A 4-years of radar-based observation of bow echo over Bandung basin Indonesia

Abstract

Background

This study presents a 4-year (January 2019–April 2023) X-band radar network-based bow echo observation over Great Bandung Indonesia. This study provides insight into the temporal and spatial variability of bow echo distribution and presents the atmospheric condition associated with the bow echo events. Temporal analysis is categorical into monthly, seasonally, and diurnal. The analysis was performed using X-band radar network and reanalysis data (ERA5).

Result

At least 26 bow echoes were identified across the Bandung basin from X-band radar network during the study period. From this total number of bow echoes, the observation of initiation modes is primarily generated from a weakly organized cell, with few coming from the squall line. The bow echo mostly evolved from noon until afternoon. The rainy season (December–January–February) and transition season (March–April–May) is the most frequent period of bow echo occurrence, with March being the most active month. Nevertheless, this study also found bow echo occurrence in the dry season (June–July–August). For the spatial analysis, the studied area is divided into two regions representing the eastern and western part of Bandung basin. The eastern region recorded the most intense occurrences with 14 events. The movement of bow echo in this region covered a shorter distance (average distance only 4.56 km), with all initiation modes occurring inside the region. The atmospheric condition within this region has less moisture flux, with higher CAPE and slightly higher surface temperature. Meanwhile, in the western region have different characteristics with higher moisture flux, a slight effect of CAPE and CINH, with longer distance and zonal movement direction of bow echo displacement.

Conclusion

These conditions indicate that local convection is the dominant mode of bow echo initiation mode in the eastern region of Bandung basin. Meanwhile, the monsoon effect influences the bow echo initiation mode in the western region. Given that the observed 4-year bow echo has different characteristics from previous studies of bow echo in mid latitudes, developing different criteria for bow echo detection in the tropics is crucial.

Introduction

X-band weather radar is gaining popularity among meteorologists, researchers, and observers due to its high temporal and spatial resolution, low cost, compact installation, which provide advantages over lower radar bands (C and S). Several modern X-band radars are often combined to form a radar network to solve the disadvantage of shorter coverage. Besides modern radars, conventional X-band radars are still implemented for precipitation observation (Pedersen et al. 2010). Among them is the radar network in northern Germany, which demonstrates that conventional X-band radar network is a scientifically valuable instrument for investigating the spatial distribution of precipitation. Their network can enhance the quality of the retrieved precipitation field and can be set up in the mountain region (Lengfeld et al. 2014).

Bandung basin, known for its complex topography with mountains surrounding the region, poses a significant challenge for conventional C-band weather radar due to the blocking of the radar beam. To overcome this, the installation of an X-band radar network within the Bandung basin was proposed. The X-band radar network offers optimum coverage and easy installation, providing a significant advantage over other radar systems. A low-cost rain scanner radar (RSR) network within the framework of the SANTANU project was established in 2018, and several improvements and evaluations have been performed (Sinatra et al. 2023; Awaludin et al. 2021; Sinatra et al. 2021a, b). The continuous observation of RSR has captured various meteorological phenomena, including bow echoes.

Bow echo is a type of mesoscale convective system that has a rain pattern of a bow-shaped envelope. The radar can capture this distinctive bow echo shape, along with other properties such as spearhead echo, booked vortices, and rear inflow notch (Goulet 2015). The dynamic structure of bow echo is first described by Fujita (1978), where the cycle consists of large, strong, and tall echoes that deform into bow-shaped and comma-shaped echo. Bow echo size varies from ten to hundreds of kilometers horizontally (Lee et al. 1992; Johns and Doswell 1992; Weisman 1993) with a duration of more than thirty minutes (Gatzen 2013; Klimowski et al. 2004).

Bow echo is linked to strong winds on the surface and high rainfall intensity (Atkins and St. Laurent 2009; Klimowski et al. 2004; Wang et al. 2020; Mauri and Gallus 2021). In Indonesia, bow echoes appear frequently in several locations. In Lampung Sumatra, two occurrences of bow echo related to whirlwindGs were observed by weather radar in January 2015. The events inflicted light to severe damages to the settlement (Ali and Hidayati 2016). Additionally, a squall line forming a bow echo in the nearby location occurred in December 2017 (Hidayat et al. 2019). Earlier in Malacca Strait in August 2013, a squall line that reached maximum intensity and extended significantly in size as it moved, formed a bow echo as it expanded (Lo and Orton 2016). Three categories of bow echo initiation phases, classified as weakly organized cells, squall lines, and supercells, were distinguished in prior works (Burke and Schultz 2004; Klimowski et al. 2004).

