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  • View in gallery
    Fig. 1.

    Retrieved winds using two different size analysis boxes overlaid on the 3-km-height reflectivity (scale in dBZ at the bottom) at 0400 UTC 30 Apr 2013: (a) 5 km × 5 km and (b) 50 km × 50 km boxes.

  • View in gallery
    Fig. 2.

    Retrieved winds overlaid on the 3-km-height reflectivity (scale in dBZ at the bottom) at 0400 UTC 30 Apr 2013. (a) Winds at 1-km height (black) and 5-km height (red). (b) Average of 1- and 5-km winds.

  • View in gallery
    Fig. 3.

    Retrieved winds for six different heights overlaid on the 3-km-height reflectivity (scale in dBZ on the right) at 0330 UTC 30 Apr 2013: (a) 1-, (b) 2-, (c) 3-, (d) 4-, (e) 5-, and (d) 6-km heights.

  • View in gallery
    Fig. 4.

    Comparison of (a) TREC and (b) IVAP motion vectors overlaid on 3-km-height reflectivity (scale in dBZ at bottom) at 0330 UTC 30 Apr 2013.

  • View in gallery
    Fig. 5.

    Comparison of TREC and IVAP 1-h nowcasts at 0430 UTC 30 Apr 2013. (a) Observed reflectivity at 0330 UTC, (b) observed reflectivity at 0430 UTC, (c) TREC nowcast at 0430 UTC, and (d) IVAP nowcast at 0430 UTC. The black contours in (b)–(d) are the observed 40-dBZ contour at 0430 UTC. The reflectivity scale in dBZ is to the right.

  • View in gallery
    Fig. 6.

    Mean evaluation scores of POD (black), FAR (red line), and CSI (blue) within 60-min forecast for TREC (solid) and IVAP (dashed).

  • View in gallery
    Fig. 7.

    Orography with internal 100 m above mean sea level (MSL). The radar site is indicated by letter R.

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A Study of Extrapolation Nowcasting Based on IVAP-Retrieved Wind

Yi Luo State Key Laboratory of Severe Weather, Chinese Academy of Meteorological Science, Beijing, China
Meteorological Center, Central South Air Traffic Management Bureau, CAAC, Guangzhou, China

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Xudong Liang State Key Laboratory of Severe Weather, Chinese Academy of Meteorological Science, Beijing, China

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Gang Wang Meteorological Center, Central South Air Traffic Management Bureau, CAAC, Guangzhou, China

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Zheng Cao Meteorological Center, Central South Air Traffic Management Bureau, CAAC, Guangzhou, China

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Abstract

In this study, we propose a new way to obtain motion vectors using the integrating velocity–azimuth process (IVAP) method for extrapolation nowcasting. Traditional tracking methods rely on tracking radar echoes of a few time slices. In contrast, the IVAP method does not depend on the past variation of radar echoes; it only needs the radar echo and radial velocity observations at the latest time. To demonstrate it is practical to use IVAP-retrieved winds to extrapolate radar echoes, we carried out nowcasting experiments using the IVAP method, and compared these results with the results using a traditional method, namely, the tracking radar echoes by correlation (TREC) method. Comparison based on a series of large-scale mature rainfall cases showed that the IVAP method has similar accuracy to that of the TREC method. In addition, the IVAP method provides the vertical wind profile that can be used to anticipate storm type and motion deviations.

© 2021 American Meteorological Society. For information regarding reuse of this content and general copyright information, consult the AMS Copyright Policy (www.ametsoc.org/PUBSReuseLicenses).

