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A weather radar, or weather surveillance radar (WSR), is a type of radar used to locate precipitation, calculate its motion, estimate its type (rain, snow, hail, etc.), and forecast its future position and intensity.

Modern weather radars are mostly pulse-Doppler radars, capable of detecting the motion of rain droplets in addition to intensity of the precipitation. Both types of data can be analyzed to determine the structure of storms and their potential to cause severe weather.

## History

World War II, military radar operators noticed noise in returned echoes due to weather elements like rain, snow, and sleet. Just after the war, military scientists returned to civilian life or continued in the Armed Forces and pursued their work in developing a use for those echoes. In the United States, David Atlas, for the Air Force group at first, and later for MIT, developed the first operational weather radars. In Canada, J.S. Marshall and R.H. Douglas formed the "Stormy Weather Group" in Montreal. Marshall and his doctoral student Walter Palmer are well known for their work on the drop size distribution in mid-latitude rain that led to understanding of the Z-R relation, which correlates a given radar reflectivity with the rate at which water is falling on the ground. In the United Kingdom, research continued to study the radar echo patterns and weather elements such as stratiform rain and convective clouds, and experiments were done to evaluate the potential of different wavelengths from 1 to 10 centimetres.

In 1953, Donald Staggs, an electrical engineer working for the Illinois State Water Survey, made the first recorded radar observation of a "hook echo" associated with a tornadic thunderstorm.

Between 1950 and 1980, reflectivity radars, which measure position and intensity of precipitation, were built by weather services around the world. The early meteorologists had to watch a cathode ray tube. During the 1970s, radars began to be standardized and organized into networks. The first devices to capture radar images were developed. The number of scanned angles was increased to get a three-dimensional view of the precipitation, so that horizontal cross-sections (CAPPI) and vertical ones could be performed. Studies of the organization of thunderstorms were then possible for the Alberta Hail Project in Canada and National Severe Storms Laboratory (NSSL) in the US in particular.

The NSSL, created in 1964, began experimentation on dual polarization signals and on Doppler effect uses. In May 1973, a tornado devastated Union City, Oklahoma, just west of Oklahoma City. For the first time, a Dopplerized 10-cm wavelength radar from NSSL documented the entire life cycle of the tornado. The researchers discovered a mesoscale rotation in the cloud aloft before the tornado touched the ground : the tornadic vortex signature. NSSL’s research helped convince the National Weather Service that Doppler radar was a crucial forecasting tool. The Super Outbreak of tornadoes on April 3-4, 1974 and their devastating destruction might have helped to get funding for further developments.

After 2000, research on dual polarization technology has moved into operational use, increasing the amount of information available on precipitation type (e.g. rain vs. snow). "Dual polarization" means that microwave radiation which is polarized both horizontally and vertically (with respect to the ground) is emitted. Wide-scale deployment is expected by the end of the decade in some countries such as the United States, France, and Canada.

Since 2003, the U.S. National Oceanic and Atmospheric Administration has been experimenting with phased-array radar as a replacement for conventional parabolic antenna in order to provide more time resolution in atmospheric sounding. This would be very important in severe thunderstorms as their evolution can be better evaluated with more timely data.

Also in 2003, the National Science Foundation established the Engineering Research Center for Collaborative Adaptive Sensing of the Atmosphere, "CASA", a multidisciplinary, multi-university collaboration of engineers, computer scientists, meteorologists, and sociologists to conduct fundamental research, develop enabling technology, and deploy prototype engineering systems designed to augment existing radar systems by sampling the generally undersampled lower troposphere with inexpensive, fast scanning, dual polarization, mechanically scanned and phased array radars.

## How a weather radar works

A radar beam spreads out as it moves away from the radar station, covering an increasingly large volume.

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 10 cm are approximately ten times the diameter of the droplets or ice particles of interest, because Rayleigh scattering occurs at these frequencies. This means that part of the energy of each pulse will bounce off these small particles, back in the direction of the radar station.

Shorter wavelengths are useful for smaller particles, but the signal is more quickly attenuated. Thus 10 cm (S-band) radar is preferred but is more expensive than a 5 cm C-band system. 3 cm X-band radar is used only for very short distance purposes, and 1 cm Ka-band weather radar is used only for research on small-particle phenomena such as drizzle and fog.

Radar pulses spread out as they move away from the radar station. This means that the region of air any given pulse is moving through is larger for areas farther away from the station, and smaller for nearby areas, decreasing resolution at far distances. At the end of a 150–200 km sounding range, the volume of air scanned by a single pulse might be on the order of a cubic kilometer. This is called the pulse volume

The volume of air that a given pulse takes up at any point in time may be approximately calculated by the formula \, {v = h r^2 \theta^2}, where v is the volume enclosed by the pulse, h is pulse width (in e.g. meters, calculated from the duration in seconds of the pulse times the speed of light), r is the distance from the radar that the pulse has already traveled (in e.g. meters), and \,\theta is the beam width (in radians). This formula assumes the beam is symmetrically circular, "r" is much greater than "h" so "r" taken at the beginning or at the end of the pulse is almost the same, and the shape of the volume is a cone frustum of depth "h".

