Radar Tech Training IOM-88 Module-D

WEATHER RADAR TRAINING 5.0 / ISTANBUL-2010

TURKISH STATE METEOROLOGICAL SERVICE

(TSMS)

WORLD METEOROLOGICAL ORGANIZATION

(WMO)

COMMISSION FOR INSTRUMENTS AND METHODS OF OBSERVATION

(CIMO)

OPAG ON CAPACITY BUILDING (OPAG-CB/C.2.)

TRAINING ACTIVITIES AND TRAINING MATERIALS

TRAINING COURSE ON WEATHER RADAR SYSTEMS

MODULE D: RADAR PRODUCTS AND OPERATIONAL APPLICATIONS

CÜNEYT GEÇER-Meteorological Engineer

REMOTE SENSING DIVISION TURKISH STATE METEOROLOGICAL SERVICE

10-14 OCTOBER 2010 WMO RTC-TURKEY

ISTANBUL FACILITIES, ISTANBUL-TURKEY

MODULE D- RADAR PRODUCTS AND OPERATIONAL APPLICATIONS


IMPORTANT NOTE!

The main sources of the information used in those training documents, which have been

stated in the reference list, are the guides published by WMO, technical brochure and

instruction manuals of the manufacturers, all related documents issued by research

institutions, universities and manufacturers, and all the documents available for anyone in the

internet. In addition, previous version of those documents have been reviewed and updated

based on the comments of the participants of the previous trainings and view of the

instrument experts of TSMS. It must be always kept in mind that those document have been

prepared just for such training courses but not for any commercial purposes.



MODULE A: INTRODUCTION TO RADAR

MODULE B: RADAR HARDWARE

MODULE C: PROCESSING BASICS IN DOPPLER WEATHER RADARS

MODULE D: RADAR PRODUCTS AND OPERATIONAL APPLICATIONS

MODULE E: RADAR MAINTENANCE AND CALIBRATION TECHNIQUES

MODULE F: RADAR INFRASTRUCTURE





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RADAR PRODUCTS AND OPERATIONAL APPLICATIONS

CONTENTS CONTENTS 3

FIGURE LIST 4

TABLE LIST 6

ABBREVIATIONS 7

1 INTRODUCTION 9

2 SCANNING 10

3 SIGNAL PROCESSING AND RADAR PRODUCT GENERATION 14

4 RADAR PRODUCTS 17 4.1. Radar Data 17

4.1.1. Reflectivity 17 4.1.2. Velocity 20 4.1.3. Spectrum Width 22 4.1.4. Differential Reflectivity 23

4.2. Base Products 26 4.2.1. PPI Product 26 4.2.2. RHI Product 29 4.2.3. CAPPI Product 32 4.2.4. Echo Top Heights 33 4.2.5. Maximum Reflectivity 34

4.3. Derived Products 35 4.3.1. SRI Product 35 4.3.2. Vertically Integrated Liquid (VIL) 37 4.3.3. Surface Precipitation Accumulation 38 4.3.4. Subcatchment Accumulation 40 4.3.5. Wind Speed and Direction 41 4.3.6. Vertical Wind Profile 43 4.3.7. Wind Shear 45

4.4. Warning Products 46 4.5. Tracking Products 48 4.6. Dual Polarization Products 49 5 REFERENCES 51



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FIGURE LIST FIGURE 1: PPI and RHI scanning 11 FIGURE 2: Volume scan 11 FIGURE 3: Example of 15-tilt volume scan 12 FIGURE 4: Signal processing and radar product generation 15 FIGURE 5: Radar data flow infrastructure 15 FIGURE 6: Task configuration tool (from IRIS) 16 FIGURE 7: Product configuration tool (from IRIS) 16 FIGURE 8: Echo intensities 17 FIGURE 9: The interpretation of dBZ factors on colour figure 19 FIGURE 10: Echo intensity scales for clear air (left) and precipitation mode (right) 19 FIGURE 11: Doppler frequency shift by moving targets 21 FIGURE 12: Doppler radial velocities and an example image 21 FIGURE 13: Another figure of Doppler radial velocities 22 FIGURE 14: Spectrum width and its averages 22 FIGURE 15: Dual polarization 23 FIGURE 16: Raindrop diagram 24 FIGURE 17: Hydrometeor types in a radar volume coverage 25 FIGURE 18: Hydrometeor classification products 26 FIGURE 19: A PPI product display 27 FIGURE 20: A PPI(V) product display 28 FIGURE 21: PPI(W) product 29 FIGURE 22: PPI(V) product 29 FIGURE 23: A RHI product 30 FIGURE 24: A cross-section product (in this case height of the

Maximum reflectivity is 2,5 km) 31 FIGURE 25: A cross-section product 31 FIGURE 26: A CAPPI product 32 FIGURE 27: Echo top heights display product for 30 dBZ of selectable threshold value 33 FIGURE 28: The schematic diagram of the maximum reflectivities 34 FIGURE 29: A Maximum reflectivity product display (in this case, height is from 0 km to 12 km) 35 FIGURE 30: A SRI product display 37 FIGURE 31: A VIL product display 38 FIGURE 32: A surface precipitation accumulation product of the last 6 hours 39 FIGURE 33: An hourly surface precipitation accumulation product 39 FIGURE 34: Subcatchment accumulation product display 40 FIGURE 35: Subcatchment accumulation product (histogram display) 41 FIGURE 36: A typical display of horizontal wind vectors at 2 km height 42 FIGURE 37: The horizontal wind vectors displayed as an overlay product on the CAPPI(V) product 42 FIGURE 38: The horizontal wind vectors displayed as an overlay product on the maximum reflectivity product 43 FIGURE 39: A sample VVP product 44 FIGURE 40: A sample VVP product 44 FIGURE 41: A sample VVP product 45