Several methods have been proposed for radar-based echo bow detection. For example, a skeletonization method applies two fuzzy transformations for pruning the extracted skeleton and then feeds to a shape descriptor to extract bow echo features for the shape-matching algorithm and classification step (Kamani et al. 2016). Other methods applied an automatic detection method from single radar reflectivity images. The images are then analyzed based on a contour model description and a concave segment identification to indicate the bow echo (Fanlin and Jinyi 2018; Surowiecki and Taszarek 2020).

Multi-year observation of bow echo events is often studied to comprehensively determine the characteristics of bow echo at certain locations and types of seasons (Burke and Schultz 2004; Klimowski et al. 2004). An insight into the atmospheric condition during the bow echo is also necessary to unveil what type of atmospheric conditions are associated with the bow echo events.

The main objective of this study is to utilize 4 years of continuous X-band rain scanner radar (RSR) network observation from 2019 until 2023 in Bandung Basin Indonesia to investigate bow echo events by providing analysis of initial development, temporal, and spatial variability, consequences in terms of hazards, and their various footprints in observations. Bandung basin is selected as the study location since the unique mountainous topography combined with the urban environment creates a significant risk of flood disaster (Agustina et al. 2023).

Few studies have been conducted on bow echo phenomena in the Indonesia Maritime Continent region. This study is one of the first attempts in Indonesia to determine radar-based qualitative and quantitative assessments of bow echo events over such a long period using X-band radar network. Bow echo frequently caused considerable damage to infrastructure and immense societal impacts, which makes their return periods important to evaluate. This paper is organized as follows. "Bow echo detection and data processing" section describes bow echo criteria, rain scanner radar network and ERA5 dataset. "Results and discussion" section presents a statistical analysis of the bow echo. Finally, the discussion and conclusion are summarized in "Discussion" and "Conclusion" sections.

Bow echo detection and data processing

Bow echo criteria

Weather radar reflectivity can be used to observe the presence and evolution of bow echo (Klimowski et al. 2004). Several criteria can be considered to determine the occurrence of bow echo based on radar observation. This study uses six criteria of bow echo following the previous reference (Celiński-Mysław et al. 2020).

• (1) Radar echo in the form of a crescent shape or bow echo (Fujita 1978).

• (2) Sharp gradient in reflectivity at the leading edge (Klimowski et al. 2004).

• (3) The reduced channel of weak reflectivity area on the rear side is also known as the rear inflow notch (Fujita 1978; Laurent & France 2015).

• (4) The persistent arc or the increasing length along with time (Burke and Schultz 2004; Klimowski et al. 2004).

• (5) The smallest scale of bow echo used in this study is 20 km. This is different with the classification used by Lee et al (1992) where 10–25 km is considered to be the smallest type which is the cell bow echo (CBE).

• (6) The duration of the bow echo is at least 30 min or more (Gatzen 2013; Klimowski et al. 2004).

The first task is the identification of the bow shape in the radar data. A straightforward method is used by manually inspecting entire radar reflectivity data to collect the probable bow shape echoes. The manual inspection also considers the sharp gradient in reflectivity at the leading edge and the rear inflow notch of the low reflectivity area. The manually collected results were then verified with the combination method of image processing and skeletonization (Nugroho et al 2023) to confirm the crescent shape of the bow echo.

The next task is the examination of the persistent evolution of bow echo. The determination of the increased length or radius of the bow echo requires a quantitative analysis. To do so, the analysis calculates the length of the bow echo objects in the radar image. The estimated length is derived from the major axis of the desired image pixel object using the region props function of image processing (Hasenbalg et al. 2020). Once the estimated length is obtained, a length of 20 km is used as a threshold to filter the object that is not associated with a significant organized convective system. This value is referred from previous research by Lee et al. (1992) that described the very small scale of bow echo cells with a size of 10–25 km. The 20 km threshold is applied in this study to obtain more significant bow echo event but still covered the smallest bow echo type.

The duration of the bow echo evolution is also recorded to observe whether the bow echo have a longer duration (more than 30 min) or not. The duration of the bow echo criteria is used to exclude the bow echo that only appears briefly and does not form a significant organized convective system. In addition, online media news related to the large-scale bow echo occurrence is collected to confirm whether the existence of the bow echo have a significant impact on the environment (such as strong wind, floods, etc.). Several studies utilized the online media to confirm the large-scale extreme weather in Indonesia (Baranowski et al. 2020; De Bruijn et al. 2018; Holderness and Turpin 2015).

