarthy, J., 1984. Microburst wind structure and evaluation of Doppler radar for airport wind shear detection. J. Clim. Appl. Meteorol. 23, 898–915.
Iwashita, H., Kobayashi, F., 2017. Pressure transition accompanied with downburst indicated by a high dense ground surface meteorological observation network data. In: Japan Meteorological Society Fall Meeting, vol. 112, p. A209 (in Japanese). Klingle, D.L., Smith, D.R., Wolfson, M.M., 1987. Gust front characteristics as detected by Doppler radar. Mon. Weather Rev. 115, 905–918. Maeda, R., Suzuki, M., Kure, H., Morita, T., Iwasaki, H., 2014. Outline and Results of the Surface Dense Observation Stations “POTEKA” (Point Tenki Kansoku) in Gunma Prefecture, vol. 8. Society of Atmospheric Electricity of Japan, p. 44 (in Japanese). Martner, B.E., 1997. Vertical velocities in a thunderstorm gust front and outflow. J. Appl. Meteorol. 36, 615–622.
161
Contents lists available at ScienceDirect
Journal of Wind Engineering & Industrial Aerodynamics journal homepage: www.elsevier.com/locate/jweia
Transition of meteorological variables while downburst occurrence by a high density ground surface observation network Hisato Iwashita a, *, Fumiaki Kobayashi b a b
Meisei Electric Co.,Ltd, 2223 Naganumamachi, Isesaki-shi, Gunma, 372-8585, Japan National Defense Academy, 1-10-20 Hashirimizu, Yokosuka-shi, Kanagawa, 239-8686, Japan
A R T I C L E I N F O
A B S T R A C T
Keywords: gust Wind direction rotation Wind speed jump Downburst scale High density Surface observation network
A high density ground surface observation network has been realized by about 150 POTEKAs and has a resolution of approximately 2 km in Gunma and Saitama prefectures in Japan. POTEKA (POint TEnki KAnsoku in Japanese) is a compact weather station which can observe seven meteorological variables such as wind speed, wind direction, temperature, pressure, humidity, sunshine and rain. In this observation network, the verification observation has been performed for about 5 years since 2013, and we have succeeded in observing 11 cases of downbursts and damaging gusts, which included 5 events in the F1 category (Fujita scale). By detailed analysis of the F1 downbursts which were accompanied by substantial damage (particularly the downburst on June 15, 2015), the transition characteristics of meteorological variables such as wind, temperature and pressure have been clarified while downburst occurrence. Moreover, we have been able to estimate the actual scale of a downburst.
1. Introduction In recent years, extreme weather is of great interest to society. In particular, rapid onset weather events such as localized heavy rainfall, torrential rain, downburst, tornado, and so on are of great concern as they threaten human life and property. Comprehension of the reasons for the localized extreme weather is absolutely essential for the aspect of not only meteorology but also disaster prevention. However, these events are said to be meso-γ scale and the size is defined as 2–20 km. Because the installation resolution of the Japan Meteorological Agency's (JMA) Automated Meteorological Data Acquisition system (AMeDAS) is approximately 17 km, it is difficult to examine these events in detail. So, we have realized a high density ground surface observation network with a resolution of approximately 2 km composed of these compact weather stations. The observation network is called the POTEKA NET SYSTEM which has the function of data collection and analysis (Maeda et al., 2014). After Fujita's investigations of downbursts/microbursts (Fujita, 1981), the structures of downbursts have been discussed from Doppler radar observations (e.g., Goff, 1976; Wakimoto, 1982; Wilson et al., 1984; Klingle et al., 1987; Hjelmfelt, 1988; Martner, 1997). As a detection technique of gust fronts (Charba, 1974, Droegemeier and
