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References
- Frédéric Fabry, McGill University, Montréal
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- Radar Meteorology
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4 - Reflectivity patterns
- Frédéric Fabry, McGill University, Montréal
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- Radar Meteorology
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- 21 May 2015, pp 43-64
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Summary
The basic physics and measurement strategy of weather radar having been established, it is time to turn our attention on the radar observations themselves, as their interpretation forms the core of radar meteorology. Radar echoes have a variety of origins and shapes, each associated with, as well as caused by, the processes that generate the targets being observed. Understanding and differentiating echo patterns must hence come with an understanding of the phenomena that give rise to them.
Types of targets
Targets observed by radar can be separated into three broad categories:
Precipitating weather targets: These are what naturally come to mind when one thinks of weather radar observations: rain, drizzle, snow, hail, etc.
Nonprecipitating weather targets: These include ice clouds, water clouds, and refractive index gradients causing clear-air echoes. The ability to detect each of these will depend on radar sensitivity and wavelength.
Nonmeteorological targets: These include a large variety of targets. Some of these are important to detect such as ash; some others such as insects end up providing information that will prove to be useful for meteorological purposes. Unwanted targets for most meteorological purposes include birds, airplanes, and the ground and sea surfaces. Echoes from unwanted targets are referred to as clutter. Note that someone's clutter may be someone else's signal: aviation radar users refer to weather echoes as clutter!
Despite the large variety of targets, most of them can be recognized by carefully examining the horizontal and vertical structure of their echo. To achieve this, the processes governing their formation and evolution must be well understood. We must therefore explore in some detail the microphysics and dynamics of precipitation formation.
Precipitation processes: a quick overview
In a nutshell, dynamical processes determine the structure and spatial extent of the areas of vertical motion required to generate clouds and precipitation, while microphysical processes control the way water vapor supersaturation, resulting from upward motion, is ultimately transferred into precipitation.
There are several instabilities and processes in the atmosphere that may cause enough vertical motion to generate precipitation. The two most important are the baroclinic instability and the convective instability.
3 - Radar reflectivity and products
- Frédéric Fabry, McGill University, Montréal
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- 21 May 2015, pp 32-42
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9 - Radar estimation of precipitation
- Frédéric Fabry, McGill University, Montréal
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- 21 May 2015, pp 148-165
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Summary
“It may be possible therefore to determine with useful accuracy the intensity of rainfall at a point quite distant (say 100 km) by the radar echo from that point.” With that sentence, Marshall et al. (1947) launched the quest for the hydrological use of radar. And despite tremendous progress, this quest still continues. As you might have appreciated, radar is a superb tool for measuring the spatial patterns of reflectivity at the altitude where the measurement is taken, which, with some uncertainties, can be assumed to be a spatially distributed estimate of instantaneous precipitation rate. However, what is needed for hydrological purposes is a precise measurement of precipitation accumulation at the surface over a relevant time period. This is what rain gauges measure best: gauges are poor instantaneous rain rate instruments, but their measurement error diminishes rapidly with integration time. As will be explained in this chapter, this is not the case for radars. The art of radar hydrology hence consists in transforming a time sequence of instantaneous estimates of reflectivity and of dual-polarization parameters aloft into an unbiased estimate of precipitation accumulation at the surface. Properly integrating precipitation rates in time to obtain accurate accumulations requires a systematic fight against every source of error that can build up in time. These include systematic fractional differences for every measurement, or bias errors, such as a 15% underestimation due to a 1 dB radar calibration error. An equally problematic and less obvious challenge comes from errors that have long correlation times and distances, meaning that they are variable in time and/or space, but the time constant of their variation is slow compared with the accumulation time of interest. An example of such an error would be the one associated with extrapolating measurements made aloft at 1 km to the surface: climatologically, the difference between rainfall aloft and at the surface may be zero, but on any given day, there could be net evaporation or net low-level growth depending on low-level moisture availability.
