An Automated System to Quantify Aircraft Encounters with Convectively Induced Turbulence Over Europe and the Northeast Atlantic

VOLUME 55 JOURNAL OF APPLIED METEOROLOGY AND CLIMATOLOGY MAY 2016

An Automated System to Quantify Aircraft Encounters with Convectively Induced Turbulence over Europe and the Northeast Atlantic

ELENA MENEGUZ,HELEN WELLS, AND DEBI TURP Met Ofﬁce, Exeter, United Kingdom

(Manuscript received 23 June 2015, in ﬁnal form 28 January 2016)

ABSTRACT

It is well known that encounters with moderate or severe turbulence can lead to passenger and crew injuries and incur high insurance costs for airlines. Atmospheric convection is thought to induce a significant proportion of turbulence experienced by commercial aircraft, but its relative importance over Europe and the northeastern Atlantic Ocean area has not yet been quantified in a systematic way. In this study, a new approach is developed to automatically detect turbulent events associated with convective sources. Observations of convection over Europe and the northeastern Atlantic were obtained from the Met Office Arrival Time Detection system (ATDnet) and from Meteosat Second Generation satellite imagery. The system is run for all in situ reports of turbulence received from a commercial airline for two 6-month periods (summer 2013 and summer 2014). It is found that, as a monthly average, 14% of all aircraft encounters with turbulence occur in the proximity of a convective storm. These findings are interpreted and discussed together with the limitations of the system and observations that were used in this study.

1. Introduction upper-level fronts, breaking mountain waves, tropopause folds, and convection. It is known that convectively in- Turbulence continues to be one of the main meteoro- duced turbulence is experienced by aircraft not only within logical hazards for en route air traffic, with turbulence convective clouds but also above the cloud (e.g., Lane and encounters accounting for approximately 65% of weather- Sharman 2006; Lane et al. 2012) and at some distance from related commercial aircraft incidents (Sharman et al. the cloud (e.g., Kaplan et al. 2005). Information on tur- 2006). In particular, encounters with severe turbulence bulence sources is vital for two reasons: first to focus can lead to injuries to passengers and crew, aircraft dam- (limited) research resources on the sources that have the age, and (costly) redirection of aircraft around turbulent greatest impact on aviation and second to allow verifica- regions. Therefore, there is considerable ongoing research tion of forecasts of turbulence from a particular source effort directed toward improving the accuracy of turbu- only against turbulence encounters that are likely to be lence forecasts. associated with that source and thus to enable both as- Data from commercial aircraft provide a good basis sessment and improvement of turbulence forecasts. for gaining a better understanding of turbulence. There Past studies indicate that, among all possible causes, are several types of observations available, including pilot moist convection is likely to play a significant role in air- reports (PIREPs) and automated in situ measurements of craft encounters with turbulence. A study by Kaplan et al. turbulence reported as vertical acceleration, eddy dissi- (2005) aimed at characterizing the conditions associated pation rate (EDR), or derived equivalent vertical gust with severe turbulence that led to commercial aviation (DEVG). These observations (with the exception of a accidents. From an analysis of 44 events (37 over the small fraction of PIREPs) do not contain any information United States and 7 over warm ocean regions), the authors regarding the likely source of turbulence. There are many concluded that the majority (86%) occurred within possible causes for atmospheric turbulence at cruise level, 100 km of moist convection. Wolff and Sharman (2008) including wind shear associated with jet streams and more recently attempted to identify the sources of upper- level turbulence over the contiguous United States using Corresponding author address: Elena Meneguz, Met Office, 12 yr of PIREPs of encounters with moderate-or-greater FitzRoy Road, Exeter EX1 3PB, United Kingdom. (MOG) intensity turbulence. The authors identified a con- E-mail: elena.meneguz@metoffice.gov.uk siderably higher number of aircraft turbulence encounters

