Weather_radar - Wikiparency

# Weather radar A **weather radar**, also called a weather surveillance radar, is a radar system built to locate precipitation, track how it moves, and estimate what kind it is and how heavily it is falling, rather than to find and track solid objects such as aircraft or ships. Almost every modern weather radar is a [[Pulse-Doppler_radar|pulse-Doppler]] system, timing each returning pulse to find range exactly as any pulsed radar does while also reading the pulse-to-pulse phase shift that moving precipitation itself imposes, which gives the radial speed of the rain, snow or hail passing through the beam. A three.js microsim built for the site's aviation set and reused here renders a weather radar's own display; two neighbouring Wikitube sketches, named in Microsims below, cover the pulse-timing and Doppler physics underneath it in more general form. Weather radar grew directly out of a wartime nuisance: operators trying to find aircraft on early radar found precipitation echoes producing unwanted [[Clutter_(radar)|clutter]] on their screens, and the effort to filter that clutter out became, once the war ended, the deliberate use of surplus radar sets to observe the weather instead. National weather services, university research groups and television forecasters now all rely on networks of these radars, and the raw reflectivity and velocity data they collect is routinely processed by automated software into short-term forecasts of where existing precipitation is headed next. ## History Radar operators during the Second World War were the first to notice precipitation echoes at all, since a shower or a storm cell scattered enough of an early radar's pulse to show up on a scope built to find aircraft, and the immediate operational problem, telling a real target from a rain-cluttered screen, drove the earliest work on filtering weather returns out of a radar picture.[^wwii] Studying the echoes for their own sake followed quickly once the war ended and surplus radar sets became available to meteorologists rather than only to the military, turning a source of unwanted clutter into a new instrument for observing storms. The step that mattered most for the science came decades later, when Doppler processing was added to weather radars so that the radial velocity of precipitation, not only its reflected intensity, could be measured directly; the United States National Weather Service, together with the Federal Aviation Administration and the [[United_States_Air_Force|United States Air Force]], completed a nationwide changeover to Doppler weather radar, the WSR-88D network, beginning in the late 1980s and continuing through the following decade.[^wsr88d] Dual-polarization capability, adding a second, independent measurement of each pulse's returned polarization, followed as a later upgrade to the same national network and to comparable networks elsewhere.[^dualpol] ## Principle ### Sending radar pulses A weather radar transmits a short pulse of [[Microwave|microwave]] energy from a large, usually parabolic antenna and then falls silent to listen for whatever the pulse illuminates. The choice of [[Pulse-repetition_frequency|pulse-repetition frequency]] sets two limits at once, in the same trade every pulsed radar faces: a higher rate shortens the unambiguous range before a distant echo could be confused with the next pulse's own return, while a lower rate narrows the unambiguous Doppler velocity the radar can measure without a genuinely fast wind being folded back and read as a slower, wrong-signed one. ### Listening for return signals Only a tiny fraction of the transmitted power ever returns, scattered back by raindrops, snowflakes or hailstones that are, next to the radar's own dish, extremely small and extremely numerous. As with any radar receiver, the faintest reflectivity a weather radar can still report is set by the receiver's own noise floor, the same sensitivity limit any coherent radio receiver has to clear before a signal can be told from the noise around it.[^noisefloor3] ### Determining height Because the beam leaves the antenna at a chosen elevation angle rather than travelling flat, the altitude of the volume of air a given range cell samples rises steadily with range, an effect the [[Atmosphere_of_Earth|atmosphere]]'s own refraction bends only slightly under normal conditions; a radar therefore samples ever higher, and ever coarser, slices of a storm the farther that storm sits from the antenna. ### Effective volume coverage A single elevation angle cannot cover a useful volume of atmosphere by itself, so an operational radar steps through a whole sequence of elevation angles, a volume-coverage pattern, sweeping fully around in azimuth at each one before stepping to the next; the full pattern trades