### Listening for return signals

Between each pulse, the radar station serves as a receiver and listens for return signals from particles in the air. The duration of the "listen" cycle is on the order of a millisecond, which is a thousand times longer than the pulse duration. The length of this phase is determined by the need for the microwave radiation (which travels at the speed of light) to propagate from the detector, to the weather target, and back again, a distance which could be several hundred kilometers. The horizontal distance from station to target is calculated simply from the amount of time that lapses from the initiation of the pulse to the detection of the return signal. (The time is converted into distance by multiplying by the speed of light). If pulses are emitted too frequently, the returns from one pulse will be confused with the returns from previous pulses, resulting in incorrect distance calculations.

### Determining height

Echos height above ground

Assuming the Earth is round, with knowledge of the variation of the index of refraction through air and the distance to the target, one can calculate the height above ground of the target. After each scanning rotation, the antenna elevation is changed for the next sounding. This scenario will be repeated on many angles in order to scan all the volume of air around the radar within the maximum range. Usually, this scanning strategy is completed within 5 to 10 minutes in order to have data within 15 km above ground and 250 km distance of the radar.

Due to the Earth curvature and change of index of refraction with height, the radar cannot “see” below the height above ground of the minimal angle or closer to the radar than the maximal one. This image shows the height of a series of typical angles done by a 5 cm weather radar in Canada. They range from 0.3 to 25 degrees.

### Calibrating intensity of return

Because the targets are not unique in each volume, the radar equation has to be developed beyond the basic one:

P_r = \left [P_t{{ G^2 \lambda^2 \sigma^0}\over{{(4\pi)}^3 R^4}} \right] \propto \frac {\sigma^0} {R^4}
where \,P_r is received power, \,P_t is transmitted power, \,G_t is the gain of the transmitting antenna, \,\lambda is radar wavelength, \,\sigma is the radar cross section of the target and \,R is the distance from transmitter to target.

In this case, we have to add the cross sections of all the targets:
\sigma^0 = \bar \sigma^0 = V \sum \sigma^0_j = V \eta
:\begin{cases} V\quad= scanned\ volume \\ \qquad= pulse\ length\ \ X\ beam\ width \\ \qquad= \left[\frac {c\tau}{2} \right] \left[\frac {\pi R^2 \theta^2}{4} \right] \end{cases}

where \,c is the light speed, \,\tau is temporal duration of a pulse and \,\theta is the beam width in radians.

In combining the two equations :

P_r = \left [P_t{{ G^2 \lambda^2 }\over{{(4\pi)}^3 R^4}} \right] \left[\frac {c\tau}{2} \right] \left[\frac {\pi R^2 \theta^2}{4} \right] \eta = \left [P_t \tau G^2 \lambda^2 \theta^2 \right] \left[\frac {c}{512(\pi^2)} \right] \frac {\eta} {R^2}

P_r \propto \frac {\eta} {R^2}

Notice that the return now varies inversely to \, R^2 instead of \,R^4. In order to compare the data coming from different distances from the radar, one has to normalize them with this ratio.

## Data types

### Reflectivity (in decibel or dBZ)

Return echoes from targets, reflectivity, are analyzed for their intensities in order to establish the precipitation rate in the scanned volume. The wavelengths used (1 to 10 cm) ensure that this return is proportional to the rate because they are within the validity of Rayleigh scattering which states that the targets must be much smaller than the wavelength of the scanning wave (by a factor of 10).

Reflectivity perceived by the radar (Ze) varies by the 6th power of the rain droplets' diameter (D), the square of the dielectric constant (K) of the targets and the drop size distribution (e.g. N[D] of Marshall-Palmer) of the drops. This gives a truncated Gamma function,
of the form:


Z_e = \int_{0}^{Dmax} |K|2 N0e -\Lambda D D6dD

Precipitation rate (R), on the other hand, is equal to the number of particles, their volume and their fall speed (v[D]) as:

R = \int_{0}^{Dmax} N0e-\Lambda D (\piD3/6) v(D)dD

So Ze and R have similar functions that can be resolved giving a relation between the two of the form:

Z = aRb

Where a and b depend on the type of precipitations (snow, rain, convective or stratiform) which have different \Lambda, K, N0 and v.

• As the antenna scans the atmosphere, on every angle of azimuth it obtains a certain number of return from each targets encountered. Reflectivity is then averaged for that target in order to have a better data set.

• Since variation in diameter and dielectric constant of the targets can lead to large variability in power return to the radar, reflectivity is expressed in dBZ (10 times the logarithm of the ratio of the echo to a standard 1 mm diameter drop filling the same scanned volume).

Radar returns are usually described by colour or level. The colours in a radar image normally range from blue or green for weak returns, to red or magenta for very strong returns. The numbers in a verbal report increase with the severity of the returns.

