An Automatic Recognition Method for Airflow Field Structures Of

atmosphere

Article An Automatic Recognition Method for Airﬂow Field Structures of Convective Systems Based on Single Doppler Radar Data

Ping Wang, Kai Gu, Jinyi Hou * and Bingjie Dou School of Electrical and Information Engineering, Tianjin University, Tianjin 300072, China; [email protected] (P.W.); [email protected] (K.G.); [email protected] (B.D.) * Correspondence: [email protected]; Tel.: +86-182-0251-1093

Received: 23 October 2019; Accepted: 24 January 2020; Published: 27 January 2020

Abstract: Airflow structures within convective systems are important predictors of damaging convective disasters. To automatically recognize different kinds of airflow structures (the convergence, divergence, cyclonic rotation, and anticyclonic rotation) within convective systems, an airflow structure recognition method is proposed, in this paper, based on a regular hexagonal template. On the basis of single Doppler radar data, the template is designed according to the appearance model of airflows in radial velocity maps. The proposed method is able to output types and intensities of airflow structures within convective systems. In addition, the outputs of the proposed method are integrated into a projection map of the airflow field structure types and intensities (PMAFSTI), which is developed in this work to visualize three-dimensional airflow structures within convective cells. The proposed airflow structure automatic recognition method and the PMAFSTI were tested using three typical cases. Results of the tests suggest the following: (1) At different evolution stages of the convective systems, e.g., growth, split, and dissipation, the three-dimensional distribution of the airflow fields within convective systems could be clearly observed through the PMAFSTI and (2) on the basis of recognizing the structures of the airflow field, the complex airflow field, such as a squall line, could be further divided into several small parts making the analysis of convective systems more scientific and elaborate.

Keywords: single doppler radar; airflow field; automatic recognition; convective systems; airflow type; airflow intensity

1. Introduction Updrafts and downdrafts in convective systems can form complex and variable airflow fields. The convergence, divergence, cyclonic rotation, and anticyclonic rotation are the four structure types of the airflow field. If two prevailing flows in the atmosphere meet and interact, it is defined as a convergence [1]. The inverse of a convergence is a divergence. A cyclonic rotation [2] is defined as a circulation of winds around a central region of low atmospheric pressure, counterclockwise in the Northern Hemisphere and clockwise in the Southern Hemisphere. An anticyclonic rotation is defined as a circulation of winds around a central region of high atmospheric pressure, clockwise in the Northern Hemisphere and counterclockwise in the Southern Hemisphere. The types and intensities of these four airflow structures (the convergence, divergence, cyclonic rotation, and anticyclonic rotation) usually have a high correlation with the development and evolution of the convective systems and have significant effects on convective weathers. For example, mesocyclones form as warm core cyclones over land and can lead to tornado formation. Convergences usually cause a mass accumulation that eventually leads to a vertical movement and the formation of clouds and precipitation. Therefore, it is important to recognize these structures of airflow field.

Atmosphere 2020, 11, 142; doi:10.3390/atmos11020142 www.mdpi.com/journal/atmosphere Atmosphere 2020, 11, 142 2 of 19

At present, Doppler weather radar is commonly used to observe the airflow field structures. It is a type of radar used to locate precipitation, calculate its motion, and estimate its type (rain, snow, hail, etc.). It is meaningful to design algorithms that automatically recognize airflow structures from Doppler radar radial velocity data. However, this task is difficult because the airflow structures are complex and change over time. In previous studies, based on single Doppler radar, researchers have developed some algorithms to recognize typical structures of the airflow field, such as mesocyclones [3] and the mid-altitude radial convergence (MARC) [4]. A mesocyclone is a vortex of air within a convective storm, which is localized, approximately 2 to 10 km in diameter within strong thunderstorms [1]. Mesocyclones include anticyclones and cyclones. It has been found that over 90% of the mesocyclones are associated with some severe convective disasters [5]. Researchers have developed some automatic mesocyclone recognition algorithms. For example, Stumpf et al. [6] developed the mesocyclone detection algorithm (MDA). It obtains three-dimensional features by the vertical association of two-dimensional features and, then, determines the development history of mesocyclones by time-dependent analysis of three-dimensional features. Smith et al. [7] proposed the two-dimensional local linear least squares derivative (LLSD) method to estimate the vertical vorticity field. Hou et al. [8] also proposed an automatic mesocyclone recognition method based on the detection of velocity couplets. MARC is a signature of a strong convergence which occurs between upper rear-to-front inflow (originating from the backside of the storm) and front-to-rear updraft flow (originating ahead of the storm). The intensity of a convergence appears to be related to wind damage [9]. Researchers have also developed some automatic recognition algorithms for MARC. Wang et al. [10] proposed a method based on the recognition of “positive–negative velocity region pairs” in Doppler radar velocity maps to identify the MARC automatically. Wang et al. [11] proposed an automatic MARC recognition method based on the convergence points. In general, two or more types of airflow field structures can coexist during the evolution of large convective systems (such as squall lines). However, at present, there are no recognition algorithms that can recognize the four structure types simultaneously. The recognition of these four types mainly relies on the experience of meteorologists. The meteorologists infer the three-dimensional structure of the local airflow field from the distributions of positive and negative velocities on radial velocity maps and further determine the weather conditions, however, this work is time consuming and the results are usually subjective. In contrast, if there is an automatic method, it reduces the manual workload and provides more objective assessment results. On the basis of single Doppler radar data, the purpose of our study is to develop an algorithm using computer vision technology to automatically recognize four types of airflow field structures and achieve results similar to those of humans. In order to achieve this purpose, we designed a method based on a regular hexagonal template to automatically recognize convergence, divergence, cyclonic rotation, and anticyclonic rotation. Our method has several advantages over earlier approaches. First, our method innovatively integrates the four structure types into one recognition template, which can recognize these four structure types at the same time. Secondly, our template recognition is automatic, which can reduce workload and provide more objective results. Third, our template recognition results are presented using a projection map of the airflow field structure types and intensities (PMAFSTI) to visualize three-dimensional airflow structures within each convective system. The technology proposed in this paper is a supplement to the existing radar products. In fact, the technology proposed in this paper also depends on existing radar products. For example, before applying our technology, a velocity de-aliasing algorithm is required to preprocess the radar radial velocity, because the aliasing velocity causes many false alarms. In addition, the estimated environmental wind velocity (e.g., using the velocity-azimuth display (VAD) algorithm [12]) should be subtracted from the radar velocity field, otherwise, the proposed technology misses some airflow field. This paper is structured as follows: Section2 introduces a detailed description of our recognition algorithm of airflow field structures and a PMAFSTI is also proposed. Section3 presents the case Atmosphere 2020, 11, 142 3 of 19 analysis that involves three typical convective weather cases and the discussion of our method. Finally, a conclusion of our work is presented in Section4.

2. Recognition Algorithm of Airﬂow Field Structures

2.1. Structure Model of Airflow Field and Template Design The aim of this work is to design an algorithm to automatically recognize different kinds of airflow fields, including the convergence, divergence, cyclonic rotation, and anticyclonic rotation. Inputs of the algorithm are velocity maps of airflow fields observed via Doppler weather radar. Doppler weather radar [13], also called weather radar or weather surveillance radar (WSR), is a type of radar used to locate precipitation, calculate its motion, and estimate its type (rain, snow, hail etc.). In addition to the reflectivity factor, it can also obtain radial velocity information of raindrops. As the raindrops are carried by winds, the velocity so detected provides a good estimation of the wind velocity. This velocity is the component of the wind velocity vector along the radar beam direction and is known as “radial velocity”. If the wind at the detection point is moving away from the radar, the radial velocity is positive and is called “outflow velocity”. Conversely, if the wind at the detection point is moving towards the radar, the radial velocity is negative and is called “inflow velocity”. When airflow fields are observed using Doppler weather radar, their appearances are changed. For example, Figure1 shows airflow field structures and their appearances on the radial velocity maps of Doppler weather radar. Figure1 is an adaptation of the Figure 3.27 in [ 14]. In Figure1, blue ellipse represents the maximum inflow velocity center, and red ellipse represents the maximum outflow velocity center. The grey and black solid arrows in Figure1 represent the wind direction, the black dashed arrows represent the radar beams, and the central point of each subgraph represents the center Atmosphereof the airflow 2020, 11 field., 142 The radar is located directly below the center of the airflow field. 4 of 19

Radar beams Radar beams Radar beams Radar beams (a) (b) (c) (d)

Radar beams Radar beams Radar beams Radar beams (e) (f) (g) (h)

γ Figure 1. VelocityVelocity structures of the Meso- γ scale system: ( a)) Pure Pure cyclonic cyclonic rotation flow flow field.field. ( (b) pure pure c d anticyclonic rotati rotationon flow flow field, field, (c () )pure pure convergence convergence flow flow field, field, (d) ( pure) pure divergence divergence flow flow field, field, (e) (e) cyclonic convergence flow field. (f) cyclonic divergence flow field, (g) anticyclonic convergence cyclonic convergence flow field. (f) cyclonic divergence flow field, (g) anticyclonic convergence flow flow field, and (h) anticyclonic divergence flow field. The central point of each subgraph represents field, and (h) anticyclonic divergence flow field. The central point of each subgraph represents the the center of the airflow field and the radar is located directly below the center of the airflow field. center of the airflow field and the radar is located directly below the center of the airflow field. Blue Blue ellipses represent the maximum inflow velocity centers, and red ellipses represent the maximum ellipses represent the maximum inflow velocity centers, and red ellipses represent the maximum outflow velocity centers. Grey and black solid arrows represent the wind directions and black dashed outflow velocity centers. Grey and black solid arrows represent the wind directions and black dashed arrows represent the radar beams. arrows represent the radar beams.