2 - Fundamentals of weather radar measurements
- Frédéric Fabry, McGill University, Montréal
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- 21 May 2015, pp 8-31
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Summary
Radar: an active remote sensor
To gather information about the world around us, we rely on a variety of sensing mechanisms. Some, such as touch or taste, are based on in situ sensing: the sensor must be in direct contact with an object to gather information about it. Others, such as sight, use remote sensing: the sensor can be some distance away from the object. For remote sensors to work, information must travel between the object and the sensor. Most remote sensors rely on the detection of acoustic waves (sound) or electromagnetic (EM) waves (light, heat, and radio waves, among others) to gather that information.
While the applications of remote sensing are extremely varied, the principles and the process of data gathering are extremely similar. Most remote sensors basically measure the energy received for certain ranges of wavelengths, preferably from a given direction, as a function of time. That energy is either emitted or reflected by the object observed.
Radar, an acronym for RAdio Detection And Ranging, refers to an instrument that emits a strong signal at radio or microwave frequencies and then listens for echoes that occur if the signal reflects off objects known as targets (remember, radar was first a military instrument). And since it provides illumination to the target – like a camera and a flash do, and unlike the way our eyes rely on an external source such as the Sun – it is referred to as an active remote sensor.
Because of the need for an energy source, active remote sensors such as radars tend to be more complex than passive remote sensors such as satellite imagers. But that extra complication comes with benefits. Since we know what was transmitted and when, active remote sensors can make additional measurements compared to passive sensors: How much time elapsed between the transmission of the signal and the reception of the echo? How strong is the signal compared to what was transmitted? Has the frequency or the polarization of the signal changed? These crucial pieces of information give us additional clues on the object being studied, as well as on the medium between the sensor and the object.
Frontmatter
- Frédéric Fabry, McGill University, Montréal
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Preface
- Frédéric Fabry, McGill University, Montréal
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“Radar meteorology” is an odd specialty in atmospheric science. As opposed to synoptic meteorology or cloud physics, its focus has been on the instrument from the start. Radar enabled us to observe and understand many previously unknown phenomena. What we could do with this wonderful tool drew together a vibrant community of researchers whose main point in common was the use or the development of radars for meteorology. The instrument became the center of this community. As a result, when radar meteorologists meet, many often talk about instrument characteristics such as frequency, beam width, and transmit power before they talk of science. Early influential textbooks reflected that state of affairs, and many current introductory ones follow the same mold: they tend to be very radar focused, even those that do not describe in great detail the radar and its workings, and they are not very application oriented, despite the fact that the very reason we use radars is for what it allows us to see and do, from meteorological studies to short-term forecasting. Introductions to satellite meteorology, another technically oriented specialty, have managed to free themselves from their heritage: textbooks on how to use satellite imagery can be found, as well as more traditional books focused on radiative transfer. But somehow, introductions to radar meteorology have failed to do so.
Yet, the average weather radar user has changed. Radar is an operational instrument in many countries. It offers the forecasters what is generally the best opportunity to detect rapidly developing storms, and the last one to evaluate whether the weather evolves as expected or not. Also, the twenty-first-century researcher has a different focus: while efforts to improve and better understand radar data remain the principal objective of a necessary core of specialists, the emphasis has shifted toward meteorology and how to make the best use of the rich information provided by radar. Traditional books introducing radar meteorology, and the courses that rely on them, have gradually become disconnected from this changing reality.
Radar Meteorology
- Principles and Practice
- Frédéric Fabry
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This practical textbook introduces the fundamental physics behind radar measurements, to guide students and practitioners in the proper interpretation of radar reflectivity, Doppler velocity and dual-polarization imagery. Operational applications are explored, such as how radar imagery can be used to analyze and forecast convective and widespread weather systems. The book concludes with an overview of current research topics, including the study of clouds and precipitation using radars, signal processing, and data assimilation. Numerous full-color illustrations are included, as well as problem sets, case studies, and a variety of supplementary electronic material including animated time sequences of images to help convey complex concepts. This book is a valuable resource for advanced undergraduate and graduate students in radar meteorology and other related courses, such as precipitation microphysics and dynamics. It will also make a useful reference for researchers, professional meteorologists and hydrologists.
