Hail detection methods using radar...

Hail detection methods using radar data

Author: Dejan Kolarič

Co-advisors: assist. dr. Gregor Skok and prof. dr. Jože Rakovec

Ljubljana, March 2013

Abstract

Different methods for hail detection are presented in the seminar. The presented methods use radar

reflectivity measurements to determine the occurrence of hail. We compared observations made on

meteorological stations, radar derived product developed by Faculty of mathematics and physics (FMF) and

radar derived product VIL (Vertically integrated liquid). Comparison is performed for the period from 2006 to

2009 for ten selected days with observed hail. The biggest advantage of radar measurements of hail is that it

gives a good estimate of the occurrence of hail in areas where there are no direct observations.

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Contents 1. Introduction .................................................................................................................................................... 2

2. Overview of meteorological radar ............................................................................................................. 2

3. Products derived from radar data.............................................................................................................. 5

3.1 Radar product VIL .............................................................................................................................. 5

3.2 Hail product developed by FMF ........................................................................................................ 5

4. Ground observations of hail....................................................................................................................... 7

5. Precipitation and formation of hail ............................................................................................................ 7

5.1 Clouds formation ............................................................................................................................... 7

5.2 Precipitation formation ..................................................................................................................... 8

5.3 Hail formation ................................................................................................................................... 9

6. Comparison of methods and radar products against ground observations ............................................ 10

7. Conclusion .................................................................................................................................................... 15

8. References .................................................................................................................................................... 15

1. Introduction Large hail related to summertime thunderstorms can cause severe damage to crops and goods, and it can be

dangerous for aviation and traffic. Hailstorms are a small-scale weather phenomenon and due to the wide

spacing of the surface meteorological observation stations it remains mostly undetected by these stations.

Although ambiguous, the reflectivity factor observed by a single-polarization weather radar provides

information about the presence of large hail.

The main purpose of the hail detection methods is improving nowcasting of thunderstorms for general public,

traffic, agriculture and aviation. The results can be also used as reference information to insurance companies

investigating the legitimacy of hail damage claims.

This seminar will discuss operating principles of the meteorological radar and available radar products. It will

also explain the basics of precipitation, clouds and hail formation. A specific attention will be given to the

methods, used to detect hail. At the end of this seminar the results of different comparisons between ground

observations, radar products and other methods for hail detection are discussed.

2. Overview of meteorological radar The measurement of certain variables in the atmosphere requires remote sensing methods. These include

measurements by meteorological satellites and radars. Meteorological radar is also commonly used for

measurements of precipitation. In this section we will learn the basics of meteorological radar.

Radar (Radio Detection and Ranging) is a device that emits targeted electromagnetic waves and catches the

proportion of emitted energy reflected from potential targets. The target may be a ship or an aircraft, or any

other object in the air or on the ground. In the case of meteorological radar the target are hydrometeors – rain

droplets or ice particles.

Weather radars send directional pulses of microwave radiation, on the order of a microsecond long, using a

cavity magnetron or klystron tube connected by a waveguide to a parabolic antenna. The wavelengths of 1 to

30 cm are approximately ten times the diameter of the droplets or ice particles of interest, which causes

Rayleigh type of scattering. This means that part of the energy of each pulse will backscatter on these small

particles in the direction of the radar station. [1]

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Given the size of the targets we want to measure and the radar range, which is strongly dependent on

wavelength due to absorption, we divide meteorological radars in the L, S, C, X and K bands:

Table 1: Doppler radar bands [2].

Band Wavelength [cm] Usage

L 15-30 Long range; for studying turbulence at high altitude S 8-15 Range up to 250 km; C 4-8 Shorter range; X 2.5-4 For studies of cloud dynamics K <2.5

The result of radar measurements is the ratio between the emitted and the received radiation power. Hydrometeors will radiate as dipoles, if the emitted wavelength of the radar beam is much larger than the diameter of droplets. Their backscattering cross section is [3]:

𝜍𝑠 =2𝜋5

3|𝐾2|

𝐷6

𝜆4 𝐾 =𝑚2−1

𝑚2+2

where m is the complex refractive index, D diameter of droplets and λ the wavelength of radar radiation. Constant |K|

2 is strongly dependent on the type of hydrometeors.