X-band rain scanner radar (RSR) network

In this study, the RSR network is the primary instrument used for the bow echo observation (Awaludin et al. 2013; Nugroho et al. 2015; Hidayatulloh et al. 2022). RSR network consists of two X-band radars installed over Bandung basin area separated by a distance of 27.79 km. The first RSR is installed on the rooftop of a four-floor building in Djundjunan Bandung (6.895° S, 107.587° E, 752 m a.s.l). The second RSR is installed on the rooftop of a one-floor building in Tanjungsari Sumedang (6.899° S, 107.831° E, 884 m a.s.l) (Sinatra et al. 2023).

The X-band radar, a non-coherent radar employing a fan beam, fixed-elevation, and single-polarized antenna, is used to observe rainfall. To overcome its limitations, the signal processing applies volume correction, attenuation correction, and clutter removal. The radar product is a rainfall map with a reflectivity value (Awaludin et al. 2021; Lengfeld et al. 2014; Nugroho et al. 2018; Pedersen et al. 2010). Postprocessing from these two RSR implements the Cressman method to obtain RSR network mosaic data (Sinatra et al. 2021a, b). The continuous data from RSR network every two minutes provides a unique and valuable dataset of rain observation within the Bandung basin area. This high temporal resolution dataset could provide important information on rainfall evolution with many factors involved in the process, especially in the mountainous areas.

RSR network data spanning from 1 January 2019 to 30 April 2023 is used in this study. The average percentage of data availability is 89% with lower data availability in the year 2019 (70.71%) due to frequent power outages. This study examines daily to monthly observation data because rainfall events occur almost throughout the year, even during the dry season. To verify the bow echo detection from RSR network, another X-band weather radar (Furuno WR-2100) is also used in this study. The WR-2100 is in Husein Sastranegara airport (6,9036° S, 107,577° E, 738 m a.s.l), and the distance between the nearest RSR location is 1.5 km. The maximum radius coverage of the WR-2100 is 70 km. Furuno WR-2100 has been tested for observing atmospheric conditions in several locations in West Java, Indonesia (Arbain et al. 2018). Figure 1 illustrates the location of RSR network coverage and the WR-2100.

Fig. 1

The map of Java Island in Indonesia, representing the primary area of this study, is depicted in the inset. The thick black rectangle denotes Greater Bandung in Western Java. The black circles in Greater Bandung indicate the coverage area of RSR with each 44 km radius and the blue dash circle depicts the scope of WR2100

Atmospheric condition from ERA 5

The atmospheric condition during the bow echo event is investigated using ERA5, a reanalysis dataset released by the European Centre for Medium-Range Weather Forecasts (ECMWF). ERA5 reanalysis data has a spatial and temporal resolution of 0.25° × 0.25°, and 1 h, respectively. Several studies have verified ERA5 data by comparing it with observation data (Celiński-Mysław et al. 2020; Minola et al. 2020).

In this study, the coverage of the ERA5 grid data is limited specific region within the scope of the research area. ERA5 hourly single levels (Hersbach et al. 2023a) and pressure levels data (Hersbach et al. 2023b) are selected to provide the atmospheric condition during the bow echo as well as the background condition. Selection of the ERA5 grid data is determined from the analysis of the bow echo location that will be described in "Temporal variability" section. Methodology and research structure framework from this study is detailly describe in Fig. 2.

Fig. 2

Methodology and research structure framework for Bow echo analysis in Bandung basin

Results and discussion

Comparison with other weather radar and analysis of the bow echo events are discussed in this section. Analysis of the bow echo comprises the frequency occurrence, spatial and temporal variability, displacement variability, and investigation of atmospheric conditions.

Comparison of bow echo detection between RSR network and WR-2100 radar

A case study of heavy rainfall on 24 March 2021 is used as a comparison for the bow echo detection. During this period, heavy rainfall information was reported by online media starting at 13:00 LT. This heavy rainfall caused a flood in the Rancaekek subdistrict in the East Bandung region, located in the eastern part of the WR-2100 radar location.

Figure 3 demonstrated the bow echo evolution observed by both RSR network and WR-2100, which developed from weakly organized cell. Two or more cells that did not interact with one another performed the initial mode, and then a deviant movement of one or more cells caused the cells to merge. In this example, at first, there are two cells at 12:44 LT, then they merged and develop even further, creating the bow echo at 13:10 LT. The bow echo started to dissipate at 13:26 LT and dissolved at 13:40 LT. The total duration of the bow echo occurrence is 30 min, with the process cycle from the initiation, merging, and dissipation taking 72 min. Both the RSR network and WR-2100 observe these processes well. The strong agreement between the RSR network and WR-2100 radar observation increases our confidence in utilizing the RSR network data to investigate the bow echo in Bandung basin.

