Weather radar

as seen on a ship's radar screen in December 1944. During World War II, military radar operators noticed noise in returned echoes due to rain, snow, and sleet. 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 at first working for the Air Force 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 rainwater is falling. 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 centimeters. By 1950 the UK company EKCO was demonstrating its airborne 'cloud and collision warning search radar equipment'. -producing supercells over the Minneapolis-Saint Paul metropolitan area. Between 1950 and 1980, reflectivity radars, which measure the position and intensity of precipitation, were incorporated by weather services around the world. The early meteorologists had to watch a cathode-ray tube. 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. The first use of weather radar on television in the United States was in September 1961. As Hurricane Carla was approaching the state of Texas, local reporter Dan Rather, suspecting the hurricane was very large, took a trip to the U.S. Weather Bureau WSR-57 radar site in Galveston in order to get an idea of the size of the storm. He convinced the bureau staff to let him broadcast live from their office and asked a meteorologist to draw him a rough outline of the Gulf of Mexico on a transparent sheet of plastic. During the broadcast, he held that transparent overlay over the computer's black-and-white radar display to give his audience a sense both of Carla's size and of the location of the storm's eye. This made Rather a national name and his report helped in the alerted population accepting the evacuation of an estimated 350,000 people by the authorities, which was the largest evacuation in US history at that time. Just 46 people were killed thanks to the warning and it was estimated that the evacuation saved several thousand lives, as the smaller 1900 Galveston hurricane had killed an estimated 6000-12000 people. 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 cross-sections 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. In Canada, Environment Canada constructed the King City station, with a 5 cm research Doppler radar, by 1985; McGill University dopplerized its radar (J. S. Marshall Radar Observatory) in 1993. This led to a complete Canadian Doppler network between 1998 and 2004. France and other European countries had switched to Doppler networks by the early 2000s. Meanwhile, rapid advances in computer technology led to algorithms to detect signs of severe weather, and many applications for media outlets and researchers. After 2000, research on dual polarization technology 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 was done by the end of the decade or the beginning of the next in some countries such as the United States, France, and Canada. In April 2013, all United States National Weather Service NEXRADs were completely dual-polarized. ==Principle==

Sending radar pulses Weather radars send directional pulses of microwave radiation, on the order of one microsecond long, using a cavity magnetron or klystron tube connected by a waveguide to a parabolic antenna. The wavelengths of 1 – 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 towards the radar station. , France, housing a fixed microwave antenna used for long-range atmospheric monitoring. 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 short-range units, and 1 cm Ka-band weather radar is used only for research on small-particle phenomena such as drizzle and fog. The volume of air that a given pulse takes up at any point in time may be approximated 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". 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 Since the Earth is round, the radar beam in vacuum would rise according to the reverse curvature of the Earth. However, the atmosphere has a refractive index that diminishes with height, due to its diminishing density. This bends the radar beam slightly toward the ground and with a standard atmosphere this is equivalent to considering that the curvature of the beam is 4/3 the actual curvature of the Earth. Depending on the elevation angle of the antenna and other considerations, the following formula may be used to calculate the target's height above ground: :H = \sqrt{r^2+(k_ea_e)^2+2rk_{e}a_{e}\sin(\theta_e)} - k_{e}a_{e} + h_{a}, where: :r = distance radar–target, :ke = 4/3, :ae = Earth radius, :θe = elevation angle above the radar horizon, :ha = height of the feedhorn above ground. Effective volume coverage A weather radar network uses a series of typical angles that are set according to its needs. After each scanning rotation, the antenna elevation is changed for the next sounding. This scenario will be repeated on many angles to scan the entire volume of air around the radar within the maximum range. Usually, the scanning strategy is completed within 5 to 10 minutes to have data within 15 km above ground and 250 km distance of the radar. For instance in Canada, the 5 cm weather radars use angles ranging from 0.3 to 25 degrees. The accompanying image shows the volume scanned when multiple angles are used. Due to the Earth's curvature and change of index of refraction