1. Introduction
Aviation turbulence, or simply turbulence, is the cause of 71% of aircraft accidents that cause injuries (Gultepe et al. 2019). For the safety of aircraft, it is necessary to accurately predict when and where aviation turbulence occurs in the atmosphere. If aviation turbulence is predicted accurately, aircraft can take actions in advance in order to change the altitude or flight path, which prevents incidents that possibly result in injuries, loss of fuel, and flight delays. However, it is difficult to accurately predict the location and time of turbulence by numerical weather prediction models (Sharman et al. 2012; Chun et al. 2017; Kim et al. 2018).
Aviation turbulence is classified into three types (Sharman et al. 2012; Lane et al. 2012; Kim et al. 2018): turbulence associated with convective clouds [convectively induced turbulence (CIT)], turbulence affected by flows produced by the presence of mountains [mountain-wave turbulence (MWT)], and turbulence that occurs in cloud-free regions and not from the effects of mountains [clear-air turbulence (CAT)].
It has been verified that aviation turbulence is often associated with jet streams, convective clouds systems, or midlatitude cyclones. In any case, aviation turbulence is suggested to be caused by flows or waves in which the spatiotemporal scale is larger than that of the turbulence itself. The mechanisms of turbulence are suggested to be different among the large-scale flows (e.g., Dutton and Panofsky 1970; Dutton 1971; Ellrod and Knapp 1992; Knox 1997; Ellrod et al. 1992; Knox et al. 2008). Knox et al. (2008) showed that inertia–gravity waves emitted as a spontaneous imbalance of atmospheric flow play a role in producing the turbulence.
Dutton (1971) proposed that the cause of turbulence is due to Kelvin–Helmholtz (K–H) instability, which is a hydrodynamic instability produced by a strong vertical wind shear of horizontal winds under a weak density stratification. K–H billows have often been observed in the middle and upper troposphere (e.g., Ludlam 1967; Atlas et al. 1970; Browning and Watkins 1970; Dutton and Panofsky 1970; Browning 1971; Reiss and Corona 1977; Petre and Verlinde 2004; Luce et al. 2010; Conrick et al. 2018; Barnes et al. 2018; Luce et al. 2018; Grasmick and Geerts 2020) and in the planetary boundary layer (e.g., Blumen et al. 2001). The K–H billows, observed by Luce et al. (2018), were characterized by couplets of upward and downward velocities with the width of 3.7 km around an altitude of 6.5 km, which lasted for about 20 min. The K–H waves are generated not only in cloud-free regions but also in cloudy regions. Kudo (2013) observed turbulence below midlevel clouds where sublimation of snow reduces static stability that likely causes K–H instability. Barnes et al. (2018) reported the observation of K–H waves in midlatitude cyclones.
When horizontally flowing air is forced to rise up in a stably stratified layer due to the presence of a mountain, a gravity wave forms. If the amplitude of the mountain wave is large, winds with strong vertical motion affect airplanes, or it may result in wave breaking that causes strong turbulence affecting airplanes. The mountain waves can be propagated both vertically and horizontally, and thus, airplanes can be affected by the waves not only near mountainous areas but also far above or in distant regions (e.g., Doyle and Smith 2003; Doyle et al. 2005; Ehard et al. 2017). Therefore, predicting mountain waves is important for the forecast of turbulence (Hopkins 1977; Lane et al. 2009; Sharman et al. 2012; Kim et al. 2015; Park et al. 2016; Elvidge et al. 2017; Kim et al. 2018, 2019).
In summary, for a better understanding and better prediction of aviation turbulence, it is fundamentally important to reveal the environmental features of aviation turbulence and statistical features of turbulence, such as intensity, location and time of occurrence, and seasonal variations.
Wolff and Sharman (2008) analyzed pilot reports (PIREPs) from 1994 to 2005 to examine the statistics of upper-level aviation turbulence over the United States and revealed that many cases in western regions are caused by mountain waves, while cases in the south are associated with convective clouds. The number of cases associated with mountain waves is less frequent in summer, whereas those with convective clouds are more dominant in summer. Lane et al. (2009) investigated the turbulence statistics over Greenland based on the PIREPs and showed that low-level cyclones play a role in producing turbulence. Their analysis showed that the breaking of mountain waves produced by the mountainous terrain in Greenland resulted in turbulence. Kim and Chun (2011) conducted a statistical analysis of aviation turbulence over South Korea using PIREPs from the years 2003 to 2008, except for 2005. They showed that the number of cases is concentrated between 20 000 and 30 000 ft (1000 ft ≈ 300 m) in spring, and the total number increases from 2003 to 2008. The number of cases associated with convection is largest in summer followed by spring. Whereas the number of cases is dominant along major flight routes, there are local peaks in the spatial distribution.