Radar Beam

R+

C+

∆θ d P L

C-

R-

Radar

Figure 2. The velocity couplet to represent the airflow field structure. R+ and R− are the local maximum and minimum velocity regions of the velocity couplet, respectively. Their geometric

centers are C+ and C− , respectively. Point P is the point at the boundary line between positive and negative velocity regions. A polar coordinate system is established by setting P as the pole and radar

beam ray as the pole axis. ∆θ is the polar angle of C+ in this polar coordinate system and 0° ≤ Δθ < 360° , L is the distance between C+ and C− , and d is the projection of L on the radar beam ray.

Atmosphere 2020, 11, 142 4 of 19

As shownRadar beams in Figure1a, in a smallRadar region beams within the e ffectiveRadar detectionbeams range of radar,Radar beams there is (a) (b) (c) (d) a Meso-γ scale [15] rotation. The cyclone appears as a velocity couplet that is composed of a maximum inflow and a maximum outflow. The center of the maximum inflow velocity region is as close to the radar as the outflow one, and the outflow one is on the right side. Figure1b shows an anticyclonic rotation. The center of the maximum inflow velocity region is on the right side of the center of the airflow field and the outflow one is on the left side. Figure1c,d shows a radial convergence area and a radial divergence area, respectively. In Figure1c, the centers of the maximum inflow and outflow velocity regions are in the same radial direction of the radar, and the center of the maximum outflow velocity region is closer to the radar. Conversely, this region is a radial divergence region. Radar beams Radar beams Radar beams Radar beams As shown(e) in Figure1, structure types (f) of the airflow field (g) can be determined by orientation (h) relationships among the radar, the positive and negative velocity extremum regions. Specifically, all kindsFigure of airflow 1. Velocity field structures structures of the appear Meso-γ as scale a velocity system: ( coupleta) Pure cyclonic in Doppler rotation radar flow radicalfield. (b) velocity pure images.anticyclonic This couplet rotati canon be flow described field, (c using) pure threeconvergence parameters, flow field, length (d ()L pure), orientation divergence (∆ flowθ), and field, velocity (e) differencecyclonic (dv). convergence These three flow parameters field. (f) arecyclonic further divergence illustrated flow in field, Figure (g2). anticyclonic In Figure2, convergenceR+ and R flowarethe − local maximumfield, and ( andh) anticyclonic minimum divergence velocity regions flow field. of the The velocity central point couplet, of each respectively. subgraph Theirrepresents geometric the centerscenter are C of+ theand airflowC , respectively. field and the Pointradar Pis islocated the point directly at thebelow boundary the center line of the between airflow positivefield. Blue and − negativeellipses velocity represent regions. the maximum A polar coordinateinflow velocity system centers, is establishedand red ellipses by settingrepresentP asthe themaximum pole and radar beamoutflow ray velocity as the centers. pole axis.Grey and∆θ blackis the solid polar arro anglews represent of C+ inthe this wind polar directions coordinate and black system dashed and arrows represent the radar beams. 0◦ ∆θ < 360◦ ,L is the distance between C+ and C , and d is the projection of L on the radar beam ray. ≤ − Radar Beam

R+

C+

∆θ d P L

C-

R-

Radar

Figure 2. The velocity couplet to represent the airflow field structure. R+ and R are the local maximum − Figure 2. The velocity couplet to represent the airflow field structure. R and R− are the local and minimum velocity regions of the velocity couplet, respectively. Their geometric+ centers are C+ andmaximumC , respectively. and minimum Point P velocityis the point regions at the of boundary the velocity line between couplet, positive respectively. and negative Their geometric velocity − regions.centers A are polar C+ coordinate and C− , respectively. system is established Point P is bythe setting point atP asthe the bounda pole andry line radar between beam positive ray as the and polenegative axis. ∆ velocityθ is the regions. polar angle A polar of C coordi+ in thisnate polar system coordinate is established system by and setting0 P∆ asθ the < 360 pole,L andis radar the ◦ ≤ ◦ distancebeam ray between as theC+ poleand Caxis., and ∆θd is thethe projection polar angle of L ofon theC+ radarin this beam polar ray. coordinate system and − 0° ≤ Δθ < 360° , L is the distance between C+ and C− , and d is the projection of L on the radar beam Through the three parameters of the velocity couplet, the structure types of the related airflow field ray. such as the convergence, divergence, cyclonic rotation, and anticyclonic rotation can be determined. By combining Figures1 and2, we find out that the types of the airflow fields are related to ∆θ of the velocity couplet. The relationships we set are summarized in Table1. In addition, the distance L between the inflow and outflow velocity extreme points should be less than a given value. Atmosphere 2020, 11, 142 5 of 19

Through the three parameters of the velocity couplet, the structure types of the related airflow field such as the convergence, divergence, cyclonic rotation, and anticyclonic rotation can be determined. By combining Figures 1 and 2, we find out that the types of the airflow fields are related Atmosphereto ∆θ of the2020 ,velocity11, 142 couplet. The relationships we set are summarized in Table 1. In addition,5 ofthe 19 distance L between the inflow and outflow velocity extreme points should be less than a given value.

Table 1. Relationships between airﬂowairflow ﬁeldfield types and ∆θ.

Types of AirﬂowAirflow FieldField Ranges Ranges of ∆ ofθ ∆θ 150° ≤ Δθ < 210° Convergence 150 ∆θ < 210 ◦ ≤ ◦ Divergence 0° ≤ Δ 0θ <∆ 30°θ < or30 330°or 330 ≤ Δθ∆ <θ 360° < 360 ◦ ≤ ◦ ◦ ≤ ◦ Cyclonic rotation 30° ≤30 Δθ <∆ 150°θ < 150 ◦ ≤ ◦ Anticyclonic rotation 210° 210 ≤ Δθ <∆θ 330° < 330 ◦ ≤ ◦

OnOn the the basis basis of of the the relationships relationships between between airflow airflow fi fieldeld structures structures and velocity couplets, the complex airflowairflow field field structures in convective systems could be recognized by recognition of velocity couplets in radar velocity radial maps. For For example, example, a a meso mesocyclonecyclone could could be be identified identified at at the place where the Δ∈θ ° ° velocity couplets that have anan ∆θ [][30,1530 , 1500 ]exit.exit. In In this this work, work, a a regular hexagonal template is ∈ ◦ ◦ designed to recognize four types of airflow airflow field field st structures.ructures. Figure Figure 3 showsshows thethe shapeshape ofof thethe template.template. The template is composedcomposed of twotwo subtemplates.subtemplates. Th Thee first first subtemplate is is designed as two regular triangles with the same vertex (Regions A and B in FigureFigure3 3).). ItIt isis usedused toto recognizerecognize velocityvelocity coupletscouplets that correspond to convergences or divergences. Sim Similarly,ilarly, the second subtemplate is designed as two diamonds with a common endpoint (Regions (Regions C C and D in Figure 33).). ItIt isis usedused toto recognizerecognize velocityvelocity couplets that that correspond correspond to tocyclonic cyclonic rotations rotations or anticyclonic or anticyclonic rotations. rotations. By combining By combining the above the triangle above andtriangle diamond and diamond subtemplates, subtemplates, a regular a regularhexagonal hexagonal templa templatete for recognizing for recognizing four types four typesof airflow of airflow field structuresfield structures could could be obtained. be obtained.

Radar Beam

A H ∆θ CDp d

Radar

Figure 3. The regular hexagonal template for airflowairflow fieldfield structuresstructures recognition.recognition. The blue ellipse represents the maximum inflow inflow velocity center, and the red ellipse represents the maximum outflow outflow velocity center.center. PointPointP Pis is the the point point atthe at boundarythe boundary line betweenline between positive positive and negative and negative velocity velocity regions. Theregions. dashed The arrow dashed represents arrow represents the radar beam, the radard and beam,∆θ are d theand same ∆θ are as in the Figure same2, as and in HFigureand l represent2, and H andthe heightl represent and basethe height of the triangularand base of Region the triangular A, respectively. Region A, respectively.

ForFor the the designed hexagonal template, the height H of the triangletriangle subtemplatesubtemplate is determined according to to the the maximum maximum statistical statistical distance distance (max{ (max{d} = d6} km)= 6 between km) between positive positive and negative and negative velocity velocitypoints. We points. set WeH = set max{H =d}max{ = 6d } km.= 6 km.As the As thetriangle triangle subtemplate subtemplate is isa aregular regular triangle, l ==×=≈2 tan(30 ◦) H = 6.93 7 km. lH2 tan(30 )× 6.93 7≈ km .