Wilhelmson, 1987), Bedard et al. (1977) installed a high density ground surface pressure observation network around the airport and showed an effect for the safety of airport operation. However, the surface structures of near downbursts are not well known, because of few observation data. So, the common characteristics about the time transition relation between temperature and pressure at the nearest POTEKA station for the particularly severe three downbursts such as on August 11, 2013, on June 15, 2015 and on July 14, 2016, were reported (Iwashita and Kobayashi, 2017). In the previous paper (Norose et al., 2016), the pressure and temperature fields of downbursts and gust fronts in Gunma prefecture on August 11, 2013, on the basis of simultaneous surface observations of meso-γscale network data were presented. However, the spatial structure and time change of downbursts/microbursts remain largely unknown. The purpose of the current study is to clarify the actual horizontal scale of a downburst of severe F1 downburst on June 15, 2015 which was accompanied by substantial damage, and describe the time transition relation between wind and other variables while downburst occurrence. 2. POTEKA weather station The compact weather station included in a POTEKA weather observation equipment was developed in 2011 according to the concept that it
* Corresponding author. E-mail address: [email protected] (H. Iwashita). https://doi.org/10.1016/j.jweia.2018.10.007 Received 29 June 2018; Received in revised form 29 September 2018; Accepted 10 October 2018 Available online 24 November 2018 0167-6105/© 2018 The Authors. Published by Elsevier Ltd. This is an open access article under the CC BY license (http://creativecommons.org/licenses/by/4.0/).
H. Iwashita, F. Kobayashi
Journal of Wind Engineering & Industrial Aerodynamics 184 (2019) 153–161
Fig. 1. POTEKA weather station and weather observation equipment.
should be compact, light and capable of being installed anywhere. A POTEKA weather station can observe seven meteorological variables such as wind speed, wind direction, temperature, pressure, humidity, sunshine and rain. By utilizing the spare channels, a POTEKA weather station can observe other variables such as precipitation, lightning, snowfall, water level, soil index, PM 2.5 and so on. Moreover, wind speed, temperature, pressure, humidity and precipitation passed the JMA's variable verification test. About wind observation, a POTEKA weather station uses an ultrasonic sensor which has the accuracy within 5% of the actual wind speed value. The height of observation position is approximately 1.5 m from the installation surface such as ground surface, a building roof and so on. The ultrasonic sensor has the sampling frequency of 50 Hz and the weather station records the value every 1 s 1-second value is averaged from the raw data of the nearest 3 s. Moreover, the weather station can calculate the average wind (direction/speed) value and the peak wind (direction/ speed) value and transmit these data to a leased cloud server every 1 min. The average value is calculated from 600-s values for 10 min. The peak value is the maximum among 60-s values for 1 min. An area of 25 m2 (5 m multiplied by 5 m) and an external electric power supply are required for JMA's AMeDAS, whereas a POTEKA weather station can be installed anywhere such as at school, on a building's roof or on electricity pole because of its compact size and solar panel. The installation is straightforward and typically takes less than 1 h. Photographs of the station and equipment are shown in Fig. 1. Several examples are shown in Fig. 2. 3. High density ground surface observation network and POTEKA NET SYSTEM POTEKA weather stations, which have been recording observations since 2013, have been installed at about 500 locations in Japan as of May
Fig. 3. High density ground surface observation network in Gunma and Saitama (POTEKA network) and Image of POTEKA network area size.