Since there are a number of international and domestic flights over Japan, it is expected that a significant number of airplanes encounter turbulence. However, few studies have focused on the detailed statistics of aviation turbulence over Japan, except for those focusing on global turbulence climatologies that include Japan (Jaeger and Sprenger 2007; Storer et al. 2017). Therefore, the purpose of this study is to conduct a statistical analysis of the occurrence of aviation turbulence events over Japan using PIREPs with flight data and cloud data observed by ground-based radars. The remainder of this paper is as follows: data used in this study are introduced in section 2, results of statistical analysis are provided in section 3, the obtained results are discussed in section 4 by comparing the previous studies that focused on different regions, and this study is summarized in section 5.
2. Data
a. PIREPs
This study utilized PIREPs as turbulence data, which include location and intensity of turbulence as well as meteorological phenomena influencing aircraft, such as icing, lightning, and hail. PIREPs are provided by individual aircraft regardless of whether they encounter turbulence. Strictly speaking, the turbulence data in a PIREP do not represent the exact location and intensity of turbulence, because the information of turbulence in a PIREP is reported subjectively after turbulence occurs (Sharman et al. 2014). The spatial and temporal uncertainties of PIREPs are approximately 50 km in horizontal and 70 m in vertical directions and 200 s in time, respectively (Sharman et al. 2006; Kim and Chun 2011). According to Sharman et al. (2006), the uncertainties of turbulence data can be ignored when studying climatology. Since PIREPs were the only available data of turbulence for a long period, this study utilized the data.
PIREPs include eight categories of intensity of aviation turbulence: smooth (SMTH), light minus (LGTM), light (LGT), light plus (LGTP), moderate (MOD), moderate plus (MODP), severe (SEV), and extreme (EXT). We considered cases in which the flight level is higher than or equal to 18 000 ft (FL180) and less than or equal to FL600, which is defined as the class-A airspace in the United States (Wolff and Sharman 2008). Note that the flight level does not stand for an actual height from the surface but rather represents a constant pressure surface based on the U.S. Standard Atmosphere with a sea–level pressure of 1013.25 hPa. As we focus on turbulence cases in the upper troposphere and lower stratosphere, and the cause of turbulence would be different in the lower troposphere, only turbulence data in which the altitude is higher than or equal to FL180 were analyzed in this study. The period we analyzed in this study covered 1 January 2006–31 December 2018, in which 83 172 turbulence events were reported in total (Table 1). Of the total, the number of cases that were stronger than or equal to MOD, namely, moderate or greater (MOG), was 81 639.
Intensity category and number of turbulence cases.
b. Flight data
In addition to the turbulence data, we also used Collaborative Actions for Renovation of Air Traffic Systems (CARATS) open data for flight paths over the area of Japan, which is provided by the Ministry of Land, Infrastructure, Transport and Tourism (MLIT) of Japan. The CARATS data cover paths of regular flights in a week in odd-numbered months from 2012 to 2015. The time resolution of reported spatial location of aircraft was approximately 10 s. The CARATS data were used to normalize statistical quantities associated with turbulence by number of flights. Since flights are concentrated around major airports and major flight routes, as shown below, the statistical quantities obtained from PIREPs were normalized by the number of flights at every location for discussion on the likelihood of occurrence of aviation turbulence. In this study, the number of flights in May 2012 was applied to all the analyzed years, because the flight data before 2012 are missing in CARATS, and the spatial distribution has not significantly changed every year. Figure 1 displays the horizontal locations of flight data in May 2012 that were obtained from the CARATS data. Only the cases where altitudes are above FL180 are plotted. The flight points cover almost the entire portion of land and areas near the coastal region.
Horizontal locations of the CARATS flight data in May 2012 as shown by gray dots. Major airports in Japan are denoted by blue squares. Brown contour represents topography with an interval of 500 m.