Corresponding author: Xudong Liang, liangxd@cma.gov.cn

Abstract

In this study, we propose a new way to obtain motion vectors using the integrating velocity–azimuth process (IVAP) method for extrapolation nowcasting. Traditional tracking methods rely on tracking radar echoes of a few time slices. In contrast, the IVAP method does not depend on the past variation of radar echoes; it only needs the radar echo and radial velocity observations at the latest time. To demonstrate it is practical to use IVAP-retrieved winds to extrapolate radar echoes, we carried out nowcasting experiments using the IVAP method, and compared these results with the results using a traditional method, namely, the tracking radar echoes by correlation (TREC) method. Comparison based on a series of large-scale mature rainfall cases showed that the IVAP method has similar accuracy to that of the TREC method. In addition, the IVAP method provides the vertical wind profile that can be used to anticipate storm type and motion deviations.

© 2021 American Meteorological Society. For information regarding reuse of this content and general copyright information, consult the AMS Copyright Policy (www.ametsoc.org/PUBSReuseLicenses).

Corresponding author: Xudong Liang, liangxd@cma.gov.cn

1. Introduction

Doppler weather radar is a useful tool in detecting convective storm, which provides reflectivity, radial velocity, and velocity spectral width at high temporal and spatial resolution. Radar reflectivity has been used in storm nowcasting via extrapolation methods, which are still being used in many operational nowcasting systems. An extensive review of earlier studies on thunderstorm nowcasting techniques can be found in Wilson et al. (1998).

Ligda (1953) did the earliest trial of extrapolating radar echoes for the purpose of forecasting precipitation. Hilst and Russo (1960) and Noel and Fleisher (1960) introduced the cross-correlation method to estimate the motions of radar echoes. In this method, cross-correlation coefficients are calculated between radar echoes at different times, the locations with maximum cross-correlation coefficients are considered the distance that radar echoes moved in the past period, and then the distance divided by time interval is used to estimate the motions of the echoes. Initially, the cross-correlation method was used to determine the motion of the entire echo (i.e., entire convective system), and the echo was then extrapolated without changes in size and intensity. Rinehart and Garvey (1978) improved this method; and the improved method is known as the tracking echo by correlation (TREC) technique, which uses cross-correlation technique to obtain motions of different sections of the whole echo field. The TREC method was developed into variant schemes, such as the continuity of the TREC (COTREC; Li et al. 1995) and multiscale tracking radar echoes by correlation (MTREC; Wang et al. 2013). The COTREC uses the continuity equation as a constraint, and utilizes variation technique to smooth the motion vectors derived from the TREC, which improves the performance of precipitation nowcasting related to complex orography and severe storms. The MTREC synthesizes different motion vectors by tracking echoes of different scales, which removes zero vector and provides smoother motion vectors compared with those from the TREC method. Another technique involves spectral algorithm, such as the Spectral Prognosis (S-PROG; Seed 2003) and Short-Term Ensemble Prediction System. The S-PROG utilizes Fourier functions to decompose radar reflectivity field into a set of characteristic scale components and applies a model for the scale-dependent Lagrangian evolution to smooth the forecast field. However, the advection vectors of the S-PROG are from the pattern-matching method, which is like original cross-correlation method and only provides one single extrapolation vector for the entire field. In the 1980s, the optical flow method (Horn and Schunck 1981; Lucas and Kanade 1981) was introduced to improve the advection of radar echoes. The optical flow method is used to obtain motion vectors through the calculation of the optical flow field from consecutive radar reflectivity pictures, and this method has been applied to some nowcasting systems, such as the Generating Advanced Nowcasts for Deployment in Operational Land surface Flood Forecasts (GANDOLF; Bowler et al. 2004) and Short-Range Warning of Intense Rainstorms in Localized Systems (SWIRLS; Cheung and Yeung 2012).