Index
- Frédéric Fabry, McGill University, Montréal
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Contents
- Frédéric Fabry, McGill University, Montréal
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Appendix A - Mathematics and statistics of radar meteorology
- Frédéric Fabry, McGill University, Montréal
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Many branches of meteorology rely heavily on mathematics to help explain and describe phenomena. Most, especially those rooted in atmospheric dynamics, primarily use calculus and differential equations; as a result, these mathematical concepts are well taught and often used in meteorology programs and courses. In many ways, the mathematical foundations of instrumentation in general and of radar in particular are more basic, being rooted in geometry, algebra of real and complex numbers, and introductory statistics. But since these do not get applied as much in meteorology programs, they tend to be forgotten. Furthermore, geophysical fields such as winds and rainfall pose special challenges to their analysis that are often ignored in introductory statistics courses, leading to mistakes that are too often observed even in scientific publications. This section introduces some of these topics by focusing on particular radar research problems that require a specific mathematical treatment or statistical approach to study them, and use these problems as means to introduce the relevant mathematical or statistical concepts. It will, however, not be a complete surrogate to proper reference texts in mathematics and statistics, but should hopefully help the reader ask the right questions and find the relevant material in these books. This appendix also gives me the opportunity to complement or introduce topics that could not be easily covered in previous chapters.
Geometry of ground-based radar measurements
To interpret or simulate radar measurements, it is essential to understand the complexity of the geometry of radar measurements. This complexity is due, among others, to the fact that both the radar measurements and the Earth-centric coordinate system follow different near-spherical geometry. On the one end, radar makes measurements in range–azimuth–elevation (r, ϕ, θ) along beams that have widths and that bend due to propagation in the atmosphere. On the other end, the position and axis of measurement must be reported with respect to an Earth-centric coordinate system such as latitude–longitude–height (Lp, lp, z), where the pointing direction of axes such as “east” and “up” changes from location to location because of the curvature of the Earth.
10 - Nowcasting
- Frédéric Fabry, McGill University, Montréal
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Nowcasting needs and approaches
Almost everyone who has looked at a real-time animation of radar imagery has wondered: What is the weather going to do next and where is it heading? Will it reach my location? If yes, when, and for how long? Will the weather be severe? These are the questions that nowcasting systems and techniques are designed to answer.
For a variety of applications, very short-term forecasts of a few minutes to a few hours are needed. These may include the determination of the future track of a particularly severe storm for warning purposes, or the estimation of the amount of additional precipitation that will fall in a given area in the next few hours. Since weather varies rapidly in unexpected ways, such forecasts must be recreated often, typically several times per hour. Because numerical weather forecasting models must first wait several minutes for all the needed data to be available and then produce a correct analysis at the initial time before finally generating their forecast, they cannot at present satisfy our needs for very frequent forecast updates. Furthermore, they often do not perform very well for short lead times (Fig. 10.1). Therefore, there exists a niche for simpler and faster forecasting approaches.
Etymologically, nowcasting comes from the contraction of the words “now” and “fore-casting.” It refers to techniques dedicated to make forecasts over relatively short periods, generally with a lead time within 12 h. These are generally less complicated and designed to function more efficiently at short time scales than the traditional weather forecasting based on numerical modeling covering large portions of continents. For example, when we make a short-term forecast based on the pressure tendency of our home barometer, we are in essence performing a nowcast. Nowcasting techniques are particularly suited to use data from remote sensors such as radar and satellite.
12 - Cloud and spaceborne radars
- Frédéric Fabry, McGill University, Montréal
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- 21 May 2015, pp 189-200
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There is a new role for radar since the eve of the twenty-first century. It arose from the growing interest in clouds and global precipitation, partly fueled by the wish to better understand the Earth energy balance and hydrological cycle, and partly in response for the need to improve the simulation of clouds and of precipitation in climate models. Both of these needs require shorter wavelength radars: ground-based cloud studies are best made with radars that have narrower beam widths and greater sensitivity to cloud droplets and ice crystals, while global cloud and precipitation monitoring requires radars that are both small enough to be carried on satellites, yet have sufficient resolution and sensitivity to make useful measurements. These radars also tend to be used differently than weather radars, the focus being more on cloud and precipitation microphysics and the characterization of long-term statistics rather than on storm processes and minute-to-minute weather surveillance and nowcasting. These differences in focus have led to the emergence of two distinct communities of radar meteorologists who largely work in isolation of one another. The use of shorter wavelengths either from a ground-based or a spaceborne platform gives rise to a combination of challenges and opportunities that are unique to these radars and deserve a special discussion in this chapter.