how often the whole volume is refreshed against how many elevation angles that volume is sliced into. ### Calibrating return intensity Turning a receiver's measured power into a physically meaningful reflectivity value requires calibrating the whole chain, antenna, receiver and processing alike, against a known reference signal or target, since an uncalibrated radar can report a plausible-looking but wrong reflectivity that later throws off any rainfall estimate built from it. ## Data types ### Reflectivity Reflectivity describes how much of the transmitted power a volume of precipitation scatters back, and for raindrops much smaller than the radar's wavelength it scales with the sum of the sixth power of each drop's diameter within that volume, which is why a few large drops or hailstones can dominate a reflectivity measurement far out of proportion to their share of the total water present. Because the values involved span many orders of magnitude, reflectivity is reported logarithmically, in decibels relative to a reference reflectivity, abbreviated dBZ, and radar displays commonly translate the dBZ scale into a graduated colour scheme so an operator can judge at a glance where the heaviest precipitation sits within a storm. ### Velocity Estimating the radial velocity of precipitation from a train of pulses, rather than from a single pulse's own frequency shift the way a continuous-wave radar might, is normally done by pulse-pair processing: comparing the phase of consecutive returns from the same range cell is fundamentally a problem of [[Estimation_theory|estimation]], giving both a mean Doppler velocity and a measure of how much that velocity varies within the cell. The same pulse-repetition-frequency choice that sets unambiguous range also sets an unambiguous, or [[Nyquist_frequency|Nyquist]], velocity beyond which a genuinely fast wind folds back and reads as a slower speed of the opposite sign, a trade-off radar meteorologists call the Doppler dilemma, since raising the pulse rate to see faster winds shortens the unambiguous range in exactly the proportion that widening the range would narrow the velocity coverage. Once unfolded, velocity data is typically displayed by direction and speed, letting forecasters pick out the tight, paired zones of inbound and outbound velocity that mark rotation inside a thunderstorm. ### Polarization A dual-polarization radar transmits and receives horizontal and vertical polarization together rather than only one, and the extra comparison this provides, how much the two polarizations differ in returned power and how well their phases stay correlated inside a volume, helps distinguish precipitation type: raindrops flatten as they fall and so return more horizontal than vertical power, while hail, snow and non-meteorological targets each leave a somewhat different signature on the same pair of measurements. ## Radar display methods A weather radar's raw measurements are turned into more display products than any one idea of "a radar image" suggests. ### Plan position indicator The [[Plan_position_indicator|plan position indicator]] is the basic display, a single elevation angle's sweep drawn as a top-down map centred on the radar; combining several elevation angles into one constant-altitude slice, stacking a column's worth of angles into a single maximum-reflectivity composite, or slicing vertically along one azimuth in a range-height indicator, each answer a question the plain plan-position view cannot. Further products build on these same underlying scans: accumulation maps sum reflectivity-derived rainfall over an interval to estimate storm-total precipitation, echo-top products report the highest altitude a storm's reflectivity reaches as a proxy for its intensity, and animated loops of successive scans, overlaid on a geographic map, let a forecaster or a member of the public watch a storm's motion directly rather than infer it from a single frame. Because one radar's coverage is limited by range and by the curvature of the [[Earth]], national weather services combine many individual radars into a single composite, or mosaic, and increasingly hand the raw data to automated [[Machine_learning|machine-learning]] algorithms that flag likely hail, rotation or [[Hydrology|hydrological]] flash-flood signatures faster than a human operator scanning every site could manage alone. ## Limitations and artifacts Weather radar's chief limitation is geometric: the beam samples the atmosphere at whatever height its elevation angle and the Earth's curvature put it at a given range, so the same volume-coverage pattern that images a nearby storm's low levels well can completely overshoot a similar storm's low levels at long range, a limit closely related to the plain [[Radar_horizon|radar horizon]] any