Fig. 3

Case study of bow echo in March 24, 2021, initiation mode (a. RSR network: 12.44 LT, b. WR-2100: 12.45 LT), Merge-first bow echo (c. RSR network: 13.10 LT, d. WR-2100: 13.19 LT), mature bow echo (e. RSR network: 13.20 LT, f. WR-2100: 13.20 LT), started to dissipated (g. RSR network: 13.26 LT, h. WR-2100: 13.25 LT), fully dissipated (i. RSR network: 13.40 LT, j. WR-2100: 13.40 LT)

Frequency occurrence of bow echo

In total 26 bow echo events over 1426 days were identified over the period from 1 January 2019 until 30 April 2023. Table 1 summarizes all properties of the identified bow echo that consist of the initial modes, duration, and location of the bow echo. The initial modes of bow echo are categorized into three types: weakly organized cell (WO), evolution from squall line (SL), and supercell (SC), referred to in previous research by Klimowski et al. (2004). This table includes the subdistrict name, the region (two region A and B, which will be described in more detail in "Temporal variability" section), the duration, the length, and the reported impact of the bow echo from online media source.

Table 1 List of bow echo events properties

Table 2 shows that the dominant initial mode in the Bandung basin is WO with 23 cases (88.5%). Meanwhile, the average duration was 37.88 min, with region B showed longer duration (40.91 min), compared to region A (34.85 min). Please note that the duration of the bow echo covers only the appearance of the bow echo. There is a possibility that one event has a longer duration of precipitation than the bow echo duration. The average length was 23.57 km, with the largest length occurs in 2022 case no 21 with the initiation comes from squall line.

Table 2 Bow echo properties in each year

Temporal variability

With respect to monthly variation, March is the most active month contributing 6 bow echo events, the largest is in March 2022 (Fig. 4a). There is no record of bow echo occurrence in May and November. Note that there are few data in 2023 since the RSR dataset is available only until the end of April 2023.

Fig. 4

Temporal variability of Bow echo, a frequency occurrence per month, b frequency occurrence per season, c frequency occurrence per hour

An analysis based on the seasonal variation showed that December–January–February (DJF) and March–April–May (MAM) is the dominant season of the bow echo occurrence (9 events) (Fig. 4b). This result is reasonable since Java Island is mostly affected by the monsoon effect, especially in the DJF period (Faidah et al. 2022; Pujiastuti and Nurjani 2018). Meanwhile, MAM is the transition period from rainy to dry season. In dry season on June–July–August (JJA), surprisingly there are 6 bow echo events, with the lowest bow echo frequency events occurs in September–October–November (SON) with only two events. According to the hourly variation data (Fig. 4c), the period from noon until the afternoon is the highest frequency of bow echo occurrence. Among them, the early morning and late-night bow echoes only happened on 21 April 2020 and 14 August 2022, respectively.

Spatial variability

The selection of the bow echo area provides a more detailed analysis of its spatial variability. The selection procedure creates a bounding box representing the x and y boundaries encircling the bow echo object and determines the center location calculated from the obtained boundary area. The center location then overlay with the bow echo image along with a map of Bandung to verify the coordinate of the center location of the bow echo.

Several challenges emerged while investigating the spatial variability of the bow echo. These include the need for caution in the area near the center of the radar location, the edges of radar coverage, and the uncovered area of the mountain region in the northern part of the radar location. Despite these challenges, Fig. 5 reveals an interesting spatial pattern of bow echo in the Bandung basin over the course of 4 years of observation.

Fig. 5

Spatial variability of bow echo occurrence. Black rectangle represent the two regions. Color represents the bow echo occurrence based on seasonal variation. The marker shape represents the bow echo occurrence based on its year. The Inset map is the Indonesia region with the location of Bandung city. The contour area represents the topography of Bandung Basin. Circle plus sign marker represent the two RSR location

The spatial pattern of the bow echo seems to occur in a specific regions within the Bandung basin. To simplify the analysis, the spatial variability of bow echo events is divided into two regions (A and B) as illustrated by the two black rectangles in Fig. 5. These regions were selected based on the ERA 5 coordinate grid, which will be discussed in "Variability of the bow echo displacement" section. The location in Fig. 5 represents the center position where the first bow echo is observed. While there are two bow echo locations in Fig. 5 that fall outside of the region (event no 8 and 23), they are still considered within the scope as their movement remains within the rectangle region. The calculation of the number of bow echoes within each region revealed that region A had more bow echo events, with 14, compared to 12 in region B. Although the difference in number is not significant, there is a noticeable distinction in terms of their displacement.