For example, the U.S. National Doppler Radar sites use the following scale for different levels of reflectivity:[69415]

• magenta: 65 dBZ (extremely heavy precipitation)
• red: 52 dBZ
• yellow: 36 dBZ
• green: 20 dBZ (light precipitation)

Strong returns (red or magenta) may indicate not only heavy rain but also thunderstorms, hail, strong winds, or tornadoes, but they need to be interpreted carefully, for reasons described later in this article.

#### Aviation conventions

When describing weather radar returns, pilots, dispatchers, and air traffic controllers will typically refer to three return levels:[69416]

• level 1 corresponds to a green radar return, indicating usually light precipitation and little to no turbulence, leading to a possibility of reduced visibility.
• level 2 corresponds to a yellow radar return, indicating moderate precipitation, leading to the possibility of very low visibility, moderate turbulence and an uncomfortable ride for aircraft passengers.
• level 3 corresponds to a red radar return, indicating heavy precipitation, leading to the possibility of thunderstorms and severe turbulence and serious structural damage to the aircraft.

Aircraft will try to avoid level 2 returns when possible, and will always avoid level 3 unless they are specially-designed research aircraft.

### Velocity

Idealized example of Doppler output.
Approaching velocities are in blue and receding velocities are in red.
Notice the sinusoidal variation of speed when going around the display at a particular range.

#### Pulse pair

Any rain drops or snow flakes in motion affect the frequency of the returned radar beam according to the Doppler effect. With velocities of less than 70 m/s (150 miles/h) for weather echos and radar wavelength of 10 cm, it amounts to only 10−5%. This difference is too small to be noted by electronic instruments. However, as the targets move slightly between each pulse, the returned wave has a noticeable phase difference or phase shift from pulse to pulse.

Doppler weather radars are using this phase difference (pulse pair difference) to calculate the precipitation's motion. The intensity of the successively returning pulse from the same scanned volume where targets have slightly moved is :

I = I_0 Sin \left(\frac{4\pi (x_0 + v \Delta t)}{\lambda}\right) = I_0 Sin \left(\Theta_0 + \Delta\Theta\right)\begin{cases} x = \mathrm{distance\ radar\ to\ target} \\ \lambda = \mathrm{radar\ wavelength} \\ \Delta t = \mathrm{time\ between\ two\ pulses} \end{cases}

So \Delta\Theta = \left(\frac{4\pi v \Delta t}{\lambda}\right)

v = target speed = \frac{\lambda\Delta\Theta}{4\pi \Delta t}

This speed is called the radial Doppler velocity because it gives only the radial variation of distance versus time between the radar and the target. The real speed and direction of motion has to be extracted by the process described below.

#### Doppler dilemma

Maximum range from reflectivity (red) and unambiguous Doppler velocity range (blue) with pulse repetition frequency.

The phase between pulse pairs can vary from -\pi and +\pi, so the unambiguous Doppler velocity range is

Vmax = \pm\frac{\lambda}{4\Delta t}

This is called the Nyquist velocity. This is inversely dependent on the time between successive pulses: the smaller the interval, the larger is the unambiguous velocity range. However, we know that the maximum range from reflectivity is directly proportional to\Delta t:

x = \frac{c\Delta t}{2}

The choice becomes increasing the range from reflectivity at the expense of velocity range, or increasing the latter at the expense of range from reflectivity. In general, the useful range compromise is 100 to 150 km for reflectivity. This means for a wavelength of 5 cm, like on the image, an unambiguous velocity range of 8.3 to 12.5 m/s but the double for a 10 cm radar like the NEXRAD.

Some techniques using two alternating pulse repetition frequencies (PRF) permit to extend the Doppler range. The velocities noted with the first pulse rate could be equal or different with the second. For instance, if the maximum velocity with a certain rate is 10 m/s and the one with the other rate is 15 m/s. The data coming from both will be the same up to 10 m/s and differ afterward. It is then possible to find a mathematical relation between the two returns and calculate the real velocity beyond the limitation of the two PRF.

#### Doppler interpretation

Radial component of real winds when scanning on 360 degrees.
If one thinks of an autumn rain uniformly filling the radar area coverage and moving from west to east, one notes that a radar beam pointing west will "see" the raindrops moving toward itself, while a beam pointing east will "see" the drops moving away. On the other hand, looking north or south, since there is no motion toward the radar in those directions, the radial velocity is null.

As the beam is scanning 360 degrees around the radar, data will comes from all those angles and be the radial projection of the actual wind on the individual angle. The intensity pattern formed by this scan can be represented by a cosine curve, as seen on the right. One can then calculate the direction and the strength of the motion of particles as long as there is enough coverage on the radar screen.

However, the rain drops are falling. As the radar only sees the radial component and has a certain elevation from ground, the radial velocities are contaminated by some fraction of the falling speed. This component is negligible in small elevation angles, but must be taken into account for higher scanning angles.