with height, the radar cannot "see" below the height above ground of the minimal angle (shown in green) or closer to the radar than the maximal one (shown as a red cone in the center). Calibrating return intensity Because the targets are not unique in each volume, the radar equation has to be developed beyond the basic one. Assuming a monostatic radar where G_t=A_r (\mathrm{or} \, G_r) =G: :P_r = P_t{{G^2 \lambda^2 \sigma}\over{{(4\pi)}^3 R^4}} \propto \frac {\sigma} {R^4} where \scriptstyle P_r is received power, \scriptstyle P_t is transmitted power, \scriptstyle G is the gain of the transmitting/receiving antenna, \scriptstyle \lambda is radar wavelength, \scriptstyle \sigma is the radar cross section of the target and \scriptstyle R is the distance from transmitter to target. In this case, the cross sections of all the targets must be summed: :\sigma = \bar \sigma = V \sum \sigma_{j} = V \eta ::\begin{cases} V\quad= \mathrm{scanned \, \, volume} \\ \qquad= \mathrm{pulse \, \, length} \times \mathrm{beam \, \, width} \\ \qquad= \frac {c\tau}{2}\frac {\pi R^2 \theta^2}{4} \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 = P_t{{G^2 \lambda^2 }\over{{(4\pi)}^3 R^4}} \frac {c\tau}{2} \frac {\pi R^2 \theta^2}{4} \eta = P_t \tau G^2 \lambda^2 \theta^2 \frac {c}{512(\pi^2)} \frac {\eta} {R^2} Which leads to: :P_r \propto \frac {\eta} {R^2} The return 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 Return echoes from targets ("reflectivity") are analyzed for their intensities to establish the precipitation rate in the scanned volume. The wavelengths used (1–10 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 sixth 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 N_0 e^{-\Lambda D} D^6dD 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} N_0 e^{-\Lambda D} {\pi D^3 \over 6} v(D)dD So Ze and R have similar functions that can be resolved by giving a relation between the two, in the form called Z-R relation: : Z = aRb Where a and b depend on the type of precipitation (snow, rain, convective or stratiform), which has different \Lambda, K, N0 and v. • As the antenna scans the atmosphere, on every angle of azimuth it obtains a certain strength of return from each type of target encountered. Reflectivity is then averaged for that target 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). How to read reflectivity on a radar display 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 NEXRAD radar sites use the following scale for different levels of reflectivity: • magenta: 65 dBZ (extremely heavy precipitation, > per hour, but likely hail) • red: 50 dBZ (heavy precipitation of per hour) • yellow: 35 dBZ (moderate precipitation of per hour) • 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 below. Aviation conventions When describing weather radar returns, pilots, dispatchers, and air traffic controllers will typically refer to three return levels: • 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 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. Precipitation types Some displays provided by commercial television outlets (both local and national) and weather websites, like The Weather Channel and AccuWeather, show precipitation types during the winter months: rain, snow, mixed precipitations (sleet and freezing rain). This is not an analysis of the radar data itself but a post-treatment done with other data sources, the primary being surface reports (METAR). Over the area covered by radar echoes, a program assigns a precipitation type according to the surface temperature and dew point reported at the underlying weather stations. Precipitation types reported by human operated stations and certain automatic ones (AWOS) will have higher weight. Then the program does interpolations to produce an image with defined zones. These will include interpolation errors due to the calculation. Mesoscale variations of the precipitation zones will also be lost. :* Differential Reflectivity (Zdr) – Differential reflectivity is proportional to the ratio of the reflected horizontal and vertical power returns as ZH / ZV. Among other things, it is a good indicator of droplet shape. Differential reflectivity also can provide an estimate of average droplet size, as larger drops are more subject to deformation by aerodynamic forces than are smaller ones (that is, larger drops are more likely to become "hamburger bun-shaped") as they fall through the air. :* 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, or in extreme cases debris aloft, usually coinciding with a tornado debris signature and a tornado vortex signature. :* 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. :* Differential Phase (\Phi_{dp}) – The 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 differential phase is a "propagation effect." It is a very good estimator of rain rate and is not affected by attenuation. The range derivative of differential phase (specific differential phase, Kdp) can be used to localize areas of strong precipitation/attenuation. With more information about particle shape, dual-polarization radars can more easily distinguish airborne debris from precipitation, making it easier to locate tornados. 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. NOAA established a test deployment for dual-polametric radar at NSSL and equipped all its 10 cm NEXRAD radars with dual-polarization, which was completed in April 2013. In 2004, ARMOR Doppler Weather Radar in Huntsville, Alabama was equipped with a SIGMET Antenna Mounted Receiver, giving Dual-Polarmetric capabilities to the operator. McGill University J. S. Marshall Radar Observatory in Montreal, Canada has converted its instrument (1999) and the data were used operationally by Environment Canada in Montreal until its closure in 2018. Another Environment Canada radar, in King City (North of Toronto), was dual-polarized in 2005; it uses a 5 cm wavelength, which experiences greater attenuation. ==Radar display methods==