Variability of the bow echo displacement

The bow echo displacement is calculated by identifying and tracking the bow echo at frame-t that equivalent with frame t + 1 using combination of edge detection and K-Nearest Neighbour (K-NN) method (Sinatra et al. 2022). Centre point coordinate of the tracking attribute is then overlaid in a map that will result in a curved line (bow echo track) with different colors based on season category. This curved line is also marked with a start–end marker on each end (Fig. 6).

Fig. 6

Bow echo displacement. The curved line colors represent bow echo displacement in different seasons. The circle (star) marker represents the start (end) location of bow echo movement from the early start until the last appearance of bow echo. Plus sign marker represent the two RSR location. The contour area represents the topography of Bandung Basin

The analysis of bow echo displacement reveals a sharp contrast between region A and B. A longer curved line indicates more displacement. In region A, the bow echo displacement characteristic is shorter, with an average displacement of 4.56 km. However, significantly more displacement in region B, with an average distance of 11.8 km are observed.

Another interesting characteristic can be observed in the direction of the bow echoes movement. In region A, the bow echo movement is dominantly meridional from north to south or vice versa. Meanwhile, the movement in region B is dominantly zonal from west to east or vice versa. In region B, the bow echo movement in DJF (red curve line) is dominantly westerly. Meanwhile, in JJA (blue curve line) it is reasonably easterly. These movement seems to follow the wind pattern in Indonesia. The wind pattern during wet monsoon (DJF) has a westerly wind pattern. Meanwhile in dry monsoon (JJA), easterly wind pattern is more dominant (Aldrian and Susanto 2003; Chang et al. 2005). This result suggests that there are distinct characteristics of bow echo in region A and B. The impact of monsoon seems to be stronger in region B, probably due to a more open area. While the effect of local circulation might be more significant in region A, possibly due to the surrounding mountainous topography. Further investigation of atmospheric conditions in those two areas is discussed in the next section.

Investigation of the atmospheric condition in each region using ERA5

The bow echo is an indication of organized deep convection in the atmosphere. Four ingredients could generate deep convection which are the amount of moisture, sufficient lapse rate that could trigger the atmospheric instability, a force that could initiate and preserve the convection, and strong wind shear (Celiński-Mysław et al. 2020; Kuchera and Parker 2006). Further analysis is conducted to investigate atmospheric condition patterns during the bow echo event and compare them to the background condition.

Following Celiński-Mysław et al. (2020), five atmospheric parameters were selected from the ERA5 dataset to represent the ingredients for deep convection. These parameters are instantaneous moisture flux (MF), which contributes to the amount of moisture; temperature at 2 m above the surface (TA), which could initiate convection; convective available potential energy (CAPE), which measures atmospheric instability; convective inhibition energy (CINH), which measures the energy required to initiate convection; and vertical wind shear (VS), which could support bow echo formation.

The five atmospheric parameters were retrieved from the ERA 5 dataset at the center coordinates of each region. Drawing insight from the bow echo spatial variability from "Temporal variability" section, the estimated centre coordinates of the two regions (A and B) are determined. The center area of region A is − 6.9° S, 107.7° E that represents the east side of the Bandung basin. Meanwhile, the center area of region B is − 6.9° S, 107.45° E that represents the west side of the Bandung basin.

The retrieved parameters are categorized into two time scales: first is a 1-day of the bow echo event (bow echo days) on each region for all the 26 events, and second is a full 4-year of five atmospheric parameter from 2019 until 2022 (background days) on each region. Four atmospheric parameters (MF, TA, CAPE, and CINH) are obtained from ERA 5 single level. Meanwhile, VS is calculated from the wind component (zonal and meridional) of ERA 5 pressure level in four levels (1000 hPa, 900 hPa, 700 hPa, and 450 hPa).

The first parameter, which is MF represents the net rate of moisture exchange between surface and atmosphere (Eley et al. 2021; Roman-Stork et al. 2020). The positive value represents the condensation process, meanwhile the negative value represents evaporation process. The negative phase of MF pattern showed an increase after 6 LT in the morning and a decrease until around 18 LT in the afternoon on both bow echo days and background (Fig. 7a). This pattern represents natural behaviour of diurnal evaporation process. Higher value of this negative MF during bow echo days compares with the background, indicate more water vapor content during the bow echo event. The significant discrepancies of MF between bow echo days and background days particularly from 12 LT until 14 LT (even in MF region B until 19 LT).