The weather radar equation describes the relationship between the received power, the properties of the

radar, the properties of the targets and the distance between the radar and the targets. The weather radar

equation becomes [4]:

𝑃 = 𝐶 𝐾 2

𝑟2𝑍

where P is the mean power received from hydrometeors at range r, C is the so-called radar constant, K is a

coefficient related to the dielectric constant of water(or ice – it is assumed that there are spherical droplets in a

cloud) and Z is the radar reflectivity.

The signal strength received by radar can be used to estimate the radar reflectivity of the reflecting volume. We define the radar reflectivity Z. If we consider that the distribution of droplet size is approximately exponential [5]:

𝜕𝑁

𝜕𝐷= 𝑁0𝑒

−𝛬𝐷

𝑍 ≡ 𝐷6𝜕𝑛

𝜕𝐷

∞

0

𝑑𝐷

than

𝑍 =𝑁0𝛤(7)

𝛬7

Radar reflectivity is expressed in units of decibel (dBZ). The decibel (dB) is a logarithmic unit that indicates the

ratio of a physical quantity (usually power or intensity) relative to a specified or implied reference level. In our

case: 𝑍0 = 1 𝑚𝑚6/𝑚3. A ratio in decibels is ten times the logarithm to base 10 of the ratio of two power

quantities. [6]

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The proportion of the reflected wave depends on the diameter of dominant precipitation particles to the sixth

power. A number of particles with a diameter of 2 mm cause 64-times greater radar reflectivity than the same

number of particles with a diameter of 1 mm. The radar reflectivity is related to the rainfall intensity by the

following equation [7]:

𝑍∗ = 𝑎𝑅∗𝑏

𝑍∗ =𝑍

𝑍0 , 𝑅∗ =

𝑅

1 𝑚𝑚 /ℎ

where Z is radar reflectivity, R rainfall intensity, a and b are empirically determined constants. The parameters

a and b in the Z-R relationship usually change from one area to another and depend upon the variations of

raindrop size distribution in both space and time. Consequently, there is no universal relationship that can be

applied to all rainfall fields [1]. The meteorological radar on the Mount Lisca uses the value of a = 200 and b =

1.5.

For a successful detection the most important fact is that the size of droplets has a limit, because the larger

droplets split when falling into smaller raindrops. Radar reflectivity above 50 dBZ can therefore only be caused

by precipitation elements that are larger than the maximum possible raindrops - these are wetted ice grains.

Large, partially melted snowflakes, surrounded by a layer of water, are another possibility. Radar detects them

the same as large drops or wetted ice grains. This fact represents a complication of hail detection by radar to

some degree, but it can be bypassed if we know the temperature profile of the atmosphere. The new

generation of radars that emit dual polarized electromagnetic waves, can distinguish between the raindrops,

snowflakes and ice grains, but these types of radars are not yet commonly used.

Figure 1 shows the operation of meteorological radar. Radar emits a pulse of electromagnetic waves, while it

rotates around its vertical axis at a current azimuth and elevation . It is desirable that the beam is narrow (

Figure 1: Schematic illustration of radar [2].

< 200 m

200 - 400 m

400 - 600 m

600 - 800 m

800 - 1000 m

1000 - 1500 m

1500 - 2000 m

2000 - 2500 m

2500 - 3000 m

> 3000 m

Figure 2: Occultation map [8].