Citation: Journal of Applied Meteorology and Climatology 62, 5; 10.1175/JAMC-D-21-0191.1
Figure 2 shows the histogram of altitude, longitude, and latitude of the flight points. The flight data increase with height, except for the range below FL200. There is a sharp peak at 140° longitude, where Tokyo is located. Except for the peak, the number is large at 136° longitude, which includes the Kanasai area in which Osaka, the second-largest city in Japan, and a couple of major airports are located (cf. Fig. 1). There are double peaks at the bins of 136° and 140° longitude. At 130° longitude, a major airport, Fukuoka, is located, and many aircraft heading to or coming from Naha airport pass through this longitude. There is a peak around the latitude from 33° to 37°, and the number of aircraft decreases from the peak latitude. Several major cities with big airports, such as Tokyo, Osaka, and Fukuoka, are located in this range of latitude.
Histogram of (a) altitude, (b) longitude, and (c) latitude of aircraft over Japan in May 2012, obtained from the CARATS flight data.
Citation: Journal of Applied Meteorology and Climatology 62, 5; 10.1175/JAMC-D-21-0191.1
Figure 3 displays the spatial distributions of flight data at five different ranges of flight levels. At FL200 and below, the flight points are concentrated around the major airports (cf. Fig. 1). For FL300 or higher, a large number of flight points are observed along flight routes between the major airports.
Horizontal distribution of number of flights in an area 40 × 40 km2 in May 2012 obtained by CARATS data at the five different altitudes: (top left) lower than FL200, (top center) from FL200 to FL250, (top right) from FL250 to FL300, (bottom left) from FL300 to FL350, and (bottom right) higher than FL350.
Citation: Journal of Applied Meteorology and Climatology 62, 5; 10.1175/JAMC-D-21-0191.1
c. Cloud data
As introduced above, a number of turbulence cases are associated with clouds. To diagnose whether the reported turbulence is associated with clouds (i.e., CIT), we utilized observations by ground-based radars by the Japan Meteorological Agency (JMA). The radar observation network covers the domain in which most turbulence cases are reported in PIREPs in the present analysis. The radar reflectivity data and echo-top data were analyzed. Based on previous studies (Dixon and Wiener 1993; Hilgendorf and Johnson 1998; Shimizu and Uyeda 2012), convective clouds were defined as grid points where the reflectivity is greater than or equal to 20 dBZ and the echo top is higher than or equal to 5 km in this study.
We calculated the distance from the location of reported turbulence to points at which the conditions are satisfied at the nearest time. If the distance from the point of turbulence to nearest convective clouds is shorter than or equal to 100 km, the case was defined as turbulence associated with convective clouds (i.e., CIT). The analyses using radar data were conducted from 2013 to 2018.
d. Reanalysis data
To diagnose MWT, we used reanalysis data provided by the Japan Meteorological Agency, that is, the mesoscale reanalysis (MANAL), which is produced by using the JMA nonhydrostatic model (Saito et al. 2006). The horizontal grid spacing of the MANAL data is 5 km, the number of levels is 15 up to 50 hPa, and the temporal resolution is 3 h. The MANAL data contain meteorological quantities, such as wind speed in the horizontal directions, and temperature. The data cover the area where the turbulence in PIREPs used in the present study is located. Additionally, we used GTOPO30 data developed by the U.S. Geological Survey as topography data for the diagnosis of MWT.
To identify the MWT, we applied the method used by Kim and Chun (2011). Specifically, we first calculated the variance of topography at a grid point of reanalysis data on the surface. Then, Richardson number, Froude number, and the gravity-wave stress were calculated from the reanalysis data at a grid point in the three-dimensional domain. If turbulence was located within 30 km in the horizontal direction and 500 m in the vertical direction from where the mountain-induced gravity-wave drag had a nonzero value, the turbulence was diagnosed as MWT.
Figure 4 shows an example of detected MWT cases at 0000 UTC 6 October 2017 by the method of Kim and Chun (2011). The horizontal distribution of surface mountain-wave stress is large in the mountainous region (Fig. 4a). There are three MWT cases around longitude 140° and latitude 36° at the time. Figure 4b displays the meridional and vertical cross sections of vertical wind shear, static stability, and Richardson number, including the effects of wave drag, Rim, for MWT cases along longitude 140°. The vertical shear is large in the middle–upper troposphere around latitude 40°, which is below the peak of horizontal velocity (figure not shown) and in the low troposphere including the region where MWT cases are detected. The static stability is low in the upper troposphere in latitudes lower than 36° and below 1-km altitude. The gravity-wave drag is large near the surface in the mountainous region and around latitude 36° and z of 3–4 km where the cases of aviation turbulence are reported. The wave drag is large where the Richardson number with the wave effects is lower than the critical value of 0.25. The MWT cases are identified around the region where the mountain-induced gravity-wave drag is nonzero.