### Polarization

liquid hydrometeors have a larger horizontal axis due to the drag coefficient of air while falling (water droplets). This causes the water molecule dipole to be oriented in that direction so radar beams are generally polarized horizontally to receive the maximal return.

If we decide to send simultaneously two pulses with orthogonal polarization: vertical and horizontal, we receive two sets of data proportional to the two axis of the droplets that are independent:

* Differential Reflectivity (Zdr) – The differential reflectivity is a ratio of the reflected horizontal and vertical power returns. Among other things, it is a good indicator of drop shape and drop shape is a good estimate of average drop size.
* Correlation Coefficient (ρhv)– A statistical correlation between the reflected horizontal and vertical power returns. High values, near one, indicate homogeneous precipitation types, while lower values indicate regions of mixed precipitation types, such as rain and snow, or hail.
* Linear Depolarization Ratio (LDR) – This is a ratio of a vertical power return from a horizontal pulse or a horizontal power return from a vertical pulse. It can also indicate regions where there is a mixture of precipitation types.
* Specific Differential Phase (θdp) – The specific differential phase is a comparison of the returned phase difference between the horizontal and vertical pulses. This change in phase is caused by the difference in the number of wave cycles (or wavelengths) along the propagation path for horizontal and vertically polarized waves. It should not be confused with the Doppler frequency shift, which is caused by the motion of the cloud and precipitation particles. Unlike the differential reflectivity, correlation coefficient and linear depolarization ratio, which are all dependent on reflected power, the specific differential phase is a "propagation effect." It is a very good estimator of rain rate and is not affected by attenuation.

With this new knowledge added to the reflectivity, velocity, and spectrum width produced by Doppler weather radars, researchers have been working on developing algorithms to differentiate precipitation types, non-meteorological targets, and to produce better rainfall accumulation estimates. In the U.S., NCAR and NSSL have been world leaders in this field.

For more details:

US polarization development

McGill University operational output

## Main types of radar outputs

All data from radar scans are displayed according to the need of the users. Different outputs have been developed through time to reach this. Here is a list of common and specialized outputs available.

### Plan position indicator

Thunderstorm line viewed in reflectivity (dBZ) on a PPI (NOAA)
data are obtained one angle at a time, the first way of displaying them has been the Plan Position Indicator (PPI) which is only the layout of radar return on a two dimensional image. One has to remember that the data coming from different distances to the radar are at different heights above ground.

This is very important as a high rain rate seen near the radar is relatively close to what reach the ground but what is seen from 160 km (100 miles) away is about 1.5 km above ground and could be far different from the amount reaching the surface. It is thus difficult to compare weather echoes at different distance from the radar.

PPIs are afflicted with ground echoes near the radar as a supplemental problem. These can be misinterpreted as real echoes. So other products and further treatments of data have been developed to supplement its shortcomings.

USAGE: Reflectivity, Doppler and polarimetric data can use PPI.

N.B.: In the case of Doppler data, two points of view are possible: relative to the surface or the storm. When looking at the general motion of the rain to extract wind at different altitudes, it is better to use data relative to the radar. But when looking for rotation or wind shear under a thunderstorm, it is better to use the storm relative images that subtract the general motion of precipitation leaving the user to view the air motion as if he would be sitting on the cloud.

### Constant Altitude Plan Position Indicator

The zigzag represent data angles used to make CAPPIs at 1.5 & 4 km of altitude

To avoid some of the problems on PPIs, the CAPPI or Constant Altitude Plan Position Indicator has been developed by researchers in Canada. It is basically a horizontal cross-section through radar data. This way, one can compare precipitation on an equal footing at difference distance from the radar and avoid ground echoes. Although data are taken at a certain height above ground, a relation can be inferred between ground stations reports and the radar data.

CAPPIs call for a large number of angles from near the horizontal to near the vertical of the radar in order to have a cut that is as close as possible at all distance to the height needed. But even then, after a certain distance, there isn’t any angle available and the CAPPI becomes the PPI of the lowest angle. The zigzag line on the angles diagram above shows the data used to produce a 1.5 and 4 km height CAPPIs. Notice that the section after 120 km is using the same data.

Usage
Since the CAPPI uses the closest angle to the desired height at each point from the radar, the data can originate from slightly different altitudes, as seen on the image, in different points of the radar coverage. It is therefore crucial to have a large enough number of sounding angles to minimize this height change. Furthermore, the type of data must be changing relatively gradually with height in order to produced an image that is not noisy.

Reflectivity data being relatively smooth with height, CAPPIs are mostly used for displaying them. Velocity data, on the other hand, can change rapidly in direction with height and CAPPIs of them are not common. It seems that only McGill University is producing regularly Doppler CAPPIs with the 24 angles available on their radar. However, some researchers have published papers using velocity CAPPIs to study tropical cyclones and development of NEXRAD products. Finally, polarimetric data are recent and often noisy. There doesn't seem to have regular use of CAPPI for them although the SIGMET company offer a software capable to produce those type of images.