Another method for estimating the motion of thunderstorm is the storm cell centroid identification and tracking technique (Wilk and Gray 1970; Zittel 1976; Brady et al. 1978; Crane 1979; Rosenfeld 1987). This method was implemented to determine cell characteristics, especially the echoes’ centroid position. Then the storm location, motion, and precipitation distribution were estimated mainly based on the centroid position. An early, typical centroid-type algorithm was the WSR-88D storm series algorithm (Boak et al. 1977; Forsyth 1979). Later on, the Storm Cell Identification and Tracking (SCIT) algorithm was proposed by Johnson et al. (1998), which preset several reflectivity thresholds instead of only one reflectivity threshold used in the WSR-88D storm series algorithm. The SCIT algorithm not only improved the identification performance on individual storms, but also had the ability to detect line or cluster storms. Handwerker (2002) combined cell identification and echoes splitting and merging, and extended the centroid tracking method from 2D to 3D based on radar volume data, which led to the development of the TRACE-3D technique. The TRACE-3D can identify, and track convective cells, and provide temporal development of several properties of a thunderstorm.

Correlation tracking algorithms tend to provide more accurate speed and direction information on larger-scale echoes, but they are not efficient at identifying and tracking individual storms. Centroid identification and tracking algorithms can track isolated individual storms more effectively, and provide temporal characteristic information of storms. However, centroid-type methods depend on predefined threshold to distinguish individual convective storms, which makes them inappropriate for nowcasting of stratiform precipitation (Keenan et al. 2003; Pierce et al. 2004). The Thunderstorm Identification, Tracking, Analysis and Nowcasting (TITAN; Dixon and Wiener 1993; Han et al. 2009) method was thus developed, which combines both techniques, and is continuously being updated. The TITAN has the capability to handle splitting and merging of storms, track and forecast convective systems, and even analyze storms’ trends to grow or dissipate based on their past variation.

All of the aforementioned methods use the motions of echoes to represent the motion of a storm, without considering the motion of the air. As we already know, echo advection is correlated to the mean wind (steering flow). Wilson (1966) showed that echo motion was related to the scale of the convective storm, and the convection with small-scale features tended to move with the mean wind while thunderstorms with larger scales tended to move more slowly and to the right of the mean wind. Andersson and Ivarsson (1991) used model-forecasted winds on 850 hPa as the steering flow to extrapolate radar echoes for nowcasting accumulated precipitation.

As we all know, in the early stage of weather radar development, the weather radar without Doppler detective capacity only collected reflectivity data. Such data can be used to make prediction using echo tracing methods (such as TREC and TITAN). Then the Doppler weather radar was used in operation; it provides reflectivity and radial velocity simultaneously. Turner et al. (2004) developed the McGill algorithm for precipitation nowcasting by Lagrangian extrapolation (MAPLE) nowcasting system that uses retrieval winds by variational analysis technique. Liu et al. (2015) applied the velocity–azimuth display (VAD) winds to extrapolate radar echoes at different heights; and their forecasting results coincided well with observations when large-scale storms approached the radar location.

Researchers (Turner et al. 2004; Liu et al. 2015) obtained some encouraging results of nowcasting with retrieval wind. However, the retrieval method of Turner et al. (2004) is complicated, and the VAD wind used by Liu et al. (2015) is relatively coarse. The question is how to obtain a better wind field from this kind of method. There are some existing techniques for obtaining the wind field, such as the dual-Doppler wind analysis method (Armijo 1969; Miller and Strauch 1974; Brandes 1977; Ray et al. 1980; Kessinger et al. 1987; Shapiro and Mewes 1999); and some sophisticated methods that can be used to retrieve 2D or 3D winds using prognostic equations, kinetic equations, and even numerical models as strong or weak constraints in a variational framework (Shapiro et al. 2003, 2009; Snyder and Zhang 2003; Tong and Xue 2005; Xiao et al. 2008). The dual-Doppler radar wind analysis method can provide more accurate wind estimates, but it requires overlapped observations by at least two radars, which is difficult to use in operation. Meanwhile, there are some methods, which retrieve winds only using radar radial velocity, including the VAD (Lhermitte and Atlas 1961; Caton 1963; Browning and Wexler 1968), the velocity area display (VARD; Easterbrook 1975), the velocity volume processing (VVP; Waldteufel and Corbin 1979; Koscielny et al. 1982), the uniform winds (UW; Persson and Anderson 1987), the velocity–azimuth process (VAP; Tao 1992), and the velocity plan processing (VPP; Lang et al. 2001). Liang (2007) proposed the integrating velocity–azimuth process (IVAP) method to obtain the wind field using data from a single Doppler radar. This method can obtain wind field with higher resolution than the VAD, and is easier to implement than the sophisticated methods mentioned above.