Atmosphere 2020, 11, 142 6 of 19 Atmosphere 2020, 11, 142 6 of 19 2.2. Template Recognition Process

2.2.1.2.2. Data Template Preprocessing Recognition Process 2.2.1.In this Data paper, Preprocessing we use a two-dimensional template to recognize three-dimensional airflow fields. Before usingIn this the paper, two-dimensional we use a two-dimensional template, the template radar data to recognize is preprocessed three-dimensional as described airflow below. fields. BeforeFirst of using all, thea two-dimensionalvelocity-azimuth template, display the (VAD) radar dataalgorithm is preprocessed [12] is asemployed described below.to estimate the environmentalFirst of wind all, a velocities velocity-azimuth at different display heights. (VAD) Then, algorithm the es [12timated] is employed environmental to estimate winds the are subtractedenvironmental from the wind original velocities radar at veloci differentty fields heights. to eliminate Then, the their estimated influence. environmental winds are subtractedIn order to from use the the original two-dimensional radar velocity recognition fields to eliminate template their influence.conveniently, the variables, radial distance Inr and order azimuth to use theθ, in two-dimensional polar coordinate recognition system templateof the radar conveniently, map, are thetaken variables, as two radial mutually distance r and azimuth θ, in polar coordinate system of the radar map, are taken as two mutually perpendicular coordinate axes to form a θ-r rectangular coordinate system. The azimuth θ is taken perpendicular coordinate axes to form a θ-r rectangular coordinate system. The azimuth θ is taken as as the horizontal axis with the resolution of 1° and radial distance r as the vertical axis with the the horizontal axis with the resolution of 1◦ and radial distance r as the vertical axis with the resolution resolution of 1 km. To prevent the airflow field near the azimuth angle of 0° from being truncated in of 1 km. To prevent the airflow field near the azimuth angle of 0◦ from being truncated in the θ-r the θrectangular-r rectangular coordinate coordinate system, system, the coordinate the coordinate system is sy extendedstem is horizontallyextended horizontally by 20 degrees, by as 20 shown degrees, as shownin Figure in 4Figureb. 4b.

r/km θ/° 359 0…20 0…20 θ/° 1 data reuse data reuse

230 r/km

(a) (b) FigureFigure 4. Conversion 4. Conversion from from a θ a-r θpolar-r polar coordinate coordinate system system to to a aθθ-r-r rectangularrectangular coordinate system:system: (a) Circular(a) Circular data region data region in a inθ-r a θpolar-r polar coordinate coordinate system system and and ((bb)) rectangularrectangular data data region region in ainθ-r a θ-r rectangularrectangular coordinate coordinate system. system. In In (b), (b), the azimuthazimuthθ θis is taken taken as theas the horizontal horizontal axis withaxis thewith resolution the resolution of 1 and radial distance r as the vertical axis with the resolution of 1 km. To prevent the airflow field near of 1° and◦ radial distance r as the vertical axis with the resolution of 1 km. To prevent the airflow field the azimuth angle of 0◦ from being truncated in the θ-r rectangular coordinate system, the coordinate near the azimuth angle of 0° from being truncated in the θ-r rectangular coordinate system, the system is extended horizontally by 20 degrees. coordinate system is extended horizontally by 20 degrees. Aiming at the severe convection systems, for the rectangular reflectivity data in a θ-r rectangular coordinateAiming at the system, severe we convection only select systems, the storms for whosethe rectangular reflectivity reflectivity is higher thandata in 35 a dBZ θ-r rectangular on the coordinatereflectivity system, maps andwe whoseonly areasselect are the larger storms than 150wh gridsose reflectivity in a θ-r rectangular is higher coordinate than 35 system dBZ [ 16on]. the reflectivityTheir boundaries maps and are whose extracted areas and are expanded larger than appropriately 150 grids to in obtain a θ-r the rectangular high reflectivity coordinate regions system at [16].nine Their elevation boundaries angles. are extracted and expanded appropriately to obtain the high reflectivity regions atNext, nine we elevation select the angles. area to be recognized. We project the high reflectivity regions at nine elevation angles horizontally. For these regions, we need to establish vertical matching by calculating the area Next, we select the area to be recognized. We project the high reflectivity regions at nine coincidence degree of projections. If the overlapping area [17] of two projection areas of two high elevation angles horizontally. For these regions, we need to establish vertical matching by calculating reflectivity regions at two adjacent elevation angles is larger than 60% of the smaller projection area, the areathe two coincidence regions are degree considered of projections. to be correlated If the in ov theerlapping vertical direction area [17] and of located two projection in the same areas airflow of two highfield, reflectivity as shown regions in Figure at two5. The adjacent minimum elevation bounding angles rectangle, is larger which than we60% set of as thea (smaller) b(km), projection of ◦ × area,correlated the two regions high reflectivity are considered regions’ projections to be correlated at all elevation in the vertical angles is direction taken to determine and located the positionin the same airflowof the field, air flowas shown field and in asFigure the recognition 5. The minimum area. bounding rectangle, which we set as a(°) × b(km), of correlated high reflectivity regions’ projections at all elevation angles is taken to determine the position of the air flow field and as the recognition area.

Atmosphere 2020, 11, 142 7 of 19 Atmosphere 2020, 11, 142 7 of 19

Atmosphere 2020, 11, 142 7 of 19 h/km

h/km

Two adjacent elevation angles Two adjacent elevation angles

θ/° Horizontalθ/° r/km Horizontalprojections r/km projections FigureFigure 5. 5. DeterminationDetermination of of the the rectangular rectangular recognition recognition area, area, where where h hisis the the height height from from the the ground. ground. TheTheFigure high high reflectivity5. reflectivity Determination regions regions of at the at di di fferentrectangularfferent elevation elevation recognition angles angles area, are are projected projectedwhere h ishorizontally. horizontally. the height from If If the the the overlapping overlapping ground. areaareaThe of of high two two reflectivityprojection projection regionsareas areas at atat different di fferentfferent elevation elevation anglesan anglesgles are is is largerprojected larger than than horizontally. 60% 60% of of the the If smaller smallerthe overlapping projection projection area,area,area the the of two two regionsprojection regions are are areas considered considered at different to to be be elevation located located inan in thegles the same is same larger airflow airflow than field.60% field. of The The the minimum minimumsmaller projection bounding bounding area, the two regions are considered to be located in the same airflow field. The minimum bounding rectangle,rectangle, which wewe setset as as a( ◦a(°)) b(km),× b(km), of correlatedof correlated high high reflectivity reflectivity regions’ regions’ projections projections at all elevation at all rectangle, which we set as a(°)× × b(km), of correlated high reflectivity regions’ projections at all elevationangles is angles taken tois determinetaken to determine the position the ofposition the airflow of the field airflow and field as the and recognition as the recognition area. area. elevation angles is taken to determine the position of the airflow field and as the recognition area. SinceSince we we aim aim to to recognize recognize the the three-dimensional three-dimensional ai airflowrflow field, field, the the radial radial velocity velocity data data of of the the Since we aim to recognize the three-dimensional airflow field, the radial velocity data of the rectangularrectangular recognition recognition area area at nine elevationelevation anglesangles inin a aθ θ-r-rrectangular rectangular coordinate coordinate system system should should be interpolatedrectangular bi-linearlyrecognition [18area] into at ninem layers’ elevation rectangular angles in dataa θ-r withrectangular a height coordinate resolution system of 0.25 should km and be beinterpolated interpolated bi-linearly bi-linearly [18] [18] into into m m layers’ layers’ rectangular rectangular data withwith a a height height resolution resolution of of 0.25 0.25 km km and and a horizontal resolution of (1 km 1 km). For the place with a horizontal distance of less than 50 km, a horizontala horizontal resolution resolution of of (1 (1 km km × ×× 1 1 km). km). For For thethe placeplace with aa horizontalhorizontal distance distance of of less less than than 50 50 km, km, the maximum elevation of the radar does not detect an altitude of more than 17.5 km. Therefore, we thethe maximum maximum elevation elevation of of the the radar radar does does not not detectdetect an altitude ofof more more than than 17.5 17.5 km. km. Therefore, Therefore, we we set m = 17.5/0.25 = 70. setset m =m 17.5/0.25 = 17.5/0.25 = 70.= 70. 2.2.2. Template Recognition 2.2.2.2.2.2. Template Template Recognition Recognition When applying the template in the θ-r rectangular coordinate system, the template needs to be WhenWhen applying applying the the template template in in the the θ θ-r-r rectangularrectangular coordinate system, system, the the template template needs needs to tobe be transformed from the polar to the rectangular coordinate system. If the template designed in the θ-r transformedtransformed from from the the polar polar to to the the rectangular rectangular coorcoordinate system. IfIf the the template template designed designed in in the the θ-r θ -r polar coordinate system is used in the θ-r polar coordinate system, the template needs to be rotated as polarpolar coordinate coordinate system system is is used used in in the the θ θ-r-r polar polar coordinatecoordinate system,system, the the template template needs needs to to be be rotated rotated the azimuth changes to ensure that the axis of the template coincides with the radar beam direction, as theas the azimuth azimuth changes changes to to ensure ensure that that the the axis axis of of thethe template coincidescoincides with with the the radar radar beam beam direction, direction, as shown in Figure6a, which is complex for image processing. While in the θ-r rectangular coordinate as shownas shown in inFigure Figure 6a, 6a, which which is is complex complex for for image image processing.processing. WhileWhile in in the the θ θ-r-r rectangular rectangular coordinate coordinate system, the direction of the vertical axis and the radar beam direction are the same. Therefore, if the system,system, the the direction direction of of the the vertical vertical axis axis and and thethe raradar beam directiondirection are are the the same. same. Therefore, Therefore, if theif the template is used in the θ-r rectangular coordinate system, it does not need to be rotated, as shown templatetemplate is usedis used in in the the θ -rθ-r rectangular rectangular coordinate coordinate system,system, itit doesdoes notnot need need to to be be rotated, rotated, as as shown shown in in in Figure6b. FigureFigure 6b. 6b.

0° O 0° O θ/° - + Radar beams θ/° - + Radar beams B B - C p D - C p D + A + A

Radar beams Radar beams 270° O 90° 270° O 90° r/km (a) (b) r/km (a) (b) FigureFigure 6. 6.Template Template applicationapplication diagram: (a (a) )Application Application of of the the template template in ina θ a-rθ -polarr polar coordinate coordinate Figuresystemsystem 6. and andTemplate ( b(b)) applicationapplication application of of the thediagram: template template (a in) inApplicationa θ a-rθ rectangular-r rectangular of the coordinate template coordinate system. in a system. θ -Inr polar(a) and In coordinate ( a()b), and the (b), systemthegreen green and background background (b) application areas areas represent of represent the template the the areas areas in with a with θ -negar rectangular negativetive radial radial coordinate velocity, velocity, while system. while the the pink In pink ( abackground) and background (b), the greenareasareas background represent represent the the areas areas areas represent with with positivepositive the areas radial with velocity. negative The The radialarrows arrows velocity, represent represent while the the radar the radar pink beams. beams. background areas represent the areas with positive radial velocity. The arrows represent the radar beams.