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is minimized). Radar antennae achieve the beam width of one degree. Temporal and angular resolution of

radar, t and determine the size of the smallest volume of air that radar is able to discern. If the distance

between the radar and cloud is about 100 km, this volume is about 1 km3. Target height above the ground, h,

depends on the elevation of a radar beam. The height of lowest observable target increases with the distance

from the radar, due to the curvature of the Earth's surface, although refraction in the atmosphere bends the

beam slightly downwards. At a distance of 250 km, the lowest height is already about 6 km high. The extreme

northeastern part of Slovenia is located at a distance of 120 km from Lisca radar, which ensures that the

minimum height is low enough to not affect the quality of the measurements. Figure 2 is an occultation map of

the Lisca radar that shows the minimum observable height of precipitation across Slovenia.

Lisca radar uses the wavelength of 5 cm, scans whole atmosphere around every 10 minutes, the measurement

itself takes 5 minutes. This time limit is due to limited antenna rotation speed and the need to do multiple

scans with different elevations in order to obtain three-dimensional measurement of the whole atmosphere.

Using data from Z-R equation of the radar reflectivity of each of the measured volumes, which is represented

as a three-dimensional matrix, we can estimate rainfall intensity at ground level [2].

3. Products derived from radar data

3.1 Radar product VIL

Vertically Integrated Liquid (VIL) product provides an estimate of atmospheric liquid water content in a vertical

column for an area of precipitation. It is another excellent indicator of severe storm activity, especially with

regard to the rainfall potential of a storm.

Empirically VIL is a function of reflectivity, and converts reflectivity data into an equivalent liquid water content

value based on studies of a drop-size distribution and empirical studies of a reflectivity factor and liquid water

content. The general empirical equation for VIL as used with the WSR-88D (Weather Surveillance Radar, 1988,

Doppler) is:

𝑉𝐼𝐿 = 3.44 𝑥 10−6 [(𝑍𝑖∗ + 𝑍𝑖+1∗)]47𝑑ℎ

𝑖

and has units of kg m-2

. Zi* and Zi+1* are radar reflectivity at the bottom and top of the layer dh, whose thickness

(dh) is in meters [9].

The VIL product is compiled from extensive reanalysis of base reflectivity data. It totals reflectivity within a

given column of the atmosphere and then displays a product of tallied values. The function of the VIL algorithm

is to estimate the amount of liquid water contained in a storm. In addition to this, VIL is directly related to

updraft strength. The VIL product was designed to distinguish severe from non-severe storms.

The output shows the estimated precipitation contained within a user-defined layer. If the layer height is above

the freezing level, high VIL values are an excellent indicator of severe storm and hail. If the layer height extends

from the surface up to 3 km, then the VIL values serve as a forecasting guide as to how much precipitation is

likely to fall during the next few minutes. [10]

In stratiform clouds a VIL value of 10 kg/m2 is rarely exceeded. In areas with strong convection, however, a VIL

value of 10 kg/m2 is easily exceeded and VIL values of 25 kg/m

2 or higher are possible. [11]

3.2 Hail product developed by FMF

At the Faculty of Mathematics and Physics (FMF), University of Ljubljana, they have created a model for hail

detection using methods described by Waldvogel [12] and Gmoser [13](AMS method). In this section those

methods are discussed.

A very general criterion for the detection of hail (diameter ice precipitation elements at least 5 mm) in the

cloud is that the maximum reflectivity measured in the column exceeds the threshold of 45 dBZ. For this value,

it is likely that the cloud contains grains of hail.

Hail is formed in parts of the cloud where the temperature is below freezing, because that is a condition for the

formation and growth of ice particles. By using this criterion it is possible to exclude some events with only

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intense rain and associated large droplets. Of course, for this purpose it is necessary to know the vertical

temperature profile, which can either be measured with the radiosonde, or taken from numerical weather

prediction model. Demonstration of different variants of hail detection method is shown in Figure 3.

