**2. How weather radar works?**

*Technology, Science and Culture - A Global Vision*

disdrometers are often used to validate radar rainfall and satellite observations and require very little maintenance in comparison with rain gauges. Satellite rainfall measurement is improving and the latest global precipitation measurement (GPM) mission will help to improve our understanding of the water and energy cycles across the globe and to improve our capabilities to forecast extreme rainfall events. The GPM core observatory includes active and passive instruments such as the dualfrequency phased-array precipitation radar (DPR), infrared (IR) sensors, and the GPM microwave imager (GMI) which can provide the three-dimensional structure of storms [10]. GPM provides rainfall measurements from space with spatial and temporal resolutions of 0.1° (approximately 10 km) every 3 h, respectively, from 65° south to 65° north in latitude. Satellite precipitation is particularly important in places where there are no other ground precipitation observations available. For instance, the measurement of precipitation over the oceans is an active area of research and the early detection of hurricanes, tropical cyclones, and large precipitation systems allows meteorologists to forecast these large-scale events several days in advance. Microwave links (MLs) measure the signal attenuation due to rain from commercial communication MLs (e.g., from mobile telephone networks), and the precipitation rates along the link can be estimated from the measured attenuation in rain [11]. Although the application of this technique is very promising in urban areas due to both, the lack of rain gauge stations and the large number of MLs available, it is not straightforward to get access to ML data from mobile network operators. Weather radars, on the other hand, provide distributed rainfall measurements with good spatial and temporal resolutions over a larger area. For instance, the operational C-band weather radar network in the UK, consisting of 15 radars, produces rainfall measurements at 1 km every 5 min over the UK (see **Figure 1**). Mobile polarimetric X-band weather radars can produce rainfall measurements at even higher spatial and temporal resolutions (e.g., 250 m/1-min) which make them suitable for urban flash flooding applications [12]. Radar technology was developed during the World War II to detect enemy aircraft at long distances. Early radar systems used long wavelengths that require huge antennas to operate, but the development of the magnetron allowed radar systems to use shorter wavelengths typically

**42**

**Figure 1.**

*Real-time radar rainfall mosaic over the UK.*

A weather radar typically sends a high-power signal in the microwave frequency range (S-band at 3 GHz, C-band at 5 GHz, and X-band at 10 GHz), and if precipitation particles lie along the path of the radar beam, then a small percentage of energy is reflected back to the radar antenna. This reflected power is related to a measurement known as the radar reflectivity (*Z*), which is commonly used to estimate the rainfall rate. If the average diameters (*D*) of the precipitation particles are small compared to the radar wavelength (λ), then the approximation of the Rayleigh scattering applies (i.e., D < <λ) and the radar reflectivity can be expressed as a function of the sixth moment of the drop size distribution *N*(*D*), that is, *Z* = ∫*D* <sup>6</sup>*N*(*D*)*dD*. The rainfall rate, on the other hand, is a function of the 3.67 moment of the drop size distribution (DSD), that is, *R* = ∫*D* 3.67*N*(*D*)*dD*, with *Z* more sensitive to large drops than *R*. This produces a source of uncertainty because both *Z* and *R* depend to different extend on the DSD, which can continuously change between storms and even during the same storm. The radar reflectivity (*Z*) can be related to the rainfall rate (*R*) by using a nonlinear *Z-R* equation of the form *Z* = *aR <sup>b</sup>* , where *a* and *b* are parameters that depend on the DSD. The parameters can be obtained empirically by establishing a climatological *Z-R* relationship or by simulating *Z* and *R* over a wide range of DSDs. However, updrafts and downdrafts can cause the *Z-R* relationship to vary from the one obtained in still air. The *Z-R* relationship is critically dependent on the calibration of the radar system and *Z* is subject to attenuation due to precipitation at frequencies higher than 3 GHz. In the US, the relationship *Z* = 300*R*1.4 is often used due to the convective nature of the precipitation, whereas in the UK, the equation *Z* = 200*R*1.6 is more suitable for stratiform precipitation. However, there are many different equations quoted in the literature, often very specific to the type of precipitation or the climatology of the area.

Radar rainfall can be affected by different error sources. Weather radars do not measure rainfall directly, but the power reflected from precipitation particles, which gives a measure of reflectivity, which in turn can be used to estimate the rainfall rate. In general, the quality of radar rainfall decreases with range (distance from radar location) because the radar sampling volume also increases with range and the radar beam height may be at several kilometers above the ground at long ranges. As a result, the precipitation particles intercepted by the radar sampling volume might be due to rain, melting snow, snow, ice, etc., or a combination of these. This variability affects reflectivity measurements and the estimation of precipitation may not be representative of the rainfall rate at the ground. The variation of the vertical profile of reflectivity (VPR) is due to factors such as growth or evaporation of precipitation, melting of precipitation particles, thermodynamic phase of precipitation (rain, snow, hail, etc.),

wind effects that can cause considerable errors in radar rainfall measurements. For instance, interception of the radar beam with melting snow precipitation particles can cause overestimation of precipitation up to a factor of 5. Typical errors in radar rainfall include radar calibration, radar signal attenuation due to rain, radar measurements contaminated with nonmeteorological echoes (e.g., echoes due to ground or sea, buildings, ships, airplanes, birds, insects, wind farms, etc.), variation in the reflectivity-rainfall (*Z-R*) equation, variations of the vertical profile of reflectivity, extrapolation of reflectivity measured aloft to the ground, wind drift effects, radar beam overshooting the shallow precipitation, radar beam blockage and occultation, etc. Substantial work has been carried out to correct these errors in radar rainfall measurements and the literature is too large to summarize here. Perhaps the most significant progress has been the development of dual-polarization radar (or polarimetric radar), which has demonstrated significant improvements in terms of both data quality and accuracy of rainfall estimation [14] and this is why many countries have upgraded (or replaced) their operational weather radar networks with polarimetric capability.