2018. The number of installations is now increasing steadily. A high density ground surface observation network with a resolution of approximately 4 km has been realized in many prefectures in Japan. In
Fig. 2. Several examples of installation locations. 154
H. Iwashita, F. Kobayashi
Journal of Wind Engineering & Industrial Aerodynamics 184 (2019) 153–161
2018. The number of installations is now increasing steadily. A high density ground surface observation network with a resolution of approximately 4 km has been realized in many prefectures in Japan. In particular, a very high density ground surface observation network (hereinafter, the POTEKA network) has been realized, consisting of about 150 POTEKAs, with a resolution of approximately 2 km over a wide range of about 30 km in the north-south direction and about 60 km in the eastwest direction in Gunma and Saitama prefectures. The plains of Gunma and Saitama prefectures have a climatological feature that a cumulonimbus is generated over the surrounding mountains and proceeds over the plains accompanied by the growth in the summer. Moreover, the developing cumulonimbus causes severe gusts such as downburst and gust fronts frequently. The plains of Gunma and Saitama are characterized by extreme weather. The POTEKA network is shown in Fig. 3. Also, the image for the area size and the resolution of the POTEKA network is shown in Fig. 3. The area size of Japan country (380,000 km2) is approximately the one twenty-fifth of the area size of United States (9,500,000 km2). The area size of Kanto region (32,000 km2) is approximately the one eleventh of the area size of Japan country (380,000 km2). The POTEKA network size (1800 km2) is approximately the one eighteenth of the area size of Kanto region (32,000 km2). To summarize, the
Fig. 4. POTEKA net system.
particular, a very high density ground surface observation network (hereinafter, the POTEKA network) has been realized, consisting of about 150 POTEKAs, with a resolution of approximately 2 km over a wide range of about 30 km in the north-south direction and about 60 km in the eastwest direction in Gunma and Saitama prefectures. POTEKA weather stations, which have been recording observations since 2013, have been installed at about 500 locations in Japan as of May
Fig. 5. Weather charts of June 15, 2015. 155
H. Iwashita, F. Kobayashi
Journal of Wind Engineering & Industrial Aerodynamics 184 (2019) 153–161
POTEKA network size (1800 km2) is approximately the one five thousandth of the area size of United States (9,500,000 km2). We could have installed many stations of about 150 POTEKAs in this size area and have realized the resolution of approximately 2 km. As soon as a POTEKA weather observation equipment is installed and turned on, observations can begin due to the electric power storage of the solar panel and 3G module built into the electric power supply box. The observation data are transmitted at 1-min intervals. ALL POTEKA observation data are collected in a leased cloud server automatically and in real time. If the electric storage of the solar panel is fully charged, a POTEKA weather station can operate for several days. Therefore, the observation, transmission and collection of data are rarely interrupted. Moreover, a POTEKA weather station includes a data recovery function that is called past compensation. Even if the real time transmission or collection is interrupted, if the observations are not interrupted, late transmission or collection will be automatically performed after reconnecting. The POTEKA NET SYSTEM is resilient against external disturbances and missing observations are rare. The data collected in a leased cloud server can be utilized from “POTEKA NET” website. POTEKA NET has several output formats for the data such as maps, contours, graphs and tables. The POTEKA NET SYSTEM is shown in Fig. 4.
4.1. Time transition relation characteristics between wind and other variables The POTEKA network was able to record observations for the severe downburst. The observation data for the nearest POTEKA station from the most damaged point is shown in Fig. 7. The purple solid line indicates peak wind velocity for each 1 min. We found that peak wind speed increases and decreases complicatedly while downburst occurrence. Fig. 7 shows the observation data at a distance of about 1 km from the most damaged point. A first steep jump of peak wind speed of 9.2 m/s (from 3.4 to 12.6 m/s) over 2 min (from 15:55 to 15:57 JST) was observed. This time was 8 min before (15:57 JST) the time that the damage was reported to have occurred (16:05 JST) by JMA. Around the same time, a small pressure jump of 1 hPa (from 1005.2 to 1006.2 hPa) over 9 min (from 15:47 to 15:56 JST) and a steep temperature drop of 3.9 C (from 26.7 to 22.8 C) over 1 min (from 15:57 to 15:58 JST) were observed. We estimated that these transitions occurred owing to the gust front passing. Also, a second steep jump of peak wind speed of 7.7 m/s (from 10.8 to 18.5 m/s) over 2 min (from 15:59 to 16:01 JST) was observed 4 min before the damage occurrence time (16:05 JST). Around the same time, a steep pressure jump of 4.4 hPa (from 1004.7 to 1009.1 hPa) over 4 min (from 15:59 to 16:03 JST) was observed. We estimated that these transitions occurred owing to the downdraft from downburst itself. A steep pressure jump of 3–4 hPa accompanied with a second steep jump of peak wind speed was observed only the limited area near to the most damaged point. On the other side, many points except for the limited area observed only a small pressure jump of approximately 1 hPa. Generally, the downburst area of increasing in pressure is more localized compared with the widespread area of previous pressure jump associated with gust front (e.g., Wilson et al., 1984; Hjelmfelt, 1988; Norose et al., 2016). Therefore, we estimated that a steep pressure jump had been caused by the downdraft from downburst itself. Moreover, in contrast to the temperature, which monotonically decreased, pressure and peak wind speed increased and decreased complicatedly. We have already reported the transition characteristics of pressure and temperature just before downburst occurrence on not only June 15, 2015 but also August 11, 2013 and July 14, 2016 (Iwashita and Kobayashi, 2017). In this section, we have found the following characteristics for peak wind speed.