radar faces. A beam also widens with range, so at long range it may only partially fill with precipitation, biasing the reported reflectivity low compared with what a closer, fully filled beam would show of the same storm. Refraction departs from its normal, gentle bending whenever the atmosphere itself is unusually layered: a temperature inversion can duct the beam back toward the ground, a condition called anomalous propagation or super-refraction that paints false, storm-shaped echoes from ordinary ground clutter, while the opposite, under-refraction, can lift the beam enough to miss precipitation it should otherwise have seen. Large, wet hailstones and heavily rimed ice violate the small-particle assumption behind the ordinary reflectivity relation, biasing any rainfall estimate built from reflectivity alone; a band of unusually high reflectivity at the altitude where falling snow melts into rain, the bright band, can likewise be mistaken for a layer of unusually heavy rain rather than the phase change it actually marks. Ground clutter, insects, birds and aircraft all return echoes with nothing to do with precipitation, and modern wind turbines add a particularly persistent version of the same problem, since their moving blades generate strong, Doppler-shifted returns that can contaminate both reflectivity and velocity products near a wind farm.[^windfarm] Attenuation, the beam's own energy being absorbed and scattered by rain lying between the radar and a more distant storm, worsens at shorter wavelengths, which is one reason long-range weather radars favour longer wavelengths over the finer resolution a shorter one would give; and an echo that bounces between the ground and precipitation more than once before returning can appear as a spurious, duplicate echo at a false range or height entirely. ## Solutions and future solutions ### Filtering Software filtering removes much of the clutter described above after the fact, in a technique closely related to ordinary [[Moving_target_indication|moving-target indication]]: discarding returns whose Doppler velocity sits too close to zero to be real precipitation moving with the wind, or that fail tests built around how a genuine storm's reflectivity should vary smoothly from one range cell to the next. ### Mesonet A dense mesonet of ground weather stations complements the radar's view from above with direct surface measurements of rainfall, temperature and wind, giving forecasters ground truth that a purely remote-sensing instrument, however well calibrated, cannot supply on its own. ### Scanning strategies Faster, adaptive scanning strategies concentrate on the low elevation angles most relevant to severe weather while a storm is active, and research into [[Phased_array|phased-array]] weather radar aims to replace a mechanically rotating dish with an electronically steered beam, cutting the time needed to refresh a full volume scan from minutes to seconds. ### Electronic sounding Radar-derived data is also combined with other remote-sensing measurements to estimate the atmosphere's own temperature and moisture profile electronically, a complement to the weather-balloon soundings that have traditionally supplied the same information less often and at far fewer locations. ## Specialized applications ### Avionics weather radar An aircraft carries its own small weather radar in its nose, using the same reflectivity principle at much shorter range and with a much smaller antenna to let the flight crew see and route around severe [[Turbulence|turbulence]] and hail ahead rather than through it. Its antenna, and its receiver and transmitter alike, are built to the same aircraft-weight and aircraft-power budgets that shape every other piece of avionics, trading the range and sensitivity of a ground-based dish for something that fits in an aircraft's nose. ### Thunderstorm tracking Comparing successive scans of the same storm cell lets software estimate its motion and its trend in intensity, feeding the short-term, minutes-to-an-hour warnings that give the public time to take shelter before a storm arrives rather than only a report of where it already is. ### Doppler radar and bird migration The same sensitivity that lets a weather radar see light rain also picks up the diffuse, spreading Doppler signature of a mass bird migration at night, and researchers now use weather-radar network data deliberately to study when and where birds migrate, not only to filter that signature out as unwanted clutter. ### Meteorite fall detection On occasion, the same networks have caught the radar signature of meteorite fragments falling through the lower atmosphere after a bright bolide breaks apart, an echo with nothing to do with weather that has, in at least a few documented cases, helped searchers narrow down where to look for the fallen fragments.