Various methods of displaying data from radar scans have been developed over time to address the needs of its users. This is a list of common and specialized displays: Plan position indicator Since data is obtained one angle at a time, the first way of displaying it has been the Plan Position Indicator (PPI) which is only the layout of radar return on a two dimensional image. Importantly, 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 reaches the ground but what is seen from 160 km 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 distances from the radar. PPIs are affected by ground echoes near the radar. These can be misinterpreted as real echoes. Other products and further treatments of data have been developed to supplement such shortcomings. Usage: Reflectivity, Doppler and polarimetric data can use PPI. 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 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 To avoid some of the PPI problems, the constant-altitude plan position indicator (CAPPI) has been developed by Canadian researchers. It is 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 to have a cut that is as close as possible at all distance to the height needed. 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 1.5 km 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 change relatively gradually with height to produce 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 types of images. 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 assures that no strong echo is missed in the layer and a treatment using Doppler velocities eliminates the ground echoes. Comparing base and composite products, one can locate virga and updrafts zones. Accumulations Another important use of radar data is the ability to assess the amount of precipitation that has fallen over large basins, to be used in hydrological calculations; such data is useful in flood control, sewer management and dam construction. The computed data from radar weather may be used in conjunction with data from ground stations. 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 image to image 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 scan 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 always extend above 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-section product of the radar data is available to meteorologists. 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 the vertical axis, it can obtain data at the same resolution as PPI scans, as opposed to the often coarse interpolation from volumes, of which the scans therein are often separated in time by several minutes and thousands of feet. This output is called a Range Height Indicator (RHI), which is excellent for viewing the detailed smaller-scale vertical structure of a storm. As mentioned, this is different from the vertical cross section mentioned above, namely due to the fact that the radar antenna is scanning solely vertically, and does not scan over the entire 360 degrees around the site. This kind of product is typically only available on research radars. Radar networks Australia Over the past few decades, radar networks have been extended to allow the production of composite views covering large areas. For instance, countries such as the United States, Canada, Australia, Japan, and much of Europe, combine images from their radar network into a singular display. 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 when deciding 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 by passing through a thunderstorm. Composite images of precipitations using a network of radars are made with all those limitations in mind. Automatic algorithms . Notice the inbound/outbound doublet (blue/yellow) with the zero velocity line (gray) parallel to the radial to the radar (up right). It is noteworthy to mention that the change in wind direction here occurs over less than 10 km. To help meteorologists spot dangerous weather, mathematical algorithms have been introduced in the weather radar treatment programmes. These are particularly important in analyzing the Doppler velocity data as they are more complex. The polarization data will even need more algorithms. Main algorithms for reflectivity: A new popular presentation of weather radar data in United States is via Radar Integrated Display with Geospatial Elements (RIDGE) in which the radar data is projected on a map with geospatial elements such as topography maps, highways, state/county boundaries and weather warnings. The projection is often flexible giving the user a choice of various geographic elements. It is frequently used in conjunction with animations of radar data over a time period. ==Limitations and artifacts==