(a) Horizontal distribution of mountain-wave stress (kg m−1 s−2) at the surface at 0000 UTC 6 October 2017. (b) Meridional and vertical cross sections of vertical shear of horizontal velocity (shaded; s−1) and static stability with an interval of 1 × 10−2 (s−1) at 140° longitude at 0000 UTC 6 October 2017 (the brown line at the bottom indicates terrain height), and (c) Same as (b), but for the minimum Richardson number including the wave effects (shaded) and mountain-induced gravity-wave drag (contour). Yellow and gray crosses indicate the locations of MWT detected at this time.
Citation: Journal of Applied Meteorology and Climatology 62, 5; 10.1175/JAMC-D-21-0191.1
3. Results
a. Statistics of reported turbulence
Figure 5 shows the locations of all the cases of aviation turbulence in PIREPs where the intensity category is greater than or equal to MOD during the analysis period from 2006 to 2018. The locations of turbulence in PIREPs are widely distributed over Japan. In particular, a number of cases are concentrated along the popular flight routes between Tokyo and major cities: Osaka, Fukuoka, and Sapporo (cf. Fig. 1). Additionally, many cases are reported around Okinawa, which is located in the southwestern region of the main island.
Spatial distribution of turbulence around Japan reported in PIREPs from 2006 to 2018.
Citation: Journal of Applied Meteorology and Climatology 62, 5; 10.1175/JAMC-D-21-0191.1
Figure 6 depicts the spatial distribution of horizontal velocity at the 250-hPa level averaged for the four seasons with location of turbulence reported above 300 hPa in PIREPs. Spring, summer, autumn, and winter are defined in this study as three months from March to May (MAM), from June to August (JJA), from September to November (SON), and from December to February (DJF), respectively. Westerly wind is dominant in most of the analysis domain in all the seasons except for summer. The strong westerly wind (i.e., the jet stream) flows around the locations of turbulence in the three seasons, of which maximum wind speed exceeds 60 m s−1. The strong winds tend to result in strong vertical wind shear (figure not shown), which is a key factor in producing the turbulence. Whereas the averaged wind directs eastward in summer, the magnitude is much weaker than that in the other seasons. This indicates that many cases of aviation turbulence in Japan are associated with the jet stream.
Spatial distribution of seasonally averaged horizontal velocity at 250 hPa in (a) spring, (b) summer, (c) autumn, and (d) winter with location of turbulence above 300 hPa in PIREPs from 2006 to 2018.
Citation: Journal of Applied Meteorology and Climatology 62, 5; 10.1175/JAMC-D-21-0191.1
Figure 7 displays the horizontal location of turbulence in an area of 1.2° longitude × 1.2° latitude at the five different altitudes as in Fig. 3. In the altitudes lower than FL300, especially below FL200, the number of cases is high around Tokyo, Osaka, and Fukuoka (cf. Fig. 1). This indicates that the turbulence cases at lower altitudes are reported mainly around the major airports. As altitudes increase, the total number of turbulence cases in general increases, but the spatial distribution is dispersed. In other words, the region with a large number of cases is not concentrated in a specific area but is instead scattered.
Horizontal distribution of number of turbulence cases in an area of 1.2° longitude × 1.2° latitude at the five different altitudes: (a) lower than FL200, (b) from FL200 to FL250, (c) from FL250 to FL300, (d) from FL300 to FL350, and (e) higher than FL350.
Citation: Journal of Applied Meteorology and Climatology 62, 5; 10.1175/JAMC-D-21-0191.1
Figure 8 shows the time series of the monthly number of cases of turbulence in PIREPs from 2006 to 2018. A periodic annual variation is clearly observed; the number of cases is in general large in spring and small in summer, which will be discussed later. It is consistent with the findings for the United States by Wolff and Sharman (2008) and South Korea by Kim and Chun (2011). It is also observed that the total number of cases of turbulence increases with time, especially after 2014. To explore the reason for the recent increase in the number of turbulence cases, we investigated the monthly number of flights departing from domestic airports. Although information on the number of flights is available only after 2011, the number of flights also increases at least after 2012 (figure not shown). By normalizing the monthly number of turbulence cases by the number of flights, it is shown in the bottom panel of Fig. 8 that the normalized number does not increase, indicating that the recent increase in the number of turbulence cases is due to the increase in the number of flights.