DOI: 10.1175/JAMC-D-15-0194.1

1077 Unauthenticated | Downloaded 09/29/21 10:58 AM UTC 1078 JOURNAL OF APPLIED METEOROLOGY AND CLIMATOLOGY VOLUME 55 over significant topography than would be expected a convective systems (e.g., Morel and Senesi 2002) but to priori, indicating the importance of mountain-wave- quantify the fraction of aircraft encounters with con- induced turbulence in winter and orographically in- vectively induced turbulence over Europe and the north- duced thunderstorm activity in the summer. They also eastern Atlantic Ocean region. Two independent sets of found correlations as high as 20% over certain regions observations are used as a proxy for convection: lightning between MOG PIREPs and lightning-flash data within data from the Met Office ATDnet system and MSG 30 min and 40 km, which is indicative of the importance of (composite and IR) imagery. In this study, NWP di- convectively induced turbulence in those regions. Gill agnostics are deliberately omitted to minimize the de- and Stirling (2013) categorized in situ reports of turbu- pendence of results on numerical model errors (since the lence collocated (in space and time) with the lightning output of the categorization system is intended for usage in observations from the Met Office Arrival Time Detection NWP verification). The new automated system is now network (ATDnet), which indicated that 82% of MOG used routinely to obtain a dataset of turbulence encounters turbulence encounters over an 8-month period were in occurring close to moist convection. the proximity of lightning strikes. They also showed that The remainder of this paper is organized as follows. the skill of upper-level turbulence forecasts could be Section 2 describes the in situ observations of turbulence significantly enhanced if, in addition to wind shear and from commercial aircraft used in this study. Section 3 mountain-wave predictors, convective predictors such as illustrates the main features of the observations used as the convective precipitation rate were also used. Recent a proxy for atmospheric convection—namely, the studies such as Krozel et al. (2015) and Al-Momar et al. ATDnet system, the MSG severe-convection red– (2015) investigated the relation between total (i.e., cloud green–blue (RGB), and IR 10.8-mm satellite imagery. to ground and inter-/intracloud) lightning and EDR tur- Section 4 is devoted to illustrating the working princi- bulence measurements and found a close correlation for ples of the automated system, including a description of the cases studied. Taken together, these five studies in- the algorithm used to automate identification of con- dicate the importance of convective sources for hazard- vective clouds from satellite imagery, and in section 5 ous (moderate or severe) turbulence. results of a preliminary climatological study over the A number of automated approaches for detection of European and northeastern Atlantic area are presented severe convective storms over continental Europe have and discussed. Last, conclusions and recommendations recently been constructed. Morel and Senesi (2002) for future work are the focus of section 6. proposed an automated method for identifying the ex- istence and development of mesoscale convective systems using Meteosat infrared (IR) satellite images. 2. Aircraft measurements of turbulence Their technique used a single IR brightness temperature The current ICAO standard metric for turbulence threshold to identify deep convective cloud. An algo- reporting is the eddy dissipation rate (ICAO 2010), but rithm was used to extract the structural properties of aircraft measurements of EDRarecurrentlyonlywidely these systems (e.g., area and orientation), which were in available over the United States. An alternative acceptable turn used to track their temporal evolution. More recent measure is the derived equivalent velocity gust (Sherman techniques are based on the exploitation of Meteosat 1985; Truscott 2000). DEVG is an aircraft-independent Second Generation (MSG) satellite data combined with metric with units of meters per second and is defined as numerical weather prediction (NWP) model data both to identify overshooting tops, which are found in the DEVG 5 AmjDnj/V , (1) proximity of 47% of severe-weather episodes in Europe (Bedka 2011), and for use in nowcasting (e.g., Zinner where jnj is the peak modulus value of fractional de- et al. 2013). Other approaches in the literature include viation of aircraft vertical acceleration from 1 g in units pattern recognition to identify cloud signatures associ- of gravitational acceleration g, m is the total aircraft ated with the development of mesoscale convective mass in metric tons, V is the calibrated airspeed at the systems and satellite imagery of transverse bands time of occurrence of the acceleration peak in knots 2 (DiMaio 2013) and use of the difference between IR and (1 kt ’ 0.51 m s 1), and A is an aircraft-specific param- water vapor brightness temperature to identify deep eter that varies with flight conditions [for details see convection (Martin et al. 2008). Truscott (2000)]. Values of DEVG can be interpreted as In this paper, an automated approach for identifying shown in Table 1. aircraft encounters with turbulence that occur close to This study uses automated aircraft measurements observed convective storms (CSs) is presented. Here, from the Global Aircraft Data Set (GADS; Gill 2014), the aim is not to track the development of mesoscale from which values of DEVG are calculated and used

