Radar meteorológico

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Un radar meteorológico o radar meteo es un tipo de radar usado en meteorología para localizar lluvias, calcular sus trayectorias y estimar sus tipo (lluvia, nieve, granizo, etc.). Además, los datos tridimensionales pueden analizarse para extraer la estructura de las tormentas y su potencial de daño. Finalmente, los ecos de precipitaciones y de atmósfera clara del radar meteo permiten estimar la dirección y velocidad del viento en las zonas bajas de la atmosfera.

El Radar Meteo suele usarse junto con detectores de rayos, para ubicar la actividad mayor de una tormenta.

Radar meteo en Norman, Oklahoma, con domo antilluvia (NOAA)

Tabla de contenidos

1 Historia
2 Principios del radar en meteorología
3 Tipos de datos
4 Tipos principales de "salidas" del radar
5 Limitaciones y defectos
6 Soluciones de hoy y en el futuro
7 Fuente
8 Bibliografía
9 Enlaces externos

[editar] Historia

En la Segunda Guerra Mundial, los operadores de radar notaban ruido en ecos de retorno debido a elementos climáticos (lluvia, nieve, celisca, etc.)

Poco después del conflicto, los científicos militares volvían a la vida civil ó continuaban en las Fuerzas Armadas, investigando el desarrollo de uso de aquellos ecos:

En EE.UU.: David Atlas^[1], Fuerza Aérea de EE.UU., y el grupo que primero y más tarde lideró los primeros radares meteo MIT.
En Canada : J.S. Marshall y R.H. Douglas forman el «Grupo de Tiempo Tormentoso ^[2]» en Montreal. : Marshall y, el estudiante de doctorado, Walter Palmer son bien conocidos por su trabajo sobre el tamaño de gotas y su distribución en latitudes medias de la lluvia que fijan la relación lluvia - reflectividad al radar (relación Z-R)
En Gran Bretaña: continuaron los estudios de patrones de "ecos de radar y tiempo" (lluvias estratiformas, nubes convectivas, etc.) y experimentos evaluando el potencial de diferentes longituds de onda de 1 a 10 cm

Entre 1950 y 1980, los radares de reflectividad (que dan posición e intensidad de la lluvia) se construyeron por los Servicios Meteorológicos de países muy desarrollados. Los meteorólogos tenían que observar con tubo de rayos catódicos.

En los años 1970, los radares se estandarizan y se organizan en redes. Se desarrolla el primer artefacto para capturar imágenes de radar. El Nº de ángulos escaneados sube para obtener vistas tri-dimensionales de la lluvia, se mejoran los barridos horizontales (CAPPI) y verticales. Estudios de la organizactión de tormentas se hacen posible con el Proyecto Alberta Hail y el NSSL en EE.UU. El NSSL se crea en 1964, comenzando a experimentar sobre señales de polarización dual y en usos de Efecto Doppler (ver Radar Doppler.

Entre 1980 y 2000, las redes de radares meteo son norma en Norteamérica, Europa, Japón y otos países desarrollados. Los radares convencionales son reemplazados por los Doppler para añadir información sobre velocidad. En EE.UU., desde 1988, la red son radares de longitud de onda de 1 dm, los NEXRAD ó WSR-88D.

En Canadá, la Environment Canadá construye la estación King City^[3], con un radar Doppler de 5 cm, en 1985; y la Universidad McGill doppleriza ese radar (CWMN) en 1993. Así completa la Red Canadiense de Radares Meteo ^[4] entre 1998 y 2004. Francia y otros países europeos cambian a redes Doppler a fines de los años 1990 y principios de los 2000. Se desarrollan computadoras para procesar los algoritmos de detección de signos de tiempo severo.

Después de 2000, la investigación de la polarización dual pasa al uso de información adicional para tipificar precipitaciones.

Los Radares se actualizan hacia fines de los años 1990 en EE.UU, Francia^[5], y Canadá.

Desde 2003, NOAA experimenta con radar de fase, reemplazando antenas convencionales parabólicas para dar más resolución temporal al ruido atmosférico. Esto podría ser muuy importante en tormentas severas, dando su evolución con mejor evaluación de los datos temporales.