4. Downburst observation on June 15, 2015 As discussed in No.3 chapter, the plains of Gunma and Saitama prefectures experience severe gusts such as downbursts and gust fronts in the summer. The POTEKA network has succeeded in observing 11 cases of downbursts and damaging gusts, which included five in the F1 category (Fujita scale) over about 5 years since 2013. In this chapter, we introduce the result of observation and detailed analysis for particular severe downburst on June 15, 2015 that was in the F1 category and was accompanied by substantial damage. Because the cold air of less than 9 C was covered at high altitudes of 500 hPa and the highest temperature approached to 32 C on the ground surface in many areas, the lapse rate of approximately 8 ( C/km) was generated and the atmosphere was being unstable over eastern Japan throughout the day of June 15, 2015. The ground surface weather charts of 9 JST and 21 JST for this day are shown in Fig. 5–1 and Fig. 5–2 respectively. The 500 hPa upper air weather charts of 9 JST and 21 JST for this day are shown in Fig. 5–3 and Fig. 5–4 respectively. In Gunma and Saitama prefectures, a cumulonimbus was developing over the mountains in the northwest. In the afternoon, the cumulonimbus that was located to the northwest of the POTEKA network produced an F1 downburst resulting in damage in Maebashi and Isesaki cities while advancing to the southeast.
・ A first steep jump of peak wind speed is observed about 10 min before the damage time and it is estimated to be caused by the gust front passing. ・ A second steep jump of peak wind speed is observed about 5 min before the damage time and it is estimated to be caused by the downdraft from downburst itself.
Fig. 6. Damage situation on the most damaged point (Cursors indicate the direction of damaging such as falling and blowing affected from the north wind).
Fig. 7. Observation data from the POTEKA station nearest the most damaged point on Jun 15, 2015. 156
H. Iwashita, F. Kobayashi
Journal of Wind Engineering & Industrial Aerodynamics 184 (2019) 153–161
Fig. 8. Ground surface transition for wind direction/speed while downburst occurrence on June 15, 2015.
・ In contrast to temperature, which monotonically decreases, peak wind speed increases and decreases complicatedly.