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TABLE 1. Turbulence severity measured by aircraft, with the cor- uses a surface-based network of very low frequency radio responding DEVG values and indices (Truscott 2000). receivers across Europe that record the absolute arrival Turbulence severity DEVG value DEVG index time of the signal emitted by lightning. By comparing the arrival-time differences among several stations, the po- None DEVG , 20 Light 2 # DEVG , 4.5 1 sition of the lightning strike is found [for more details Moderate 4.5 # DEVG , 92about the lightning-detection technique, the reader is Severe DEVG $ 93referred to Gaffard et al. (2008) or Anderson and Klugmann (2014)]. The data are then stored in a data- to identify encounters with turbulence. The database has base. This technique for lightning detection is sensitive to been used in previous meteorological studies by several the lightning-strike geometry, and therefore not all forecasting centers (e.g., Cardinali et al. 2004; Rickard lightning is detected. In particular, the system detects et al. 2001). Aircraft observations from GADS have mainly cloud-to-ground strikes, which account for ;25% been more recently used for development and verifica- of all activity, and the rest is inter-/intracloud. This dis- tion of World Area Forecast Centre turbulence fore- advantage is counterbalanced by the capability to detect casts (Turp and Gill 2008; Gill and Stirling 2013; Gill flashes over a large area and to provide relatively con- 2014; Gill and Buchanan 2014). The best data coverage tinuous coverage over most of Europe, where the best is over Europe, North America, and the northeastern performance of ATDnet is found (location uncertainty is Atlantic, with fewer data in the Southern Hemisphere. on the order of a few kilometers, and 90% of cloud-to- Observations are reported every 4 s for several different ground strikes are detected), with a more limited capability parameters, including latitude, longitude, altitude, air- to detect lightning in North Africa and the northeastern speed, aircraft mass, and vertical acceleration. The max- Atlantic. imum and minimum vertical acceleration values are b. MSG imagery obtained from 32 measurements taken during the previous second. To eliminate erroneous values, standard Meteosat satellites are spin stabilized, with instruments quality-control measures are applied (e.g., duplicates and designed to provide permanent visible and infrared im- corrupted records are removed, and observations from aging of Earth. In particular, MSG satellites are equipped blacklisted aircraft are discarded). In addition, only ob- with a visible–infrared radiometer for Earth radiation servations at cruise level [i.e., altitude above 28 000 ft budget and the Spinning Enhanced Visible and Infrared (;8500 m)] are used to eliminate false alarms resulting Imager (SEVIRI), which observes Earth in 12 spectral from maneuvering aircraft, although maneuvers by large channels (Schmetz et al. 2002). SEVIRI records data in a passenger aircraft are generally kept at a minimum to 15-min cycle, providing an accurate weather-monitoring reduce the risk of passenger discomfort (Sherman 1985). service. The data are then projected on maps: the satellite imagery retrieved in this study covers Europe and the 3. Observations of convection northeastern Atlantic area and is projected using a polar Severe convective weather is often associated with stereographic projection. In this type of projection, each deep cumulonimbus clouds and thunderstorms, and it is point on Earth (assumed to be a sphere) is projected from common practice to use lightning-detection networks and the South Pole onto a plane perpendicular to the Earth satellite images to assess the occurrence of convection by axis. The stereographic projection is a so-called conformal means of observations. From recent studies it is clear that projection; that is, an angle on the sphere remains the the characteristics of convective storms vary significantly same in the projection plane and vice versa. It is not an among different regions of the world (e.g., Kaltenböck equal-area projection (grid points are equally spaced et al. 2009). For example, continental thunderstorms are among meridians but not among parallels), and the aver- associated with more lightning flashes than oceanic age spatial resolution of a pixel is 6 km 3 6km.Thisstudy thunderstorms are (Christian et al. 2003; Orville and makes use of two MSG products (in a mutually exclusive Henderson 1986). To capture the variability of convective way): the severe-convection RGB and the IR 10.8 mm. characteristics over the domain of interest, in this study The IR-window-channel imagery is a 24-h product two sets of independent observations are used: ATDnet corresponding to a wavelength of 10.8 mm. This IR and MSG imagery. channel is used to measure cloud top and surface temperature (at midlatitudes, the cloud-free atmosphere is a. ATDnet data transparent at these wavelengths). Temperature in- The Met Office has been using a lightning-location tensities are converted into grayscale tones: lighter areas network since 1987. The most recent version, ATDnet, of cloud correspond to higher cloud tops. Convective

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FIG. 1. The IR 10.8-mm-channel product valid at 0800 UTC 13 FIG. 2. The severe-convection RGB product valid at 1100 UTC 11 Oct 2014. Jun 2014. cloud tops are usually found below a brightness temper- the time, location, and severity of all turbulence en- ature (BT) threshold and are approximately circular in counters over Europe and the northeastern Atlantic, shape. Figure 1 shows an example of the IR 10.8-mm defined as the area within latitude 308–608N and longi- imagery that is valid at 0800 UTC 13 October 2014 in tude 308W–308E. which convective cells are visible over northern Italy. The system also uses the severe-convection RGB b. Retrieval of lightning observations product (http://oiswww.eumetsat.org/IPPS/html/MSG/ ATDnet data (latitude and longitude of flash events) RGB/). RGB images are composite images that are are retrieved over the domain of interest, with lightning- generated by combining two or more channels and dis- strike times recorded to the nearest minute. playing the output in color. The naming convention describes which channel is assigned to the red, green, and c. Retrieval and postprocessing of satellite imagery blue colors. The severe-convection product uses the 6.3– The system retrieves either the severe-convection 7.3-mm difference (colored red) to indicate the height of RGB or the IR 10.8 mm with hourly frequency. Be- the cloud in the atmosphere, the 1.6–0.6-mm difference cause the first product has been exclusively designed to (colored blue) to highlight the phase of the clouds, and identify convective cloud tops, it was desirable to use it the 3.9–10.8-mm difference (colored green) to extract preferentially over the IR imagery; its availability is information on the particle size. In this product, convec- restricted to daylight hours, however. To automate the tion is shown as either bright orange, which indicates that capability of distinguishing between daylight and the ice cloud is high in the troposphere but has fairly large nocturnal hours, the solar zenith angle (SZA) is cal- ice particles, or bright yellow, which indicates that the culated. This angle is measured from the zenith to the convection is severe and air has ascended rapidly into the geometric center of the sun’s disk, and it is a function troposphere, leading to very small ice particles. Figure 2 of time, yearday, and latitude of each turbulent event illustrates an example of this RGB product valid at such that 1100 UTC 11 June 2014 in which a convective cell is present over northeastern Germany close to the Polish SZA 5 sina sinb 1 cosa cosb cosg , (2) border. Because of its use of visible channels, the product is available only during daylight hours. where a, b, and g are respectively the declination of the sun, latitude (defined as positive in the Northern Hemisphere), and hour angle (a measure of the local 4. Description of the automated system to identify time). If SZA , 82, it is considered to be daytime and turbulence events close to convective storms the corresponding severe-convection RGB imagery is The semioperational system is run every month in retrieved. Otherwise, the IR 10.8 mm is retrieved. verification mode and performs the operations de- 1) TEST ON BRIGHTNESS TEMPERATURE scribed in the following sections. For each satellite image retrieved, a filtering tech- a. Retrieval of turbulence observations nique is applied to isolate convective clouds. For IR A file containing the GADS turbulence data (de- 10.8-mm images, a BT threshold of 223 K is imposed and scribed in section 2) is read in to the system. It includes all cloud tops above this temperature are discarded. A