Fig. 7

Atmospheric condition in different area, a MF region A, b TA region A, c CAPE region A, d CINH region A, e VS: 0–6 km region A, f VS: 0–3 km region A, g VS: 0–1 km region A, h MF region B, i TA region B, j CAPE region B, k CINH region B, l VS: 0–6 km region B, m VS: 0–3 km region B, n VS: 0–1 km region B. The red and black line represent the characteristic during a 1-day of bow echo and background from 2019 until 2022, respectively

The starting phase of the negative pattern shows slight differences with the negative phase of MF in region B occurs earlier (7 LT in the morning) than in region A (9 LT in the morning). Overall, the MF of region B exhibits a larger negative peak on both bow echo day and background day than of MF region A. The results suggest that MF region B during bow echo event have more water vapour content compared to region A. This finding is crucial as it suggests that region B obtained more water vapor content than region A, which is in line with the longer-duration bow echo in the region. Higher water vapor content during bow echo days in both regions signify the role of moisture in enhancing deep convective activities leading to the formation of bow echo described by Celiński-Mysław et al. (2020).

The second parameter (TA) is the result of a non-linear interpolation between model temperatures at the lowest model level (at about 10 m above the surface) and temperatures forecast at the model earth's surface (Hersbach et al. 2023a). Figure 7b shows that TA on bow echo days is slightly higher than background days during early in the morning, but lower in the afternoon until early in the evening. Comparing on both regions, higher discrepancy between bow echo days and background is more distinctive in region B, with the peak of TA in bow echo days is lower than background. Different TA condition occurred in region A, where the bow echo days and background peak are almost similar. Furthermore, there are periods where TA in region A showed slightly higher during bow echo days at early morning (0 until 7 LT) and daytime (9 until 11 LT) compared with background. The reduction of TA in the afternoon might indicate the increase of convective activities and cloud development during bow echo days in both regions. Moreover, higher TA in region A might indicate a more significant role of local convection in the region.

In relation to the atmospheric instability condition, two parameters were used: CAPE and CINH. CAPE represents the potential energy (buoyancy) available to be converted into convective motions and is a measure of atmospheric instability. Higher CAPE indicates an unstable atmosphere with a greater potential for stronger thunderstorm development (Cohen et al. 2007). On the other hand, CINH represents the energy barrier that must be overcome for convection to begin. Lower CINH indicates a higher potential for convective initiation, while higher CINH inhibits convection but allows CAPE to accumulate, which will be released once the CINH is removed.

The CAPE condition showed a distinctive pattern, where CAPE during bow echo days was higher than background days in both regions. These discrepancies become higher started at 11 LT until 17 LT, with the maximum CAPE occurs after 12 LT. Meanwhile, the CINH condition showed more varied but with consistent pattern with CAPE, where the minimum peak occurs after 12 LT. CINH on bow echo days have lower value compared to that in background days during the minimum phase.

The results suggest that the presence of atmospheric instability indicated by CAPE is an essential ingredient for bow echo formation, a finding that supports the previous research by Celiński-Mysław et al. (2020). Moreover, lower CINH might also play a crucial role in elevating the potential for convection and bow echo development in the afternoon. Notably, CAPE starts to decrease when CINH reaches minimum, which might indicate the conversion of available potential energy into convective movement. These findings have significant implications for understanding bow echo formation and its relationship with atmospheric instability.

Comparing between the two areas, region A have higher peak of CAPE (Fig. 7c) during bow echo days compared to region B (Fig. 7j), even though the background condition is almost the same on both regions. In addition, CINH value during bow echo in region A (Fig. 7d) is lower than that during background days. This combination of CAPE and CINH indicate a stronger influence of atmospheric instability during bow echo events in region A.

The examination of VS variation within two regions in three different levels (low level: 0–1 km, mid level: 0–3 km, and deep layer: 0–6 km) are also investigated during bow echo and background days. In region A, VS in bow echo days is generally lower than in the background days. However, there is a slightly increased from 7 to 9 LT at each level (Fig. 7e–g). In region B, occasionally VS in bow echo days is significantly increased in each level (Fig. 7l–n), particularly in the morning (7–10 LT) and around midnight (22–23 LT). The findings suggest that the role of VS might be limited in the formation of bow echo in the afternoon.