Atmosphere 2020, 11, 142 8 of 19

The template is used to recognize airflow structures in the entire flow fields. As the template is designed with the boundary point between the positive and negative velocity regions as the center, the template recognition process is only conducted on boundary points in the radial velocity rectangular maps of 70 layers. Specifically, the template is placed on each point of the boundary line, and the axis of the template coincides with the radar beam, as shown in Figure6b. The boundary points can be detected using an image segmentation algorithm. All boundary points are recorded as P . Using the { i} template, the types and intensities of the two-dimensional airflow field of P are calculated as follows: { i} 1. Determine airflow directions of the subregions A, B, C, and D covered by the template by counting the numbers of positive and negative radial velocity points in the regions. Take Region A for + instance, suppose nA and nA− are numbers of positive and negative velocity points within Region + + A, Region A is marked as positive (ωA) if nA> nA− ; otherwise, Region A is marked as negative (ωA− ). Similarly, airflow directions of Regions B, C, and D are determined. 2. Calculation of properties of the regions in the template. Take Region A for instance, if Region A is marked as positive in the previous step, only positive velocity points within this region are used to calculate the properties of the region, including geometric center, average velocity vA, max and maximum velocity vA . Similarly, if this region is marked as negative, use negative velocity points within this region to calculate the geometric center, average velocity vA, and minimum min velocity vA . 3. Calculation of properties of the region pairs within the template. There are two region pairs in the template, (Region A, Region B) and (Region C, Region D). Take (Region A, Region B) for instance, if Region A and Region B have different directions, i.e., one is positive and the other is negative, the differences between the average velocities of two regions ∆vAB and the maximum velocity max difference of two regions ∆vAB are calculated as:

∆v = v v AB A − B  max min  v v , (Region A is positive and Region B is negative) (1) max =  A − B ∆vAB   vmax vmin, (Region B is positive and Region A is negative) B − A

The azimuth difference ∆θAB and radial distance difference ∆rAB between the geometric centers of two regions are calculated as illustrated in Figure2. Conversely, if Region A and Region B have the same direction. All properties are set to zero. Properties of region pairs (Region C, Region D) are calculated using the same method. 4. Determine the type of the airflow field and calculate its properties. The airflow structures have the following four types: the convergence, divergence, cyclonic rotation, and anticyclonic rotation. The specific classification rules of the airflow field types are shown in Table2. After obtaining the max max max max results of two subtemplates, we compare ∆vAB and ∆vCD . If ∆vAB > ∆vCD , the subtemplate max max for distinguishing between the convergence and divergence prevails. If ∆vAB < ∆vCD , the subtemplate for distinguishing between the cyclonic rotation and anticyclonic rotation max = max prevails. In particular, if ∆vAB ∆vCD , the result depends on the larger one between ∆vAB and ∆vCD.

Once the type of the airflow field is determined, we also calculate its four properties, as shown in Table3. Finally, the airflow field is described using a five-dimensional feature vector, ( p1, p2, p3, p4, p5), where p1 belongs to {1, 2, 3, 4} and denotes the airflow field structure type; 1,2,3,4 denote the convergence, divergence, cyclonic rotation, and anticyclonic rotation, respectively; and p2~p5 record four properties of the airflow field. Atmosphere 2020, 11, 142 9 of 19 Atmosphere 2020, 11, 142 9 of 19 Atmosphere 2020, 11, 142 9 of 19

TableTable 2. Classification 2. Classification rules rules of airflow of airflow field field structure structure types. types. Subtemplates Diagrams Conditions Structure Types SubtemplatesSubtemplates Diagrams Diagrams ConditionsConditions StructureStructure Types Types vv⋅>0 Null A vvAB⋅>0 Null The subtemplate for A v ABv > 0 Null TheThe subtemplate subtemplate for for A · B CD ()vv<∩00() > Convergence distinguishingdistinguishing between between the the CD ()vvAB<∩00() > Convergence distinguishing between the (v AB< 0) (v > 0) Convergence convergenceconvergence and and divergence divergence A ∩ B convergence and divergence B >∩ < B ()vvAB>∩00() < Divergence (()vvvAB> 000) (v()< 0) DivergenceDivergence A ∩ B <∩ > A ()vvCD<∩00() > Cyclonic rotation The subtemplate for A (()vvvCCD< 000) (v()D > 0) CyclonicCyclonic rotation rotation The subtemplate for ∩ distinguishingdistinguishing between between the the CD >∩ < CD(()vvvCCD>>∩000) (v()D < <0) AnticyclonicAnticyclonic rotation rotation cyclonedistinguishing and anticyclone between the ()vvCD00() Anticyclonic rotation cyclone and anticyclone ∩ cyclone and anticyclone B B ⋅> vCvvCDv⋅>D > 00 Null Null vvCD· 0 Null

Table 3. Properties of the airﬂow ﬁeld. Table 3. Properties of the airflow field.

Types p1 p2 p3 p4 p5 Types Types p1 pp1 2 p2 p3 p3 pp44 p5 p5 max Δvmax Δv Δθ Δr Convergence 1 ΔvAB ΔvAB ΔθAB ΔrAB ConvergenceConvergence 1 ∆1v max AB ∆v AB ∆θ AB ∆ABr AB max AB AB AB Δvmax Δ Δθ Δr Divergence 2 maxΔ AB ΔvAB ΔθAB ΔrAB DivergenceDivergence 2 ∆2v vAB ∆vABvAB ∆θABAB ∆ABrAB AB max maxΔvmax Δ Δθ Δr Cyclonic rotationCyclonic rotation 3 ∆ 3v Δ CD ∆v ΔvCD ∆ΔθθCD Δ ∆CDr Cyclonic rotation 3 CD vCD CDvCD CDCD rCD CD max max Δvmax Δ Δθ Δr AnticyclonicAnticyclonic rotation rotation 4 ∆ 4v CD Δ CD ∆vCDΔvCD ∆ΔθθCDCD Δ ∆CDrCD Anticyclonic rotation 4 vCD vCD CD rCD

2.3.2.3. Output Output Visualization Visualization OutputOutput of the of templatethe template recognition recognition process process is recorded is recorded as a function as a functionV (r, θ, hV)( (hr,is θ the, h) height(h is the from height the ground),from the where ground), each where output each of V outputat the position of V at the (r, θ position, h) is a five-dimensional (r, θ, h) is a five-dimensional vector, (p , p , vector,p , p , p (p).1, p2, from the ground), where each output of V at the position (r, θ, h) is a five-dimensional1 2 vector,3 4 (5p1, p2, V (r,p3,, ph4,) p reflects5). V (r, θ the, h) airflowreflects structurethe airflow around structure the around boundary the boundary point (r, point, h). To (r, furtherθ, h). To analyze further analyze the pθ3, p4, p5). V (r, θ, h) reflects the airflow structure around the boundaryθ point (r, θ, h). To further analyze airflowthe structureairflow structure within a convectivewithin a cell,convective the three-dimensional cell, the three-dimensional radial velocity radial region’s velocity recognition region’s resultsrecognition within a convectiveresults within cell a should convective be obtained. cell should be obtained. ForFor a three-dimensional a three-dimensional recognition recognition region, region, i.e., i.e., 70 70 layers’ layers’ rectangles, rectangles, 70 70 ×a (a(°)) × bb(km),(km), its For a three-dimensional recognition region, i.e., 70 layers’ rectangles, 70× × a◦(°) ×× b(km), its its correspondingcorresponding values valuesV V(r ,(θr,, θh,) h describe) describe the the airflow airflow structures structures within with thein the detection detection area. area. To visualize To visualize andand analyze analyze these these airflow airflow structures, structures, values valuesV (r ,Vθ (,rh, )θ are, h) projectedare projected to two to two two-dimensional two-dimensional maps, maps, the map of airflow typesF F1h (θ, h) and the map of maximum velocity differencesF F2h (θ, h). To obtainF F1 the mapthe map of airflow of airflow types types1 (θ F,1 ()θ and, h) and the mapthe map of maximum of maximum velocity velocity differences differences2 (θ F, 2 ().θ, To h). obtain To obtain1 F1 h(θ, h) andF F2h (θ, h), the boundary points that have the maximum velocity differences alongr r direction (θ, ()θ and, h) and2 (θ F,2 (),θ, the h), boundarythe boundary points points that that have have the maximumthe maximum velocity velocity diff erencesdifferences along alongdirection r direction are firstare first identified, identified, and theirand their airflow airflow types types and maximum and maxi velocitymum velocity differences differences (regard (regard as intensities) as intensities) are are recorded in two-dimensionalF mapsh F1 (θF, h) andh F2 (θ, h), respectively. Specifically,p r forh p1(r, θ, h) recordedare recorded in two-dimensional in two-dimensional maps 1 maps(θ, ) F and1 (θ, h2 )( θand, ), F respectively.2 (θ, h), respectively. Specifically, Specifically, for 1( , θfor, )p1 and(r, θ, h) θ p (rand, θ, h p),2 find(r, θ,r h),s.t. findp (rrm s.t., θ ,ph2)(r=m,max θ, h()r =, θ ,max(h), whererh ,θ , )r, iswhere the radial r is the range radial of therange detection of the detection area. Then area. 2 and p2 (r, θ, mh), find2 rmm s.t. p2(rm, θ, h) = max(rR∈ rh , , ) , where r is the radial range of the detection area. r R ∈ ∈ rR F1 (θThen, h) = Fp11 ((θrm, h, )θ =, hp)1( andrm, θF, 2h()θ and, h) =F2 p(2θ(,r mh), =θ ,ph2().rm, θ, h). Then F1 (θ, h) = p1(rm, θ, h) and F2 (θ, h) = p2(rm, θ, h). Figure7 shows an example to illustrate how these two maps are achieved. In Figure7a, Figure 7 shows an example to illustrate how these two maps are achieved. In Figure 7a, the the recognition region is marked using a sector box in a polar coordinate system, namely, a rectangular recognition region is marked using a sector box in a polar coordinate system, namely, a rectangular boxbox in the in rectangularthe rectangular coordinate coordinate system. system. Figure Figu7c,bre shows7c,b shows the final the final projection projection maps maps of F1 of(θ F, 1h ()θ and, h) and box in the rectangular coordinate system. Figure 7c,b shows the final projection maps of F1 (θ, h) and F2 (θF,2h ().θ, To h). make To make the projection the projection maps maps clearer, clearer, in Figure in Fi7b,gure the 7b, velocity the velocity di fference difference values abovevalues 20 above are 20 F2 (θ, h). To make the projection maps clearer, in Figure 7b, the velocity difference values above 20 discretized with an interval of five, and therefore shallow-to-deep red isolines of this map are present; are discretized with an interval of five, and therefore shallow-to-deep red isolines of this map are in Figure7c, the di fferent structure types are labeled using four colors of yellow (cyclonic rotation), blue present; in Figure 7c, the different structure types are labeled using four colors of yellow (cyclonic (anticyclonic rotation), green (divergence), and grey (convergence). In addition, the two-dimensional rotation), blue (anticyclonic rotation), green (divergence), and grey (convergence). In addition, the maps of the velocity difference isolines and the structure types can be integrated into one map (see two-dimensional maps of the velocity difference isolines and the structure types can be integrated Figure7d), namely the “projection map of the airflow field structure types and intensities” (PMAFSTI). into one map (see Figure 7d), namely the “projection map of the airflow field structure types and Obviously, with the help of the PMAFSTI, the three-dimensional spatial structure and intensity intensities” (PMAFSTI). distribution of the whole airflow field can be clearly observed. All this information can be used to Obviously, with the help of the PMAFSTI, the three-dimensional spatial structure and intensity infer the development stage of the convective cell and furthermore, to forecast the severe convective distribution of the whole airflow field can be clearly observed. All this information can be used to weathers. Taking Figure7a for example, there is a strong rotation structure in the radial velocity map at infer the development stage of the convective cell and furthermore, to forecast the severe convective weathers. Taking Figure 7a for example, there is a strong rotation structure in the radial velocity map