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FIGURE 42: A wind shear product 46 FIGURE 43: A hail warning product 48 FIGURE 44: Track with two centroids 48 FIGURE 45: A sample tracking product 49 FIGURE 46: Dual polarization products 50



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TABLE LIST TABLE 1: PPI and RHI scanning types 11 TABLE 2: Example of defined parameters for precipitation mode volume scan configuration 13 TABLE 3: Example of defined parameters for clear air mode

volume scan configuration 14 TABLE 4: The range of radar reflectivity factor 18 TABLE 5: The interpretation of dBZ factors 18 TABLE 6: Average Spectral Width values 22 TABLE 7: ZDR values of hydrometeors 24 TABLE 8: Input parameters and output types of a sample

hydrometeor classification study 50



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ABBREVIATIONS: RADAR : Radio Detecting and Ranging TSMS : Turkish State Meteorological Service AWOS : Automated Weather Observing System PPI : Plan Position Indicator CAPPI : Constant Plan Position Indicator RHI : Range Height Indicator MAX : Maximum Display VVP : Velocity Volume Processing VAD : Velocity Azimuth Display SRI : Surface Rainfall Intensity VIL : Vertically Integrated Liquid SL : Squall Line Cb : Cumulonimbus Cloud RDA : Radar Data Acquisition RPG : Radar Product Generation AP : Anomalous Propagation PRF : Pulse Repetition Frequency Vmax : Maximum Unambiguous Velocity Rmax : Maximum Unambiguous Range Hz : Hertz PRT : Pulse Repetition Time Sec. : Second μsec. : Microsecond cm : Centimetre dB : Decibel dBZ : Logarithmic Scale for Measuring Radar Reflectivity Factor c : Speed of light f : Frequency λ : Wavelength Z : Reflectivity Factor of the Precipitation hPa : Hectopascal



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1. INTRODUCTION

The meteorological radar is a basic remote sensing tool which uses electromagnetic waves. It

is also an atmospheric observation tool for detecting and tracking severe weather phenomena

in real time covering large areas. The meteorological radar senses energy from the radar beam

that is reflected back to the radar by various targets in the atmosphere. The targets in the

atmosphere can be precipitation (like raindrops, hailstones, snowflakes, or sleet particles) or

they can be non-meteorological (such as birds, insects, or bats). Among its main

characteristics are:

Rainfall accumulation and intensity

Detection and analysis of wind velocity

Vertical and horizontal wind profile

Echo Top Heights

Range selection presentation

Storm speed and development

As you aware from global warming, from the researchers’ point of view; global warming

threatens all the world. Heavy rainfalls and flash flooding will be experienced more

frequently in near future. Consequently, we should consider this problem.

It is attached importance to weather radars worldwide recently. Meteorological radars are one

of the most important component of a flood warning system. They can be used to issue early

warning and to reduce risk of future floods. Although a flood warning system will not prevent

floods, it will allow minimisation of damage and reduce the loss of life.

A number of products are obtained from Doppler weather radars. These products include

radar reflectivity factor, radial velocity, spectrum width and differential reflectivity data

information which are directly observed/measured by Doppler weather radars. The radar

products are classified in two groups generally as Base and Derived products. A radar product

generation software provides for the determination and visualization of a set of base and

derived products.



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The following base products are:

Plan Position Indicator (PPI)

Range Height Indicator (RHI)

Constant Altitude PPI (CAPPI)

Echo Top Heights

Maximum Reflectivity

The derived products are produced from some algorithms by the radar product generation

softwares. The following derived products are grouped:

Hydrological products:

Surface Rainfall Intensity (SRI)

Vertically Integrated Liquid (VIL)

Surface Precipitation Accumulation

Subcatchment Accumulation

Wind products:

Wind Speed and Direction

Vertical Wind Profile

Wind Shear

Warning products (hail, wind shear, flash flood, etc.)

Tracking Products

Dual polarization products

2. SCANNING

Firstly, it should be explained briefly how the radar products are generated. Raw data collection from the atmosphere via scanning is the first step in order to generate the radar products which were listed above. Scanning is the motion of the radar antenna during data collection. We can move the antenna up and down (elevation) and round in horizontal plane (azimuth). There are two base scanning techniques; PPI and RHI. Meteorological radars usually employ one of these two scanning techniques.



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Scan Mode PPI Full PPI Sector RHIAzimuth Full Circle: antenna scans

0°~359° in azimuth continuously.

Antenna scans between two azimuthal angles.

Antenna is fixed in an azimuthal angle.

Elevation Single angle in elevation or different elevation angles from -2° to 90° in elevation can be chosen for volume scan.

Single angle in elevation or different elevation angles from -2° to 90° in elevation can be chosen for volume scan.

Antenna scans between two elevation angles.

Table 1: PPI and RHI scanning types.

Plan Position Indicator (PPI): The radar antenna holds its elevation angle constant but varies its azimuth angle. If the radar antenna rotates through 360 degrees in azimuth continuously, this is the “PPI Full Scan” and also called “Surveillance Scan”. If the radar antenna rotates through less than 360 degrees, the scan is called “Sector Scan”. It’s good surveillance scan and good in operational setting. Range Height Indicator (RHI): The radar holds its azimuth angle constant but varies its elevation angle. The elevation angle normally is rotated from near the horizon to near the zenith (the point in the sky directly overhead). It’s excellent for determining the detailed vertical structure of a storm.