Unauthenticated | Downloaded 09/29/21 10:58 AM UTC MAY 2016 M E N E G U Z E T A L . 1081 value of 223 K was chosen because it is similar to the 228-K threshold used by Morel and Senesi (2002) to isolate mesoscale convective systems but is a weaker constraint than the 215-K threshold used by Bedka (2011) to identify only overshooting cloud tops (i.e., tops that are located in the lower stratosphere). For composite images (i.e., the severe-convection RGB product), postprocessing is performed to asso- ciate each pixel with its corresponding latitude and longitude, and a filtering technique is applied to select only those pixels that through a combination of channels correspond approximately to a bright yellow/ orange color. FIG. 3. Result of test on cloud shape (red contours) placed on top 2) ADDITIONAL TEST ON CLOUD SHAPE (ONLY of clouds identified by the BT filter (green contours), for the same image that is shown in Fig. 1. FOR IR 10.8-mM IMAGERY) In addition to the BT test, a shape test is applied to d. Assessment of the proximity of convective storms identify convective cloud tops by identifying clouds with to each turbulent event an approximately circular shape. The starting point of the numerical and statistical analysis that is employed For each turbulent encounter listed in the aircraft for shape detection is the boundaries of each cloud that database, the system searches for the nearest lightning has passed the BT filter that was previously described strike and satellite convective observation occurring (i.e., BT # 223 K). Then, only those cloud patches whose within 30 min before or after the event. Then it calcu- size is bigger than 5 3 5 pixels and for which the lat- lates the distance between the observation of convection itudinal and longitudinal extents, in pixels, are approx- and the aircraft’s location to classify the turbulent event imately equal are considered. This is done to discard as being in the proximity of or far from a convective small clouds and clouds that are not sufficiently circular storm. For the sake of clarity, in this study the definition in shape to be indicative of convective storms. Following of a CS depends on the type of observation used as a a boundary scalar technique (Levner 2002), the absolute proxy for convection: a CS is identified by each lightning distances di between the center of each cloud patch and its flash when using lightning data and by each convective boundaries as well as the average distance d obtained by cloud top when using satellite data. In the remainder of summingallofthesedistancesanddividingbythenumber this paper, the word ‘‘proximity’’ is used as a general of pixels around the boundaries are calculated. This is term that includes both the ‘‘near storm’’ and ‘‘in/above based on the theory of mean deviation and has been de- storm’’ categories, explained as follows and with refer- fined by Ritter and Cooper (2009) as a ‘‘mean roundness ence to Table 2. For observations of lightning, it is as- measure.’’ Then, only those clouds for which the dis- sumed that the flash occurs at the center of a CS and persion falls within an acceptable tolerance interval de- therefore that a turbulent event is inside a CS when the fined as di/d 2 (0:1, 2) are selected and classified as distance between the aircraft and the flash is shorter convective clouds. than a typical storm radius [taken to be 15 km, as in The shape-identification technique that is developed Jerrett and Turp (2005)]. A turbulent event is instead and employed in this paper has been tested by visual classified as near storm if it is within a threshold of 53 km, assessment of a large number of satellite images, and roughly corresponding to 15 km 1 20 n mi (;38 km), the threshold intervals have been altered until its perfor- second being the distance by which pilots are currently mance was satisfactory. An example of the outcome of advised to avoid severe thunderstorms (e.g., Lane and this technique is shown in Fig. 3, which displays the Sharman 2008). contours of all of the convective clouds identified by the For observations provided by RGB satellite imagery, shape-identification technique (in red) laid on the con- it is assumed that the aircraft is in/above a CS when there tours of all the clouds that passed the BT test (in green), is at least one cloud pixel at a distance shorter than resulting in fewer clouds being classified as convective. 3.63 km—this being the theoretical maximum distance Coastlines and lines of meridians and parallels are re- between the aircraft’s position and the nearest cloud moved from the original image (e.g., Fig. 1) to avoid boundary (identified with the center of the pixel), as clouds being split artificially and resulting in spurious sketched in Fig. 4. This distance is calculated as the av- additional clouds when applying this technique. erage between the maximum distance r1, equivalent to