[editar] Principios del radar en meteorología

[editar] Pulso en microonda electromagnética (orden del microsegundo)

Trayectoria del haz y volumen escaneado

Los radares meteo son radares de pulso. El generador de microonda es usualmente un magnetrón ó un klistrón con 1 a 10 cm de ancho de banda. La onda es transmitida por una guía de ondas a una antena parabólica, y hacia el blanco.

A diferencia del radar de vuelo, el radar meteo tiene un blanco numeroso en volumen, haz del radar: $\, {h r^2 \theta^2}$ (h ancho del pulso, r distancia al radar y $Θ$ ancho de haz).

Con un radar típico de pulso y su ancho de rayo, el volúmen escaneado varía grandemente, hasta 250 ó 300 km. Por ej., el retorno de una distancia dada será promedio de los ecos en un volumen del orden de 1 km³ de aire.

[editar] Ecuación para un radar con blancos meteorológicos

Debido a que los blancos varían en volumen, la Ecuación del Radar se desarrolla:

$P_r = \left [P_t{{ G^2 \lambda^2 \sigma^0}\over{{(4\pi)}^3 R^4}} \right] \propto \frac {\sigma^0} {R^4}$

donde $\,P_r$ es potencia recibida, $\,P_t$ es potencia trasmitida, $\,G_t$ es la ganancia de la antena trasmisora, $\,\lambda$ es el ancho de banda del radar, $\,\sigma$ es la sección del blanco de radar, $\,R$ es la distancia del trasmisor al blanco.

En este caso, hay que agregar las secciones a todos los blancos:

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

$\begin{cases} V\quad= escaneado\ volumen \\ \qquad= pulso\ longitud\ \ X\ haz\ ancho \\ \qquad= \left[\frac {c\tau}{2} \right] \left[\frac {\pi R^2 \theta^2}{4} \right] \end{cases}$

donde $\,c$ es la velocidad de la luz, $\,\tau$ es la duración del pulso y $\,\theta$ es el ancho del haz medido en radianes.

Combinando las dos ecuaciones:

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

Donde lidera a:

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

Notar que el retorno ahora varía inversamente a $\, R^2$ en vez de $\,R^4$ .

Comparando los datos viniendo de diferentes distancias al radar, UNO las ha normalizado con esta relación.

[editar] Tiempo de escucha receptiva (~ 1 ms)

Altura de ecos por encima del horizonte

Entre cada pulso, la antena se comporta como receptor para recibir el retorno del blanco. La distancia se calcula:

distancia = c x

Δ

t /2 (c = vel. de la luz).

Así el rango máximo no ambiguo depende de $Δ$ t entre pulsos. Cualquier pulso retornando DESPUÉS de que uno nuevo se ha emitido será perdido, como si se asumiera que proviene del 2º (siguiente) pulso.

Asumida la redondez terráquea, la variación del índice de refracción a través del aire y la distancia al blanco, se calcula la altura desde el horizonte.

[editar] Estrategia de escaneado

Típicos ángulos de escaneo en Canadá

. El zigzag representa la data de ángulos usados para hacer CAPPIs (Indicaciones Radáricas de Plan de Posición de Altitud Constante) entre 1,5 y 4 km de altitud)

Después de cada rotación de escaneo, la elevación (azimut de la antena se cambia para el siguiente sondeo. Este escenario se repite en muchos ángulos de modo de escanear el máximo del volumen atmosférico alrededor del radar meteo, con el máximo alcance. Usualmente, la estrategia de escaneo se completa en 5 a 10 min para resolver datos entre 0 y 15 km de altitud y 120-240-480 km de distancia del radar.

Debido a la curvatura de la Tierra y a cambios del índice de refracción con la altitud, el radar queda “ciego” debajo de una altura dada por el ángulo mínimo o cerrado al radar que su máximo. La imagen muestra la altura de una serie de ángulos típicos en un dardar meteo de 5 cm en Canadá; va de 0,3 a 25º.