[^meteorite] ## Microsims This article carries no p5.js sketch of its own. A three.js companion already built for the site's aviation and flight-instrumentation set renders a weather radar's own display and is reused here rather than duplicated. Two neighbouring Wikitube sketches cover the underlying physics in more general form. The Radar sketch models the pulse-timing fundamentals, pulse-repetition frequency, pulse width and unambiguous range, that a weather radar's own timing choices, including the Doppler dilemma described above, build directly on. The Doppler effect sketch shows the frequency shift a moving source produces, the same geometric effect that lets pulse-pair processing turn a phase change between pulses into the radial wind speed carrying precipitation through the beam. *Try:* in the [[Radar]] sketch, raise the pulse-repetition frequency and watch the dashed unambiguous-range ring shrink; a weather radar trades that same ring against its unambiguous velocity every time it chooses a pulse rate. *Try:* in the [[Doppler_effect]] sketch, change the source speed and watch the frequency shift ahead of and behind it move together, the same relationship pulse-pair processing extracts from precipitation's own echoes. ## Wikipedia : Wikitube **Strict pair:** [Wikipedia](https://en.wikipedia.org/wiki/Weather_radar) : [Wikitube](https://en.wikitube.io/wiki/Weather_radar) Skeleton mirrored at revision 1375129657. Prose, emphasis and the microsims are Wikitube's own. ## References The sixth-power reflectivity relation for small raindrops, the pulse-repetition-frequency trade between unambiguous range and unambiguous velocity, and the pulse-pair Doppler estimator are standard radar-meteorology theory and are not separately footnoted here, per the Wikitube style guide's §6.1. Page numbers below are PDF pages of the open edition linked in Bibliography. [^wwii]: Citation needed: a specific wartime radar unit's log or after-action report describing the first identified precipitation echoes would fix the date, location and unit involved. [^wsr88d]: Citation needed: the US National Weather Service's WSR-88D programme history would confirm the exact start date and completion date of the nationwide Doppler-radar changeover. [^dualpol]: Citation needed: the National Weather Service's dual-polarization upgrade programme record would confirm the exact dates the WSR-88D network's changeover began and finished. [^noisefloor3]: Ellingson, S. *Radio Systems Engineering, Revised First Edition*. 2023, pp. 96-101 (PDF pages). Open Textbook Library: https://open.umn.edu/opentextbooks/textbooks/radio-systems-engineering . CC BY-NC. [^windfarm]: Citation needed: a specific FAA or National Weather Service study of wind-turbine interference with weather radar would confirm the affected sites and the dates studied. [^meteorite]: Citation needed: a specific, documented case of a weather radar network detecting falling meteorite fragments would confirm the event's date, location and the radar network involved. ### Bibliography - Christian Tiberius; Max Mulder. *Engineering Signal Analysis: From Fourier to filtering: Theory*. 2026. Open Textbook Library: https://open.umn.edu/opentextbooks/textbooks/engineering-signal-analysis-from-fourier-to-filtering-theory . CC BY. - Don Johnson. *Fundamentals of Electrical Engineering I*. 2014. Open Textbook Library: https://open.umn.edu/opentextbooks/textbooks/fundamentals-of-electrical-engineering-1 . CC BY. - Michael Stiber; Bilin Stiber; Eric Larson. *Signal Computing: Digital Signals in the Software Domain*. 2020. Open Textbook Library: https://open.umn.edu/opentextbooks/textbooks/signal-computing-digital-signals-in-the-software-domain . CC BY-SA. - Allen Downey. *Think DSP: Digital Signal Processing in Python*. 2012. Open Textbook Library: https://open.umn.edu/opentextbooks/textbooks/think-dsp-digital-signal-processing-in-python . CC BY-NC. - Steven Ellingson. *Radio Systems Engineering, Revised First Edition*. 2023. Open Textbook Library: https://open.umn.edu/opentextbooks/textbooks/radio-systems-engineering . CC BY-NC. - John Dyer; Chad Davis. *Measurement and Instrumentation: An Introduction to Concepts and Methods, 1st Edition*. 2020. Open Textbook Library: https://open.umn.edu/opentextbooks/textbooks/measurement-and-instrumentation-an-introduction-to-concepts-and-methods . CC BY-NC-SA. ## See also - [[Pulse-Doppler_radar]] - [[Doppler_effect]] - [[Radar]] - [[Plan_position_indicator]] - [[Radar_horizon]] - [[Phased_array]] - [[Atmosphere_of_Earth]] - [[Weather_forecasting]] - [[Turbulence]] ## External links This article carries no p5.js sketch of its own; its three.js companion, reused from the aviation and flight-instrumentation set, is embedded in Microsims, above.