Atmosphere 2020, 11, 142 10 of 19 Atmosphere 2020, 11, 142 10 of 19 theat the elevation elevation angle angle of 2.3 of◦ 2.3°(the (the dashed dashed ellipse ellipse in Figure in Figure7a). This 7a). structureThis structure is also is clear alsoon clear the on PMAFSTI the atPMAFSTI the same at height the same (the height dashed (the rectangular dashed rectangular areas in Figureareas in7b–d). Figure 7b,c,d).

h/km h/km h/km 17.5 17.5 17.5 2.3° Convergence 20-25m/s Divergence (d) 15 15 15 -27 25-30m/s -20 100 km Cyclonic rotation -15 Above 30m/s Anti-cyclonic -10 -5 rotation -1 h2 10 10 10 0 θ2 1 5 θ1 10 15 h1 5 5 5 20 h 27 50 km h2 2 h2 θ θ θ1 θ2 θ θ RF 1 2 1 2 m/s h1 h1 h1 0 0 0 320 340 360(0) θ/° 320 340 360(0) θ/° 320 340 360(0) θ/°

(a) (b) (c) (d) FigureFigure 7.7.Diagrams Diagrams of of the the projection projection map map ofthe of airﬂowthe airflow ﬁeld structurefield structure types andtypes intensities and intensities (PMAFSTI):

((PMAFSTI):a) A radial velocity(a) A radial map velocity at the radar’s map at elevationthe radar’s angle elevation of 2.3 angle◦ and of (b –2.3°d) PMAFSTI.and (b,c,d) InPMAFSTI. (b), the airflowIn field(b), the intensities airflow field greater intensities than 20 greater m/s are than discretized 20 m/s ar ate intervalsdiscretized of at 5 tointervals draw the of 5 isolines to draw from the isolines light red to darkfrom red.light In red (c ),to the dark di ffred.erent In ( structurec), the different types arestructure labeled types using are four labeled colors using and four (d) colors is the superpositionand (d) is ofthe (b superposition) and (c). In ( ofa), ( ab) strong and (c). rotation In (a), a structurestrong rotation in radial structure velocity in radial map isvelocity marked map by is a dashedmarked by ellipse, whicha dashed corresponds ellipse, which to the corresponds dashed rectangular to the dashed areas rectangular in (b), (c ),areas and in (d ().b), (c), and (d).

3.3. CasesCases Analysis and and Discussion Discussion of of Method Method

3.1.3.1. AA SquallSquall Line Case Case in in Tianjin, Tianjin, China China on on 13 13 June June 2005 2005 Taking a squall line line [19,20] [19,20 ]case case in in Tianjin Tianjin on on 13 13 June June 2005 2005 as asan anexample, example, the theproposed proposed recognition recognition algorithmalgorithm of the the airflow airflow field field structures structures was was tested tested.. The recognition The recognition results resultsat 08:29 atUTC 08:29 and UTC 08:54 and UTC 08:54 UTCwere wereanalyzed. analyzed. Figure 8a,d Figure shows8a,d the shows extracted the extracted sectors of sectorsthe high ofreflectivity the high regions reflectivity of the regions squall line of the squallon the linereflectivity on the reflectivitymaps at the maps elevation at the angle elevation of 0.5°. angle Local of radial 0.5◦. velocity Local radial maps velocity at different maps elevation at different elevationangles of anglesthe corresponding of the corresponding high reflectivity high reflectivity region of region the squall of the squallline are line shown are shown in Figure in Figure 8b,e,8 b,e, respectively.respectively. The The PMAFSTIs PMAFSTIs are are shown shown in in Figure Figure 8c8c,f.,f. In In addition, addition, to tovisualize visualize the the corresponding corresponding compositecomposite reflectivityreflectivity maps maps of of the the PMAFSTIs, PMAFSTIs, the the sect sectoror regions regions of composite of composite reflectivity reflectivity (the (themaximum maximum reflectivityreflectivity from any any of of the the reflectivity reflectivity angles angles of of the the radar) radar) maps maps were were also also transformed transformed into intothe θ the-r θ-r rectangular coordinate system, which were expanded according to the azimuth angle θ and drawn rectangular coordinate system, which were expanded according to the azimuth angle θ and drawn below the PMAFSTIs, as shown in Figure 8c,f. In this case, the squall line is composed of a group of below the PMAFSTIs, as shown in Figure8c,f. In this case, the squall line is composed of a group convective cells. Considering that the reflectivity at the core of the cell is the strongest and the of convective cells. Considering that the reflectivity at the core of the cell is the strongest and the reflectivity weakens outwards, the squall line system at 08:29 UTC and 08:54 UTC could be divided into reflectivity weakens outwards, the squall line system at 08:29 UTC and 08:54 UTC could be divided five micro convective cells. The cells and their corresponding airflow structures are labeled using into five micro convective cells. The cells and their corresponding airflow structures are labeled using rectangular boxes in Figure 8c,f, respectively. rectangularThe local boxes radial in Figurevelocity8c,f, maps respectively. of the squall line at different elevation angles reveal the three- dimensionalThe local airflow radial field velocity structures maps of the of thesquall squall line. At line 08:29 at diUTC,fferent at the elevation elevation angles angle of reveal 0.5°, the three-dimensionalthere was a relatively airflow weak convergence field structures near of the the left squall boundary line. of At the 08:29 delineated UTC, atarea the (the elevation red circular angle of 0.5region◦, there in the was subfigure a relatively of 0.5° weak of convergence Figure 8b); at near the theelevation left boundary angle of of1.5°, the the delineated convergence area (theand red circularrotation region near the in theleft subfigureboundary ofof 0.5the◦ delineatedof Figure8 b);area at became the elevation stronger, angle a convergence of 1.5 ◦, the phenomenon convergence and rotationappeared near in the the middle, left boundary and a rotation of the characteristic delineated area was became presented stronger, on the right a convergence side; at the phenomenonelevation appearedangle of 2.5°, in the there middle, was a androtation a rotation in the left characteristic boundary (the was red presented circular onregion the rightin theside; subfigure at the of elevation 2.5° angleof Figure of 2.5 8b).◦, thereIn the wasmiddle a rotation of the sector in the area, left boundarythe three-dimensional (the red circular airflow region field structures in the subfigure from low of 2.5◦ ofto Figurehigh elevation8b). In the angles middle were of convergence the sector area, (0.5°), the ro three-dimensionaltation (2.5°), and divergence airflow field (3.4°), structures respectively. from low toA highweak elevation rotation structure angles were could convergence be seen at the (0.5 bottom◦), rotation of the right (2.5◦ ),side and (the divergence red circular (3.4 region◦), respectively. in the Asubfigure weak rotation of 1.5° structureof Figure 8b). could be seen at the bottom of the right side (the red circular region in the subfigure of 1.5◦ of Figure8b).