In preparation for the attempt of seeding clouds “Grossversuch” [12] in central Switzerland in 1979 Waldvogel

and coauthors have demonstrated with hail pad measurements that the probability of hail is greater if it was

possible to observe reflections stronger than 45 dBZ above the level of the 0°C isotherms. Based on this data,

the calculated relationship between the probability of hail on the ground and the height difference between

the isotherm of 0°C and highest reflection (Δh0) is in polynomial form:

p = −1.20231 + 1.001841

𝑚 Δh0 – 0.17018

1

𝑚2 Δh0

2 + 0.01086 1

𝑚3Δh0

3 ,

which is limited between 0 and 1. 0 corresponds to hail probability 0% and 1 to 100%. From such polynomial

they determine minimal Δh which is approximately 2 km for occurrence of hail. In this study every event with

probability of hail of at least 50 percent was considered as an occurrence of hail. In Figure 3, which

schematically represents the operation of the detection algorithm, the distance is marked Δh0 which is used in

Waldvogel’s method and on the basis of which they determine the probability of hail, is marked.

Figure 3: Examples of vertical distribution of radar reflectivity and temperature, critical for the functioning of algorithms to objectively identify hail [2].

In this study they used the radiosonde from Ljubljana, which was available once a day. In the case of missing

data, interpolation of measured data from adjacent days was used.

In figure 3 full squares represent the values of radar reflectivity above the threshold required for the presence

of hail. h0 means the height of 0°C isotherm. Method according to Waldvogel considers only parameter Δh0,

AMS method considers reflectivity in the columns of hz, if they are at least 5 km thick. Waldvogel’s modified

method takes into account the continuous part of the reflection above h0.

The original method described by Waldvogel does not require that the entire column Δh below the highest

measured reflection contains reflectivity above a threshold of 45 DBZ. Therefore this method has high level of

detection of hail events. In the Austrian Meteorological Service (AMS method, Gmoser and coauthors, 2006)

they use a method that checks reflectivity of all the elements in a column of 5 km above the 0°C isotherm h0.

Appropriate to more stringent criteria used in the method there are lower thresholds (possible hail 38 dBZ,

plausible hail 42 dBZ, very plausible hail 46 dBZ). In this study they counted the statistics of events over 42 dBZ,

which corresponds approximately to Waldvogel probability of 50 percent. In Figure 3, in columns with labels hz

they determine the total maximum reflectivity, but only for columns reaching a thickness of 5 km.

Identification of hail by radar has several possible sources of error. Radars measure in discrete elevations with

interruptions, leading to uneven horizontal and vertical resolution at different distances from the radar.

Another important limitation is discrete time measurement. Radars measure 5-minutes in intervals every 10

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min, during this time a thunderstorm cell could have noticeably moved. Because of that it can be left blank area

between two successive recordings. [2]

4. Ground observations of hail For measurements of kinetic energy of hail they use hail pads as shown on the picture below. The hail pads are

usually about half square meter large plates made from Styrofoam coated with aluminum foil. When hail hits

the hail pad it leaves a trace, which is then analyzed visually or with special equipment. Hail pads are placed in

regular grid over selected area. The more are placed, the greater the probability of storm hitting them. Such

measurement of hail is used primarily for research of hail occurrence, it is not appropriate for an operational

monitoring of hail. [2]

Another method of observation of occurrence of hail is within network of meteorological stations across the

country. In Slovenia there are about two hundred meteorological stations. When observing the weather the

station observers also indicate the occurrence of hail in weather reports, so there is daily data of hail presence.

5. Precipitation and formation of hail

5.1 Clouds formation

In the atmosphere clouds form, if the raising air is cooled below the dew point. Then tiny water droplets begin

to form around the cloud condensation nuclei, for example mineral dust, sea salt crystals and anthropogenic

organic materials. Condensation nuclei in the aerosol can be solid, but they can be also liquid or even solid,

coated with a layer of liquid. If the condensation nucleus is hygroscopic - (e.g., soot, watered with H2SO3), the

condensation may begin at a relative humidity that is up to 25% lower than the saturation. In the air there are

always enough aerosols, therefore homogeneous condensation of pure air never occurs. Supersaturation

(relative humidity of more than 100%) in the atmosphere is very rare and even then only slight.

