Iwan Holleman, KNMI and Elena Saltikoff, FMI

Hail in weather radar observations

Introduction

Hail can be a problem in precipitation measurement because dBZ values are ”too big” so dBZ/R equations are not valid. For big hail, it's not pure Rayleigh scattering so the assumptions made in the radar equation are perhaps not valid. So the hail data should be removed from the measurement before calculating estimates of accumulated precipitation. On the other hand, hail detection can be very useful for warnings for aviation and general public.

Detection Method of Waldvogel:

At the end of the seventies Waldvogel et al. developed a method for the detection of summer (large) hail which is based on a combination of radardata and information on the temperature distribution of the atmosphere.

It is quite obvious that the CAPPI-image contains hardly any information on the vertical structure of the thunderstorm. The method for detection of hail from Waldvogel is based on the observation that the presence of high radar reflectivities, i.e, the presence of large amounts of liquid and solid waterparticles, at high altitudes points to the presence of a strong updraft. When radar reflectivities of 45 dBZ or higher are observed above the freezing level, and thus large amounts of undercooled or solid water is present at high altitudes, the presence of summer hail is likely. Finally, Waldvogel et al. reached the conclusion that the presence of hail is likely when radar reflecitivties of 45 dBZ are observed 1.4 km or more above the freezing level and that probability increases when this high reflecticty is higher above the freezing level.

Verification:

An extensive comparison has been conducted at KNMI between the results of the radar-based hail detection methods and reported hail by on-ground observers. The collection of on-ground hail observations for these periods turned out to be quite a challenge. Because summer hail is usually a small scale phenomenon, it is almost never observed by the network of synop stations. In order to obtain a more dense observer network, hail reports of the 325 voluntary rainfall observers have been taken into account as well. In addition, cases of damage caused by hail as reported to three large insurance companies during the summers of 1999 and 2000 have been used. The reports of hail damage to agricultural property will surely not be distributed homogeneously across the Netherlands. This inhomogeneity can, however, be taken into account by looking at the distribution of the land use. Despite this extensive search for reference data, there still will be cases of hail that have not been reported. Eventually this will result in an underestimation of the performance of the hail detection product.



Implementation at KNMI:

In May of 2001, the hail detection product based on the method of Waldvogel became operational. For this, an radar composite, including both the De Bilt and Den Helder radars, with the 45dBZ echotops is produced every 15 minutes. In addition, a map of the height of the freezing level is calculated from Hirlam forecasts (+3h,+6h) every 3 hours. When a 45dBZ-echo is present in the radar image, the difference between the height of this echo and height of the freezing level is calculated. Making use of the verification results of the summer of 2000, this height difference can be converted to the Probability-Of-Hail (POH).

For referencing by the Climatological Services Department and for verification purposes, the 96 hail detection images are overlayed into a single images every day (day bin). For this, all images are collected and the maximum Probability-Of-Hail that has occurred that day is determined for each pixel.

The construction of the hail day bin using interpolation is a three-step process. First of all, a pattern recognition technique is used to detect hail cells in each of the 96 hail images per day. Hail cells are detected using a recursive algorithm which looks for neigboring pixels above a certain threshold. For each of the detected cells, the average position and the number of pixels is stored. Then, detected hail cells in subsequent images are matched. For each pair of cells in subsequent images, the displacement vector/velocity is calculated. The displacement vector (Vd) is compared with the (predicted) Hirlam wind vector at 700 hPa (V700). The quality of the match is expressed in a quality index:

| Vd - V700 |

Q =

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| V700 |



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