Figure 1: PPI and RHI scanning.

Most of products require Volume Scan. Volume Scan is a radar scanning strategy in which

sweeps are made at successive antenna elevations (tilts). In other words, volume scan is taken

at multiple elevation angles either PPI Full or PPI Sector scans (generally PPI Full).

Figure 2: Volume scan.

ZN

S

EW

Azimuth

ElevationStep

Range

Elevation

VolumeScan



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A typical volume scan for precipitation mode is shown in Figure 3.

Figure 3: Example of 15-tilt volume scan. As you see in 15-tilt volume scan, the scans are made at 15 different elevations starting at 0.8°

and increasing up to 44.9°.

Precipitation Mode volume scan is the standard mode of operation whenever precipitation is

first detected. When rain is occurring, the radar does not need to be as sensitive as in clear air

mode as rain provides plenty of returning signals. When the weather conditions turn severe,

the Precipitation Mode can be activated. The Precipitation Mode provides a faster scan rate to

monitor a larger volume of space in a shorter time. This permits the tracking of rapidly

moving meteorological phenomena found in convective weather patterns. This mode is

characterized by the use of a short pulse width at both high and low PRFs. It consists of the

Surveillance Task with Monitor Task. In addition, a RHI Task can be scheduled for observing

storm structure in detail, especially for storms close to the radar (max range 120 km). In

precipitation mode, the radar products update every 6 minutes.



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Elevation Angles (°) 0.5-45.0 (16 angles) Resolution (°) 1.0 Pulse Width (µsec) 1.00 Scan Speed (°/sec) 12.00, 24.00 Data Z, V, W, ZDR Samples 64, 32, 32 Number of Bins 1200 Bin Spacing (m) 250.0 Max Range (km) 120.0PRF (Hz) 1200-900 Unambiguous Velocity (m/s) 48 (4:3) Processing RPHASE Data Quality Thresholding T: LOG, Z: LOG&CSR, V:SQI&CSR,

W: SIG&SQI&LOG LOG (dB) 0.8 SIG (dB) 10 CSR (dB) 18 SQI 0.4 Speckle Z on, V on

Table 2: Example of defined parameters for precipitation mode volume scan configuration.

Another kind of precipitation mode volume scan type contains 14 elevation angles. The scans

are made at 14 different elevations starting at 0.5° and increasing up to 19.5°. It uses a short

pulse wave and sweeps in 5 minutes.

Clear Air Mode volume scan is preferred when significant precipitation is not estimated in the

radar coverage. In this mode, the radar is in its most sensitive operation. This mode has the

slowest antenna rotation rate which permits the radar to sample a given volume of the

atmosphere longer. This increased sampling increases the radar's sensitivity and ability to

detect smaller objects in the atmosphere than in precipitation mode. This mode allow to

meteorologists, detecting clear air phenomena, such as dry lines, dry microbursts, and wind

shift lines. In clear air mode, the radar products update every 10 minutes. It uses a long pulse

and the radar is operated at a relatively slow scan rate that allows the sampling of five

contiguous elevation angles (0.5° to 4.5°) in a period of 10 minutes. When a radar system

detects precipitation of a specified intensity and extent (30 dBZ), it automatically switches

from clear air to the precipitation mode by using Automatic Mode Switch Menu for two

plans.



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Elevation Angles (°) 0.5, 1.5, 2.5, 3.5, 4.5 Resolution (°) 1.0 Pulse Width (µsec) 2.00 Scan Speed (°/sec) 12.00 Data Z, V, W, ZDR Samples 55 Number of Bins 1200 Bin Spacing (m) 250.0 Max Range (km) 300.0PRF (Hz) 500-375 Unambiguous Velocity (m/sec) 20 (4:3) Processing PPP Data Quality Thresholding T: LOG, Z: LOG&CSR, V: SQI&CSR,

W: SIG&SQI&LOGLOG (dB) 0.8 SIG (dB) 10 CSR (dB) 18 SQI 0.4 Speckle Z on, V on

Table 3: Example of defined parameters for clear air mode volume scan configuration. Surveillance Scan is used to generate PPI at a single elevation close to zero for long range

weather monitoring (e.g., Elevation Angle:0.5°, Max Range:300 km, Pulse Width:2 µsec). It

can be used for winter and summer conditions. As it’s mentioned before, PPI is the fastest of

all radar products and therefore suitable for studying the fast-developing mesoscale storms.

We are most concerned with the PPI scan. The TSMS radars are operated by collecting a

series of surveillance scans at increasing elevation angles. It takes a radar ~ 8 minutes to

collect the data, depending on how many elevation angles are used. The radar then repeats the

cycle.

3. SIGNAL PROCESSING AND RADAR PRODUCT GENERATION

The processing of radar data generally involves two distinct steps. The first step, called signal

processing, is the extraction of raw radar parameters like echo strength (reflectivity) or

Doppler velocity from the radar signals coming out of the receiver. The second step, called

data processing or product generation, is the further processing of raw radar parameters in

order to obtain information that is useful for meteorological or hydrological purposes. In



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general, these two steps are done by different computers, signal processing being done at the

radar site, while product generation can be done everywhere the data are sent to.

Figure 4: Signal processing and radar product generation.

Figure 5: Radar data flow infrastructure.

Every product is associated with a TASK (scanning definition), defines a radar TASK such

as a volume scan, single PPI sweep or sector scan. TASKS collect information from signal

processors and store the raw data on disk. These raw data is used for product generation.