Supersaturation of moisture in the air is a factor which affects the size and growth of cloud droplets. Given

Claussius-Clapeyron equation, curvature of the droplet surface and affect of the solute we get to the equation

for the supersaturation S around droplets [7]:

𝑆 =𝑒𝑠

𝑒𝑠 𝑇𝑑 = 1 +

𝑎

𝑅−

𝑏

𝑅3

where es means saturated vapor pressure drops at given conditions, es(Td) saturated vapor pressure above a

flat surface of water without solute under the given conditions, R radius of droplets, a is related to the surface

tension constant for water and b to the solute content.

Figure 4: Hail pad [14].

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Figure 5 shows that the saturated vapor pressure around droplets depends on droplet size. Larger droplets

tend to increase at the expense of nearby smaller ones, due to their higher saturated vapor pressure. The

process of diffusion of water vapor from smaller to larger droplets is slow, because the difference in

supersaturation is small. In this way, the proportion of larger cloud droplets begins to increase at the expense

of smaller cloud droplets [7].

With decreasing temperature, more and more nuclei are suitable for the ice deposition and also some

supercooled droplets begin to freeze. At temperatures below -20°C the clouds contain only small amounts of

liquid water. Saturated vapor pressure over ice crystals is slightly lower than over the droplets, so in parts of

the cloud where ice crystals and droplets are present, cloud ice crystals grow at the expense of cloud droplets.

Cloud droplets and cloud ice crystals are small (r ≈ 10 µm). In the stationary air they fall very slowly: (w ≈ 1

cm/s). Two forces affect the movement of cloud elements: gravity acting downwards and air drag, acting in the

opposite direction of the relative movement of cloud elements to the surrounding air. Buoyancy is small and

can be neglected in comparison to ~103 times bigger gravity. Fall speed is increasing until it reaches

equilibrium rate. For turbulent flow the velocity of a droplet depends on the root of the radius:

𝑤 = 8

3

𝑔

𝑐𝑢

𝜌𝑎

𝜌𝑟

cu is the drag coefficient of droplets, ρa density of liquid water and ρ the density of air. For small hydrometeors

we have to consider laminar flow and the viscosity of air μ and we get the terminal velocity dependence on the

square of the diameter of droplets:

𝑤 =1

3

𝑔𝜌𝑎

μ𝑟2

In both regimes large raindrops fall through the air faster than small drops.

The formation and shape of clouds are different, which mainly depends on the conditions in which a single

cloud formed. Clouds can be spread throughout the whole troposphere and up to 15 km high, but they can also

be shallow and only a few meters thick. Liquid water content of a cloud and with it the amount of able water,

depends on the occurrence and type of the cloud. Cloud particles must grow to precipitation size droplets in

order to cause the formation of precipitation [15].

5.2 Precipitation formation

Two processes are important for the formation of precipitation. The process of growth of ice crystals at the

expense of supercooled droplets and coalescence; this is a process by which two or more droplets or ice

particles merge during a collision into bigger hydrometeor.

In the most vertically extended clouds (Cu-cumulus, Cb-cumulonimbus, Ns-nimbostratus), the cloud particles at

different heights are different: at the top of the cloud there are cloud ice crystals, in the middle there are

supercooled cloud droplets, and lower above the base of clouds there are liquid droplets. Cloud particles are in

thermodynamic equilibrium with the air; around them there is saturated vapor pressure. Saturated vapor

Figure 5: Degree of supersaturation around the droplets as a function of radius for different masses of solute NaCl (m = 10

-17g (blue), 10

-16g (green), 10

-15g (red)).

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pressure depends on the temperature. In the Claussius-Claperyron equation in addition to temperature, we can

also see latent heat h.