[editar] Tipos de datos

[editar] Reflectividad (en decibel o dBZ)

Los ecos, proveniente del reflejo sobre los blancos detectados, es analizado de acuerdo a sus intensidades para establecer los indices de precipitaciones del volumen explorado. La longitud de onda utilizada (1 a 10 cm) asegura que el reflejo será proporcional al indice, dado que está en el rango de la dispersión de Rayleigh, que indica que los blancos deben ser mucho mas pequeños que la longitud de onda con la cual se explora (por un factor de 10).

La reflectividad (Z) varia de acuerdo a la sexta potencia del diametro de las gotas de lluvia (D) y al cuadrado de la constante dieléctrica (K) del blanco. A medida que la distribución de las gotas (N[D]) es una función gamma truncada^[6], su ecuación toma la siguiente forma:

Z = $\int_{0}^{Dmax}$ |K|² N₀e ^{$- Λ$ D / D₀} D⁶dD

El índice de precipitaciones (R), por otro lado, depende del numero de particulas, su volúmen, y su velocidad de caída (v[D]), de la siguiente forma:

R = $\int_{0}^{Dmax}$ N₀e ^{$- Λ$ D / D₀} (

π

D³/6) v(D)dD

Por lo tanto, Z y R estan correlacionadas por:

Z = aR^b

Donde a y b dependen del tipo de precipitaciones (nieve, lluvia, stratus o convección), quienes presentan distinto $Λ$ , K, N₀ y v.

A medida que la antena barre la atmósfera, en cada ángulo de acimut obtiene de cada blanco encontrado un valor determinado de retorno. La reflectividad is promediada para ese blanco para aproximar mejor el conjunto de valores obtenidos.

Dado que la variación del diametro y constante dieléctrica de los blancos pueden producir una gran variabilidad en la intencidad del retorno del radar detectado, la reflectividad es expresada en dBZ (10 veces el logaritmo de la relacion con el eco de una gota estádard de 1 mm de diametro ocupando el mismo volumen rastreado).

[editar] Velocidad

Idialized example of Doppler output. Approaching velocities are in blue and receeding one in red in the usual convention. Notice the sinuosidal variation of speed when going around the display along a particular ring. (Source: Environment Canada).

See also: Pulse-doppler radar and Doppler radar

[editar] Pares de Pulsos

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 :

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

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.

[editar] Dilema Doppler

Maximum range (Red) and non ambiguous Doppler velocities (Blue) variation with Pulse Repetition Rate.

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

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

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 $Δ t$ :

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

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.

[editar] Interrpetación Doppler

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. 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.

[editar] Polarización

Targeting with dual-polarization will reveal the form of the droplet

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 ^[7]:

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 ( $K d p$ ) 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 ( $ρ$ _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

[editar] Tipos principales de "salidas" del radar

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.

[editar] Indicador de Plan de Posición

See : Plan Position Indicator

Thunderstorm line viewed in reflectivity (dBZ) on a PPI (NOAA)

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.: en el caso de datos Doppler, son posibles dos puntos de vista: 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

[editar] Indicador de Plan de Posición de Altitud Constante

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:

[editar] Composición vertical

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

[editar] Acumulaciones

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. (Source: Environment Canada)

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.

[editar] Topes de ecos

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.

[editar] Secciones verticales de cruces

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.

[editar] Redes radáricas

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 :

[editar] Algoritmos automáticos

The square in this Doppler image has been automatically placed by the radar program to spot the position of a mesocyclone. It is noteworthy to mention that the change in wind direction this feature point to is less than 10 km (6 miles) in diameter (Source: Environment Canada).

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.

Algoritmos principales para velocidades Doppler:

Mesociclón 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.

[editar] Animaciones

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

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.

[editar] Limitaciones y defectos

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.

[editar] Propagación anómala (atmósfera no estándar)

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.

[editar] Super refracción

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.

[editar] Baja refracción

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.

[editar] Blancos invisibles

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.

[editar] Volumen escaneado parcialmente lleno

Profiler high resolution view of a thunderstorm (top) and by a weather radar (bottom).

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.