In summary, the basic idea of nowcasting using radar data is to obtain the motion vectors of radar echoes. The motion of the echo can be obtained from echo tracing methods, such as cross-correlation and centroid-type methods. On the other hand, the echo motion is correlated to the wind field (steering flow), which plays a predominant role in the movement of the convective system (Wilson 1966). As we know, the Doppler radar has an advantage to obtain retrieval winds, which can be used as motion vectors for nowcasting. In this paper, we will explore the feasibility of nowcasting by using the retrieved winds from the IVAP method, whose advantage is easy to implement and has a high spatial resolution (Liang 2007).

The remainder of this paper is organized as follows. In section 2, we introduce the IVAP method briefly, and describe the frame of extrapolation forecasting. In section 3, we use the case of a squall line to verify the practicability of the IVAP nowcasting. In section 4, we compare the nowcasting performances using the traditional TREC method and the IVAP method for a series of weather events to obtain a statistical evaluation. Finally, conclusions and discussion are presented in section 5.

2. Method and algorithm

a. Data preprocessing and quality control

We use the data from an S-band Doppler radar in Guangzhou, China, in this study. The radar type is CINRA-SA, which is similar to WSR-88D. The radar site is at (22.43°N, 113.34°E). The sampling mode is the volume coverage pattern (VCP) 21, which consists of nine elevation scans (ranging from 0.5° to 19.5°, namely, 0.5°, 1.5°, 2.4°, 3.4°, 4.3°, 6.0°, 9.9°,14.6°, and 19.5°) in 6 min. The rainfall case on 30 April 2013 is selected to verify the practical application of the IVAP method. Then the cases from April to June in 2016 were selected for statistic verification. We performed all data quality control processes, which include ground clutter suppressing (Hubbert et al. 2009), terminal vertical velocity of precipitation particle removal (Orr and Kropfli 1999) and velocity dealiasing (Liang et al. 2019).

b. Scheme of IVAP extrapolation

Radar reflectivity at 3-km height is chosen to symbolize the location and pattern of target convective system, as many extrapolation nowcasting methods do (Steiner et al. 1995). The retrieval winds as motion vectors for extrapolation should meet two requirements: 1) mean winds within a certain area (to filter out small-scale wind pattern), and 2) mean winds within a certain vertical range to represent the steering flow of the convective system. The IVAP method satisfies both requirements.

In Liang (2007), the locally averaged wind (u, υ) within [θ1, θ2] and [r1, r2] can be obtained using Eqs. (2.1) and (2.2) as follows,
u=r1r2θ1θ2VR*cosθdθdrr1r2θ1θ2sin2θdθdrr1r2θ1θ2VR*sinθdθdrr1r2θ1θ2sinθcosθdθdrr1r2θ1θ2cos2θdθdrr1r2θ1θ2sin2θdθdrr1r2θ1θ2sinθcosθdθdrr1r2θ1θ2sinθcosθdθdr,
υ=r1r2θ1θ2VR*sinθdθdrr1r2θ1θ2cos2θdθdrr1r2θ1θ2VR*cosθdθdrr1r2θ1θ2cosθsinθdθdrr1r2θ1θ2sin2θdθdrr1r2θ1θ2cos2θdθdrr1r2θ1θ2sinθcosθdθdrr1r2θ1θ2sinθcosθdθdr,
where VR* is radar radial velocity, θ is azimuth angle, and r is radar range. The retrieved wind depends on both the azimuth angle range of [θ1, θ2] and the radar range of [r1, r2] (analysis box). For convenience, the analysis box is defined in the Cartesian coordinate system in this study.

