Performance of a HDR-based hail detection algorithm in Northern Italy

Phys. Chem. Earth (B), Vol. 25, No. 10-12, pp. 949-952, 2000 0 2000 Elsevier Science Ltd All rights reserved 1464- 1909/00/S - see front matter

Pergamon

PII:

S1464-1909(00)00131-3

Performance of a HDR-Based Hail Detection Algorithm in Northern Italy P. Mezzasalma, S. Nanni and P. P. Alberoni ARPA - Servizio Received

Meteorologico

15 June 2000;

accepted

Regionale,

Bologna,

Italy

3 July 2000

Abstract. Hail is the most frequent damaging

phenomena affecting northern Italy during severe weather. At the Servizio Meteorologico Regionale of the Emilia-Romagna region the analysis of the differential hail signal Hoa (Aydin et al., 1986) which is a function of horizontal reflectivity and differential reflectivity, was carried out and checked against the ground truth coming from a dense hail pad network. That study, which used the data related to four hailstorms occurred in 1997, allowed the implementation of an operational technique since early summer of 1998. Since then, an automatic hail detection algorithm runs during the stormy season (March through October) with the issue of a hail warning for nowcasting purpose. The performance of such an automatic method is investigated in an operational and statistical perspective. First results highlight the difficult in correctly estimating the position of a hail shaft with respect to the coarse resolution of the pad network, especially when hailstorms of small extension are involved. 0 2000 Elsevier Science Ltd. All rights reserved.

1

‘; Adriatic

Introduction

Fig 1. The hail pad network in the radar field. The circular markers are every 25 km and thin lines indicate administrative borders of EmiliaRomagna region.

Polarimetric radars have greatly improved remote detection of hail, especially after the introduction of dual polarisation differential reflectivity “ZDR’.by Seliga and Bringi (1976). Aydin et al. (1986) obtained a statistical relationship between horizontal reflectivity “Zn” and Zn, from surface disdrometers and introduced a new differential hail signal, named Hoa, as a possible discriminator of hail in regions below the melting layer. An early attempt to remotely detect hail was carried out at SMR in 1997 (Nanni et al., 2ooO). That work consisted in the use of the multiparametric radar data related to four hailstorms, which occurred in 1997 and were analysed and checked against the ground truth coming from a dense hail pad network. The overall network consists of about 350 pads, but just those pads (330) inside an area with a radius Correspondence

Sea

of 75 km from the radar (see Fig. 1) were taken into account. All pads lay on a flat region bounded to the north by the PO River and to the south by the foothills of the Apennines. That study allowed the implementation of an automatic hail detection algorithm that uses the differential hail signal and was being run in real time during the 1998 stormy season. All storms that occurred from June through October 1998 (no matter the kind of related precipitation) were taken into account. The response of the HDR-based hail detection method was checked against the pad reports. Note that the 1998 comparison is quite different from the previous one: all kinds of storms were considered, while in 1997 just four main hailstorms were selected.

to: P. Meuasalma 949

P. Mezzasalma

950

er al.: HDR-Based Hail Detection

An outlook of the HDR hail detection method at SMR

2

The differential hail signal HoR is the net distance of the observed ZH from hail-rain boundary in the ZH-ZDR space, see Fig. 2:

Algorithm

Number 50 ONot hit n Hit 40 30 20

H DR =zH

-.f(zDR) IO

where: ZDR

21 dB f(ZDR)=

19 ZDR +2ldB

OcZDR ZDR

60 dB

<-6.5

< OdB < 1.74dB > 1.74dB

-6.5

0

6.5 13 HDR(dB)

19.5

26

Fig. 3. Distributions of maximum HDK values for the hit (heavy grey) and not hit (light grey) hail pad areas referred to the four 1997 events after the application of constraints.

2, WJZ) 70

contingency table defined in Table 1, i. e. the hail detection by means of radar (yes = Ho,>13 dB) is compared to the hail observation at the ground as deduced by hail pads reports. The score of the radar estimate is quantitatively expressed by the following scalar indices derived from the quantities reported in Table 1:

rain region

. 7.

ZOT 0

I

3

2

4

5

&a (dB) Fig 2. Rain-hail region in the Z-ZDR plane as defined by Aydin’s formula.

Aydin et al. (1986) suggested that a value of HoR greater than 0 dB indicates the presence of hail. The HER assessment test that was conducted on four north Italy hailstorms leaded to a somewhat different result. Each hail pad of the dense network (mesh size of 4 km) was considered as the centre of an area with a two-kilometre radius, so that several radar bins were inside a pad area. Each pad area was involved in the inter-comparison against the hail pad report whenever it lay inside the storm (i.e. at least one Zn bin value above 40 dBZ), was not affected by differential attenuation (i.e. the area averaged ZoR value was positive) and its HoR representativeness was significant (i.e. the amount of positive HDR bin values were either zero or above a fixed threshold, in order to reject those pad areas having a peripheral position with respect to the hail swath or being affected by spurious echoes). When these “constraints” were applied to the radar parameters, it was found that the maximum HoR value, related with a “hit” hail pad report, was always greater than 13 dB, see Fig. 3. Those hit pads with negative HoR represent pads that were actually hit by hail, but the radar was not able to detect: some of them are real radar underestimates but most of them, although inside the storm, were hit at a different time. Indeed, just four polarimetric scans were performed every hour. Assuming this value as the threshold for radar based hail retrieval, quantitative results wsre expressed through the

. .