Signal ProcessorRVP 7

RDAWorkstation

RPGWorkstation

Display

Composite WEB Server

RADAR Site

VSAT Terminal

VSAT Terminal

The RDA unit consists of the antenna, transmitter, receiver and signal processor. These components generate/transmit the energy pulses, receive the reflected energy and process the received energy into base data.

The RPG serves as the command centre for the entire system. The RPG processes the digital data and creates the Base and Derived Products, providing clutter filtering and other functions.



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Figure 6: Task configuration tool (from IRIS). To configure the details of the product generation for each product type such as the range and

resolution of the product as well as product-specific information such as the CAPPI heights.

Figure 7: Product configuration tool (from IRIS).



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4. RADAR PRODUCTS

At this point, it should be given short information about reflectivity, velocity, spectrum width

and differential reflectivity. Recall, a number of products are obtained from Doppler weather

radars. These products include radar reflectivity factor, radial velocity, spectrum width and

differential reflectivity data information which are directly observed/measured by Doppler

weather radars.

4.1. Radar Data

4.1.1. Reflectivity

Some degree of transmitted energy (power) is likely to be returned to the radar antenna

(receiver) as a result of backscattering. Reflectivity is simply a measure of how much power

was scattered back to the radar from any targets. Stronger targets have higher levels of

reflectivity and return more energy. Thus, stronger targets have higher reflectivity values; that

is, higher dBZ levels.

dBZ is also related to the number of drops per unit volume and the sixth power of their

diameter (and also it can be related to rainfall rate through an empirical relationship called the

“Z-R relationship”).

Energy backscattered from a target as seen on the radar display, i.e. echo intensities are

displayed as on color in Figure 8 below:

Figure 8: Echo intensities.

This energy is converted into a logarithmic (base10) unit so that a wide range of reflectivity

can be expressed with a short number scale.



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Z=∑Di6 (mm6/m3) Linear Radar Reflectivity Factor

dBZ=10log10Z Logarithmic Radar Reflectivity Factor

Linear Value

Z (mm6/m3)

Logarithm

Log10Z

Decibels

dBZ

1000 3 30

100 2 20

10 1 10

1 0 0

0.1 -1 -10

0.01 -2 -20

0.001 -3 -30

Table 4: The range of radar reflectivity factor.

Radar reflectivity factor can take on a tremendous range of values: 0.001(fog)-40,000,000

mm6/m3 (large hail) (-30 ~ +76 dBZ). Radar reflectivity factor of the clouds which do not

produce rainfall or produce little rainfall is generally low. So, most of the meteorologists do

not interested in very light precipitation. The reflectivity values less than 0 dBZ are not

displayed on the colour scale.

dBZ values are what you typically see on radar displays (e.g., on TV.). General interpretation,

reflectivity values lower than about 35 dBZ are light rain. Typically, light rain is occurring

when the dBZ value reaches 20. Values between 35 and 50 dBZ are moderate rain. And

values above about 50 dBZ are heavy rain. Reflectivity values above about 55 dBZ are

usually hail.

In Table 5 below a guideline on the interpretation of dBZ factors is given in Figure 9.

dBZ Rain Rate Comments 10 ~0.2 Significant but mostly

non-precipitating clouds 20 ~1 Drizzle, very light rain 30 ~3 Light rain 40 ~10 Moderate rain, showers 50 ~50 Heavy rain, thundershowers, some hail possible 60 ~200 Extremely heavy rain, severe thunderstorm, hail likely

Table 5: The interpretation of dBZ factors.

No Precipitation



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Figure 9: The interpretation of dBZ factors on colour figure.

A radar reflectivity image includes one of two colour scales. One scale represents dBZ values

when the radar is in clear air mode (dBZ values from -28 to +28). The other scale represents

dBZ values the radar is in precipitation mode (dBZ values from 0 to 75). (Notice the colour

on each scale remains the same in both operational modes, only the values change). Clear air

mode is more sensitive than precipitation mode.

Figure 10: Echo intensity scales for clear air (left) and precipitation mode (right).

The colour scale on the radar image corresponds the reflectivity values. By clicking on a

given colour in the legend, the corresponding reflectivity values are toggled off and on in the

radar image. The process of toggling the colours allows visual filtering to more easily focus

on higher reflectivity values, which are usually more significant. Another method by the

different radar software, by clicking on an image, the corresponding reflectivity value is

shown.



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Corresponding “dBZ” values of fog and hail: Z=0.001 mm6/m3 (fog) dBZ=10log10Z =10log10(0.001) =10x(-3) dBZ=-30 Definition of this fog: Assume that there is a cloud in a radar scope which has 1,000,000,000 drops and average diameter of the drops is 0,01 mm in 1 m3; For each drops Di

6=0,016 mm6=10-12 mm6 Z=∑Di

6 Z=1,000,000,000 m-3x10-12 mm6 Z=0.001 mm6/m3 Z=156,25 mm6/m3 (heavy rain with some hail possible) dBZ=10log10Z =10log10(156,250) =10x(5.19) dBZ=51,9 Definition of this hail: Assume that there is a cloud in a radar scope which has 10 drops and average diameter of the drops is 5 mm in 1 m3; For each drops Di

6=56 mm6=15625 mm6 Z=10 m-3x15625 mm6 Z=156,25 mm6/m3

4.1.2. Velocity

Until now, we have only considered power measurements with radar. Most modern radars now easily measure velocities of targets. These are Doppler radars. Doppler is a means to measure motion. Doppler radars not only detect and measure the power received from a target, they also measure the motion of the target toward or away from the radar. This is called the “Radial Velocity”. Radial velocity is determined from Doppler frequency shift of the target. Doppler frequency shift caused by a moving target. Moving targets change the frequency of the returned signal. This frequency shift is then used to determine wind speed. Doppler radars routinely measure velocities and used to detect wind speeds, tornadoes, mesocyclones.