Radar data interpretation depends on many hypotheses about the atmosphere and the weather targets, including: • International Standard Atmosphere. • Targets small enough to obey the Rayleigh scattering, resulting in the return being 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 similarly polarized. • There is no return from multiple reflections. These assumptions are not always met; one must be able to differentiate between reliable and dubious 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 Temperature inversions often form near the ground, for instance by air cooling at night while remaining warm aloft. As the index of refraction of air decreases faster than normal 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. In the former case, it could be difficult to notice. 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. 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 2000 metres of the Earth's atmosphere, contaminates wind profiles gathered by weather radar, particularly the WSR-88D, by increasing the environmental wind returns by 30–60 km/h. Other objects within radar imagery include: The closer the wind farm, the stronger the return, and the combined signal from many towers is stronger. In some conditions, the radar can even see toward and away velocities that generate false positives for the tornado vortex signature algorithm on weather radar; such an event occurred in 2009 in Dodge City, Kansas. As with other structures that stand in the beam, attenuation of radar returns from beyond windmills may also lead to underestimation. Attenuation s moves over (from left to right images) a 5 cm wavelength weather radar (red arrow). Source: Environment Canada Microwaves used in weather radars can be absorbed by rain, depending on the wavelength used. For 10 cm radars, this attenuation is negligible. Attenuation correction in weather radars for snow particles is an active research topic. Bright band A radar beam's reflectivity depends on the diameter of the target and its capacity to reflect. Snowflakes are large but weakly reflective while rain drops are small but highly reflective. When snow falls through a layer above freezing temperature, it melts into 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 compensates 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 a kind of triangle of false weaker reflections placed radially behind the hail. ==Solutions and future solutions==

Filtering These two images show what can be achieved to clean up radar data. On the first image made from the raw returns, it is difficult to distinguish the real weather. Since rain and snow clouds are usually moving, Doppler velocities can be used to eliminate a good part of the clutter (ground echoes, reflections from buildings seen as urban spikes, anomalous propagation). The other image has been filtered using this property. However, not all non-meteorological targets remain stationary (birds, insects, dust). Others, like the bright band, depend on the structure of the precipitation. Polarization offers a direct typing of the echoes which could be used to filter more false data or produce separate images for specialized purposes, such as clutter, birds, etc. subsets. In recent years, fuzzy logic-based techniques have emerged as an alternative for clutter mitigation. Automated methods for processing 2D reflectivity data to identify ground clutter based on typical clutter properties like stationarity of echoes, narrow spectrum width, and limited vertical extent were extensively used. Texture-based algorithms based on the horizontal variation of reflectivity to generate a probabilistic clutter map which assigns a likelihood of clutter to each radar pixel, can be used to capture persistent clutter. Mesonet Another question is the resolution. As mentioned, 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 the regular NEXRAD (a network in the United States) 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 otherwise available. Using 3 cm radars, the antenna of each radar is small (about 1 meter diameter) but the resolution is similar at short distance to that of NEXRAD. The attenuation is significant due to the wavelength used but each point in the coverage area is seen by many radars, each viewing from a different direction and compensating for data lost from others. and in 2014, additional intra-cycle scanning of the lowest level elevation (MESO-SAILS). Electronic sounding With 5 to 10 minutes between complete scans of weather radar, much data is lost as a thunderstorm develops. A Phased-array radar is being tested at the National Severe Storms Lab in Norman, Oklahoma, to speed the data gathering. A team in Japan has also deployed a phased-array radar for 3D NowCasting at the RIKEN Advanced Institute for Computational Science (AICS). ==Specialized applications==