Atmosphere 2020, 11, 142 11 of 19 Atmosphere 2020, 11, 142 11 of 19

08:29 UTC 08:54 UTC 0.5° 0.5° 230 km 230 km

200 km 200 km

150 km 150 km 100 km

100 km 50 km (a) (d) 0 1 5 -5 -1 10 15 20 27 -27 -20 -15 -10 RF 0 1 5 -5 -1 10 15 20 27 -27 -20 -15 -10 RF m/s m/s 4.3° 4.3° Convergence Divergence / km /

Cyclonic rotation h / km 17.5 h Anti-cyclonic rotation Convergence 17.5 Divergence 20-25m/s 15 20-25m/s Cyclonic rotation 25-30m/s 3.4° 15 25-30m/s 3.4° Anti-cyclonic rotation Above 30m/s Above 30m/s

10 10

2.5° 2.5° 5 5

IIIIII IV V Rotation 0 III III IV V 0 120 100 Rotation 1.5° 1.5° II 140 120 II III I I III IV IV Convergence Rotation V 160 0.5° 140 θ/° 300 320 340 360(0) 0.5° / km r

180 V θ/° 320 340 360(0) / km / Convergence r (b) (c) (e) (f) FigureFigure 8.8. TheThe airﬂowairflow ﬁeldfield structuresstructures ofof thethe squallsquall lineline (13(13 JuneJune 2005)2005) atat 08:2908:29 andand 08:5408:54 UTC:UTC: ((aa)) TheThe

reflectivityreflectivity map map at at the the elevation elevation angle angle of of 0.5 0.5°◦ (08:29 (08:29 UTC), UTC), (b )( radialb) radial velocity velocity maps maps (08:29 (08:29 UTC), UTC), (c) the (c) PMAFSTIthe PMAFSTI and theand corresponding the corresponding composite composite reflectivity reflectivity map in map the θin-r therectangular θ-r rectangular coordinate coordinate system ofsystem the study of the region study(08:29 region UTC), (08:29 ( dUTC),) the reflectivity(d) the reflectivity map at map the elevation at the elevation angle of angle 0.5◦ (08:54of 0.5° UTC),(08:54 (UTC),e) radial (e) velocity radial velocity maps (08:54 maps UTC), (08:54 and UTC), (f) the and PMAFSTI (f) the PMAFSTI and the corresponding and the corresponding composite reflectivitycomposite mapreflectivity in the θ map-r rectangular in the θ-r coordinaterectangular system coordinate of the system study of region the study (08:54 region UTC). (08:54 UTC).

ByBy synthesizingsynthesizing the informationinformation providedprovided by radial velocity maps at didifferentfferent elevationelevation angles,angles, thethe airflowairflow structuresstructures withinwithin thethe squallsquall lineline cancan bebe summarizedsummarized asas follows:follows: (1)(1) TheThe convectiveconvective airflowairflow fieldfield nearnear thethe leftleft boundaryboundary ofof thethe selectedselected sectorsector regionregion hadhad matured,matured, showingshowing aa structurestructure ofof thethe bottombottom convergence,convergence, middlemiddle rotation,rotation, andand upperupper divergence;divergence; (2)(2) therethere werewere manymany rotationrotation pairspairs ofof positivepositive andand negative velocities velocities in in the the middle middle of of the the selected selected sector, sector, but but they they we werere mainly mainly located located in the in themiddle middle altitude altitude layer. layer. There There were were no no strong strong conver convergencesgences in in the the bottom bottom layer layer because it waswas stillstill developing;developing; andand (3)(3) thethe weakweak rotationrotation dominateddominated nearnear thethe rightright boundaryboundary ofof thethe selectedselected sectorsector regionregion andand itit waswas inin thethe early stage of development.

Atmosphere 2020, 11, 142 12 of 19

Through the above comprehensive analysis of the radial velocity maps at different elevation angles, we can indirectly understand the airflow field structures of the study region. However, using artificial observation to infer the airflow field within a convective system is inefficient and is limited to qualitative analysis. In contrast, the PMAFSTI (the upper subgraph of Figure8c) obtained by our method is a more effective analytical tool. It can be used to qualitatively and quantitatively analyze the airflow field structure within a convective system. Through the PMAFSTI, it is easy to see that near the left boundary of the selected study area, the bottom layer was convergent, and the cyclonic rotation’s height ranged from 1.75 km to 7 km. However, the overall convective intensity was low. In the middle of the study region, there were four pairs of cyclonic and anticyclonic rotations and structures tended to be complete. There was also a strong cyclonic rotation in the low-middle layer and a strong divergence in the upper layer. There was mainly a rotation field with low intensity near the right side of the study region. All this information provided by the PMAFSTI is consistent with the airflow field structure information obtained from the radial velocity maps at different elevation angles. At 08:54 UTC, 25 min later, the squall line system had further developed. In the composite reflectivity map (Figure8f), we can see that the reflectivity of the middle and right sides in the squall line had enhanced, while the reflectivity of the left side had weakened. In the radial velocity maps (Figure8e), there was a strong convergence on the left side at the elevation angle of 0.5 ◦; at the elevation angle of 1.5◦, the convergence was obvious and strong, and the range of the convergence was expanded (the red circular region in the subfigure of 1.5◦ of Figure8e); at the elevation angle of 2.5 ◦, the convergence coexisted with a rotation (the red circular region in the subfigure of 2.5◦ of Figure8e) and; at the elevation angle of 3.4◦ and 4.3◦, the divergence dominated. In addition, in the PMAFSTI (the upper subgraph of Figure8f), the convective cell I was strong, and its structure was relatively complete. The intensity of the MARC (at the altitude of 3 km) exceeded 25 m/s. Strong airflow pairs consisting of cyclonic and anticyclonic rotations in the middle layer on the right sides were in the development stage. In addition, a strong convergence field with an intensity over 30 m/s was detected in the middle part and the thickness of the part with intensity over 25 m/s was close to 3 km. The PMAFSTI can be used to infer the airflow structures within convective cells. Table4 summarizes the characteristics of the identified airflows within the five convective cells at 08:29 UTC and 08:54 UTC, including the airflow type, the intensity, and the height of the airflow. Each column of Table4 corresponds to a cell. If there are di fferent kinds of airflows within a cell, the corresponding column of the cell is divided into sub-columns. For example, cell II has two sub-columns and three sub-columns at 08:29 UTC and 08:54 UTC, respectively. Each sub-column of cell II records the information of airflow within the cell. As we can see, each cell contains a complex airflow structure. By comparing the radar composite reflectivity maps in Figure8c,f, we can find the squall line system is further developed. The cells at the middle and right sides of the squall line (cells III and IV at 08:29 UTC and the cells III, IV, and V at 08:59 UTC) had enhanced. From Table4, we find that the airflow structures of these cells revealed by the PMAFSTI are also significantly changed. At 0859 UTC, the intensities of the cells III, IV, and V are larger than that of the cells III and IV at 08:29 UTC; meanwhile, the cyclonic rotational structure become the dominant airflow structure at 08:59 UTC. From the above analysis of the airflow field recognition results in two moments, the velocity field information of nine elevation angles can be synthesized by our recognition results of the airflow field structures. The qualitative and quantitative structure analysis of the squall line system can also be given. It is intuitive and effective. The development stage of the convective cells can be analyzed by the structure integrities and intensity levels, and therefore we can make effective predictions of the stage. At the same time, the complex structure of the squall line can be clearly displayed. It can be integrated with the cell segmentation algorithms, which makes it possible to segment the micro convective cells from the large convective system, thus, promoting the refinement and scientization of convective weather disaster prediction [21]. Atmosphere 2020, 11, 142 13 of 19

Table 4. Five micro convective cells of the squall line (13 June 2005).

Convective Time I II III IV V Cells

p1 (Structure types, see 3 1 3 1 3 3 3 Table3) Intensity Low Low 25~30 Low 20~25 Low Low (m/s)

08:29 The height UTC of the 1.8~7 1.8~7 3~7 3~5 3~6 3~7 1.7~5 airﬂow (km)

p1 (Top structure 3 3 2 2 1 types, see Table3) Core reﬂectivity 50 40 45 40 45 (dBZ)

p1 (Structure types, see 31431414134313 Table3) Intensity Low 25~30 Low Low Low Low 25~30 >30 >30 25~30 >30 25~30 25~30 (m/s)

08:54 The height UTC of the 1~5 1~3 2~5 2~6 2.2~6.5 2.2~7 2~7.5 2~4 1~6 airﬂow (km)

p1 (Top structure 2 2 2 2 2 types, see Table3) Core reﬂectivity 50 50 55 55 55 (dBZ)

3.2. A Local Gale Case in Tianjin, China on 30 July 2015 Taking the local gale in Tianjin on 30 July 2015 as an example, two events of this episode were selected for analysis; one is the event of development and dissipation of a cell (Figure9) and the other is the event of split of another cell (Figure 10). The first event is depicted in Figure9. The cell began to take shape from 15:30 UTC. The whole cell was below an altitude of 10 km. The bottom layer of the cell was convergent, and the middle layer of the cell were cyclonic and anticyclonic rotations. At 15:36 UTC, the airflow intensity, p2, reached above 25 m/s for the first time. Then, the high-intensity region of the cell (the region that has a p2 larger than 20 m/s) gradually expanded upwards and downwards, as shown by the black dashed lines in Figure9. At 16:30 UTC, the thickness and intensity of the high-intensity region reached the maximum and a region with reflectivity over 55 dBZ appeared in the reflectivity map. At 16:36 UTC, the divergence was detected at the bottom of the cell and a gale was observed at the corresponding position of the divergence (the black circular region in the subfigure of 16:36 in Figure9). At 16:42 UTC, the thickness of the high-intensity regions began to decrease, and the cell began to dissipate. At 17:06 UTC, the cell collapsed completely. From the PMAFSTI, the MARC (the gray regions) was dominant in this process. The intensity of the MARC at an altitude of 3 km exceeded 25 m/s for the first time at 16:00 UTC and, then, continued to increase, with the maximum exceeding 30 m/s. The divergence occurred at the bottom 36 min later, resulting in a surface gale at 16:36 UTC. In addition, another predictor of the surface gale was a weak rotation structure included in the main convergence field. At 16:24 UTC, the rotation structure gradually reached the intensity of 25 m/s (the black circular region in the subfigure of 16:24 in Figure9) and presented a strong horizontal wind shear. After 12 min, when the surface gale appeared, the cyclonic rotation had extended from the altitude of 1 km up to 11 km, with the intensity exceeding 30 m/s. At this time, the cyclonic field dominated. Atmosphere 2020, 11, 142 14 of 19 the cyclonic rotation had extended from the altitude of 1 km up to 11 km, with the intensity exceeding Atmosphere 2020, 11, 142 14 of 19 30 m/s. At this time, the cyclonic field dominated.