The retrieval wind of the IVAP method is related to the size of the analysis box used, which is shown by a squall line observed by Guangzhou radar at 0400 UTC 30 April 2013. Given a smaller analysis box of 5 km × 5 km (Fig. 1a), some internal wind information of the thunderstorm, such as wind shear or convergence zone, can be identified. Obviously, a wind field like this is not smooth enough to represent the movement of the convection system. In contrast, a larger analysis box of 50 km × 50 km is more suitable for extrapolation forecasting (Fig. 1b). These results indicate that a large analysis box is essential for obtaining motion vectors used for extrapolating. The analysis box size of 50 km × 50 km was chosen in this study.

Fig. 1.
Fig. 1.

Retrieved winds using two different size analysis boxes overlaid on the 3-km-height reflectivity (scale in dBZ at the bottom) at 0400 UTC 30 Apr 2013: (a) 5 km × 5 km and (b) 50 km × 50 km boxes.

Citation: Journal of Atmospheric and Oceanic Technology 38, 4; 10.1175/JTECH-D-19-0180.1

Another essential issue of extrapolation nowcasting is to determine the vertical levels at which the winds can represent the motion of the convective system. Early research demonstrated the interdependency between convective system motion and mean environmental wind. Battan (1953) used the wind within the height range of 3.0–3.7 km, and Andersson and Ivarsson (1991) used the wind at 850 hPa (about 1.5 km above mean sea level) as the steering flow. Liu et al. (2015) studied the performance of extrapolation using VAD winds at different heights, and indicated that the deeper the convection system developed, the higher the height of the steering wind was. All these studies showed that the mean winds within a layer of several kilometers are better as the motion vectors for extrapolation. In this study, the mean winds between 1.0 and 6.0 km were used as the motion vector for extrapolating, which are demonstrated in Fig. 2. The IVAP retrieval winds at two different altitudes and their averages are shown in Figs. 2a and 2b. Note that this layer can be adjusted according to the depth of the convective system.

Fig. 2.
Fig. 2.

Retrieved winds overlaid on the 3-km-height reflectivity (scale in dBZ at the bottom) at 0400 UTC 30 Apr 2013. (a) Winds at 1-km height (black) and 5-km height (red). (b) Average of 1- and 5-km winds.

Citation: Journal of Atmospheric and Oceanic Technology 38, 4; 10.1175/JTECH-D-19-0180.1

3. Case study

Here, we verify the practicability of extrapolation nowcasting using the IVAP retrieval winds, and show the characteristics of the IVAP algorithm for short-term forecasting by comparing with the results from the TREC method. The main distinction of the two methods is the way to obtain the extrapolation motion vectors: the TREC method obtains motion vectors from tracking echoes at two or more times, while the IVAP method uses retrieval winds from the radial velocity at a single time, which is collected concurrently with the echoes.

A squall line case observed by the Doppler radar in Guangzhou on 30 April 2013 was selected. The convective system was caused by the combination of an upper-level trough, middle-level wind shear and lower-level jet. This squall line moved ESE rapidly, with little changes in intensity and shape before it arrived at the radar site.

The radar reflectivity at 3-km height was chosen to represent the location and pattern of the convective system. The retrieval winds were obtained with the following settings. 1) The observations sampled in a vertical layer with 1-km thickness were used to retrieve the wind at the middle of the layer, i.e., the observations sampled from 0.5 to 1.5 km were used to retrieve wind at the height of 1.0 km. 2) The horizontal box size of 50 km × 50 km was adopted. 3) The winds were not retrieved, if the radial velocity observations covered less than 60% of the retrieval unit (50 km × 50 km × 1 km). To obtain the motion vectors using the IVAP method for extrapolation, the processes adopted by the TREC method, such as elimination of isolated motion vectors, filling and smoothing, were also applied.