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Figure 11: Doppler frequency shift by moving targets.

Motion towards a doppler radar is expressed in negative values and green (cool) colours on a

display screen. Motion away from a Doppler radar is expressed in positive values and red

(warm) colours.

Figure 12: Doppler radial velocities and an example image.



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If the target is moving sideways so that its distance relative to the radar does not change, the

radar will record zero radial velocity for that target.

Figure 13: Another figure of Doppler radial velocities.

4.1.3. Spectrum Width

Spectrum Width data is a measure of dispersion of velocities within the radar sample volume.

In other words, it is the distribution of velocities within a single radar pixel. One pixel on

radar represents a volume within which there can be literally millions of individual

hydrometeors. Each individual hydrometeor will have its own speed and direction of

movement. The radar averages the individual radial velocities with a volume sample to

produce a single average radial velocity that is displayed for that pixel. In a situation, where

shear and turbulence is small within a pixel, the spectrum width will be small. In a situation,

where shear and radial velocity is large within a pixel, the spectrum width will be large. A

technical way of defining spectrum width is the standard deviation of the velocity distribution

within a single pixel.

Figure 14: Spectrum width and its averages. Table 6: Average Spectral Width values.

≥ 8 74

Extreme Severe Moderate

Turbulence

Average Spectral Width (m/sec)

Strong shear and turbulence, thunderstorm



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4.1.4. Differential Reflectivity

Differential reflectivity parameter is a kind of data produced by polarimetric radars. In

general, weather radars send and receive microwaves at one polarization, usually horizontal,

because raindrops are usually oblate. By transmitting and/or receiving radar waves at more

than one polarization, additional information can be obtained on the nature of the targets.

Differential reflectivity is a ratio of the reflected horizontal and vertical power returns.

Amongst other things, it is a good indicator of drop shape. In turn, the shape is a good

estimate of average drop size.

The signals that are received from each polarization channel are averaged separately, and

radar reflectivity factors are determined from each, giving ZH and ZV. The reflectivity

depolarization ratio is defined as:

ZDR=10log10(ZH/ZV)

where ZH and ZV are the linear radar reflectivity factors at horizontal and vertical polarization, respectively. ZDR is measured in decibels.

Figure 15: Dual polarization.



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The precipitation particles of different size and type can be distinguished by using differential

reflectivity ZDR. The shape of raindrops falling in the atmosphere varies from nearly perfect

spheres for small droplets up to a couple of millimetres in diameter to more flattened drops up

to 5 or 6 mm across. These flattened drops give stronger returns at horizontal polarization

than at vertical. Thus, ZDR varies from near zero for spherical droplets to values as large as +5

dB for echoes from large water drops. This added information is useful for refining rainfall

measurements made by radar. Relation of the hydrometeor types and ZDR values are shown in

Table 7.

Target ZDR (dB)

Drizzle 0

Rain 0.5 - 4

Snow, Graupel (-1) - (+1)

Hail ~ 0

Table 7: ZDR values of hydrometeors.

ZDR is also useful for indicating the presence of hail. When hail is present, ZDR often goes to

near zero.

In moderate to heavy rain, the rain drops are large and as they fall they flatten to become

oblate spheroids, giving a stronger echo for horizontal polarization. Raindrop diagram is

shown in Figure 16.

Figure 16: Raindrop diagram.



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Perhaps a more significant result of polarization measurements is the ability to perform

hydrometeor identification (classification), to differentiate liquid water from ice using their

different dielectric properties and to identify various form of ice (snow, hail, crystals).

Figure 17: Hydrometeor types in a radar volume coverage.

Modern polarimetric radars have the capability of successful hydrometeor classification by

obtain Z, ZDR, ΦDP, KDP, ρHV and LDR data. You can see a sample hydrometeor classification

product in Figure 18.



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Figure 18: Hydrometeor classification products.

4.2. Base Products

The following base products are generally available from a Doppler weather radar.

4.2.1. PPI Product

The Plan Position Indicator (PPI) product is a natural radar product. In other words, PPI is the

most common (classic) display of radar data. It is used primarily for weather surveillance

purposes. It is produced in much shorter time than volume scan. So, this product is available

for display immediately on completion of the scan (quick). Therefore, PPI is advantageous



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product for especially airport meteorology services. This is most widely used form of weather

radar display. A typical PPI display is shown in Figure 19. It shows the distribution of the

selected data parameter (Z, R, V, W or ZDR) on a constant elevation angle surface (near to 0°).

PPI product is possible for all elevations at which data are collected.

Figure 19: A PPI product display.

The PPI(Z) is display of reflectivity for a given elevation at all azimuth values.

The PPI(V) gives the radial velocity (for a selected elevation) on a PPI scope (see Figure 20).

The radial wind component towards (-ve) and away (+ve) from the radar site is of some

importance for the tracking of weather systems and in aviation forecasting.



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Figure 20: A PPI(V) product display.

PPI(W), which shows the velocity spectrum width indicating turbulence, is of immense use in

nowcasting the occurrence of microburst, wind shear, etc. for aviation. This product is used

for issuing airfield warnings in some countries based on their experience to nowcast the

possibility of turbulence in the airfield. But, this product is rarely used or shown except for

specialized applications. A typical display of PPI(W) is shown in Figure 21 and the PPI(V) at

the same time is shown in Figure 22.