15:30 UTC 15:36 UTC 15:42 UTC 15:48 UTC 15:54 UTC 17.5

/ km / Convergence h 15 Divergence Cyclonic rotation Anti-cyclonic 10 rotation

5 20-25m/s 25-30m/s Above 30m/s

0 100

120

140 / km /

r 160 θ/° 310 330 350 310 330 350 310 330 350 310 330 350 310 330 350 16:00 UTC 16:06 UTC 16:12 UTC 16:18 UTC 16:24 UTC 16:30 UTC 17.5

/ km 15 h

10 Rota tion

0 100

120

140 / km /

r 160 θ/° 310 330 350 310 330 350 310 330 350 310 330 350 310 330 350 310 330 350 16:36 UTC 16:42 UTC 16:48 UTC 16:54 UTC 17:00 UTC 17:06 UTC 17.5 / km / h 15

Divergence 0 90

110 / km / r θ/° 320 340 320 340 320 340 320 340 320 340 320 340 FigureFigure 9. 9. TheThe PMAFSTIs PMAFSTIs and and the the corresponding compositecomposite reflectivityreflectivity mapsmaps inin the theθ θ-r-rrectangular rectangular coordinatecoordinate system system ofof thethe event event of of a cell’s a cell’s development development and dissipationand dissipation (30 July (30 2015). July Two 2015). black Two dashed black dashedlines in lines the figure in the connect, figure respectively,connect, respectively, the highest the and highest lowest pointsand lowe of thest points high-intensity of the high-intensity region in the regionPMAFSTIs in the of PMAFSTIs different moments. of different moments.

Figure 10 shows how our algorithm performs on the second event of another cell’s split. At 17:24 Figure 10 shows how our algorithm performs on the second event of another cell’s split. At 17:24 UTC, in the cell, a strong cyclonic rotation was detected extending upwards from an altitude of 1 km to UTC, in the cell, a strong cyclonic rotation was detected extending upwards from an altitude of 1 km 8 km. The intensity of the cyclonic rotation exceeded 20 m/s. At 17:36 UTC, 12 min later, the divergence to 8 km. The intensity of the cyclonic rotation exceeded 20 m/s. At 17:36 UTC, 12 minutes later, the was detected at the bottom of the cell (the black circular region in the subﬁgure of 17:36 in Figure 10) divergence was detected at the bottom of the cell (the black circular region in the subfigure of 17:36 and a gale was detected at the corresponding ground station. At the next moment, the cell divided into in Figure 10) and a gale was detected at the corresponding ground station. At the next moment, the

Atmosphere 2020, 11, 142 15 of 19 Atmosphere 2020, 11, 142 15 of 19 cell divided into two parts. In the one part (the sequence of the second row in Figure 10), the bottom layer was divergent, and the middle layer was rotational. The intensity gradually decreased, and the two parts. In the one part (the sequence of the second row in Figure 10), the bottom layer was divergent, cell dissipated at 18:18 UTC. In the other part (the sequence of the third row in Figure 10), severe and the middle layer was rotational. The intensity gradually decreased, and the cell dissipated at convection began to develop upwards and downwards from the rotational part of the middle layer. 18:18 UTC. In the other part (the sequence of the third row in Figure 10), severe convection began to Atdevelop 18:00 upwardsUTC, a mature and downwards convective from structure the rotational of the bottom part of theconvergence, middle layer. middle At 18:00 rotation, UTC,a and mature upper divergenceconvective structurehad been of formed the bottom and the convergence, intensity of middle the MARC rotation, within and the upper cell divergencehad increased had from been 20 m/sformed at 17:42 and UTC the intensity to 30 m/s of at the 18:00 MARC UTC. within The botto the cellm divergence had increased was fromdetected 20 m near/s at the 17:42 right UTC boundary to 30 ofm/ sthe at delineated 18:00 UTC. Thearea bottom(the rectangular divergence figure) was detected and the neargale thewas right detected boundary at the of corresponding the delineated ground area station(the rectangular at 18:18 UTC. ﬁgure) and the gale was detected at the corresponding ground station at 18:18 UTC.

17:24 UTC 17:30 UTC 17:36 UTC 17.5

/ km Convergence h 15 Divergence Cyclonic rotation Anti-cyclonic rotation 10

20-25m/s 5 25-30m/s Above 30m/s

0 Divergence 80

100

/ km 120 θ/° r 310 330 350 310 330 350 310 330 350 17:42 UTC 17:48 UTC 17:54 UTC 18:00 UTC 18:06 UTC 18:12 UTC 18:18 UTC 17.5 / km

h 15

0 90

110

/ km θ/° r 330 350 330 350 330 350 330 350 330 350 330 350 330 350 17:42 UTC 17:48 UTC 17:54 UTC 18:00 UTC 18:06 UTC 18:12 UTC 18:18 UTC 17.5 / km h 15

0 60

80 / km r

100 θ/° 300 330 300 330 300 320 340 300 320 340 300 320 340 300 320 340 300 320 340 Figure 10.10. TheThe PMAFSTIsPMAFSTIs andand the the corresponding corresponding composite composite reﬂectivity reflectivity maps maps in in the theθ- rθrectangular-r rectangular coordinate systemsystem ofof the the event event of of another another cell’s cell’s split split (30 (30 July July 2015). 2015).

Atmosphere 2020, 11, 142 16 of 19

Through the discussion of the above two events, it is easy to find that the PMAFSTI clearly shows the types and corresponding intensities of the convective system during different stages of growth, development, split, and dissipation of the cell. On the basis of previous observations of mesocyclones, the life history of mesocyclones consists of three stages [22], i.e., the generation stage, the mature stage, and the dissipation stage. The phenomena in these three stages are consistent with the recognition results of the above two events, which proves the validity of our airflow field structure recognition method.

3.3. An Extreme Gale Case in Jianli, China on 1 June 2015 On 1 June 2015, the “Oriental Star” passenger ship was traveling on the Yangtze River in Jianli, Hubei Province, China with 454 people on board when it capsized in a severe thunderstorm, resulting in 442 deaths [23,24]. The recognition algorithm of the airflow field structures was also tested by this extreme gale case. The test results are shown in Figure 11. Two black dashed lines in the figure connect, respectively, the highest and lowest points of the high-intensity region in the PMAFSTIs of different moments. The upper ends of the two-way black arrows point to the highest points of the high-intensity region and the lower ends of the arrows point to the position of the shipwreck. It is easy to see from the black dashed lines in Figure 11 that the thickness of the high-intensity region had gradually increased from 20:23 UTC. At 21:09 UTC, a strong couplet consisting of a cyclonic rotation and an anticyclonic rotation at the altitude of about 8 km appeared (the black rectangular region in the subfigure of 21:09 in Figure 11) and its azimuth was almost the same as that of the shipwreck.

3.4. Discussion of Method The proposed algorithm in this work first detects boundary points in the radar radial velocity maps and, then, identifies the airflow types around the points based on a template recognition method. Therefore, the boundary points that are not related with the airflows would lead to many false alarms of our algorithm. For example, when wind blows perpendicular to the radar beams, or reverses its direction by the altitude at high elevation angles, the radar observations have some boundary points without airflows. These false alarms can hardly be distinguished using single Doppler radar. One possible method to solve this problem is to identify airflows using multiple Doppler radars, because these false alarms would change their structures significantly on multiple radar radical velocity maps. In addition, the performance of the proposed algorithm is highly affected by the environment wind estimation algorithm, such as the VAD algorithm. If the environment wind is estimated incorrectly, the boundary lines of positive and negative velocity points disappear, and thus lead to many misses of airflows. To solve this problem, the environment wind could be set manually before the algorithm is applied. Atmosphere 2020, 11, 142 17 of 19 Atmosphere 2020, 11, 142 17 of 19

20:23 UTC 20:29 UTC 20:34 UTC 20:40 UTC 20:46 UTC 17.5 15 / km h

0 60

100 / km / r 110 θ/° 300 320 340 360(0)10 300 320 340 360(0) 10 300 320 340 360(0) 10 300 320 340 360(0) 10 300 320 340 360(0) 10 20:52 UTC 20:57 UTC 21:03 UTC 21:09 UTC 17.5 Convergence 15 Divergence / km /

h Cyclonic rotation

10 Anti-cyclonic rotation

5 20-25m/s 25-30m/s 0 Above 30m/s 40

100 / km / r 110 θ/° 280 300 320 340 360(0) 280 300 320 340 360(0) 280 300 320 340 360(0) 280 300 320 340 360(0) 21:15 UTC 21:20 UTC 21:26 UTC 21:32 UTC 17.5 15 / km / h