length of about 18 km. In the red area, the points which can observe wind (direction/speed) are only three points of ①, ② and ③ in Figs. 8–1. Also, a back ground color indicates temperature. A purple broken arrow in Figs. 8–6 has been estimated to be the advancing path of the cumulonimbus center with tracing the temperature drop points (Iwashita and Kobayashi, 2017). Whereas the eminent wind from the east and the southeast was passing all over the POTEKA network, we found that sudden rotation of average wind direction and steep jump of average wind speed was observed about 16:05 JST. The 16:05 was the time that the cumulonimbus which was seemed to have caused downburst was passing over. Fig. 9 is the only northwest area of the POTEKA network at 16:05 JST. Same as Fig. 8, X is the most damaged point and the red realm indicates the approximately damaged area. The observation points of ①, ② and ③ correspond with the three points on Figs. 8–1. ① is about 2 km of the northwest from X, ② is about 2 km of the west from X and ③ is about 1 km of the west from X. The ③ point is the nearest of most damaged point where the observation data is shown in Fig. 7. Our estimated advancing line of the cumulonimbus center indicates by the blue broken arrow. The cumulonimbus advancing speed is estimated to be approximately 3 m/s while downburst occurrence. The observation data for average wind (direction/speed) and peak wind (direction/speed) on the points of ①, ② and ③ are shown in Fig. 10(1–3). As shown in Fig. 9, we estimated that the cumulonimbus center which was seemed to have caused downburst was passing on the
4.2. Ground surface transition for wind direction/speed We have already reported that the advancing direction of the cumulonimbi which caused downbursts on not only June 15, 2015 but also August 11, 2013 and July 14, 2016 had clarified with tracing the steep temperature drop points or the steep pressure jump points. About the downburst on June 15, 2015, we have found that the cumulonimbus which was generated on the northwest of the POTEKA network was advancing to the southeast and the actual damage occurred in the process of advancing (Iwashita and Kobayashi, 2017). In this section, we introduce the localized transition for wind direction/speed on ground surface while downburst occurrence. Wind (direction/speed) observation variable was added in a new type of POTEKA weather station and was performed in about 90 observation points among the 150 POTEKAs in the POTEKA network in Gunma and Saitama prefectures after 2015. The time transition of ground surface for average wind direction/speed while downburst occurrence on June 15, 2015 is shown in Fig. 8–1 to 8-6. Arrows on the contour maps indicate average wind direction/speed for 10 min. The stronger red indicates the faster speed. An X mark is the most damaged point which has been shown in Fig. 6. The red realm surrounding the X indicates the approximately damaged area reported by JMA and has the width of about 8 km and the 157
H. Iwashita, F. Kobayashi
Journal of Wind Engineering & Industrial Aerodynamics 184 (2019) 153–161
cumulonimbus approaching. Moreover, after this rotation, the points of ② and ③ observed that the north wind direction was keeping for about 15 min (from 15:55 to 16:10 JST). On the other hand, the point of ① observed that after the north wind direction was keeping for about 5 min (from 15:55 to 16:00 JST), the sudden rotation from the north to the south occurred and the south wind direction was keeping for about 10 min (from 16:00 to 16:10 JST). In other words, for about 10 min near to the actual damage time (16:05 JST), the point of ① observed the south wind direction and the points of ② and ③ observed the north wind direction. As discussed, we have reported that the cumulonimbus center was advancing between on the south side of the point of ① and on the north side of the points of ② and ③. Therefore, we estimated that three observation points observed the divergent wind from the downburst cumulonimbus center. About wind speed, more than 15 m/s of peak wind speed and more than 5 m/s of average wind speed were observed on the observation points of ①, ② and ③ which was included in the approximately damaged area. Also, we considered that the cumulonimbus advancing speed was little influenced to wind speed observation because its speed was approximately 3.8 m/s. In this section, to summarize the above, we have found the following characteristics for the surface transition for wind direction/speed while downburst occurrence.
Fig. 9. Downburst cumulonimbus center advancing line and positioning relation around the most damaged point.
south side of observation point of ①. On the other hand, we estimated that the cumulonimbus center was passing on the north side of observation points of ② and ③. About wind direction, all observation points of ①, ② and ③ observed rotation of both average and peak from approximately the east-southeast to the north about 10 min before the actual damage time (16:05 JST). We estimated that these observation results of rotation were caused by the
・ A dominant wind direction (of the east-southeast) suddenly changes (to the north) about 10 min before the damage time owing to the downburst center approaching.