Real time examples:

### Vertical composite

Another solution to the PPI problems is to produce images of the maximum reflectivity in a layer above ground. This solution is usually taken when the number of angles available is small or variable. The American National Weather Service is using such Composite as their scanning scheme can vary from 4 to 14 angles, according to their need, which would make very coarse CAPPIs. The Composite make sure that no strong echo is missed in the layer and a treatment using Doppler velocities eliminate the ground echoes. Comparing base and composite products, one can locate virga and updrafts zones.

Real time example: NWS Burlington radar, one can compare the BASE and COMPOSITE products

### Accumulations

24 hours rain accumulation on the Val d'Irène radar in Eastern Canada.
Notice the zones without data in the East and Southwest caused by radar beam blocking from mountains.
One of the main use of radar is to be able to assess the amount of precipitations fallen over large basins for hydrological purpose. For instance, river flood control, sewer management and dam construction are all areas where planners want accumulation data. It ideally completes surface stations data which they can use for calibration.

To produce radar accumulations, we have to estimate the rain rate over a point by the average value over that point between one PPI, or CAPPI, and the next; then multiply by the time between those images. If one wants for a longer period of time, one has to add up all the accumulations from images during that time.

### Echotops

Aviation is a heavy user of radar data. One map particularly important in this field is the Echotops for flight planning and avoidance of dangerous weather. Most country weather radars are scanning enough angles to have a 3D set of data over the area of coverage. It is relatively easy to estimate the maximum altitude at which precipitation is found within the volume. However, those are not the tops of clouds as they extend to higher altitudes than the precipitation.

### Vertical cross sections

To know the vertical structure of clouds, in particular thunderstorms or the level of the melting layer, a vertical cross sections product of the radar data is available to meteorologist. This is done by displaying only the data along a line, from coordinates A to B, taken from the different angles scanned.

### Range Height Indicator

When a weather radar is scanning in only one direction vertically, it obtains high resolution data along a vertical cut of the atmosphere. The output of this sounding is called a Range Height Indicator (RHI) which is excellent for viewing the detailed vertical structure of a storm. This is different from the vertical cross section mentioned above by the fact that the radar is making a vertical cut along specific directions and does not scan over the entire 360 degrees around the site. This kind of sounding and product is only available on research radars.

Over the past few decades, radar networks have been extended to allow the production of composite views covering large areas. For instance, all major countries (e.g., the United States, Canada, much of Europe) produce images that include all of their radars. This is not as trivial a task.

In fact, such a network can consist of different types of radar with different characteristics like beam width, wavelength and calibration. These differences have to be taken into account when matching data across the network, particularly to decide what data to use when two radars cover the same point. If one uses the stronger echo but it comes from the most distant radar, one uses returns that are from higher altitude coming from rain or snow that might evaporate before reaching the ground (virga). If one uses data from the closest radar, it might be attenuated passing through a thunderstorm. Composite images of precipitations using a network of radars are made with all those limitations in mind.

Here are some national radar networks :

### Automatic algorithms

To help meteorologists to spot dangerous weather, mathematical algorithms have been introduced in the weather radar treatment programs. These are particularly important in the analyzing the Doppler velocity data as they are more complex. The polarization data will even need more algorithms.

Main algorithms for reflectivity:
• Vertically Integrated Liquid (VIL) is an estimate of the total mass of precipitation in the clouds.
• VIL Density is VIL divided the heigh of the cloud top. It is a clue to the possibly of large hail in thunderstorms.
• Potential wind gust, which can estimate the winds under a cloud (a downdraft) using the VIL and the height of the echotops (radar estimated top of the cloud) for a given storm cell.
• Hail algorithms that estimates the presence and its potential size.

Main algorithms for Doppler velocities:
• Mesocyclone detection: it is triggered by a velocity change over a small circular area. The algorithm is searching for a "doublet" of inbound/outbound velocities with the zero line of velocities, between the two, along a radial line from the radar. Usually the mesocyclone detection must be found on two or more stacked progressive tilts of the beam to be significative of rotation into a thunderstom cloud.
• TVS or Tornado Vortex Signature algorithm is essentially a mesocyclone with a large velocity threshold found through many scanning angles. This algorithm is used in NEXRAD to indicate the possibility of a tornado formation.
• Wind shear in low levels. This algorithm detects variation of wind velocities from point to point in the data and looking for a doublet of inbound/outbound velocities with the zero line perpendicular to the radar beam. The wind shear is associated with downdraft, (downburst and microburst), gust fronts and turbulence under thunderstorms.
• VAD Wind Profile (VWP) is a display that estimates the direction and speed of the horizontal wind at various upper levels of the atmosphere, using the technique explained in the Doppler section.

### Animations

PPI reflectivity loop(in dBZ) showing the evolution of a hurricane (NOAA)

The animation of radar products can show the evolution of reflectivity and velocity patterns. The user can extract information on the dynamics of the meteorological phenomena, including the ability to extrapolate the motion and observe development or dissipation. This can also reveal non-meteorological artifacts (false echoes) that will be discussed later.