𝑒𝑠(𝑇𝑑) = 𝑒𝑠0exp ℎ

𝑅𝑣

1

𝑇0

−1

𝑇

This latent heat is different for the cases of water vapor condensation into supercooled water and deposition

of vapor into ice. At the temperatures below freezing, where water can be either supercooled or frozen, the

saturated vapor pressure of subcooled liquid water is greater than the saturation vapor pressure over ice at the

same temperature. If ice crystals happen to occur in a certain area of the cloud where there are also

supercooled cloud droplets, the air surrounding is oversaturated for the ice crystal and water vapor will flow to

the ice crystal from its surrounding by diffusion. Water vapor will therefore start to deposit on the ice crystals

and the crystals will be slightly warmed up because of the released latent heat. This heat will then flow out to

the surrounding area for crystal not to warm up. Because of water vapor deposition on ice crystals the vapor

pressure drops in the surrounding area occupied by droplets and for the droplets the new vapor pressure is not

saturated anymore. Supercooled droplets therefore evaporate, and the vapor is deposited on the ice crystal. By

this mechanism the ice crystal grows at the expense of evaporation of supercooled droplets. Ice crystals

become so large by this deposition, that their speed becomes higher than the speed of falling cloud droplets

and thus fall in lower layers of the cloud. In the upper parts of clouds at moderate latitudes it always snows,

and the kind of precipitation that will arrive to the ground level depends on the temperature and humidity

conditions [15].

In the cloud, where precipitation particles of different sizes are present, a coalescence of droplets may occur.

The coalescence happens due to collisions between droplets, which fall at different speeds. Big droplets or ice

crystals fall faster than small ones. Due to increased speed of falling, large particles catch up smaller particles

and bump into them.

Table 2: Typical size, concentration and velocity of the aerosol, cloud and precipitation particles [16].

type R[μm] N [/dm3] w [m/s]

Aerosol 0.001-10 106 10

-6

Cloud drop 1-102 10

2-10

6 10

-2

Rain drop 102-5·10

3 1-10

2 0.7-6.5

With the coalescence of rain droplets in the clouds that contain precipitation and cloud droplets, we get

droplets which quickly fall through the cloud. For the formation of such precipitation there should be at least

some slightly larger droplets in the cloud, to begin the process. At the same time the cloud must be water-rich

and thick enough, that droplets with process of coalescence may thicken enough [15].

5.3 Hail formation

Hail is formed in the stormy clouds called Culumonimbus (Cb). Just as for the formation of snow and rain, it is

also necessary for formation of hail that in the upper part of the cloud there are ice crystals and in the middle

parts supercooled droplets. Ice crystals grow during falling through the cloud colliding with supercooled

Figure 6: Hail formation [17].

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droplets and grow at their expense and form larger and larger snowflakes. These form white ice in hail (porous,

relatively lightweight, it has a lot of air). When an ice grain comes to the bottom of the cloud, it is partially

thawed in Culumonimbus (Cb). It is possible that such partially molten ice grain is grabbed by updraft again and

taken into the higher parts of the cloud again. If the ice grain repeats its route up and down through the cloud

several times it accumulates more layers of ice. The number of layers of transparent ice tells us how many

times the ice grains traveled up and down the cloud.

6. Comparison of methods and radar products against ground observations

In this chapter we will discuss results of comparison of different methods of hail observation. The results are

useful for determine the thresholds for possible occurrence of hail. We compared hail observations that

happened in ten selected days. Those days are shown in the next table.

Date Number of observations

Approximate region

23.7.2006 2 Gorenjska

22.5.2007 11 Gorenjska, Štajerska, Dolenjska

17.8.2007 11 Gorenjska, Štajerska, Koroška

13.7.2008 15 Primorska, Gorenjska, Štajerska, Prekmurje

14.7.2008 20 Primorska, Notranjska, Gorenjska, Štajerska, Dolenjska, Koroška, Prekmurje

15.8.2008 29 Notranjska, Dolenjska, Štajerska, Prekmurje

23.8.2008 26 Primorska, Gorenjska, Notranjska, Dolenjska, Štajerska

25.5.2009 11 Gorenjska, Dolenjska

3.8.2009 6 Primorska, Gorenjska, Štajerska, Dolenjska

22.8.2009 5 Gorenjska, Dolenjska, Štajerska

Table 3: Number of hail observations.