The bow-whisker diagram (Fig. 8) provides a statistical analysis of the bow echo event in the two regions. The MF showed a discrepancy between the bow echo events in both region. More negative values on both mean and median in region B (− 22.74 × 10^–5, − 9.31 × 10^–5) than in region A (− 8.85 × 10^–5, 2.21 × 10^–5) indicates that region A showed less water vapour than region B. The condition is different for TA, where region A has slightly higher mean and median value (22.69°, 21.95°) than region B (22.46°, 21.58°).

Fig. 8

The box-whisker diagram of the bow echo event in two different regions, a MF, b TA, c CAPE, d CINH, e VS. Colors represent the region where red color is region A and purple color is region B. Red dots represent outliers

Meanwhile, region A recorded a higher CAPE (mean CAPE = 329.14 J/kg, median CAPE = 280.40 J/kg) and a lower CINH (mean CINH = 88.08 J/kg, median CINH = 92.41 J/kg), compared to region B (mean CAPE = 278.10 J/kg, median CAPE = 243.89 J/kg, mean CINH = 96.04 J/kg, median CINH = 100.48 J/kg).

Regarding VS condition, mean value in region Aare lower (mean WS 0–1 km = 0.24 m/s, mean WS 0–3 km = 1.23 m/s, mean WS 0–6 km = 1.65 m/s) than region B on each level (mean WS 0–1 km = 0.45 m/s, mean WS 0–3 km = 2.31 m/s, mean WS 0–6 km = 2.60 m/s). A similar condition also occurred for the median value, where region A is lower (median WS 0–1 km = 0.17 J/kg, median WS 0–3 km = 1.20 J/kg, median WS 0–6 km = 1.64 J/kg) than region B (median WS 0–1 km = 0.32 J/kg, median WS 0–3 km = 2.11 J/kg, median WS 0–6 km = 2.57 J/kg). The results suggest that local convection effects with a slightly higher surface temperature, higher CAPE, lower CINH, and lower VS might be more dominant in region A. While large scale monsoonal circulation is more influential in region B. Table 3 and Fig. 9 illustrate the relationships between atmospheric parameters and bow echo development in regions A and B, compared to background conditions.

Table 3 The effects of atmospheric parameters and on bow echo development

Fig. 9

Illustration on the effect of the four parameters (TA, MF, CINH, CAPE) towards the bow echo event in Bandung basin. The bar colors represent each of the four parameters. The bar height differences represent the higher or lower characteristic on each parameter in different condition (background and during bow event in region A and B). The overlay bow echo represents the bow echo event no 8 (Feb, 4th, 2021) and 9 (March 24th, 2021) from Table 1

Discussion

This study presents a 4-years of bow echo in Bandung basin based on X-band radar network and ERA5 analysis. During the study period, 26 bow echoes were identified. The RSR observation shows that the rainy season DJF and transition season MAM is the most frequent period of bow echo occurrence in Bandung. Nevertheless, dry season JJA also contributes with sufficient bow echo events, where six bow echo events occurred from 2020 until 2022 (each year contributes two events). Each of these years has its condition that affects the occurrence of the bow echo. Previous studies showed that climate variability such as ENSO and IOD have a significant effect on annual rainfall in Indonesia. La Nina (El Ni$\widetilde{\text{n}}$o) has similar impact as Negative (Positive) IOD, which causes wetter (drier) conditions (Hamada et al. 2002; Kurniadi et al. 2021; Supari et al. 2018). In 2020, La Nina occurred during JJA (Blunden and Boyer 2021). Meanwhile, in July 2021, negative IOD was established with the temperature condition below average in the west Indian Ocean (Blunden and Boyer 2022). In 2019, the bow echo was only observed in DJF season, especially in December 2019. The plausible factor of this condition is because of the El Ni $\widetilde{\text{n}}$ o events (Blunden 2020). A severe drought occurred in 2019 that impacted the decrease of the annual rainfall condition below normal. For all bow echoes, the period where bow echo majority occurred is from noon until the afternoon. Only 2 events that showed bow echo in the early morning and late at night, with both events occur in the region B. There was no incidence of large-scale extreme weather during the period of those two events, which suggest that the characteristic of these events are still the same as other bow echo in region B but occur in different periods.

Comparison between the bow echo occurrence with the five parameters representing the atmospheric condition (MF, TA, CAPE, CINH, and VS) has also been examined. The utilization of ERA5 with its coarse spatial resolution to represent the two regions within the Bandung basin is still debatable. However, previous research has studied the performance of ERA5 concerning the surface temperature compared with meteorological station observation (Xu et al. 2022). Taszarek et al. (2020) compared moisture, temperature, and lapse rate from ERA5 with radiosonde data, suggesting ERA5 reliability in exploring convective environments. Others stated that there is still some bias compared with observation in a complex topography area (Abdillah et al. 2022; Minola et al. 2020; Miao et al. 2019).