## Limitations and artifacts

Radar data interpretation depends on many hypotheses about the atmosphere and the weather targets. They are :

• International Standard Atmosphere.
• Targets small enough that they obey the Rayleigh scattering so the return is proportional to the precipitation rate.
• The volume scanned by the beam is full of meteorological targets (rain, snow, etc..), all of the same variety and in a uniform concentration.
• No attenuation
• No amplification
• Return from side lobes of the beam are negligible.
• The beam is close to a Gaussian function curve with power decreasing to half at half the width.
• The outgoing and returning waves are both polarized similarly.
• There is no return from multiple reflections.

One has to keep in mind that these hypotheses are not necessarily met in many circumstances. One has to be able to recognize the truth from the false echoes.

### Anomalous propagation (non-standard atmosphere)

The first assumption is that the radar beam is moving through air that cools down at a certain rate with height. The position of the echoes depend heavily on this hypothesis. However, the real atmosphere can vary greatly from the norm.

#### Super refraction

It is very common to have temperature inversions forming near the ground, for instance air cooling at night while remaining warm aloft. As the index of refraction of air increases the radar beam bends toward the ground instead of continuing upward. Eventually, it will hit the ground and be reflected back toward the radar. The processing program will then wrongly place the return echoes at the height and distance it would have been in normal conditions.

This type of false return is relatively easy to spot on a time loop if it is due to night cooling or marine inversion as one sees very strong echoes developing over an area, spreading in size laterally but not moving and varying greatly in intensity. However, inversion of temperature exists ahead of warm fronts and the abnormal propagation echoes are then mixed with real rain.

The extreme of this problem is when the inversion is very strong and shallow, the radar beam reflects many times toward the ground as it has to follow a waveguide path. This will create multiple bands of strong echoes on the radar images.

#### Under refraction

On the other hand, if the air is unstable and cools faster than the standard atmosphere with height, the beam ends up higher than expected. This places the precipitation at a much higher altitude than it actually is. This situation is very difficult to spot.

### Non-Rayleigh targets

If we want to reliably estimate the precipitation rate, the targets have to be 10 times smaller than the radar wave according to Rayleigh scattering. This is because the water molecule has to be excited by the radar wave in order to give a return. This is relatively true for rain or snow as 5 or 10 cm radars are used.

However, for very large hydrometeors, since the wavelength is on the order of stone, the return levels off according to Mie theory. A return of more than 55 dBZ is likely to come from hail but won’t vary proportionally to the size. On the other hand, very small targets, like cloud droplets, are too small to be excited and don’t give a recordable return on common weather radars.

### Resolution and partially filled scanned volume

As demonstrated at the start of the article, radar beams have a physical dimension and data are sampled every degree, not continuously, along each angle of elevation. This results in an averaging of the values of the returns for reflectivity, velocities and polarization data on the resolution volume scanned.

In the figure to the left, at the top is a view of a thunderstorm taken by a wind profiler as it was passing overhead. This is like a vertical cross section through the cloud with 150 m vertical and 30 m horizontal resolution. We can see that the reflectivity has large variations in a short distance. Now compare this with a simulated view of what a regular weather radar would see at 60 km (40 miles) at the bottom. Everything has been smoothed out. Not only the coarser resolution of the radar blur the image but the sounding incorporate area that are echo free, thus extending the thunderstorm beyond its real boundaries.

This shows how the output of weather radar is only an approximation of the reality. The image to the right compare real data from two radars almost colocated. The TWDR has about half the beamwidth of the other and one can see twice more details than with the NEXRAD.

Naturally, resolution can be improved by newer equipment but some things cannot. As mentioned previously, the volume scanned increases with distance so the possibility that the beam is only partially filled increases too. This leads to underestimation of the precipitation rate at larger distances and fools the user into thinking that rain is lighter as it moves away.

### Beam geometry

The radar beam is not like a laser but has a distribution of energy similar to the diffraction pattern of a light passing through a slit. This is because the wave is transmitted to the parabolic antenna through a slit in the wave-guide at the focal point. Most of the energy is at the center of the beam and decreases along a curve close to a Gaussian function on each side as mentioned before. However, there are secondary peaks of emission that will sample the targets at off-angles from the center. All is done to minimize the power sent by those lobes but they are never zero.

When a secondary lobe hits a very reflective target, like a mountain or a strong thunderstorm, some of the energy is sent back to the radar. This energy is relatively weak but arrives at the same time the central peak is illuminating a different azimuth. The echo is thus misplaced by the processing program. This has the effect of actually broadening the real weather echo making a smearing of weaker values on each side of it. This causes the user to overestimate the extent of the real echoes.