[editar] Geometría del haz

Distribución de energía idealizada de un haz de radar(Lóbulo central a 0 y el secundario a cada ladoe)

$Diffraction by a circular slit simulating the energy viewed by weather targets$

Diffraction by a circular slit simulating the energy viewed by weather targets

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.

The strong echoes are returns of the central peak of the radar from a series of small hills (yellow and reds pixels). The weaker echoes on each sides of them are from secondary lobes (blue and green)

[editar] Blancos no meteorológicos

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 layman. It is possible to eliminate some of them with post-treatment of data using reflectivity, Doppler and polarization data.

[editar] Atenuación

Example of strong attenuation when a line of thunderstorms move over (from left to right images) a 5 cm wavelength weather radar (red arrow). Source: Environment Canada

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.

[editar] Bandas de brillo

1.5 km altitude CAPPI at the top with strong contamination from the brightband (yellows). The vertical cut at the bottom show that this strong return is only above ground (Source: Environment Canada).

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.

[editar] Reflexiones múltiples

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.

[editar] Soluciones de hoy y en el futuro

Radar image of reflectivity with many non weather echoes (Source: Environment Canada).

The same image but cleaned using the Doppler velocities. (Source: Environment Canada).

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) ^[8]: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. Plantilla:Met inst

[editar] Fuente

[Wikipedia en inglés http://en.wikipedia.org/wiki/Weather_radar]

↑ David Atlas, "El Radar en Meteorología", editor Sociedad Americana de Meteorología
↑ Stormy Weather Group. McGill University ((2000)). Consultado el 2006-05-21.
↑ The King City Operational Doppler Radar: Development, All-Season Applications and Forecasting (PDF). Canadian Meteorological and Oceanographic Society (1990). Consultado el 2006-05-24.
↑ Information about Canadian radar network. The National Radar Program. Environment Canada (2002). Consultado el 2006-06-14.
↑ The PANTHERE project and the evolution of the French operational radar network and products: Rain estimation, Doppler winds, and dual polarization. Météo-France. 32nd Radar Conference of the AMS, Albuquerque, NM (2005). Consultado el 2006-06-24.
↑ M K Yau and R. R. Rogers, "Short Course in Cloud Physics, Third Edition", published by Butterworth-Heinemann
↑ Carey, Larry (2003). Lecture on Polarimetric Radar. Texas A&M University. Consultado el 2006-05-21.
↑ List of lectures on CASA. American Meteorological Society ((2005)). Consultado el 2006-05-21.

[editar] Bibliografía

Atlas, David. 1990. Radar en Meteorology: Battan y la Conferencia 40ª Aniversario del Radar Meteorológico, editor American Meteorological Society, Boston, 806 p., ISBN 0-933876-86-6, AMS Code RADMET.

Blanchard, Yves. 2004. Le radar, 1904-2004: histoire d'un siècle d'innovations techniques et opérationnelles , editor Ellipses, París, Francia, ISBN 2-7298-1802-2

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

Yau, M. K. y R. R. Rogers. 1989. Curso Corto en Física de Nubes, 3ª Edición, editor Butterworth-Heinemann, 1 enero de 1989, 304 p.. EAN 9780750632157 ISBN 0750632151

Wakimoto Roger M. y Ramesh Srivastava. 2003. Radar y Ciencia Atmosférica: una Colección de Ensayos en Honor de David Atlas, editor American Meteorological Society, Boston, agosto 2003. Series: Meteorological Monograph , Vol 30, Nº 52, 270 p. ISBN 1-878220-57-8; AMS Code MM52.

[editar] Enlaces externos

Commons alberga contenido multimedia sobre Radar meteorológico.Commons
Radares Meteo Environment Canadá
Errores en interpretar radar, Environment Canadá
Curso corto de radar meteo para maestros, Environment Canadá
Radar meteo Canadá FAQ
McGill Sitio de radar
NEXRAD Red de radares Doppler radar
Radares Polarimétricos
Sitio de radares NOAA
Info NOAA
NOAA Investigación sobre radares
INformación del Tiempo con radar

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