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Figure 21: PPI(W) product.

Figure 22: PPI(V) product.

4.2.2. RHI Product

RHI is another widely used form of display of base products in the two-dimensional Cartesian

coordinate system, having the curvature corrected range as x-axis and height as y-axis for an

elevation scan at a fixed azimuth. In other words, the horizontal axis is distance from the radar

and the vertical axis is the height. In short, radar is scanned in elevation at a fixed azimuth. It

is excellent for viewing the detailed vertical structure of a storm. In general, you should

schedule the associated RHI TASK through a region of interest. During RHI scanning, the

antenna azimuth is fixed and the elevation is swept, typically from near 0 to 90 degrees to

create a vertical cross-section effect. A typical display of RHI is shown in Figure 23.



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Figure 23: A RHI product.

In addition to this, a similar display with more flexibility of selecting the cut axis is available

from a volume scan of a modern Doppler weather radar which is generally known as Vertical

Cut or XSECT is shown in Figure 24 and Figure 25. It is very useful, because of users can

produce so many cross-section products for each requested time and arbitrary line. The white

arbitrary line on the CAPPI product (bottom) marks the location of the vertical cut (upper) in

Figure 24.

RHI or cross-section?

Both of them have almost similar characteristic. Although RHI gives you a much better

resolution and RHI has always one point fixed to the radar, cross-section can be cut through

any part of the polar volume. So, you can study the two-dimensional structure of the

atmosphere by using RHI or cross-section products. Consequently, due to cross-section

product is a convenient option, it is much more preferred by users.



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Figure 24: A cross-section product (in this case height of the maximum reflectivity is 2,5 km).

Figure 25: A cross-section product.



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4.2.3. CAPPI Product

The Constant Altitude Plan Position Indicator (CAPPI) is a horizontal cut through the

atmosphere; therefore, it requires a volume scan at multiple elevation angles. The number of

angles and their spacing depends on the range and height of the CAPPI you want to produce.

A CAPPI radar image shows precipitation at a nearly constant altitude above ground. In the

case of the 1.5 km CAPPI image, it displays precipitation which is located approximately 1.5

km above the ground.

In other words, a CAPPI product is a slice through a volume scan in a plane parallel to the

earth’s surface at a desired altitude set by users. It is used for surveillance and severe storm

identification. A typical display of CAPPI is shown in Figure 26.

Figure 26: A CAPPI product.

While the CAPPI product is best to see horizontal patterns, it is more likely to be

contaminated by non-meteorological echoes from the ground!



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4.2.4. Echo Top Heights

Echo Top Heights is another special product derived from base reflectivity. Basically, this

product shows the maximum heights of precipitation echoes. The Echo Top Heights product

displays the height (in kilometres) of the highest occurrence of an arbitrary selectable

threshold dBZ contour (30 dBZ in Figure 27) using a volume scan. In other words, an Echo

Top Heights product is the highest altitude for each cell with a data value above a threshold

defined by users. The Echo Top Heights product shows how high the precipitation echoes, or

reflectivities, extend up into the sky. It is an excellent indicator of severe weather and hail.

For example, a 50 dBZ top 1 km above the freezing level can be produced only by a vigorous

convective storm, and is most probably caused by the presence of hail. For air traffic

applications, the search can be made using a lower threshold value, such as 10 dBZ, to

determine the height of surrounding precipitation.

Figure 27: Echo top heights display product for 30 dBZ of selectable threshold value.



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Echo Top Heights product provides forecasters with a valuable “first look” tool. It allows

them to monitor the highest echo tops throughout the radar coverage area. It is very good

display of current weather and make excellent indicators of things (severe storm,

precipitation) to come. For this reason, Echo Top Heights product is of particular interest to

forecasters and aviators. For example, rapidly increasing Echo Top Heights might warn of

storm intensification, while decreasing Echo Top Heights indicates weakening. A rapid

collapse of Echo Top Heights may indicate the onset of downburst conditions at the surface.

4.2.5. Maximum Reflectivity

Maximum Reflectivity product is the maximum reflectivity between two altitudes for each

cell of a volume. In other words, it shows the maximum detected reflectivities (echoes) over

each pixel between user selected heights, and includes East-West and North-South profiles of

the maximum in side panels. The product is based on a volume scan. A minimum and

maximum height may be user-defined and defaults to zero and 30 kilometres (12 km in Figure

29). It is especially useful for depicting areas of severe weather. This product is a useful,

quick surveillance of regions of convective precipitation to locate both infant and mature

thunderstorms.

Figure 28: The schematic diagram of the maximum reflectivities.



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Figure 29: A Maximum reflectivity product display (in this case height is from 0 km to 12 km).

4.3. Derived Products

The following derived products are generally available from a Doppler weather radar.

4.3.1. SRI Product

Firstly, we have to get some information about estimating precipitation with radar. Weather

radars are not able to measure precipitation directly. It was explained at the other lessons that

the radar reflectivity factor Z is directly related to the size of precipitation particles in the

radar echo. If we assume that our radar echo has known distribution of precipitation particles

(i.e., number of drops of different size categories), we can relate the reflectivity factor (Z) to

the rainfall rate (R-mm/hr) in our echo feature:



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Z= ARb

(A and b are constants determined by the assumed drop size distribution)

This kind of equation between reflectivity factor and rain rate is called “Z-R relation”.

Precipitation measurement is done automatically by radar’s software.

The values for A and b vary from season to season and place to place (radar location). Also

the values of A and b predominantly depend on the type of cloud (drop size distribution).