Fig. 10. Wind direction/speed data around the most damaged point while downburst occurrence on June 15, 2015. 158
H. Iwashita, F. Kobayashi
Journal of Wind Engineering & Industrial Aerodynamics 184 (2019) 153–161
・ A divergent wind from the downburst center have been observed for about 10 min near to the actual damage time. ・ More than 15 m/s of peak wind speed and more than 5 m/s of average wind speed are observed around the approximately damaged area.
observed on the all observation points of from A1 to A5. Whereas, neither a steep pressure jump of about 3–4 hPa nor a steep temperature drop of 2 C or less per minute was observed on the all observation points of from B1 to B5. We have estimated that the steep temperature drop and the steep pressure jump were generated because of downburst itself (Iwashita and Kobayashi, 2017). Therefore, we found the following characteristics by the detailed analysis.
4.3. Estimated result for the actual scale of a downburst As discussed in No.4.2 section, we have estimated the advancing direction of a downburst by the POTEKA network. As shown in Fig. 10, in the case of the downburst on June 15, 2015, we found that the realm of extreme variable transitions such as the temperature drop and pressure jump was advancing from the northwest to the southeast. Fig. 11 is the same map data as Fig. 10 and indicates the temperature observation data on the northwest area of the POTEKA network at the actual damage time (16:05 JST) on the F1 downburst on June 15, 2015. The most damaged point is marked with an X and the red realm indicates the approximately damaged area by the downburst. Moreover, we estimated that the blue arrow on the contour was the advancing line of the cumulonimbus center. In Fig. 11, the points marked with from A1 to A5 exist within 5 km from the center advancing line of the cumulonimbus center. Whereas, the other points marked with from B1 to B5 exist out of 5 km from the center advancing line. We plotted the graphs of temperature/pressure transition for all points of from A1 to A5 and from B1 to B5 on Fig. 11. These graphs have the same scales for both perpendicular and horizontal axes. Blue lines indicate time transition of temperature and red lines indicates time transition of pressure on these graphs. We confirmed that both a steep pressure jump of about 3–4 hPa and a steep temperature drop of 2 C or less per minute were certainly
・ A steep temperature drop point corresponds with a steep pressure jump point. ・ A steep temperature drop/pressure jump point exist within approximately 5 km from the advancing line of the downburst center. ・ A steep temperature drop/pressure jump point don't exist out of approximately 5 km from the advancing line of the downburst center. According to these characteristics, it was considered that the downdraft from the downburst itself was expanding in a radius of at least 5 km. Therefore, the downburst scale could be estimated to be approximately a radius of 5 km. Fig. 12 has the same background map of the northwest area of the POTEKA network as Fig. 11. This map indicates the pressure contour line described by hand at the just damaged time (16:05 JST). The localized high pressure area of more than 1007 hPa was appeared and has the scale of approximately 5 km. We have considered that our estimation for a downburst scale was almost correct. Downburst observation result is very rare and valuable especially on the ground surface. We strongly hope that these results will be effectively
Fig. 11. Temperature/Pressure transition data and positioning relation for each observation point while downburst occurrence on June 15, 2015. 159
H. Iwashita, F. Kobayashi
Journal of Wind Engineering & Industrial Aerodynamics 184 (2019) 153–161
Fig. 12. Pressure contour at the actual damage time (16:05 JST).
・ A dominant wind direction (of the east-southeast) suddenly changes (to the north) about 10 min before the damage time owing to the downburst center approaching. ・ A divergent wind from the downburst center have been observed for about 10 min near to the actual damage time. ・ More than 15 m/s of peak wind speed and more than 5 m/s of average wind speed are observed around the approximately damaged area.
utilized in order to investigate the real structure of downburst. In the future, with understanding and utilizing the transition characteristics of meteorological variables just before and during downburst occurrence, it will be able to predict the occurrence time and place of a downburst in advance. Moreover, the prediction technology will be directly linked to protect human life and fortunes from a downburst. If we have a high density observation network, wherever it is, we have the probability that prediction technology for downburst will be realized. 5. Conclusion
Moreover, with detailed analyzing the observation points of a steep high pressure jump and a steep temperature drop, it was considered that the downdraft from the downburst itself was expanding in a radius of at least 5 km. Therefore, the actual scale of a downburst on the plains of Gunma and Saitama prefectures could be estimated to be approximately a radius of 5 km. In the future, with utilizing these characteristics, we may be able to predict a downburst occurrence in advance and estimate a damaged area. Moreover, we may be able to analyze the advancing line of a downburst center and the surface structures of near a downburst more accurately.
