A weather radar is a type of radar used in meteorology to locate precipitation, calculate its motion and estimate its type (rain, snow, hail, etc..). Furthermore, the tridimentionnal data obtained can be analyzed to extract the structure of storms and their potential for damages. Finally, precipitations and clear air echos obtained by weather radar permit to estimate wind direction and speed in the lowest part of the atmosphere.

Weather radar is often used together with lightning detectors to detect major storm activity.

History

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• During World War II, military radar operators noticed noise in returned echoes due to weather elements (rain, snow, sleet, etc.)

• 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 AtlasDavid Atlas, "Radar in Meteorology", published by American Meteorological Society, for the Air Force group at first, and later for MIT, developed the first operational weather radars.

• In Canada : J.S. Marshall et R.H. Douglas formed le « 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 the rain rate relation to radar reflectivity (Z-R relation)
• In Britain: Research continued to study the radar echo patterns and weather elements such as stratiform rain, convective clouds, etc. and experiments were done to evaluate the potential of different wavelengths from 1 to 10 centimetres.
  • Between 1950 and 1980, reflectivity (position and intensity of precipitation) radars 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 with the Alberta Hail Project in Canada and NSSL in the US in particular. NSSL, created in 1964, began experimentation on dual polarization signals and on Doppler effect uses.

• Between 1980 and 2000, weather radar networks became the norm in North America, Europe, Japan and other developed countries. Conventional radars were replaced by Doppler ones to add the velocity information. In US, the network consisting of 10 cm wavelength radars, called NEXRAD or WSR-88D, was begun in 1988. In Canada, Environment Canada constructed the King City station, with a five centimeter research Doppler radar, by 1985;McGill University dopplerized its radar (CWMN) in 1993. This led to a complete Canadian Doppler network between 1998 and 2004. France and other European countries switched to Doppler network by the end of the 1990s to early 2000s. The fast development of computers led to algorithms to detect signs of severe weather and a plethora of products.
• After 2000, research on dual polarization has moved into operational use to add information on the precipitation type. Radars will begin to be upgraded by the end of the decade in some countries such as US, France, and Canada.

Since 2003, NOAA 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.

Principles of radar in meteorology

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Electromagnetic pulsed micro-wave (of the order of the microsecond)

Weather radars are pulsed radars. The micro-wave generators are usually magnetron or klystron with wavelength of 1 to 10 cm. The wave is transmitted through wave-guide to a parabolic antenna toward targets.

Contrary to aircraft detection, weather targets are numerous within the volume of the radar beam: $\backslash ,\; \{h\; r^2\; \backslash theta^2\}$(h is pulse width, r distance to the radar and $\backslash Theta$ the beam width).

With a typical radar pulse and beam width, the scanned volume vary greatly from close to the radar to the end of the sounding range of 150 to 200 km. For instance, the returns from a far away distance will be an average of the echoes in a volume of the order of a cube kilometer of air.

Radar equation for weather targets

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

$P\_r\; =\; \backslash left\backslash right\backslash propto\; \backslash frac\; \{\backslash sigma^0\}\; \{R^4\}$

where $\backslash ,P\_r$ is received power, $\backslash ,P\_t$ is transmitted power, $\backslash ,G\_t$ is the gain of the transmitting antenna, $\backslash ,\backslash lambda$ is radar wavelength, $\backslash ,\backslash sigma$ is the radar cross section of the target and $\backslash ,R$ is the distance from transmitter to target.

In this case, we have to add the cross sections of all the targets:

$\backslash sigma^0\; =\; \backslash bar\; \backslash sigma^0\; =\; V\; \backslash sum\; \backslash sigma^0\_j\; =\; V\; \backslash eta$

$\backslash begin\{cases\}\; V\backslash quad=\; scanned\backslash \; volume\; \backslash \backslash \; \backslash qquad=\; pulse\backslash \; length\backslash \; \backslash \; X\backslash \; beam\backslash \; width\; \backslash \backslash \; \backslash qquad=\; \backslash left\{c\backslash tau\}\{2\}\; \backslash right\backslash left\{\backslash pi\; R^2\; \backslash theta^2\}\{4\}\; \backslash right\backslash end\{cases\}$

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

In combining the two eqations :

$P\_r\; =\; \backslash left\backslash right\backslash left\{c\backslash tau\}\{2\}\; \backslash right\backslash left\{\backslash pi\; R^2\; \backslash theta^2\}\{4\}\; \backslash right\backslash eta\; =\; \backslash left\backslash tau\; G^2\; \backslash lambda^2\; \backslash theta^2\; \backslash right\backslash left\{c\}\{512(\backslash pi^2)\}\; \backslash right\backslash frac\; \{\backslash eta\}\; \{R^2\}$

Which leads to:

$P\_r\; \backslash propto\; \backslash frac\; \{\backslash eta\}\; \{R^2\}$

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

Listening time (around 1 ms)

Between each pulse, the antenna serve as receptor for the return from targets. The distance of the latter are:

distance = c x $\backslash Delta$ t /2 (c = light speed).