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TABLE 2. Thresholds (in kilometers) used to assess the proximity of the aircraft’s position with respect to that of the nearest CS, depending on the type of observation.

Classiﬁcation ATDnet Satellite RGB Satellite IR In/above storm d # 15 d # 3.63 Aircraft’s position inside cloud boundaries Near storm 15 , d # 53 3.63 , d # 53 d # 53 and aircraft’s position inside cloud boundaries Out of storm d . 53 d . 53 d . 53 the radius of the circle circumscribed on the square products are not independent because they both rely on

(i.e., the pixel), and the minimum distance r2,equivalent infrared channels and are in this sense interchangeable). to the radius of the circle inscribed in the same square. Last, when using the IR satellite product, the retrieval 5. A preliminary climatological study over the technique (which is based on Interactive Data Language summers of 2013 and 2014 software routines to extract and analyze ‘‘regions of interest’’) allows identification of the boundaries of each It is known that convection is particularly active over individual convective cloud. It is then possible to calcu- the domain of interest (Europe and the northeastern late whether the aircraft’s position lies within the range of Atlantic) during summertime, and therefore the pre- latitude and longitude associated with the nearest con- liminary climatological assessment is conducted over the vective cloud; if this condition is met, the aircraft is then summer months, specifically from May to October 2013 considered to be in/above a CS. and from May to October 2014. The area covered lies within latitudes 308–608N and longitudes 308W–308Eto e. Outcome of the system guarantee good coverage of GADS, ATDnet, and The final product of the system is a file with the number MSG data. of rows equal to the number of aircraft turbulence en- Table 3 shows the total number of GADS observa- counters and the following columns: year, month, day, tions, the total number of flights (columns 2 and 3), and hour, minute, coordinates (latitude and longitude) of the the total number of light-or-greater (LOG) and MOG turbulent event, and two classification keywords (the first turbulent events experienced by aircraft within the resulting from the comparison with lightning observa- domain of interest of this study (columns 4 and 5). It is tions and the second from the comparison with satellite clear that turbulence encounters are relatively rare, images). One of the following three scenarios can occur: with just 0.028% of observations indicating LOG tur- 1) both observations (from ATDnet and satellite, either bulence and 0.0005% indicating MOG turbulence. The IR or RGB) confirm convection is present and that the number of LOG events clearly exhibits a significant turbulence event is likely to be convectively induced, intraseasonal variability (e.g., October 2014 has more 2) only one observation (either from ATDnet or satellite) than 3 times the number of LOG events as July 2013). detects convection, and 3) neither observation detects The majority of turbulence encounters are of light in- convection and therefore the source of the turbulence tensity (98% of the turbulence events have DEVG event experienced by the aircraft is probably not index 1), with only a very small fraction of MOG convective. Figure 5 illustrates the flowchart for the automated classification system. It is worth noting that initially the system is run applying only the BT filtering technique on satellite imagery (i.e., without the test on cloud shape). This is done because the assumption on circularity is very restrictive and, when applied, it frequently discards convective clouds that are collocated with lightning strikes. For quality control, the system is run a second time for all detections of convection that are based only on satellite data (i.e., are not confirmed by lightning data). For these detections, a conservative approach is 3 adopted and the cloud-shape test is performed. For the FIG. 4. (a) Pixel size represented by a square of size 6 km 6 km, and (b) maximum distance r 5 4.25 km and minimum distance r sake of simplicity, the IR 10.8-mm images are used to 1 2 5 3 km between the aircraft (at location A1 or A2) and the nearest perform this test for both daylight and nocturnal hours cloud boundary (point C), assumed to be at the center of a pixel (ultimately the severe-convection RGB and the IR when using severe-convection RGB satellite imagery.