Figure 3 illustrates the retrieval winds obtained by the IVAP method from 1- to 6-km height (with an interval of 1 km) using the settings mentioned above. The IVAP retrieval winds became smoother and counterclockwise rotated with height, which is consistent with the typical model of a squall line. The motion vectors averaged between 1 and 6 km are shown in Fig. 4b, to compare with those by the TREC method (Fig. 4a). Clearly, the motion vectors from the IVAP method are smoother than those from the TREC method.

Fig. 3.
Fig. 3.

Retrieved winds for six different heights overlaid on the 3-km-height reflectivity (scale in dBZ on the right) at 0330 UTC 30 Apr 2013: (a) 1-, (b) 2-, (c) 3-, (d) 4-, (e) 5-, and (d) 6-km heights.

Citation: Journal of Atmospheric and Oceanic Technology 38, 4; 10.1175/JTECH-D-19-0180.1

Fig. 4.
Fig. 4.

Comparison of (a) TREC and (b) IVAP motion vectors overlaid on 3-km-height reflectivity (scale in dBZ at bottom) at 0330 UTC 30 Apr 2013.

Citation: Journal of Atmospheric and Oceanic Technology 38, 4; 10.1175/JTECH-D-19-0180.1

The motion vectors from both methods are then used to extrapolate radar echoes at 0330 UTC for 60-min forecast, respectively (Fig. 5). The forecasts are output every six minutes, the same as the radar observation frequency. Figure 5a shows the radar reflectivity at 0330 UTC (initial field), and Fig. 5b at 0430 UTC (observation for verification). The 60-min forecast of the TREC method is shown in Fig. 5c, and result of the IVAP method is illustrated in Fig. 5d. The solid contours in Fig. 5 indicate the areas with reflectivity larger than 40 dBZ. The forecasting results from both methods are consistent with the observations, especially for the shape and intensity of the squall line (Fig. 5) due to the stability of the squall line. The radar echoes extrapolated by the IVAP method generally moved eastward faster than those by the TREC method, and were closer to the observations, especially in the south part of the squall line.

Fig. 5.
Fig. 5.

Comparison of TREC and IVAP 1-h nowcasts at 0430 UTC 30 Apr 2013. (a) Observed reflectivity at 0330 UTC, (b) observed reflectivity at 0430 UTC, (c) TREC nowcast at 0430 UTC, and (d) IVAP nowcast at 0430 UTC. The black contours in (b)–(d) are the observed 40-dBZ contour at 0430 UTC. The reflectivity scale in dBZ is to the right.

Citation: Journal of Atmospheric and Oceanic Technology 38, 4; 10.1175/JTECH-D-19-0180.1

The probability of detection (POD), false alarm ratio (FAR), and critical success index (CSI) (Dixon and Wiener 1993) are used to assess the performance of the forecasts. The areas (grid points) with radar reflectivity larger than 30 dBZ are verified in this case. At each grid point, S (success) means both observation and forecast exceeding 30 dBZ, F (failure) means the observation is larger but the forecast is weaker than 30 dBZ, and A (false alarm) means the observation is weaker but the forecast is larger than 30 dBZ. The three indexes for the forecasts are given as follows,
POD=nSnS+nF,
FAR=nAnS+nA,
CSI=nSnS+nF+nA,
where nS, nF, and nA are the total numbers of grids with S, F, and A, respectively.

Evaluation scores are computed every 6 min according to forecasts and observations. To eliminate fluctuations in evaluation scores, experiments with 10 sequential initial times (with an interval of 6 min) were conducted to obtain the mean scores. Figure 6 shows the three scores in 1-h forecasts. Both POD and CSI decrease while FAR increases gradually with increasing forecast time. The results in Fig. 6 indicate that the scores of the TREC and IVAP methods are very close within 12 min of forecasts, while the IVAP method is better beyond the initial 12 min.