(top) Time series of monthly number of turbulence cases reported in PIREPs from 2006 to 2018. (bottom) Time series of monthly number of turbulence cases normalized by the number of flights departing from domestic airports. Since the data for the number of flights are available only after 2011, data from 2006 to 2010 are not plotted.
Citation: Journal of Applied Meteorology and Climatology 62, 5; 10.1175/JAMC-D-21-0191.1
Figure 9 shows the histogram of quantities that are reported in PIREPs during the analysis period. The number of reported turbulence cases around Japan was highest in spring season, specifically from March to June, and lowest in summer, July and August (Fig. 9a). This is consistent with previous studies for the United States (Wolff and Sharman 2008) and for South Korea (Kim and Chun 2011). Strictly speaking, whereas the distribution is different from that for South Korea, it is suggested to be due to the fact that the number of samples in the previous study is smaller than that for the present study. Turbulence is mostly reported mainly from 0900 to 2000 local time (Fig. 9b), which approximately corresponds to the period of active flight operation for domestic flights, as indicated by Sharman et al. (2006). Particularly, the largest number is observed from 1000 to 1400 local time, followed by the second peak from 1700 to 1900 local time.
Histogram of (a) month, (b) hour, (c) altitude (×10−2 ft), (d) longitude, and (e) latitude of the cases of turbulence over Japan in PIREPs from 2006 to 2018.
Citation: Journal of Applied Meteorology and Climatology 62, 5; 10.1175/JAMC-D-21-0191.1
Turbulence is frequently observed from FL300 to FL380, and the frequency decreases as the altitude increases or decreases (Fig. 9c). For the horizontal location of reported turbulence, the number frequencies of longitude and latitude are similar to those for the flight data (cf. Fig. 2). The number is largest at 140° longitude, while except for the peak, the number is particularly large around 135° longitude (Fig. 9d). Tokyo and Osaka are located around 140° and 135° longitude, respectively (cf. Fig. 5). In addition, many flights between Tokyo and Sapporo are operated around 140° longitude, which is one of the most active flight routes. Another major route in Japan is between Tokyo and Fukuoka, which covers longitudes approximately from 130° to 140°. This would be the main reason for the large number of turbulence cases in this range of longitudes. The frequency distribution for latitude of reported turbulence is also nearly the Gaussian distribution, with a peak around 34° (Fig. 9e), in which several major cities in Japan, such as Tokyo, Osaka, and Fukuoka, are located (cf. Fig. 1) and where many flights are constantly operated.
To examine seasonal variations, the number of turbulence cases in the four seasons is listed in Table 2. The number of cases is largest in spring at 27 941 (34%), followed by winter at 19 530 (24%) and autumn at 18 445 (23%). In comparison with the other seasons, the number of cases is relatively small in summer: 15 723 (19%).
Number of turbulence cases in the four seasons.
Figure 10 shows the quantities displayed in Fig. 9 for the four seasons. The monthly variation of number of cases in Fig. 10a is the same as that in Fig. 9a, as the samples are simply categorized into the four seasons. Meanwhile, the hourly variation of number of cases is also similar in autumn, winter, and spring; the first peak appears from 1000 to 1400 local time, and the second appears from 1600 to 2000 local time (Fig. 10b). In summer, the second peak is relatively small in comparison with the other three seasons. The number of cases is large in high altitudes in summer, whereas a large number of cases are reported in low altitudes in winter (Fig. 10c). In spring and summer, namely, from March to August, more than 50% of the total were reported in altitudes higher than or equal to FL300. In winter, the percentage decreases to 35%, and the number of cases is large in altitudes lower than FL300. Furthermore, there are more cases in southern Japan, at latitudes lower than the peak (34°), in winter and spring, while the number of cases in northern Japan, at latitudes higher than the peak, is relatively high, especially in summer (Fig. 10e).