Weather radar with radome up Avionics weather radar Aircraft application of radar systems include weather radar, collision avoidance, target tracking, ground proximity, and other systems. For commercial weather radar, ARINC 708 is the primary specification for weather radar systems using an airborne pulse-Doppler radar. Antennas Unlike ground weather radar, which is set at a fixed angle, airborne weather radar is being utilized from the nose or wing of an aircraft. Not only will the aircraft be moving up, down, left, and right, but it will be rolling as well. To compensate for this, the antenna is linked and calibrated to the vertical gyroscope located on the aircraft. By doing this, the pilot is able to set a pitch or angle to the antenna that will enable the stabilizer to keep the antenna pointed in the right direction under moderate maneuvers. The small servo motors will not be able to keep up with abrupt maneuvers, but it will try. In doing this the pilot is able to adjust the radar so that it will point towards the weather system of interest. If the airplane is at a low altitude, the pilot would want to set the radar above the horizon line so that ground clutter is minimized on the display. If the airplane is at a very high altitude, the pilot will set the radar at a low or negative angle, to point the radar towards the clouds wherever they may be relative to the aircraft. If the airplane changes attitude, the stabilizer will adjust itself accordingly so that the pilot doesn't have to fly with one hand and adjust the radar with the other. Receivers/transmitters 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 – 12,500 MHz). High-powered systems operate at 10,000 – 60,000 watts. These systems consist of magnetrons that are fairly expensive (approximately $1,700) and allow for considerable 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 100 – 200 watts, and require a combination of high gain receivers, signal microprocessors, and transistors 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. Since low-powered systems operate at considerable less wattage, they are safe from arcing and can be used at virtually all times. Thunderstorm tracking a line of thunderstorms from AutoNowcaster system Digital radar systems have capabilities far beyond their predecessors. They offer thunderstorm tracking surveillance which provides users with the ability to acquire detailed information of each storm cloud being tracked. Thunderstorms are identified by matching raw precipitation data received from the radar pulse, to a preprogrammed template. In order for a thunderstorm to be confirmed, it must meet strict definitions of intensity and shape to distinguish it from a non-convective cloud. Usually, it must show signs of horizontal organization and vertical continuity: and have a core or a more intense center identified and tracked by digital radar trackers. Once the thunderstorm cell is identified, speed, distance covered, direction, and Estimated Time of Arrival (ETA) are all tracked and recorded. Doppler radar and bird migration Using Doppler weather radar is not limited to determining the location and velocity of precipitation. It can track bird migrations as well (non-weather targets section). The radio waves from the radars bounce off rain and birds alike (or even insects like butterflies). The US National Weather Service, for instance, has reported having flights of birds appear on their radars as clouds and then fade away when the birds land. The U.S. National Weather Service St. Louis has even reported monarch butterflies appearing on its radars. This is useful information in planning windmill farm placement and operation, to reduce bird fatalities, improve aviation safety and other wildlife management. In Europe, there have been similar developments and even a comprehensive forecast program for aviation safety, based on radar detection. Meteorite fall detection An image shows the Park Forest, Illinois, meteorite fall which occurred on 26 March 2003. The red-green feature at the upper left is the motion of clouds near the radar itself, and a signature of falling meteorites is inside the yellow ellipse at image center. The intermixed red and green pixels indicate turbulence, in this case arising from the wakes of falling, high-velocity meteorites. According to the American Meteor Society, meteorite falls occur on a daily basis somewhere on Earth. However, the database of worldwide meteorite falls maintained by the Meteoritical Society typically records only about 10-15 new meteorite falls annually Meteorites occur when a meteoroid falls into the Earth's atmosphere, generating an optically bright meteor by ionization and frictional heating. If the meteoroid is large enough and infall velocity is low enough, it will reach the ground. When the falling meteoroid decelerate below about 2–4 km/s, usually at an altitude between 15 and 25 km, they no longer generate an optically bright meteor and enter "dark flight". Because of this, most of the falls occurring into the oceans, during the day, or otherwise go unnoticed. This is especially useful for meteorite recovery, as weather radars are part of widespread networks and scan the atmosphere continuously. Furthermore, the meteorites cause local wind turbulence, which is noticeable on Doppler outputs, and fall nearly vertically so their resting place on the ground is close to their radar signature. ==References==