Cloud radars
12.1.1 Radars for cloud studies
The contribution of clouds on the Earth energy balance, and how it may change as a result of man-made perturbations such as greenhouse gas and aerosol emissions, remains one of the greatest sources of uncertainties in climate simulations and predictions. There is hence a societal need to better characterize clouds. In parallel, process studies of the formation of clouds and how precipitation initiates remain an active research area. Both of these research interests called for the use of new data.
But the measurements required are particularly difficult to make.
5 - Doppler velocity information
- Frédéric Fabry, McGill University, Montréal
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13 - What does radar really measure?
- Frédéric Fabry, McGill University, Montréal
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Summary
Until this point, the radar measurement process has been treated as a black box, introducing in the early chapters only what was necessary to allow a nonresearch user to understand radar data and how they can be corrupted. But the capabilities of the instruments we use dictate what can be measured, and therefore influence our perception of reality. To properly comprehend what we observe, a good knowledge of the capabilities of the instrument we use is hence a prerequisite. For radar in particular, the dependence between what can be observed and the characteristics of the instrument is often complicated, being dictated by the complex relationships between the theoretical and engineering aspects of the measurement process together with the properties of the fields being observed. To answer the question “what does radar really measure” requires a deeper understanding of how the engineering, physical, and meteorological considerations are intertwined. This understanding is essential for a proper quantitative interpretation of observations.
In this chapter, the measurement process is reexamined by first studying it from the physics and engineering perspectives, and then completing the picture by adding meteorological considerations.
The radar system
Figure 13.1 and the electronic supplement e02.1 illustrate the basic elements of a radar system. In order to better understand them, we shall follow the many steps in the travels of a radar pulse, first focusing on the radar hardware.
13.1.1 From the transmitter to the antenna
The travels of radar waves start at the transmitter. For most weather radars, the transmit signal is generally a short high-power pulse shaped by a modulator or a waveform generator, although there are radars that use a continuous wave signal (Skolnik 2008). Transmitters can be subdivided into two families. In the first one, the radar pulse is generated by a high-power oscillator that is fed a strong pulse of current and emits a strong microwave pulse in exchange. The most common oscillator is the magnetron, which powers many radars and most microwave ovens.
6 - The added value of dual polarization
- Frédéric Fabry, McGill University, Montréal
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Why polarization matters
Operational weather radars have been undergoing another massive transformation. In addition to making reflectivity and Doppler velocity measurements, they are now collecting data at more than one polarization. This opens the possibility of measuring several dualpolarization quantities that can then be used in a variety of applications. Detailed treatment of the physics and the use of such quantities can be the subject of entire books, for example, Bringi and Chandrasekar (2001). In this chapter, only a brief introduction of some of the ideas behind multiple linear polarization measurements and of their applications is presented.
Virtually all man-made emissions of radio waves and microwaves are polarized. Most single-polarization weather radars transmit linearly and horizontally polarized EM waves (Fig. 6.1a). In the context of a wave traveling in the x direction in a (x, y, z) space, for such single-polarization radars, the electric field oscillates horizontally (in the y direction) while the magnetic field oscillates vertically (in the z direction). Hence, both the electric and magnetic fields oscillate perpendicular to the direction of propagation, assumed horizontal in this discussion. In contrast, most dual-polarization radars transmit waves at horizontal as well as at vertical polarizations, either alternatively or more often simultaneously.