Histograms of (a) month, (b) hour, (c) altitude (×10−2 ft), (d) longitude, and (e) latitude of the cases of turbulence over Japan in PIREPs from 2006 to 2018. The gray, red, green, and blue lines stand for winter (–DJF), spring (–MAM), summer (–JJA), and autumn (–SON), respectively.
Citation: Journal of Applied Meteorology and Climatology 62, 5; 10.1175/JAMC-D-21-0191.1
b. Turbulence cases normalized by the number of flights
The previous results show that the number of reported turbulence cases is high around the flight routes connecting major airports. Hence, the high number of turbulence cases would simply be due to the large number of flights. To investigate the probability of occurrence of aviation turbulence, the number frequencies of quantities associated with reported turbulence are normalized by the CARATS flight data. Thus, larger values of normalized number frequency indicate higher probability of occurrence of turbulence.
Figure 11 displays the horizontal distribution of total number of turbulence cases from 2006 to 2018 divided by the number of flights in an area of 1.2° longitude × 1.2° latitude. In comparison with Fig. 7, the distribution of normalized number of turbulence cases by the CARATS data is largely different from that of number of turbulence cases. Below FL200, the spatial distribution is relatively high over the ocean in the south of Osaka. From FL200 to FL250, there is a peak around 127°–129° longitude and 29°–32° latitude. However, above FL250, the region with a high normalized number is scattered (i.e., not concentrated in a specific area).
Spatial distribution of turbulence in PIREP data in an area 1.2° in longitude × 1.2° in latitude divided by the total number of flight in the CARATS data.
Citation: Journal of Applied Meteorology and Climatology 62, 5; 10.1175/JAMC-D-21-0191.1
Figure 12 shows the histogram of the normalized quantities associated with turbulence. The probability of occurrence of turbulence is highest between FL250 and FL300. However, it should be noted that the number of turbulence cases is large from FL300 to FL350 (cf. Fig. 9c) rather than from FL250 to FL300. Since the number of flights is also high from FL300 to FL350 (cf. Fig. 2), this indicates that the large number of reported turbulence cases in the altitude range from FL300 to FL350 is due to the large number of flights.
As in Figs. 9c–e, but for those normalized by the CARATS data.
Citation: Journal of Applied Meteorology and Climatology 62, 5; 10.1175/JAMC-D-21-0191.1
The probability of turbulence does not largely vary in longitude, with exceptions of a minimum from 137° to 139° and a large peak from 145° to 147°. The former corresponds to a region adjacent to that including Tokyo with the largest number of flights. Since the number of flights from 137° to 139°, the central part of Japan, is high (Fig. 2), the low probability appears to be robust. In contrast, for the latter peak from 145° to 147° corresponding to the ocean region in the east of Japan, the numbers of flights and turbulence are small, implying that the peak appeared by coincidence.
In terms of latitude, the probability is relatively high from 23° to 25° and from 43° to 45° and is small from 25° to 27°. Note that the probability of turbulence from 21° to 23° latitude is apparently small when compared with other latitudes but also the number of flights is very low (Fig. 2). The probability from 33° to 37° is lower than that of the nearby latitudes. Within this range of latitudes, both numbers of flights and turbulence are the largest in the analyzed domain (Figs. 2 and 9), because major cities are located there. Thus, along the major flight routes between Tokyo and two major airports, Osaka and Fukuoka, the probability of turbulence is low when compared with the nearby latitudes. In contrast, the probability of turbulence is high along another major flight route between Tokyo and Sapporo, which is located around 140° longitude and covers latitudes from 37° to 41°.
c. Turbulence associated with clouds
Figure 13 shows histograms of the CIT cases, which are diagnosed by ground-based radar observation, as described in section 2, in relation to all cases. In general, the percentage of CIT is approximately 0.1% or less, meaning that 10% or less of the reported turbulence is associated with clouds according to the present definition. In contrast to all cases (Figs. 9 and 10), the percentage of CIT is large in summer and small in winter; the largest is 9% in August, which is followed by September and July, and the smallest is 2% in January (Fig. 13a). The order of percentage of CIT is consistent with that obtained for South Korea (Kim and Chun 2011), even though the study used lightning data rather than radar observation. The percentage of CIT is relatively large in the daytime (Fig. 13b). The percentage is in general large in high altitudes, except for the peak at the low level in the summer (Fig. 13c). The number is overall large in lower longitudes and latitudes for all seasons (Fig. 13e), indicating that many turbulence cases are associated with convection in the southwestern region in Japan.