 Idealized energy distribution of a radar beam (Central lobe at 0 and secondary lobes on each side)

### Non weather targets

There is more than rain and snow in the sky. Other objects can be misinterpreted as rain or snow by weather radars. Insects and arthropods are swept along by the prevailing winds, while birds follow their own course. As such, fine line patterns within weather radar imagery, associated with converging winds, are dominated by insect returns. Bird migration, which tends to occur overnight within the lowest of the Earth's atmosphere, contaminates wind profiles gathered by weather radar, particularly the WSR-88D, by increasing the environmental wind returns by to Other objects within radar imagery include::

• Thin metal strips (chaff) dropped by military aircraft to fool enemies.
• Solid obstacles such as mountains, buildings, and aircraft.
• Ground and sea clutter.
• Reflections from buildings if the radar is close enough to a city (called urban spikes).

Each of them has their own characteristics that make it possible to distinguish them to the trained eye but they may fool a layman. It is possible to eliminate some of them with post-treatment of data using reflectivity, Doppler, and polarization data.

### Wind farms

The rotating blades of windmills on modern wind farms can return the radar beam to the radar if they are in its path. Since the blade are moving, the echoes will have a velocity and can be mistaken for real precipitation. The closer the wind farm is to the radar, the more important is this artefact as the combined signal from many towers is stronger. If the conditions are right, the radar can even see a douplet of toward and away velocities that can generate false positives for the tornado vortex signature algorithm on weather radar, as happened in 2009 in Dodge City, Kansas.

Finally, the windmills are blocking a part of the radar beam and thus attenuating the return from precipations in the lee of them, leading to underestimation.

### Attenuation

Micro-waves used in weather radars can be absorbed by rain, depending on the wavelength used. For the 10 centimeter radars, this attenuation is negligible. That is the reason why countries with high water content storms are using 10 centimeter wavelength like in the United States with NEXRAD. The cost of a larger antenna, klystron and other related equipments is offset by this benefit.

For a 5 centimeter radar, absorption becomes important in very heavy rain and this attenuation leads to underestimation of echoes in and beyond a strong thunderstorms line. Canada and other northern countries use this less costly kind of radars as their precipitations are usually less intense. However, users have to remember this effect when interpreting data. The images above show how a strong line of echoes seems to vanish as it moves over the radar. To compensate for this behaviour, radar sites are often chosen to somewhat overlap in coverage in order to give different points of view of the same storms.

Shorter wavelengths are even more attenuated and are only useful on short range radar. Many television stations in the United States have 3-centimeter radars to cover their audience area. Knowing their limitations and using them with the local NEXRAD can supplement the data available to a meteorologist.

### Bright band

As we have seen previously, the reflectivity depends on the diameter of the target and its capacity to reflect. Snow flakes are large but weakly reflective while rain drops are small but highly reflective.

When snow falls through a layer above freezing temperature, it melts and eventually becomes rain. Using the reflectivity equation, one can demonstrate that the returns from the snow before melting and the rain after, are not too different as the change in dielectric constant compensate for the change in size. However, during the melting process, the radar wave “sees” something akin to very large droplets as snow flakes become coated with water.

This gives enhanced returns that can be mistaken for stronger precipitations. On a PPI, this will show up as an intense ring of precipitations at the altitude where the beam crosses the melting level while on a series of CAPPIs, only the ones near that level will have stronger echoes. A good way to confirm a bright band is to make a vertical cross section through the data like in the picture above.

### Multiples reflections

It is assumed that the beam hits the weather targets and returns directly to the radar. In fact, there is energy reemitted in all directions. Most of it is weak, and multiple reflections diminish it even further so what can eventually return to the radar from such an event is negligible. In some cases though, this cannot be.

For instance, when the beam hits hail, the energy spread toward the wet ground will be reflected back to the hail and then to the radar. The resulting echo is weak but noticeable. Due to the extra path length it has to go through, it arrives later at the antenna and is placed further than its source. This gives a kind of triangle of false weaker reflections placed radially behind the hail.

## Solutions for now and the future

The same image but cleaned using the Doppler velocities.
These two images show what can be achieved already to clean up radar data. The output on the left is made with the raw returns and it is difficult to spot the real weather. Since rain and snow clouds are usually moving, one can use the Doppler velocities to eliminate a good part of the clutter (ground echoes, reflections from buildings seen as urban spikes, anomalous propagation, etc..). The image on the right has been filtered using this property in a somewhat complex technique.

However, not all non-meteorological targets remain still; one can think of birds for instance. Others, like the bright band, depend on the structure of the precipitations. Polarization offers a direct typing of the echoes which could be used to filter more false data or produce separate images for specialized purposes. This recent development in this field is bound to improve the quality of radar products.

Another question is the resolution. As mentioned previously, radar data are an average of the scanned volume by the beam. Resolution can be improved by larger antenna or denser networks. A program by the Center for Collaborative Adaptive Sensing of the Atmosphere (CASA): aims to supplement regular NEXRAD using many low cost X band (3 cm) weather radar mounted on cellular telephone towers. These radars will subdivide the large area of the NEXRAD into smaller domains to look at altitudes below its lowest angle. These will give details not currently available.