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FIG. 5. Flowchart of the automated verification system. turbulence. In this study, only three severe turbulence different stages of development. Lightning activity reports were recorded, all of which occurred in May (especially intracloud) peaks at the initial stage of the 2013. This is not surprising because more-intense tur- storm (Williams et al. 1989) and may therefore be as- bulence is rarer than less-intense turbulence. sociated with single-cell storms of small size, but the spatial resolution of satellite images (;6km 3 6km) a. Agreement between ATDnet detections and satellite means that any cloud below this resolution remains detections of turbulence in the proximity of CSs unresolved. Figure 7 shows an IR satellite image with Figure 6 shows the monthly percentage of con- all lightning detections in a 2-h time period laid on it, vectively induced turbulence events detected by both and this effect is clearly seen over the Balkans. As a ATDnet and IR/RGB satellite observations (in green) result of this, one would expect ATDnet to identify and by one observation only (lightning in orange and more storms in the early stages of development than satellite in blue). The percentage shown is calculated as would the satellite imagery. the ratio between the number of detections and the In addition, it is possible for flashes to reach the number of LOG events each month. As mentioned ground at a significant distance from the parent cloud. earlier, a conservative approach has been adopted for Such phenomena are called bolts from the blue. They are detections only by satellite products (which are not usually generated from the backside of a thunderstorm confirmed by ATDnet data) in that only those clouds that overcome both the BT and the cloud-shape test TABLE 3. The total number of GADS observations, total number (described in section 4c) are identified as convective. For of GADS flights, and total number of LOG and MOG turbulence completeness, Fig. 6 also shows the additional detections events recorded per month over the domain of interest. of convection obtained without the test on cloud shape Month No. obs No. flights No. LOG No. MOG (gray dashed lines), which can be loosely seen as an upper bound when interpreting the results of the system. May 2013 6 062 960 4470 1720 24 Jun 2013 5 426 512 4255 1528 5 Although the system shows good agreement between Jul 2013 5 577 728 4388 765 11 lightning-based detections and satellite-based detections, Aug 2013 5 616 325 4530 1753 13 a high fraction (.70%) of convective storms in the prox- Sep 2013 5 107 877 4067 1934 24 imity of turbulence are detected by only one type of ob- Oct 2013 4 230 554 3372 1727 23 servation (i.e., detections from either ATDnet data or May 2014 5 998 964 4430 1327 41 Jun 2014 5 688 500 4241 1020 27 satellite imagery but not both). There are a number of Jul 2014 5 609 391 4305 1022 35 reasons for this result that have to do with the character- Aug 2014 5 647 210 4280 952 47 istics of both CSs and observations. Sep 2014 5 239 628 3995 1239 25 The characteristics of CSs mean that the different Oct 2014 5 317 406 4104 3167 70 3 7 observations are likely to principally detect storms in Total 6.55 10 50 437 18 154 345

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FIG. 6. Percentage of aircraft encounters with turbulence classified as convectively induced (i.e., identified as either in the vicinity of a CS or in/above a CS) using both ATDnet and satellite observations (green), ATDnet data only (orange), and satellite data only (blue). See the text for an explanation of the gray dashed lines. and can travel a relatively large distance away from the (Gaffard et al. 2008). Given all of the above, it is un- cloud before changing direction and hitting the ground surprising that only ;30% of all detections were con- (Williams 2008). In this case lightning strikes would not firmed by both lightning and satellite imagery, and it be collocated with the convective cloud, and one would emphasizes the need for multiple sources of observations not expect the lightning and satellite observations to be to automatically detect convective storms. in agreement for this reason. b. Intraseasonal variability of turbulence in the Another reason is that lightning strikes associated with vicinity of CSs larger storms are frequently concentrated along the ‘‘leading’’ edge of the cloud system, where temperatures are not In this section we refer to turbulence observations that necessarily low enough to pass the BT filter of the auto- are identified in the vicinity of CSs (i.e., either near storm matedsystem.Again,thiswouldleadtoATDnetidenti- or in/above storm; see Table 2) on the basis of lightning fying convective storms while the satellite data would not. observations and/or satellite imagery. The histogram in It is also noted that not all convection in the upper Fig. 6 shows the sum of all detections according to both troposphere–lower stratosphere is associated with lightning and that oceanic thunderstorms typically generate fewer lightning strikes than do continental thunderstorms (Orville and Henderson 1986). Therefore, one would expect that the satellite imagery should identify a larger fraction of events over the northeastern Atlantic than over continental Europe. The other reasons for a discrepancy between ATDnet detections and satellite detections are related to the detection efficiency of ATDnet. As previously mentioned, ATDnet is designed to detect only cloud-to-ground strikes; therefore, it will not detect many convective storms with inter-/intracloud flashes. The location of the array of lightning detectors has been determined so as to optimize coverage over Europe (where it detects ;90% of all cloud-to-ground strikes), but the detection efficiency is known to be poorer over other regions (e.g., FIG. 7. Lightning strikes, as detected by the ATDnet lightning the North Atlantic) because strikes that are farther from location system, plotted on top of an IR 10.8-mm satellite image, the detectors must be more powerful to be detected. with a plain land–sea mask. The colors of the strikes indicate their Also, ATDnet has been shown to underdetect at night age in minutes, as shown in the color bar at bottom.