Since the value of A and b will be specific to each radar site configuration, many researchers

have produced a large variety of values A and b.

A and b depend on the distribution and character of precipitation.

Most common Z-R relation is:

Z=200R1.6

by Marshall and Palmer (in 1948). This is used for stratiform rain. It is generally acceptance

in mid-latitude temperature climates to use default value.

Some other typical Z-R relations are:

Z=31R1.71 for orographic rain (Blanchard, 1953)

Z=500R1.5 for thunderstorm (Joss, 1970)

Z=350R1.4 for convective rain

Z=2000R2 for snow (Marshall and Gunn, 1958)

Surface rainfall intensity (SRI) product shows the rainfall intensities based on Z-R relation for

a user-defined layer. The SRI generates an image of the rainfall intensity in a user selectable

surface layer with a constant height above ground. A typical SRI display is shown in Figure

30.



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Figure 30: A SRI product display.

4.3.2. Vertically Integrated Liquid (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.

The VIL product is complied 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 nonsevere storms.

The output shows the estimated precipitation (in millimetres) contained within a user-defined

layer. If the layer height is above the freezing level, high VIL values are an excellent indicator



38

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. A typical VIL display is shown in Figure 31.

Figure 31: A VIL product display.

4.3.3. Surface Precipitation Accumulation

It shows the precipitation estimates at surface level accumulated rainfall during a predefined

period of time. This can be calculated and displayed based on Hourly Rain product. Hourly

Rain uses the previous hour’s CAPPI or SRI data to obtain an estimate of the rainfall that fell

within that hour. Surface Precipitation Accumulation is the rainfall accumulation of the last N

hours, where N is selected by the user. So, it is product of a product. It is obtained from

hourly rainfall accumulation, you can sum any number of individual Hourly Rain product. A

sample surface precipitation accumulation product is shown in Figure 32. You can see the

accumulated rainfall on the radar coverage area for last 6 hours. And also, a typical hourly

surface precipitation accumulation product is shown in Figure 33.



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Figure 32: A surface precipitation accumulation product of the last 6 hours.

Figure 33: An hourly surface precipitation accumulation product.



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4.3.4. Subcatchment Accumulation

The Subcatchment Accumulation product calculates the precipitation accumulation in

subcatchment regions such as watershead areas. It is used for hydrometeorological

applications such as estimating the total rainfall in a river basin for the purpose of flood

forecasting. The subcatchment regions are defined by using LAT/LON vector points and

stored in a file. So, the user-defined subcatchment regions can be displayed on the radar

display. It also includes histogram display. This product can also issue warning if the

precipitation in a subcatchment region exceeds a threshold value. Sample display of

subcatchment accumulation products are shown in Figure 34 and 35.

Figure 34: Subcatchment accumulation product display.



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Figure 35: Subcatchment accumulation product (histogram display).

4.3.5. Wind Speed and Direction

Horizontal wind vectors or uniform wind vectors are displayed as wind speed and direction

with either wind barbs or wind strings, based on the radial velocity data, uniform wind

assumption and the VVP. This product shows estimated winds for a selected layer of the

atmosphere. A typical sample display of horizontal wind vectors is shown in Figure 36. In

addition, the horizontal wind vectors can be displayed as an overlay product (Figure 37 and

38).



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Figure 36: A typical display of horizontal wind vectors at 2 km height.

Figure 37: The horizontal wind vectors displayed as an overlay product on the CAPPI(V) product.



43

Figure 38: The horizontal wind vectors displayed as an overlay product on the maximum reflectivity product.

4.3.6. Vertical Wind Profile

One of the most useful and unique product generated by the Doppler weather radars is the

Velocity Volume Processing. It shows wind velocities at various altitudes above the ground.

In the Velocity Volume Processing product, wind speed and direction (windbarbs) is plotted

as a function of height and time. VVP is a vertical wind profile including wind speed,

direction, divergence, deformation, axis of dilatation, particle velocities and reflectivity versus

height. Display is either time-height cross-section or graphs. This algorithm is similar to the

so-called VAD technique. Various products are shown in the some figures below (Figure 39,

40, 41). You can see the wind barb presentation displays the horizontal wind velocity and

direction of a vertical column above the radar site over the time axis in Figure 41.



44

Figure 39: A sample VVP product.




45


4.3.7. Wind Shear

Wind shear in the atmosphere can be detected by Doppler radars. The radial, azimuthal and

elevational shears can be calculated from base velocity data and used as a tool for generating

appropriate warnings for the aviation community. They can be produced separately or any

combination (Combined Shear). Wind sheer products are especially used for microburst and

mesocyclone detection. An important point that mountain radars are not able to observe to

sufficiently low altitudes immediately above the airports to reliably detect microburst. As it is

known, severe wind shear occurs 100-200 m above ground level. You can see the wind shear

product in Figure 42, and a microburst was detected and issued warning above the airport.



46

Figure 42: A wind shear product.

4.4. Warning Products

Modern Doppler weather radars have the capability of issue various automatic warnings.

Warning products are used for detecting significant weather such as the approach of a severe

storm, hail, turbulence, lightning hazard or flood potential. For example, the occurrence of 45

dBZ at 1.5 km above the freezing level is a good indicator of hail in many mid-latitude

locations. Suppose the freezing level is at 4 km, and you run an Echo Top Heights product for

the 45 dBZ contour. If the Echo Tops product shows 45 dBZ tops at heights greater than 5.5

km, there is a high probability of hail. Because of this general approach, the automatic

warning feature can provide alerts for a wide variety of weather phenomena. The most

important advantage of the warning products is that the forecasters don’t have to spend time

searching every product for significant weather.