Critical Success Index (CSr) is the fraction of pad areas correctly estimated “hail” with respect to those either observed or estimated. Probability of Detection (POD) is the fraction of all hit pad areas being estimated as affected by hail. False Alarm Ratio (FAR) is the fraction of pad areas estimated “hail” by the radar, which were actually observed not to have hail.

For a perfect radar estimate, CSI, POD and FAR should take the value 1, 1 and 0 respectively. For the 1997 sample the main findings were that this method was able to detect the 90% of the hit pads (POD=O.9), with a false alarm ratio of 0.3; the success index was 0.6. So far, the automatic method implemented at SMR searches for any pad area containing a representative number of HDR bin values above 13 dB, with the correspondent reflectivity being not less than 40 dBZ and the area averaged differential reflectivity being positive. If a pad area meets these characteristics, it is considered as affected by hail. If HDR is, on the contrary, negative, the pad

Table 1. Contingency table for the hail pads-radar comparative analysis: yes indicates a hit or a “hail” estimated pad respectively. The definitions of the significant scalar indices Critical Success Index (CSI), Probability of Detection (POD) and False Alarm Ratio (FAR) are also reported. Hail pads Yes No CSI = A/(A+B+C) POD = A/(A+B) FAR = C/(C+A)

Radar Yes A C

No B D

P. Mezzasalma

et al.: HDa-Based

area is considered as not affected by hail. Such a scheme provides a representative sample of combinations between radar and ground hail detection.

Hail Detection

951

Algorithm

Number 200 160 160 140 120

3

The 1998 statistics

100 60 60

The automatic hail detection algorithm started to be applied since 01 June 1998. By October 1998, 27 storms passed over the hail pad network. Among these, the radar detected the presence of hail in 19 storms; the pad network reported only 13 hail events. The maximum HnR value for the six “detected but not reported” hailstorms ranges from 13 dB to 22 dB, while the “detected and reported’ storms range between 20 and 32 dB. These findings seem to suggest that the threshold fixed at 13 dB might overestimate the occurrence of hail; on the other hand, it is not possible to state that this network is able to catch every hail fall: its coarse horizontal resolution, indeed, can widely miss the hail detection (Nanni et al., 2000). The histogram in Fig. 4 is similar to the one in Fig. 3, but it is related to 1998. Some differences are well evident: first, the automatic real time algorithm finds a larger amount of not hit pads not associated to hail (negative Hnn). Further, the 1998 sample shows that the HnR values of the greatest part of the hit pads (black bins) are found above the 13 dB threshold, but also a more relevant amount of not hit pads get and exceed the same threshold. Quantitatively, the automatic method was able to detect more than 80% of hit pads, but with a similar amount of false alarms; the success index was 0.2. The three main hailstorms, which occurred in 1998, were selected as an attempt to explain the worsening of results between 1997 and 1998 and to get the two samples more comparable. The related histogram is shown in Fig. 5. For this smaller sample it is evident that the amount of not hit pads is now similar to that of hit pads, although the result is not as good as for 1997, when, anyway, the four selected hail falls were more severe and larger than the three main storms occurred in 1998. This finding suggests that hail falls having a small extension make worse the comparative statistics against the ground truth. Indeed, the statistical indices computed for this sample have improved: although the probability of detection is still about 80%, the false alarms have reduced at 60% and the success index has doubled.

4

Conclusions

The study of four main hailstorms, occurred in 1997, showed the good performance of the differential hail signal on detection of hail. This method does not seem to achieve such a skill when is routinely applied to every weather situation. Even if the radar hail detection procedure maintains a good performance in correctly estimating the pads that are actually hit by hail, a lot of not hit pads are wrongly marked as “hail”. Indeed, if a false alarm ratio of 0.3 could be considered acceptable for the 1997 statistics,

40

n

20 0 c-6.5

-6.5

0

6.5

13

19.5

26

HDRUB)

Fig. 4. As Fig. 2 but for all the events that occurred in 1998

Number

-

200

160

qNot

160

w Hit

hit

140 120 100

~~ '.

60 60 40 20

F--+-9

0' c-6.5

-6.5

0

6.5 HDR

13

19.5

26

(dB)

Fig. 5. As Fig. 3. but for the three main storms that occurred in 1998.

the same statement cannot be said for 1998, when the false alarms reached a value of 0.8. This remarkable difference seems to come out from the different intensity of the involved hailstorms: in 1998 hail falls were mainly weaker and smaller than those recorded during the 1997 stormy season. When only the three main hail falls, which occurred in 1998, are taken into account, the statistics improves and the false alarms reach a value of 0.6. It seems a hard job to find the correct correspondence between a not large hail fall and the coarse resolution of the hail pad network. During 1998 the radar marked as “ hail producing” as many as 19 storms; among these, as many as 13 storms were really recorded by the pads at the ground. Anyway, the six hail falls that were not recorded by the pad network presented smaller HuR values than the others did. Some experiments are currently underway in order to have a better definition of the hail signal threshold to be applied for a better discrimination of hail occurrence. Moreover, in order to sample more frequently short living and small hailstorms, a new acquisition scheme has been applied to radar scan strategy, so increasing from four to six the number of polarimetric volumes in an hour.

References Aydin, K., Seliga, T. A. and Balaji, V., Remott~ sensing ofhuil wirh a dual linearpolarisation radar, J. Appl. Meteorol.. 25, 1475-1484, 1986

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