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FIG. 8. Detected lightning-flash density, calculated as number of flashes per kilometer squared per year, for the region of interest of this study with data (a) from 1 May to 31 Oct 2013 and (b) from 1 May to 31 Oct 2014. Both images are obtained with a resolution of 0.28 and are plotted using the same projection as in Fig. 10, below. The color bar on the right-hand side indicates the range of density levels, with the maximum density equal to 23.2 and 34.2 flashes per kilometer squared per year in (a) and (b), respectively.

(green), ATDnet only (orange), and satellite only (blue) encounters classified as convectively induced is found to observations divided by the total number of LOG events be ;9.36% (1701 of 18 154 LOG observations; see Table in the domain of interest, for each month of this case study. 3). This result is remarkably similar to that of Kim and When the result is averaged over the 12 months, it appears Chun (2011), who used lightning-flash data to identify that 14% of LOG turbulence encounters are convectively convectively induced turbulence encounters from PIREP induced. If the test on cloud shape is not performed (gray data located over South Korea. They found that ap- dashed line in Fig. 6), the fraction increases to 24% of proximately 9.5% of PIREPs that reported LOG turbu- LOG observations. This latter figure is likely to include lence were convectively induced (180 of 1901 MOG upper-tropopause convective turbulence that is encoun- turbulence observations). Kim and Chun (2011) also in- tered in frontal systems but is also likely to include more cluded an analysis of the monthly counts of convectively erroneous (i.e., not convective cloud) detections. induced turbulence encounters and also found a maxi- Figure 6 reveals a strong monthly variability in the mum during July–September. proportion of LOG reports in the proximity of CSs, with For MOG turbulence events, the automated system the monthly proportion varying from 5% to 24% (if the described in this paper identifies 11% as being con- test on cloud shape is removed, the range is similar at vectively induced (39 of 345 MOG turbulence observa- 12%–32%). Note that generally smaller proportions of tions; see Table 3) when all observation types (both convective turbulence events are seen in May and Octo- lightning and satellite) are taken into account and 8.66% ber than in the summer months of June–September. (30 over 345 MOG encounters) when turbulent events Figure 6 also reveals an interannual variability, since are compared only with observations of lightning. This there was a greater fraction of convectively induced tur- can be considered to be in agreement with Kim and bulence events in the summer months (i.e., June– Chun (2011) who identified 11% of MOG encounters as September) of 2014 (20%) than in 2013 (12%). To in- being convective in nature (28 of 255 encounters). vestigate whether this interannual variability is related However, it would appear to be notably lower than the to a higher frequency of convective storms in summer fraction of convectively induced turbulence identified by 2014 than in summer 2013, the lightning-flash density Gill and Stirling (2013); these authors found that 82% detected by ATDnet is displayed for the period May– of a total of 442 MOG turbulence encounters assessed October for 2013 and 2014 in Fig. 8. Comparison of were in the proximity of lightning strikes observed by Fig. 8a (May–October 2013) with Fig. 8b (May–October ATDnet. Although they used the same turbulence ob- 2014) clearly shows a generally higher density of lightning servations (GADS), the same lightning observations flashes in summer 2014, suggesting that it was a more (ATDnet), and the same time/distance criteria for de- convectively active season than was summer 2013. termining the proximity of a strike as were used in this When relying only on the ATDnet system (i.e., study, there is one significant difference that makes the neglecting satellite imagery), the percentage of LOG results difficult to compare. In this paper, statistics are

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FIG. 9. Comparison of the percentage of turbulence encounters classified as in/above a CS using ATDnet data (dashed line) and satellite data (solid line) for (top) May–October 2013 and (bottom) May–October 2014. made using every single LOG event (resulting in a by the work of Wolff and Sharman (2008),whofound temporal window of up to 4 s between one event and the that 20% of MOG turbulence from PIREPs over the next), whereas Gill (2014) and Gill and Stirling (2013) contiguous United States were collocated (within 0.5 h chose a 10-min segment (corresponding to ;100 km of and 40 km) with lightning strikes. It is also supported flight) to match the relatively low size of the forecast- by the findings of Kaplan et al. (2005), who found, from model grid box. an analysis of 44 severe-turbulence encounters (37 There are also other possible factors that contribute to over the United States and 7 over warm ocean re- this large difference. Gill and Stirling (2013) focused on gions), that 86% occurred within 100 km of moist 8 months (November 2009–February 2010 and May convection. 2010–August 2010) of MOG turbulence reports over the As previously illustrated, the system distinguishes full GADS (whereas this study only focuses on the between detections occurring in/above a CS and those northeastern Atlantic and European region, which that are near (but not inside or above) a CS. To show contains about one-quarter of the total number of which observation type (lightning or satellite) gives GADS observations). more in/above detections per month, Fig. 9 compares In addition, it is noted that the Gill and Stirling the monthly percentage of detections in/above a CS (2013) dataset contains around 55 MOG reports per obtained according to lightning data (dashed line with month as compared with 29 MOG monthly reports in circles) with those obtained according to satellite im- our study. Assuming that the time period of the data- agery with the shape identification technique (solid line sets (8 and 12 months, respectively) is long enough to with diamonds), for both summers (2013 and 2014). The characterize the average number of MOG reports per percentage represented by the diamond markers in month, then it can be supposed that the additional Fig. 9 is calculated by dividing each month the total 26 reports per month in the Gill and Stirling (2013) number of in/above detections via satellite imagery by dataset lie outside our region of interest (i.e., Europe all satellite detections, irrespective of whether or not and the northeastern Atlantic). It is possible that a lightning observations give a detection. A similar pro- proportion of the discrepancy in the fraction of con- cedure is applied to estimate the monthly percentage of vectively induced MOG turbulence between our study ‘‘lightning detections’’ of convection that occurred in/ and that of Gill and Stirling (2013) could be explained above a CS. It seems that the lightning dataset gives by a higher prevalence of convectively induced tur- more in/above detections than does the satellite one bulence in other geographical regions such as North during the pick of both the 2013 and 2014 summer sea- America and the tropics. This supposition is supported sons (i.e., July and August). However, this may be due to