Fig. 6.
Fig. 6.

Mean evaluation scores of POD (black), FAR (red line), and CSI (blue) within 60-min forecast for TREC (solid) and IVAP (dashed).

Citation: Journal of Atmospheric and Oceanic Technology 38, 4; 10.1175/JTECH-D-19-0180.1

4. Evaluation using multiple cases

To further examine the practicability of the IVAP method in nowcasting, 15 precipitation cases including squall line (SQL), mesoscale convective system (MCS), multicellular (MC), and mixed rainfall, were selected to statistically evaluate the ability of the IVAP method forecasting with a longer period. All cases were observed by the Guangzhou Doppler radar in the preflood season of South China from April to June in 2016. According to the South China climate characteristics from April to June, the systems that trigged the storms commonly included the westerly trough at 500 hPa, wind shear with north–south swing and low-level jet at 700 and 850 hPa, so the advection of convection in the preflood season of South China commonly moves east with some southeast or northwest motion component (He et al. 2016). Since the observation range of radar radial velocity is 230 km and the retrieval box is 50 km × 50 km, the 15 cases selected mostly occurred in the area within the 180-km range from the radar location. The terrain of the Pearl River delta is relatively flat (Fig. 7), especially in our study area, so the influence of orography on storm advection can be omitted. As we know, the initiation and dissipation are significant factors for errors of extrapolation nowcasting, which cannot be resolved by extrapolation such as the IVAP method or the traditional echo tracing method. So our tests mainly focus on the relative mature stage of the convective system, and the forecasting time span was extended to 2 h. The date, storm type, and mean evaluation indexes of the forecasts are summarized in Table 1. These cases can be divided in two groups, depending on the performances using the TREC and IVAP methods. The IVAP method has advantages in seven cases (Table 2), and the TREC method gives better forecasting in eight cases (Table 3).

Fig. 7.
Fig. 7.

Orography with internal 100 m above mean sea level (MSL). The radar site is indicated by letter R.

Citation: Journal of Atmospheric and Oceanic Technology 38, 4; 10.1175/JTECH-D-19-0180.1

Table 1.

List of the cases with date, storm type, and mean evaluation using the TREC and IVAP methods for 2-h nowcasting. The boldface number indicates the best value for each case.

Table 1.
Table 2.

List of cases showing better performances using the IVAP method with the vertical wind azimuth shear (VWAS), vertical wind speed shear (VWSS), and vertical wind shear (VWS).

Table 2.
Table 3.

As in Table 2, but using the TREC method.

Table 3.

Though these cases have different characteristics, and were initiated by different environmental conditions, a key feature determined the performances of the two methods is the vertical wind shear. The vertical wind shear was relatively smaller (larger) in the cases that the IVAP (TREC) method (Tables 2 and 3) showed better performances. Actually, many studies (e.g., Thorpe et al. 1982; Rotunno et al. 1988; Weisman et al. 1988) based on numerical simulations and observations indicated that vertical wind shear plays an essential role in estimating storm type, initiation, and lifetime. For example, Weisman and Klemp (1986) showed that the bulk Richardson number combined with convective available potential energy (CAPE) and vertical wind shear is useful to differentiate the type, organization, and longevity of a storm. The bulk Richardson number involves the vertical wind shear from the surface to 6 km. In this study, the upper-level wind at 6 km is denoted as (u2, υ2), while the lower-level wind at 1 km is denoted as (u1, υ1); and the mean vertical wind shear (VWS) is expressed in Eq. (4.1). The wind components (u2, υ2) and (u1, υ1) can be decompose into wind direction and speed, namely, (θ2, s2) and (θ1, s1). The vertical wind azimuth shear (VWAS) and the vertical wind speed shear (VWSS) are described by Eqs. (4.2) and (4.3), respectively:
VWS=(u2u1)+(υ2υ1)2,
VWAS=|θ2θ1|,
VWSS=|s2s1|.