In the second column is the number of the meteorological stations that have recorded occurrence of hail. The

total number of hail occurrences in observed period was 136. We compared only cases with observed hail on

meteorological stations. For these cases we compare two radar derived hail estimates: the one according to

FMF methodology and the other using VIL information.

Radar product VIL gives us a vertically integrated liquid in kg/m2 in six ranges, these are:

Range [kg/m2] grade

0-3.97 0

3.98-6.30 1

6.31-9.99 2

10.0-15.84 3

15.85-25.11 4

25.12-39.81 5

39.81- 6

Table 4: Radar product VIL ranges.

FMF product which uses Waldvogel and AMS methods to detect hail gives us values from 0 to 2. 0 means that

neither of the methods detected hail, 1 means that one of the methods detected hail and 2 means that both

methods detected hail.

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The resolution of radar product and models used for calculating hail probability is 1km x 1km. To determine

maximums and minimums on certain area we take surrounding pixels. The total area with this method is 3km x

3km as shown on the next image:

3.98 10 10

15.85 15.85 10

15.85 25.12 15.85

Figure 7: Example of determination of minimum and maximum on the selected area.

The ground observation is made in central pixel. In figure 7 we notice that maximum is 25.12 kg/m

2 and

minimum is 3.98 kg/m2. With that method we can cover cases where in central pixel there is no radar

measurement or there is no indication of hail, but in the surrounding pixels hail indication was present.

For FMF products we also use 3x3 pixels as shown in figure 7 to determine minimums and maximums for the

comparison of different methods of hail detection.

In figures 8 to 16 we see the comparison between two different methods for nine different days. On the left

side are the results of FMF product and on the right side are the results of radar product VIL. On the right side

of the figures ground observations are also shown. Black and red dots represent the locations of the ground

observations. Red color means that hail was detected and black color that it was not detected. In the following

table the meaning of colors used in the right figures is shown.

Table 5: Meaning of colors on the right side figures nr. 8 to 16

The meaning of colors on left figures is shown in the next table:

Color Value of FMF product

0 1 2

Table 6: Meaning of colors on the left side figures nr. 8 to 16

In figures 8 to 16 we also notice that the radar product VIL detects a possible hail on a larger area than the FMF

product. We see that areas with VIL values higher than 25.1 match quite well with FMF product.

In figure 10, 11 and 12 we see an examples of a fast moving storm. The waves of higher and lower VIL values

we see in the figure are the result of the radar operation and can be attributed to the fact that the

measurements are performed every 10 minutes.

Color VIL [kg/m2]

________ 4, 6.3 ________ 10, 15.9 ________ >25.1

12

Figure 8: Possible hail detection on 22.5.2007. On left side FMF product, on right side radar product VIL.




13





14


The following figure shows the percentage of ground hail observations against the maximum value of the radar

product VIL.

Figure 17: Percentage of cases at maximum VIL.

Figure 18: Percentage of cases above a defined VIL.

Figure 19: Percentage of cases at different output data

of FMF product.

Figure 20: Average maximum VIL values at different

output data of FMF product.

In figure 18 we see the distribution of cases above the defined VIL value. In 30% of cases the radar value VIL

was above 39.81 kg/m2, in 55% it was above 25.12 kg/m

2 and in almost 80% the radar value VIL was above

15.85 kg/m2.

Figure 19 shows the percentage of hail observations against the maximum output data of the FMF product. In

figure 19 we see that the FMF products detected hail in only 96 cases. This is 70% of all cases. In 22 cases or

16% we notice that only one of two methods used by the FMF product detected hail. In 74 cases or

approximately 55% we see that both methods detected hail. In 40 cases or almost 30% of all observed cases

none of methods used by the FMF product detected hail.