So the maximum non ambiguous range depends on $\backslash Delta$ t between pulses. Any pulse returning AFTER a new pulse has been emitted will be misplaced as it is assumed that it comes from the second pulse.

Assuming the Earth is round, the variation of the index of refraction through air and the distance to the target, one can calculate the height above ground of it.

Scanning strategy

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.

Data types

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Reflectivity (in decibel or dBZ)

• Return echoes from targets, REFLECTIVITY, are analyzed for their intensities in order to establish the precipitations rate in the scanned volume. The wavelength 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 (Z) varies by the 6^th power of the rain droplets diameter D and the square of the dielectric constant (K) of the target. As the drop distribution (N^*) is a truncated Gamma functionM K Yau and R. R. Rogers, "Short Course in Cloud Physics, Third Edition", published by Butterworth-Heinemann, its equation becomes:

Z = $\backslash int\_\{0\}^\{Dmax\}$ |K|² N₀e ^{$-\backslash Lambda$ D / D₀} D⁶dD

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

R = $\backslash int\_\{0\}^\{Dmax\}$ N₀e^{$-\backslash Lambda$ D / D₀} ($\backslash pi$D³/6) v(D)dD

So Z and R are related by:

Z = aR^b

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

• As the antenna scan 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 leads 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).

Velocity

See also: Pulse-doppler radar and Doppler radar

Pulse pair

The frequency difference in the return from moving rain droplets or snow flakes are too small to be noted by actual electronic instruments. 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%. However, as they move slightly between each pulse, the returned wave has a noticeable phase difference from pulse to pulse.

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

$I\; =\; I\_0\; Sin\; \backslash left(\backslash frac\{4\backslash pi\; (x\_0\; +\; v\; \backslash Delta\; t)\}\{\backslash lambda\}\backslash right)\; =\; I\_0\; Sin\; \backslash left(\backslash Theta\_0\; +\; \backslash Delta\backslash Theta\backslash right)\backslash begin\{cases\}\; x\; =\; distance\backslash \; radar\backslash \; to\backslash \; target\; \backslash \backslash \; \backslash lambda\; =\; radar\backslash \; wavelength\; \backslash \backslash \; \backslash Delta\; t\; =\; time\backslash \; between\backslash \; two\backslash \; pulses\; \backslash end\{cases\}$

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

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

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

If we now look at the maximum velocity that can be deduced from pulse pairs, a sinus can vary from -$\backslash pi$ and +$\backslash pi$, so one cannot resolve a greater velocity than:

V_max = $\backslash pm$$\backslash frac\{\backslash lambda\}\{4\backslash Delta\; t\}$

This is called the Nyquist velocity. This is directly dependant on the time between successive pulses: the smaller it is, the larger is the non ambiguous range of speed. However, we know that the maximum range from reflectivity is inversely dependant on $\backslash Delta\; t$:

x = $\backslash frac\{c\backslash Delta\; t\}\{2\}$

So we have a dilemma : increasing the range for reflectivity at the expense of velocity definition or increasing the latter at the expense of range. With the wavelengths used, the compromise has been the use a Pulse Repetition Rate that gives 100 to 150 km range.

Doppler interpretation

If one thinks of an autumn rain uniformly filling the radar area coverage and moving from West to East. The radar beam pointing toward the West will “see” the rain drops moving toward it while looking East, it will notice them going 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 will be a Cosinus. One can then deduce 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. Luckily, this component is negligible in small elevation angles but must be taken into account for higher scanning angles.

Polarization

Most 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 :

• The difference between the intensities is called Zdr and gives information on the form of the target.

• Electromagnetic waves change phase while passing through denser material, the phase differential with distance or specific phase differential ($K\_\{dp\}$) can be used to estimate the amount of precipitation in the scanned volume of atmosphere: the rain rate. This measurement is not affected by attenuation.
• The Zdr relation should be stable with drops of the same form, the return from a group of droplets of different forms or a mix of drops, snow flakes, hail, etc… continually changing position will have a Zdr that change with time. This variation ($\backslash rho$_hv) will thus give an idea of the variety of forms in the scanned volume.

With this new knowledge, the reflectivity and the Doppler data, researcher have been working on developing algorithms to differentiate precipitation types, non-meteorological targets, better accumulation estimates, etc… NCAR has been one of the world leaders in this field with Dusan S. Zrnic et Alexandre V. Ryzhkov.

NOAA has set up a test bed for operational radar since 2000 and plan to equip all its 10 cm wavelength NEXRAD with polarization by the end of the decade. McGill University J.S. Marshall Radar Observatory in Montreal, Canada has converted their instrument by 1999 and the data are used operationally by Environment Canada in Montreal. Another EC radar in King City (North of Toronto) has been polarized in 2005, this one work on a 5 cm wavelength which gives new challenges. EC hope to generalize this conversion to all its network eventually. Finally, Météo-France is working too on the subject and hope to set up their first polarized radars in 2008.