As in Fig. 10, but for CIT.
Citation: Journal of Applied Meteorology and Climatology 62, 5; 10.1175/JAMC-D-21-0191.1
d. Turbulence associated with mountain waves
Figure 14 shows histograms of the MWT cases, which are diagnosed by the method of Kim and Chun (2011), as described in section 2, in relation to all cases. The percentage of MWT is also approximately 0.1% for all the seasons, similar to CIT. Particularly, the relative number of MWT cases is largest in autumn, followed by winter (Fig. 14a). MWT tends to occur at night, while the relative frequency does not largely depend on altitude. The percentage of MWT is very large in the longitudes from 139° to 141° for all the seasons (Fig. 14d). The percentage is large in latitude ranges from 33° to 37° and from 41° to 45° (Fig. 14e), which corresponds to the high-mountain regions in mid- and northern Japan.
As in Fig. 10, but for MWT.
Citation: Journal of Applied Meteorology and Climatology 62, 5; 10.1175/JAMC-D-21-0191.1
4. Discussion
The present analyses show that the number of cases of aviation turbulence is highest in spring followed by autumn and winter, while the number is smallest in July and August (cf. Fig. 9). The result is consistent with previous studies of different regions in midlatitudes (Wolff and Sharman 2008; Kim and Chun 2011), in which various weather disturbances affect the occurrence of turbulence. It is suggested that the reported turbulence over Japan is affected by midlatitudinal weather systems. Synoptic-scale low pressure systems, namely, midlatitude cyclones, move over Japan in spring and autumn (e.g., Adachi and Kimura 2007), which accompany convective and stratiform clouds within a wide area. In June (and early July depending on regions), mei-yu front stretches to Japan and often provides a huge amount of precipitation by forming mesoscale convective systems (e.g., Yihui and Chan 2005). The present analysis of CIT diagnosed by radar observation showed that the number of CIT cases relative to all cases is large in summer, including June and July (Fig. 13a). Hence, it is indicated that the large number of turbulence cases from March to June (Fig. 9a) is affected by convective clouds associated with the cloud systems. In autumn, an autumn rain front that is also a stationary front extends over Japan and may produce turbulence affecting airplanes. Also, airplanes are affected by strong westerly winds associated with the subtropical jet stream, especially in winter (Fig. 6). Stronger shears associated with jet streams tend to produce turbulence, as introduced above; hence, it is expected that a number of turbulence cases are due to flows originating from the jet stream.
Wolff and Sharman (2008) found that a large number of cases over the United States are caused by mountain waves in winter, which are mainly due to strong westerly winds associated with jets whose axes are located in latitudes around high mountains. Kim and Chun (2011) also showed that the turbulence cases over South Korea in winter are mainly produced by mountain waves. There are high mountains in Japan as well (cf. Fig. 1); they do not spatially cover large areas, as in the United States or Greenland, where many turbulence cases are associated with mountain waves (Lane et al. 2009). Thus, it is suggested that the large number of cases observed in autumn, winter, and spring in the present study are also due to mountain waves caused by strong westerly winds (Figs. 6 and 14). In particular, the high probability in the longitudes from 146° to 148° shown in Fig. 12 appears to be due to the mountain waves. High mountains are located in the central and northern parts of the main island of Japan from 37° to 41° latitude and from 139° to 141° longitude. In spring and winter, strong westerly winds associated with the jet stream flow over the mountains in the northern part of the main island, causing mountain waves in the eastern side of the domain. In fact, the normalized number of turbulence cases is relatively large from 37° to 41° latitude, corresponding to the mountainous region (Figs. 12 and 14).
The turbulence intensity in the present PIREP data is mostly MOD, which is strong enough to motivate pilots to avoid turbulence (Captain Y. Nikai 2021, personal communication). Around Japan, there are several areas where commercial airplanes, the source of the present PIREP data, are prohibited from flying. The number of turbulence cases would be high near the limited areas, because airplanes hardly change the path horizontally to avoid turbulence around the areas. For example, in the offshore area of Shikoku Island located around 33°N, 133°E and east of Okinawa around 26°N, 127°E there are local peaks of the number of turbulence cases normalized by flight data (cf. Fig. 7), which would be due to the limitation of avoidance. This would partially result in the dispersed spatial distribution of number frequency of turbulence (Fig. 11), which is inconsistent with that in the United States (Wolff and Sharman 2008).