0%5%

10%15%20%25%30%35%

1 2 3 4 5 6

pre

cen

tage

grade

0%

20%

40%

60%

80%

100%

39.81- 25.12- 15.85- 10.0- 6.31-

pe

rce

nta

ge

VIL

0%

10%

20%

30%

40%

50%

60%

0 1 2

pe

rce

nta

ge

output data of FMF product

0

5

10

15

20

25

30

35

0 1 2

VIL

[kg

/m2

]

output data of FMF product

15

The figure 20 shows the comparison between average maximum values of the radar product VIL and maximum

value of the FMF product at the station location where hail was observed. When the FMF product gives value 0

we get the average radar product VIL around 15 kg/m2, at value 1 we get 20 kg/m

2 and at value 2 we get

approximately 30 kg/m2.

7. Conclusion In summary, we observed that the FMF product and the radar product VIL posses certain skill in detecting hail.

The products we used detected hail in more than 70% of cases. Average VIL values for the cases that the FMF

product did not detect hail were between 6 and 15 kg/m2. This can be attributed to the radar shadows, which

occur in certain directions because of the nearby hills as shown on figure 2 and therefore we get lower values

of VIL. We also found out that if we set a threshold for hail detection at 10 kg/m2 we were successful in 93% of

cases. If we set the threshold higher, for example at 15.85 kg/m2, we detected hail in approximately 80% of

cases. For more accurate results we would also have to look for the cases where hail was present but observers

did not record it. Another issue would be the fact that observers can easily miss an occurrence of hail, because

usually the hail is falling only few minutes when it occurs.

8. References [1] Doviak, R. J.; D. S. Zrnic (1993). Doppler Radar and Weather Observations (Second ed.)

[2]KGZS-Zavod Maribor,UL Fakulteta za matematiko in fiziko Ljubljana,UM Fakulteta za kmetijstvo in biosistemske vede Maribor ANALIZA UPRAVLJANJA S TVEGANJEM ZARADI TOČE Z UPORABO ATMOSFERSKIH MODELOV IN DREVES ODLOČANJA

[3] Seinfeld and Pandis, Atmospheric Chemistry and Physics, 2nd Edition, John Wiley and Sons, New Jersey 2006, Chapter 15.1.1

[4] Battan, L.J., 1973. Radar observation of the atmosphere. The University of Chicago Press, Chicago, 324 pp.

[5] Collier C. G. (1989). Applications of weather radar systems: a guide to user of radar data in meteorology and hydrology. Chichester: Ellis Horwood.

[6] EEE Standard 100 Dictionary of IEEE Standards Terms, Seventh Edition, The Institute of Electrical and Electronics Engineering, New York, 2000; ISBN 0-7381-2601-2; page 288

[7] Sauvageot, H., 1992: Radar meteorology. Artech-House, Washington, 336 p.

[8] Arso - Slovenian Environment Agency

[9] Amburn, S. and P. Wolf, 1996: VIL density as a hail indicator. Preprints, 18th Conf. on Severe Local Storms. Amer. Meteor. Soc., San Francisco, CA.

[10] http://www.knmi.nl/opera/tutor/radar_tech_training_IOM-88_Module-D.pdf(20.10.2012)

[11] http://www.knmi.nl/research/weather_observations/radar/vil.html (20.10.2012)

[12] Federer, B., A. Waldvogel, W. Schmid, H. Schiesser, F. Hampel, M. Schweingruber, W.Stahel, J. Bader, J. Mezeix, N. Doras, G. D'Aubigny, G. DerMegreditchian, and D.ento, 1986: Main Results of Grossversuch IV. J. Appl. Meteor., 25, 917–957.

[13] Gmoser H., Zwatz-Meise V., 2006. Warning System of ZAMG for Austria - Concept and Applications. Subgroup on Regional Aspects of Public Weather Services, Bukarešta, 4. – 7. december 2006.

[14] http://www.cocorahs.org/Content.aspx?page=hailpadexamples (20.10.2012)

[15] Rakovec J., Vrhovec T. (2000). Osnove meteorologije za naravoslovce in tehnike. Ljubljana,DMFA.

[16] Salby M. L.(1996).Fundamentals of Atmospheric Physics. San Diego: Academic Press.

[17] http://www.physicalgeography.net/fundamentals/8f.html (20.10.2012)

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