0 30

70 / km r 90

110 θ/° 280 300 320 340 360(0) 10 280 300 320 340 360(0) 10 280300 320 340 360(0) 10 280 300 320 340 360(0) 10 FigureFigure 11. 11. TheThe PMAFSTIs PMAFSTIs and and the the corresponding corresponding compositecomposite reflectivityreflectivity maps maps in in the theθ -rθ-rrectangular rectangular coordinatecoordinate system system of of the the shipwreck shipwreck (1 (1 June June 2015).2015). Two Two black dasheddashed lineslines in in the the figure figure connect, connect, respectively,respectively, the the highest highest and and lowest lowest points points of the high-intensityhigh-intensity regionregion in in the the PMAFSTIs PMAFSTIs of of di ffdifferenterent moments.moments. The The upper upper ends ofof the the two-way two-way black black arrows arrows point point to the highestto the highest points of points the high-intensity of the high- region and the lower ends of the arrows point to the position of the shipwreck. intensity region and the lower ends of the arrows point to the position of the shipwreck. 4. Conclusions 4. Conclusions Airflow structures within convective systems are important predictors of damaging convective disastersAirflow and structures useful in within recognizing convective the evolution systems stageare important of a convective predictors cell. Automaticof damaging recognition convective disastersof different and kindsuseful of in airflow recognizing structures the evolution within convective stage of a cells convective is a challenging cell. Automatic task because recognition of their of differentirregular kinds airflow of performancesairflow structures and additional within convective noise in the cells airflow is field.a challenging In this work, task a template-basedbecause of their irregularautomatic airflow airflow performances field recognition and algorithmadditional is proposednoise in the to identifyairflow fourfield. kinds In this of airflow work, structuresa template- basedin Doppler automatic radar airflow radial field velocity recognition maps. algorithm The two-dimensional is proposed template to identify recognition four kinds results of airflow are structurescombined in to Doppler obtain the radar three-dimensional radial velocity airflow maps. fieldThe structurestwo-dimensional within convective template recognition cells. In addition, results are combined to obtain the three-dimensional airflow field structures within convective cells. In addition, a two-dimensional PMAFSTI is developed in this work to visualize the three-dimensional airflow structures of convective cells. Appearances of four kinds of ideal airflow structures (the convergence, divergence, cyclonic rotation and anticyclonic rotation) in radar radical velocity maps are first analyzed. These typical

Atmosphere 2020, 11, 142 18 of 19 a two-dimensional PMAFSTI is developed in this work to visualize the three-dimensional airflow structures of convective cells. Appearances of four kinds of ideal airflow structures (the convergence, divergence, cyclonic rotation and anticyclonic rotation) in radar radical velocity maps are first analyzed. These typical airflow structures are modeled using a common velocity couplet structure, which is described using three parameters, length (L), orientation (∆θ), and velocity difference (dv). On the basis of the appearance model of velocity couplet, a two-dimensional hexagonal template is designed that has the ability to recognize different kinds of airflow field structures simultaneously. Before using the template, the original Doppler radar data are preprocessed, including coordinates transformation, data interpolation, and determination of potential convective airflow regions of convective cells. After the preprocessing, the original radar data is transformed into grid data of 70 layers. The template recognition process is carried out on each layer to obtain airflow field structures’ types and intensities around boundary points between positive and negative velocities. Outputs of the two-dimensional template recognition results on different layers compose the three-dimensional airflow structure within each convective cell. In this work, a two-dimensional PMAFSTI is developed to visualize the three-dimensional structures of airflow filed within a cell. The map is obtained by expanding the template recognition results on a θ-h coordinate system and, then, overlapping the map of types and intensities, where the airflow types’ map is represented using four kinds of colors and the airflow intensities’ map is represented using shallow-to-deep red isolines. The proposed template-based airflow field structures recognition algorithm was tested on three cases of convective disasters. The following conclusions were drawn from these three cases:

1. At different evolution stages of the convective systems, for example, growth, split, and dissipation, the three-dimensional structure distributions of the airflow fields within convective systems can be clearly observed by the PMAFSTI, which can be used to support the evolution analysis of the convective system. 2. Through recognizing the structures of the airflow field, we can further divide the complex airflow field, such as the squall line, into several small parts, and therefore we can analyze the airflow field changes more concretely and make the analysis of convective evolution more scientific and elaborate.

Author Contributions: All authors have made great contributions to this article. Conceptualization, P.W.; methodology, K.G. and J.H.; software, K.G.; validation, B.D.; formal analysis, P.W.; writing—original draft preparation, K.G.; writing—review and editing, J.H. All authors have read and agreed to the published version of the manuscript. Funding: This study was partially supported by the Natural Science Foundation of Tianjin, China, under grant no. 14JCYBJC21800. Acknowledgments: The authors would like to thank the Tianjin Meteorological Observatory for providing radar data and weather-station data. Conﬂicts of Interest: The authors declare no conﬂict of interest.

References

1. Glickman, T.S. (Ed.) Glossary of Meteorology, 2nd ed.; American Meteorological Society: Boston, MA, USA, 2000; ISBN 9781878220349. 2. Reboita, M.; da Rocha, R.; Oliveira, D. Key Features and adverse weather of the named subtropical cyclones over the Southwestern South Atlantic Ocean. Atmosphere 2018, 10, 6. [CrossRef] 3. Krinitskiy, M.; Verezemskaya, P.; Grashchenkov, K.; Tilinina, N.; Gulev, S.; Lazzara, M. Deep convolutional neural networks capabilities for binary classiﬁcation of polar mesocyclones in satellite mosaics. Atmosphere 2018, 9, 426. [CrossRef] Atmosphere 2020, 11, 142 19 of 19

4. Schmocker, G.K.; Przybylinski, R.W.; Lin, Y.J. Forecasting the initial onset of damaging downburst winds associated with a mesoscale convective system (MCS) using the mid-altitude radial convergence (MARC) signature. In Proceedings of the 15th Conference on Weather Analysis and Forecasting, Norfolk, VA, USA, 19–23 August 1996; Volume 15, pp. 306–311. 5. Trapp, R.J.; Stumpf, G.J.; Manross, K.L. A reassessment of the percentage of tornadic mesocyclones. Weather Forecast 2005, 20, 680–687. [CrossRef] 6. Stumpf, G.J.; Witt, A.; Mitchell, E.D. The national severe storms laboratory mesocyclone detection algorithm for the WSR-88D. Weather Forecast 1998, 13, 304–326. [CrossRef] 7. Smith, T.M.; Elmore, K.L. The use of radial velocity derivatives to diagnose rotation and divergence. In Proceedings of the 11th Conference on Aviation, Range, and Aerospace, Hyannis, MA, USA, 4–8 October 2004; pp. 5–6. 8. Hou, J.; Wang, P. Mesocyclone automatic recognition method based on detection of velocity couplets. J. Tianjin Univ. Sci. Technol. 2017, 11, 64–72. 9. Nascimento, E.L.; Droegemeier, K.K. Dynamic adjustment in a numerically simulated mesoscale convective system: Impact of the velocity field. J. Atmos. Sci. 2006, 63, 2246–2268. [CrossRef] 10. Wang, P.; Niu, Z. The automatic recognition of Mid-Altitude Radial Convergence and the study of relation between it and strong convective weather based on doppler weather radar data. Physics 2014, 1, 424–436. 11. Wang, P.; Dou, B. Recognition of strong convergence field based on doppler radar data. J. Tianjin Univ. Sci. Technol. 2018, 8, 797–809. 12. Tian, L.; Heymsfield, G.M.; Didlake, A.C.; Guimond, S.; Li, L. Velocity–azimuth display analysis of doppler velocity for HIWRAP. J. Appl. Meteor. Clim. 2015, 54, 1792–1808. [CrossRef] 13. Tang, J.; Matyas, C. A nowcasting model for tropical cyclone precipitation regions based on the TREC motion vector retrieval with a semi-lagrangian scheme for doppler weather radar. Atmosphere 2018, 9, 200. [CrossRef] 14. Yu, X.; Yao, X.; Xiong, T. Principle and Operational Application of Doppler Weather Radar; China Meteorological Press: Beijing, China, 2006; pp. 60–61. ISBN 750294111. 15. Wenting, S.; Yuquan, Z.; Fei, W. Case study on the test method of artificial seeding effect for meso-γ scale convective cloud based on radar observation. Meteorol. Environ. Sci. 2017, 2, 11–19. 16. Dixon, M.; Wiener, G. TITAN: Thunderstorm identification, tracking, analysis, and nowcasting—A radar-based methodology. J. Atmos. Ocean. Technol. 1993, 10, 785–797. [CrossRef] 17. Juan, S.; Yong, H.; Yan, F. Convective cloud tracking method based on correlation matching and area overlap degree. Comput. Eng. 2011, S1, 336–340. 18. Gribbon, K.T.; Bailey, D.G. A novel approach to real-time bilinear interpolation. In Proceedings of the Second IEEE International Workshop on Electronic Design, Test and Applications; IEEE: Perth, Australia, 2004; p. 126. 19. Yeqing, Y.; Xiaoding, Y.U.; Yijun, Z. Analysis on a typical squall line case with doppler weather radar data. Plateau Meteorol. 2008, 27, 373–381. 20. Mengsong, N.; Liping, Z.; Meifang, Q.U. Study on initialization mechanism and alert of gale in squall line storm event. Meteorol. Mon. 2014, 12, 1491–1499. 21. Bin, W.; Yangfang, L.; Jie, Q. Severe convective weather automatic identification system based on weather radar data. Meteorol. Hydrol. Marine Instrum. 2014, 4, 33–37. 22. Watanabe, S.I.I.; Niino, H. Genesis and development mechanisms of a polar mesocyclone over the japan sea. Mon. Weather Rev. 2014, 142, 2248–2270. [CrossRef] 23. Meng, Z.; Yao, D.; Bai, L.; Zheng, Y.; Xue, M.; Zhang, X.; Zhao, K.; Tian, F.; Wang, M. Wind estimation around the shipwreck of Oriental Star based on field damage surveys and radar observations. Sci. Bull. 2016, 61, 330–337. [CrossRef][PubMed] 24. Ni, Z.; Tan, X.; Liu, B.; Ying, Z.; Gao, X.; Yang, F. Rapid risk assessment report for schistosomiasis epidemic in Jianli county caused by the sunk “Oriental Star” cruise ship. Disaster Med. Public Health Prep. 2018, 12, 147–153. [CrossRef][PubMed]

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