For more details:

US polarization development

McGill University operational output

Main types of radar outputs

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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

See : Plan Position Indicator

Since data are obtained one angle at a time, the first way of displaying them as 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. Here are real time example: NWS Burlington radar, one can compare the BASE and STORM Doppler products

Constant Altitude Plan Position Indicator

See: Constant Altitude Plan Position Indicator

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: Mostly for reflectivity data. McGill University is producing Doppler CAPPIs but the nature of velocity make the output a bit noisy as velocities can change rapidly in direction with height contrary to a relatively smooth pattern in reflectivity.

Real time examples:

• McGill University
• Environment Canada)

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.

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

Accumulations

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 Echo tops 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. So it is easy to produce the maximum height at which precipitation are found in this volume. However one has to remember that those are not the tops of clouds since it extended further up than the precipitations.

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.

Radar networks

For the past few decades, radar networks have been extented to the point that composite views covering large areas can be produces. For instance, all major countries like United States, Canada, European countries, etc... produce images including all their radars. This is not as trivial a task as it may seem.

The fact is that such a network can consist of different types of radar that have different characteristics like beam width, wavelength and calibrations. This has to be taken into account when matching data from one end to the other of the network. What data to use when two radars cover the same point with their PPI? If one use 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 done with all those limitations in mind.

Here are some national radar networks :

• Environment Canada
• National Weather Service in United States
• Czech Republic
• South African Republic

Automatic Algorithms

To help meteorologist 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 has they are more complex. The polarization data will even need more algorithms.

Main algorithms for reflectivity:

• VIL or Vertically Integrated Liquid is the total mass of precipitation in the clouds.
• Potential gusts which estimate winds under a cloud in case of a downdraft using the VIL and the height of the Echotops.
• Hail algorithm that estimate the presence and potential size.

Main algorithms for Doppler velocities:

• Mesocyclone detection.
• Wind shear in low levels.
• VAD or Velocity Analysis and Display that estimate the direction and speed of the echoes with the technique explain in the Doppler section.

Animations

All radar products can be animated showing the evolution of reflectivity and velocity patterns. The user can extract informations on the dynamics of the meteorological phenomena: extrapolate the motion and the development or dissipation. This will reveal non meteorological artifacts in the radar echoes that we will discuss later.

Limitations and artifacts

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

• International Standard Atmosphere.
• Target small enough 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 waves and returning one are both polarized similarly.
• There is no return from multiple reflections.

One has to keep in mind that those hypotheses are not necessarily met in many circumstances and be able to recognize when 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. This is not what is expected as the index of refraction of air increase and the radar beam bend toward the Earth instead of going up. 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 exist 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 and the radar beam reflects many time on 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 cool faster that the standard atmosphere with height, the beam ends up higher than expected. This places the precipitation at a much higher altitude they really are. 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 due to the fact that 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 of the order of stone, the return level off according to the Mie scattering. 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 usual weather radars.

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 when is passed 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.

This shows how the output of weather radar is only an approximation of the reality. Naturally, resolution can be improve by newer equipment but some things cannot. As mentioned previously, the volume scanned increase with distance so the possibly that the beam is only partially filled increase too. This leads to underestimating of the precipitation rate at larger distance and fool 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 due to the fact that 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 decrease 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 minimized 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.

Non weather targets

In the sky there is more than rain and snow. Other objects can be misinterpreted as rain by a weather radar. The main one are:

• Birds, especially in period of migration.
• Insects at low altitude.
• Thin metal strips dropped by military aircraft to fool enemies.
• Solids obstacles as mountains, buildings, aircraft.
• Ground and sea clutter.

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

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 Sates with NEXRAD. The cost of a larger antenna, klystron and other related equipments is offset by the benefice.

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 point of view to the same storms.

Shorter wavelength are even more attenuated and are only useful on short range. Many television stations in United States have 3 centimeters radars to cover their listening audience. Knowing their limitations and using them with the local NEXRAD can add information 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. If 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 case though, this could not 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 it has to go, it arrives later at the antenna and is placed further than its source. This gives a kind of triangle of false weaker reflectivities radialy behind the hail.

Solutions for now and the future

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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 usually rain and snow clouds are moving, one can use the Doppler velocities to eliminate a good part of the clutter. The image on the right has been filtered using this propriety in a somewhat complex technique.

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

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) :aim 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. They will then be giving details not available at this moment.

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

References

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Bibliography

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

• 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 0750632151

• 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.

External links

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• Weather radars by Environment Canada
• Commons errors in interpreting radar by Environment Canada
• Short weather radar course for teachers by Environment Canada
• Canadian weather radar FAQ
• McGill radar homepage
• NEXRAD Doppler radar network
• POLARIMETRIC RADAR RESEARCH
• NOAA information on radars
• More NOAA info
• NOAA research on radars
• Weather Underground on radar

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