The characteristic pattern of the atmospheric instability from CAPE and CINH in both regions showed a consistent pattern with the most frequent occurrence of the bow echo per hour. The period of significant increase (decrease) of CAPE (CINH), which is from 12 to 16 LT (Fig. 7c, d, j, k), is in good agreement with the period of the most frequent occasions of bow echoes (Fig. 4c). Similarly, the significant amount of negative MF (Fig. 7a, h) during bow echo days is also consistent with the period of the majority occurrence of bow echo (Fig. 4c). However, other variables (TA and VS) did not show significant influence based on the comparison with the background days. The limited role of VS during bow echos event is probably because of the generally lower shear in the tropics compared to other areas (such as mid latitude areas).

The comparison of characteristics of the bow echo between the two regions showed that the most frequent bow echo occurrence is observed in the eastern area of Bandung basin (region A) with 14 events. Within this region A, the condition of less MF, slightly higher TA, higher CAPE and slightly decreased CINH, suggest that local convection is relatively dominant within this region, influencing the more bow echo generation. In addition to these analyses, the dominant meridional bow echo movement at region A is noteworthy. Previous study (Oigawa et al. 2017; Syaraswati et al. 2019; Fitriani et al. 2019; Kombara et al. 2019) already investigate the diurnal precipitation cycle with meridional movement either from North to South or South to North in Bandung basin. Local influences due to Bandung basin topography may also have an impact in this region because during the day the wind shear is weak due to anabatic effect (Kombara et al. 2019). This suggests that mountainous topography surrounding region A are most likely affects this local convection.

Meanwhile in region B, monsoon effect is most likely more dominant to the bow echo characteristics. The tracked bow echo zonal movement on each monsoon season (DJF and JJA) showed a distinctive monsoon effect in this region. The longer movement of the bow echo displacement within this region are confirms the monsoon effect. The characteristic is also supported by the mountain pass in the western part of the Bandung Basin, which becomes an inlet for monsoonal wind to flow in. Higher MF on both background days and bow echo days suggest a significant water vapour input towards this region.

Observing the bow echo size, initiation, atmospheric condition, and comparing it with other bow echo event from previous studies (Zhou et al. 2023; Wang et al. 2022; Mounier et al. 2022; Surowiecki and Taszarek 2020; Peng et al. 2013; Burke and Schultz 2004), suggest that there might be distinct characteristics of bow echo in the tropics relating to its length, duration, and initiation (Ramos-Valle et al. 2023). Further comprehensive research is necessary to thoroughly investigate the bow echo in the tropics, which may lead to a new criteria of the tropical bow echo characteristics.

Conclusion

This study construct a 4-year bow echo characteristic for Greater Bandung's mountainous region in Indonesia, using data from the X-band radar network for the first time. This study presents a quantitative and qualitative analysis of bow echo occurrence in those region. The identification of 26 cases has led to several key findings, which are detailed as follows:

• (1) Heavy precipitation in the form of bow echo is a rare phenomenon in Great Bandung West Java Indonesia.

• (2) The initiation mode of bow echo mostly comes from the merger of several single weakly organized cells (WO 88.5%). The rest are squall line-induced bow echo (SL 11.5%). The average duration of bow echos is 38 min, with only one cases that have reached a duration of more than an hour.

• (3) The majority of bow echoes were observed in the DJF and MAM season from noon until afternoon.

• (4) In the eastern area of Bandung (region A), the initiation of bow echo is influenced by local convection. This can be seen by the atmospheric conditions during the bow echo that have less moisture, higher surface temperature, higher CAPE, and lower CINH. The displacement of the bow echo with a meridional movement and less distance, strengthen the analysis.

• (5) The bow echo generation in the western area of Bandung (region B) is mainly affected by monsoon conditions, as shown by the dominant bow echo movement following the monsoon path, and the longer distance of the bow echo displacement. Higher moisture in this region also contributes.

In the future, we plan to expand the bow echo database obtained in this study. We aim to incorporate additional data on the thermodynamic and kinematic conditions from sounding and reanalysis data. This comprehensive approach can enhance our understanding of the bow echo mechanism which may lead to a new criteria of tropical bow echo characteristic and improve the early identification, enabling us to better anticipate threats from heavy precipitation and severe wind.