In low altitudes, airplanes also hardly avoid turbulence even if they find it in advance, because the airplane routes are predetermined and cannot be changed near the airport. As a result, the number of turbulence cases tends to be large in low levels around airports (cf. Fig. 7). The increase in the number of turbulence cases in low levels is consistent with previous studies over the United States (Steiner 1966; Wolff and Sharman 2008; Frehlich and Sharman 2010; Sharman et al. 2014). Nevertheless, the normalized number of turbulence cases is small below FL200 when compared with that between FL200 and FL350 (cf. Fig. 12), indicating that this effect in low levels is small.
5. Conclusions
We conducted a statistical analysis of aviation turbulence over Japan by analyzing the PIREP data from 2006 to 2018. The monthly number of cases of aviation turbulence has an annual periodical variation that has been slightly increasing since 2014. Most of the reported turbulence intensity is MOD. The number of turbulence cases is dominant along the major flight routes in Japan, especially around Tokyo, for the active period between 0900 and 2000 local time. The peak altitude of the number of turbulence cases is observed at FL330. The cases of turbulence are concentrated around the major airports in the altitudes lower than or equal to FL200, while they are also observed along the flight routes between the major airports. The number of cases varies seasonally; the largest number is observed from March to June, whereas the number is smallest in July and August. On the other hand, seasonal variations of quantities associated with turbulence, such as location and time, are generally not high. The number of cases is relatively large from 1000 to 1400 local time in summer in comparison with autumn and winter, while it is large in winter from 1600 to 2000 local time. A large number of cases are observed in high (low) altitudes in summer (winter). From March to August, the altitudes of more than 50% of the total cases are higher than or equal to FL300.
Dividing the number of turbulence cases by the number of flights reveals that the turbulence likely occurs in the altitudes from FL200 to FL350, despite the fact that the number of reported turbulence cases is particularly large below FL200 and above FL350. The normalized frequency is particularly large from 145° to 147° longitude and small from 137° to 139° longitude. However, the horizontal distribution of probability of turbulence is not concentrated in a particular region; rather, regions with a high probability of turbulence are detected locally. For instance, the normalized number is locally small from 137° to 139° longitude and from 25° to 27° latitude.
Many cases in summer are associated with clouds, even though the total number of cases is smallest in summer among the four seasons. It would be likely because convective phenomena are most active in summer. This result is also consistent with the statistics in South Korea (Kim and Chun 2011), but it should be noted that effects of mei-yu front on the number of turbulence cases appear to be significant in Japan. Whereas Kim and Chun (2011) showed that the relative number of CIT cases is largest in August and September, the peak appears in June and July in the present analysis.
The number of MWT cases was relatively large around the high-mountain region that extends in the meridional direction in autumn and winter. The averaged mean wind directs eastward in this region in autumn and winter (cf. Fig. 6). It is suggested that the strong westerly winds affect MWT around the mountainous region and that a large number of turbulence cases occurring in the high-mountain region are influenced by the mountains.
The PIREP data contain a certain number of errors because the information on turbulence, such as intensity, horizontal and vertical locations, and time, is subjectively provided. Since the PIREP data are the most useful data available for a wide area and for a long period, we used the PIREP data as the turbulence data for the analyses. Thus, whereas the uncertainty of PIREPs could be neglected to construct climatology, as pointed out by a previous study (Sharman et al. 2006), it is desired to conduct statistical analyses based on quantities associated with turbulence that are objectively estimated and provide exact information on geographical location and time.
Acknowledgments.
This study is partly supported by JSPS KAKENHI; Grants 19K04849 and 19H01973; the advanced model demonstration project using satellite remote sensing data for solving social issues by Cabinet Office, Japan; the open and free satellite data project by the Ministry of Economy, Trade and Industry, Japan; Keio University Academic Development Funds for Individual Research; and the start-up grant of Keio Research Institute at SFC. This study used the CARATS open data, which are provided by MLIT, Japan. The authors thank Yohei Nikai for a fruitful discussion. The authors are grateful to the anonymous reviewers for providing constructive comments.
Data availability statement.
All data analyzed in this paper are available by emailing the corresponding author.
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