Why is the polarization of the radar wave important? The interaction of the EM wave with hydrometeors and other targets occurs via the electric field of the wave, as the electric field in air induces a field in the hydrometeor. But the field in the hydrometeor will travel slower than that in air because of the higher refractive index. It is initially along the same axis as the wave in air but is then somewhat modified by the shape, size, and refractive index of the hydrometeor. The field inside the hydrometeor then interacts back with the field in air. This interaction has three results (Fig. 6.1b): (1) a partial reflection of the wave with a larger or smaller delay with respect to what would be expected for an idealized point target; (2) a partial attenuation of the transmit wave propagating forward because of loss of power from the reflection as well as from absorption; and (3) an additional delay of the forward propagating wave compared with what would have occurred in the absence of a target.
Dedication
- Frédéric Fabry, McGill University, Montréal
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11 - Additional radar measurements and retrievals
- Frédéric Fabry, McGill University, Montréal
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Summary
The data provided by reflectivity, radial velocity, and multiple polarization measurements are extremely valuable. However, particularly in research applications, additional information about unobserved fields or data quality would be needed to better interpret radar data, understand weather phenomena, and forecast them: What are the tangential components of the wind? How are the pressure, temperature, or humidity fields? To what extent are the radar data affected by attenuation or non-Rayleigh scattering? Many of those questions cannot be answered by radar. But some can, either by combining information from multiple radars or by processing radar data in unusual ways. In this chapter, the concepts behind a few of these approaches will be introduced.
Using multiple viewing angles
One of the key limitations of radar data is that only one of the three wind components can be measured. A solution to this problem is to combine existing measurements with those from another radar, or at least from another viewing angle. Once a second wind component is measured, the third can be derived, as will be seen at the end of this section.
Analyses of winds made by multiple Doppler radars rely on having measurements from at least two partially independent components of the wind (Fig. 11.1). Ideally, one would want to measure two horizontal wind components 90° apart to be able to simply derive the u (east – west) and v (north–south) component of the wind. In practice, this can be achieved even if two components are not 90° apart, as long as they are sufficiently different. Most researchers consider that the two components must be at least 30° apart so that measurement errors on each component do not compromise the quality of the retrieved tangential component; in that scenario, the area over which an adequate dual-Doppler wind analysis can be made forms two lobes on either side of the baseline between the two radars, as illustrated in Fig. 11.1. Increasing the baseline distance enlarges the analysis area but reduces its resolution as well as limits the ability to obtain winds at the lowest levels.
Notation
- Frédéric Fabry, McGill University, Montréal
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8 - Monitoring widespread systems
- Frédéric Fabry, McGill University, Montréal
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Radar, widespread systems, and threat forecasting
8.1.1 Time to re flect
Because radar has revolutionized the way convective storms can be monitored, it is primarily thought of as an instrument to be used for observing smaller, more rapidly evolving systems (Fig. 8.1). An undesirable consequence of this mental association between convective weather and radar is that when larger, or slowly evolving widespread systems are on the forecast menu, we often stop looking at the radar data and primarily rely on other sources of information.
Admittedly, there are at least two good reasons for downplaying the role of radar in widespread systems: (1) there are many additional tools to analyze widespread systems, from satellite imagery to surface and upper-air measurements, in addition to model-based prognosis; and (2) individual radar coverage is small compared with most widespread systems (e.g., Figs. 1.4 and 8.1), making it inappropriate for a complete assessment of the weather situation.
However, since widespread systems can also be disruptive, any instrument that can help assess the possible weather threats should be used. Moreover, radars being networked, it is now possible to get radar information over wider scales and to easily access data from radars outside of one's forecast region. Finally, forecasters have a luxury that they do not have when forecasting convective systems: time. Instead of just reacting to a fast-evolving situation, they can afford to reflect a few minutes and consider the more subtle yet rich information provided by radar under these circumstances.
As a result, this extra time can allow us to do more than just warn for an existing threat. Radar is most useful in widespread systems when it provides information that shows important discrepancies between what is expected to be observed given the current forecast and what is actually happening, and then provides additional clues on how to modify the forecast. Radar hence becomes less of a primary detection tool as it was in convection, but instead assumes a key role to help adjust forecasts, particularly in the 0- to 12-h time frame.