Hail warning product scans all storms within the radar coverage area and searches for very

high reflectivity values located above the freezing level. It then provides an indication of

which storms are expected to produce hail. All storms are examined for hail potential, and



47

then categorized accordingly. While the algorithm is not foolproof, it provides a valuable first

guess. The hail warning product provides estimates on the probability of hail, probability of

severe hail, and maximum expected hail size. This product provides an extremely simple

display by using a warning text symbol like HAIL, TRW (thunderstorm warning), MBW

(microburst warning). You can see a hail warning product in Figure 43.

Some examples of warning criteria are summarized below:

Hail Detection: [45 dBZ Echo Top Heights > 1.5 km above freezing level] over an area of 10

km2

Wind Shear Detection: [Wind Shear > 10 m/s/km at 0.5° EL] .AND. [ ... at 0.7° EL] over an

area of 3 km2

(for sample product, see in Section 4.3.7 in Figure 42)

Storm Turbulence Detection: [Spectrum Width > 6 m/s] .AND. [Reflectivity > 20 dBZ]

over an area of 10 km2

The spectrum width data is used for storm turbulence detection basically.

Precipitation Surveillance Detection: [1.5 to 14 km VIL > 1mm] over an area of 10 km2

(An automatic warning issue is provided at the beginning of the precipitation over an area

especially by using VIL product).

Severe Storm Detection or Lightning Hazard: [1.5 to 15 km VIL > 10 mm] .AND. [10

dBZ Echo Top Heights > 8 km] over an area of 10 km2

(An automatic warning issue is provided especially by using VIL and Echo top heights

product).

Flash Flood Warning: [Hourly Rainfall or N-Hour Rainfall > 5 mm] over an area of 25 km2

(If a criterion for flash flood is determined over an area, an automatic warning issue is provided).



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Figure 43: A hail warning product.

4.5. Tracking Products

Many developed radar softwares have tracking and forecasting products which are used to

identify and track storm cells (centroids). These products show the calculated (estimated)

motion vectors of the centroids. With every new scan (when new data comes in), the display

of the identified cells is updated. The tracking product display contains current cells, trace

image with cells of the previous scans and forecast images (see Figure 44). You can see a

sample tracking product in Figure 45.

Figure 44: Track with two centroids.



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Figure 45: A sample tracking product.

4.6. Dual Polarization Products

As differential reflectivity was explained in the Section 4.1.4, modern polarimetric radars

have the capability of successful hydrometeor classification by obtain Z, ZDR, ΦDP, KDP, ρHV

and LDR data. You can see a sample hydrometeor classification product in Figure 18. The

detailed information regarding ZDR, ΦDP, KDP, ρHV and LDR data was given in the part of the

Radar Variables. Some examples of dual polarization products are shown in Figure 46. When

you see the rain area in Figure 46, hydrometeor types can be determined by comparing with

dual polarization data values each other (see Table 8).



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Figure 46: Dual polarization products.

Species ZHH

(dBZ) ZDR (dB)

ρHV KDP (deg/km)

Temp. (C)

Drizzle 10 to 25 0.2 to 0.7 >0.97 0 to 0.06 >-10

Rain 25 to 60 0.5 to 4 >0.95 0 to 20 >-10

Snow (dry, low density) -10 to 35 -0.5 to 0.5 >0.95 -1 to 1 < 0

Snow (dry, high density)

-10 to 35 0.0 to 1 >0.95 0.0 to 0.4 < 0

Snow (wet, melting) 20 to 45 0.5 to 3 0.5 to 0.9 0 to 1 0 to 5

Graupel (dry) 20 to 35 -0.5 to 1 >0.95 0 to 1 < 0

Graupel (wet) 30 to 50 -0.5 to 2 >0.95 0 to 3 -15 to 5

Hail, small wet < 2 cm 50 to 60 -0.5 to 0.5 > 0.92 -1 to 1 -15 to 5

Hail, large wet> 2 cm 55 to 65 -1 to 0.5 0.90 to 0.92

-1 to 2 -25 to 5

Rain & hail 45 to 80 -1 to 6 >0.9 0 to 20 -10 to 10

Table 8: Input parameters and output types of a sample hydrometeor classification study.



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5 REFERENCES:

1. Radar for Meteorologists, Ronald E. Rinehart August 1997.

2. Doppler Radar and Weather Observations, Doviak R.J. and Zrnic D.S.

3. Radar for Meteorologists: A Course of Training, Radar-Info, Karlsruhe, Gysi, H.,

1998.

4. Doppler Radar-A detecting tool and measuring instrument in meteorology,

India Meteorological Department, New Delhi, Bhatnagar, A. K., Rao, P. Rajesh,

Kalyanasundaram, S., Thampi, S. B., Suresh, R., and Gupta, J.P., 2003.

5. Images in Weather Forecasting, Cambridge University Press, Great Britain,

Bader, M. J., Forbes, G. S., Grant J. R., Lilley R. B. E., and Waters A. J., 1995.

6. IRIS Radar Manual, Version 7.30, SIGMET, Inc. Massachusetts, 2002.

7. IRIS Product & Display Manual, Version 7.30, SIGMET, Inc. Massachusetts,

2002.

8. Doppler Weather Radar System- Meteor 1000CUser Manuel and Documentation-

Gematronik GmbH, 12.July.2001.

9. Doppler Weather Radar System, Enterprise Electric Corp.

10. Radar Lecture Notes and Articles available in internet

11. Booklets, reports and guidelines published by WMO

12. Technical Brochures of Radar Manufacturers

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