A high density ground surface observation network which has a resolution of approximately 2 km in Gunma and Saitama prefectures in Japan succeeded in observing the F1 downburst on June 15, 2015 which was accompanied by substantial damage. By the detailed analysis of the severe F1 downburst, the several transition characteristics of meteorological variables have been clarified while downburst occurrence. About the time transition relation between wind and other variables, the following characteristics have been clarified. ・ A first steep jump of peak wind speed is observed about 10 min before the damage time and it is estimated to be caused by the gust front passing. ・ A second steep jump of peak wind speed is observed about 5 min before the damage time and it is estimated to be caused by the downdraft from downburst itself. ・ In contrast to temperature, which monotonically decreases, peak wind speed increases and decreases complicatedly.
References Bedard, A.J., Hooke, W.H., Beran, D.W., 1977. The dulles airport pressure jump detector array for gust front detection. Bull. Am. Meteorol. Soc. 58, 920–926. Charba, J., 1974. Application of gravity current model to analysis of squall-line gust front. Mon. Weather Rev. 102, 140–156. Droegemeier, K.K., Wilhelmson, R.B., 1987. Numerical simulation of thunderstorm outflow dynamics. PartⅠ: outflow sensitivity experiments and turbulence dynamics. J. Atmos. Sci. 44, 1180–1210. Fujita, T.T., 1981. Tornadoes and downbursts in the context of generalized planetary scales. J. Atmos. Sci. 38, 1511–1534. Goff, R.C., 1976. Vertical structure of thunderstorm outflows. Mon. Weather Rev. 104, 1429–1440. Hjelmfelt, M.R., 1988. Structure and life cycle of microburst outflows observed in Colorado. J. Appl. Meteorol. 27, 900–927.
About the surface transition for wind direction/speed, the following characteristics have been clarified.
160
H. Iwashita, F. Kobayashi
Journal of Wind Engineering & Industrial Aerodynamics 184 (2019) 153–161 Norose, K., Kobayashi, F., Kure, H., Yada, T., Iwasaki, H., 2016. Observation of downburst event in Gunma prefecture on August 11, 2013 using a surface dense observation network. J. Atmos. Electr. 35, 31–41. Wakimoto, R.M., 1982. The life cycle of thunderstorm gust fronts as viewed with Doppler radar and rawinsonde data. Mon. Weather Rev. 110, 1060–1082. Wilson, J.W., Roberts, R.D., Kessinger, C., McCarthy, J., 1984. Microburst wind structure and evaluation of Doppler radar for airport wind shear detection. J. Clim. Appl. Meteorol. 23, 898–915.
Iwashita, H., Kobayashi, F., 2017. Pressure transition accompanied with downburst indicated by a high dense ground surface meteorological observation network data. In: Japan Meteorological Society Fall Meeting, vol. 112, p. A209 (in Japanese). Klingle, D.L., Smith, D.R., Wolfson, M.M., 1987. Gust front characteristics as detected by Doppler radar. Mon. Weather Rev. 115, 905–918. Maeda, R., Suzuki, M., Kure, H., Morita, T., Iwasaki, H., 2014. Outline and Results of the Surface Dense Observation Stations “POTEKA” (Point Tenki Kansoku) in Gunma Prefecture, vol. 8. Society of Atmospheric Electricity of Japan, p. 44 (in Japanese). Martner, B.E., 1997. Vertical velocities in a thunderstorm gust front and outflow. J. Appl. Meteorol. 36, 615–622.
161