Timeliness is also a point needing improvement. With 5 to 10 minutes time between complete scans of weather radar, a lot of things can be missed in the development of a thunderstorm. A Phased-array radar is being tested at the National Severe Storms Lab in Norman, Oklahoma, to speed up the gathering of data.

## Specialized applications

Aircraft application of radar systems include weather radar, collision avoidance, target tracking, ground proximity, and other systems. For commercial Weather Radar Systems, ARINC 708 is the primary weather radar system using an airborne pulse-Doppler radar.

#### Antennas

There are two major systems when talking about the receiver/transmitter: the first is high-powered systems, and the second is low-powered systems; both of which operate in the x-band frequency range (8,000 to 12,500) MHz. High-powered systems operate at power levels between 10,000 and 60,000 watts. These systems consist of magnetrons and vacuum tubes that are fairly expensive (approximately \$1,700) and allow for considerable amounts of noise due to irregularities with the system. Thus, these systems are highly dangerous for arcing and are not safe to be used around ground personnel. However, the alternative would be the low-powered systems. These systems operate between 100 to 200 watts, and require a combination of high gain receivers, signal microprocessors, and transistors in order to operate as effectively as the high-powered systems. The complex microprocessors help to eliminate noise, providing a more accurate and detailed depiction of the sky. Also, since there are fewer irregularities throughout the system, the low-powered radars can be used to detect turbulence via the Doppler Effect. Furthermore, since the low-powered systems operate at considerable less wattage, they are safe from arcing and can be used at virtually all times.

### Thunderstorm Tracking

Digital radar systems now have capabilities far beyond that of their predecessors. Digital systems now offer thunderstorm tracking surveillance. This provides users with the ability to acquire detailed information of each storm cloud being tracked. Thunderstorms are first identified by matching precipitation raw data received from the radar pulse to some sort of template preprogrammed into the system. In order for a thunderstorm to be identified, it has to meet strict definitions of intensity and shape that set it apart from any non-convective cloud. Usually, it must show signs of organization in the horizontal and continuity in the vertical : a core or a more intense center to be identified and tracked by digital radar tracking systems. Once the thunderstorm cell is identified, speed, distance covered, direction, and Estimated Time of Arrival (ETA) are all tracked and recorded to be utilized later.

General

* More NOAA info

Real time data

## References

2. Diana Yates (2008). Birds migrate together at night in dispersed flocks, new study indicates. University of Illinois at Urbana - Champaign. Retrieved on 2009-04-26.
3. Bart Geerts and Dave Leon (2003). P5A.6 Fine-Scale Vertical Structure of a Cold Front As Revealed By Airborne 95 GHZ Radar. University of Wyoming. Retrieved on 2009-04-26.
4. Thomas A. Niziol (1998). Contamination of WSR-88D VAD Winds Due to Bird Migration: A Case Study. Eastern Region WSR-88D Operations Note No. 12, August, 1998. Retrieved on 2009-04-26.
5. Bendix Corporation. Avionics Division. RDR-1200 Weather Radar System. Rev. Jul/73 ed. Fort Lauderdale: Bendix, Avionics Division, 1973.
6. Barr, James C. Airborne Weather Radar. 1st ed. Ames: Iowa State UP, 1993.
7. 2008 IntelliWeather StormPredator is a Trademark of IntelliWeather Inc. http://www.stormpredator.com/details.htm

## Bibliography

• David Atlas, Radar in Meteorology: Battan Memorial and 40th Anniversary Radar Meteorology Conference, published by American Meteorological Society, Boston, 1990, 806 pages, ISBN 0-933876-86-6, AMS Code RADMET.

• Yves Blanchard, Le radar, 1904–2004: histoire d'un siècle d'innovations techniques et opérationnelles , published by Ellipses, Paris, France, 2004 ISBN 2-7298-1802-2

• R. J. Doviak and D. S. Zrnic, Doppler Radar and Weather Observations, Academic Press. Seconde Edition, San Diego Cal., 1993 p. 562.

• Gunn K. L. S., and T. W. R. East, 1954: The microwave properties of precipitation particles. Quart. J. Royal Meteorological Society, 80, pp. 522–545.

• M K Yau and R.R. Rogers, Short Course in Cloud Physics, Third Edition, published by Butterworth-Heinemann, January 1, 1989, 304 pages. EAN 9780750632157 ISBN 0-7506-3215-1

• Roger M. Wakimoto and Ramesh Srivastava, Radar and Atmospheric Science: A Collection of Essays in Honor of David Atlas, publié par l'American Meteorological Society, Boston, August 2003. Series: Meteorological Monograph, Volume 30, number 52, 270 pages, ISBN 1-878220-57-8; AMS Code MM52.

• V. N. Bringi and V. Chandrasekar, Polarimetric Doppler Weather Radar, published by Cambridge University Press, New York, United States, 2001 ISBN 0-521-01955-9

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