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FIG. 10. Spatial distribution (on an equidistant cylindrical projection) of all aircraft encounters with LOG turbulence as classified in this study: nonconvectively induced (gray circles), in the proximity of convection according to ATDnet (blue circles), and in the proximity of convection according only to satellite data (magenta circles). the observation-dependent choice of spatial thresholds climatological studies of convection over Europe (e.g., adopted (see Table 2). Anderson and Klugmann 2014). c. Spatial distribution of turbulence encounters in the vicinity of CSs 6. Conclusions Last, all aircraft encounters with LOG turbulence Data from commercial aircraft are currently used by considered in this study are plotted in Fig. 10 (gray color) the Met Office to improve turbulence forecasts, but there using a polar stereographic projection. Each convective is a lack of information regarding the source of the tur- encounter is laid on top: in blue when detected by bulence. This information is needed to improve un- ATDnet data (either on their own or in agreement with derstanding of turbulence experienced by aircraft and to satellite data) and in magenta when detected only by improve the accuracy of turbulence forecasts for aviation. satellite imagery (with the test on cloud shape). As ex- This paper describes an automated system for cate- pected, most convectively induced turbulence reports gorization of turbulence encounters (provided by a occur over the continent, with fewer detected events over commercial aircraft dataset) using suitable observations the northeastern Atlantic area. It is also clear that of convection that are available from the Met Office ATDnet is responsible for more detections over the ATDnet lightning location system and satellite imagery. continent than over the northeastern Atlantic, as would The severe-convection RGB satellite dataset was found be expected from both the reduced detection efficiency to be the most appropriate satellite product for the outsideEuropeandthefactthatoceanicconvective identification of convectively induced turbulent events. storms are typically associated with fewer lightning Because it is only available in daylight hours, however, strikes than continental storms (the combined effect of the system has been built with the capability to switch to these factors is clearly visible in Fig. 8,withamuch an alternative satellite product (IR 10.8 mm) to identify higher flash density over Europe than over the north- events occurring during nocturnal hours. Satellite im- eastern Atlantic). Figure 10 also highlights that the ages are postprocessed using a filtering technique to geographical distribution of the GADS data is ‘‘bi- identify convective clouds. ased’’ in two ways. Most important, aircraft observa- The automated system described in this paper has tions are only available along the routes flown by the been used to assess the proportion of turbulence en- airline (with the density of observations related to the counters in the vicinity of convection over Europe and frequency of flights on the routes). In addition, pilots the northeastern Atlantic. From an analysis of two will act to avoid turbulent regions (in general on the summer periods (May–October of 2013 and 2014) it basis of reports from nearby aircraft) where possible was found that 14% of LOG (and 11% of MOG) tur- (i.e., they are less likely to be allowed to deviate from bulence events occurred in the vicinity of convection. their route in congested airspace). These factors mean This fraction is remarkably similar to that found in that this study is not easily comparable with other Kim and Chun (2011) but is considerably lower than

Unauthenticated | Downloaded 09/29/21 10:58 AM UTC 1088 JOURNAL OF APPLIED METEOROLOGY AND CLIMATOLOGY VOLUME 55 that reported by other studies; this latter difference Acknowledgments. The authors thank the Civil Avia- might be at least partially due to different sampling tion Authority for funding this work. We are also grateful methods of aircraft encounters with turbulence (this to many colleagues at the Met Office for providing sup- work being the only one in which turbulence data from port with observational data and for fruitful discussions; aircraft are available and are used at a frequency of 4 s). in particular, we thank Peter Francis, Crispian Batstone, It may also indicate that convection is relatively less Philip Gill, Jacqueline Sugier, and Sven-Erik Enno. important as a source of turbulence over Europe and the northeastern Atlantic than over other regions (such REFERENCES as North America and the tropics). 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