As illustrated in Tables 2 and 3, the average vertical wind shear of the group that achieves higher scores using the IVAP method is 4.499 m s−1, while that using the TREC group is 7.091 m s−1. Although the mean VWSS difference between the two groups is not big, 3.241 m s−1 versus 4.55 m s−1, the mean VWAS for the TREC group is 26.916° (Table 3), which is almost twice the value for the IVAP group (11.782°; Table 2).

The verification based on the 15 cases demonstrates that both methods have strengths and weaknesses, depending on vertical wind shear. However, only the IVAP method can provide vertical shear information. The vertical wind shear information of a storm can be used to improve the nowcasting of the storm. For instance, based on observational statistical studies, Maddox (1976) proposed a simple empirical method, which used the motion vector compounded with 75% of the steering wind speed and 30° to the right of the wind direction to correct the deviation motion of the storm from the steering wind. As a preliminary test, the 15 cases in Table 1 were reexamined. The speeds of the new motion vectors were the same as the original ones obtained by the IVAP, except that the directions of the motion vectors were adjusted toward the right using the angle of the VWAS in each case. The forecast scores of most of the 15 cases were improved obviously, and 10 out of the 15 cases have better scores using the adjusted IVAP winds, as illustrated in Table 4. The correction for the motion vector is empirical, but the results are encouraging. So the information of vertical wind shear from the IVAP method can help improve the motion vectors for extrapolation nowcasting.

Table 4.

As in Table 1, but using the adjusted IVAP method. The boldface number indicates the best value for each case.

Table 4.

To statistically evaluate the performances of these methods and explore the possibility of applying the IVAP method in operation systems, 1082 cases were selected from April to June in 2016 with an interval of 1 h. The criterion for selecting the cases was that there were more than 20 points with reflectivity larger than 15 dBZ within 180-km range from the radar. The POD, FAR, and CSI scores of 1-h forecasts using the TREC method are 0.49, 0.55, and 0.35, while the scores using the IVAP method are 0.50, 0.55, and 0.36, respectively. Those statistical evaluations demonstrate that the nowcasting of the IVAP method shares a similar accuracy with that of the TREC method.

5. Conclusions and discussion

In this study, we used the IVAP method to obtain motion vectors for extrapolation-based nowcasting. While traditional tracking methods rely on tracking radar echoes of a few time slices, the IVAP method only needs the radar echo and radial velocity observations at the latest time. Our experiments demonstrated that the accuracy of the IVAP method is similar to that of the TREC method, extrapolation using the IVAP method has a better performance when the vertical wind shear is relatively smaller, while the TREC method is better under a larger vertical wind shear. In addition, the preliminary tests in section 4, which use the mean vertical wind azimuth shear to correct the direction of retrieval winds from the IVAP method, have shown that vertical wind shear provided by the IVAP method helps improve forecasting skills.

The TREC and IVAP methods have different theoretical bases. The TREC method tracks the motion of storm (represented by reflectivity) directly and requires at least two volume scans, while the IVAP method takes only the latest volume scan and calculates retrieval winds at different levels to gain the steering flow for storm extrapolation. Thereafter, the IVAP method concerns dynamic processes of storm motion. It has the potential to improve nowcasting, namely, the IVAP method can provide vertical wind shear information of each storm, which can be used to improve the nowcasting of the storm. The preliminary tests showed the forecasts using the direction-adjusted motion vectors according to the VWAS obtained by the IVAP method were improved obviously. New study that involves mean retrieval wind and mean vertical wind shear to improve the nowcasting based on the IVAP method will start soon.

Acknowledgments

This work is supported by the National Key R&D Program of China (Grant 2017YFC1501805) and the National Natural Science Foundation of China (Grant 61827901). We thank the reviewers for their help to improve the presentation of the paper.

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