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Dunkerley, D. Methods Suitable for Wide-Area Rainfall Rate Measurement. Encyclopedia. Available online: https://encyclopedia.pub/entry/50298 (accessed on 18 May 2024).
Dunkerley D. Methods Suitable for Wide-Area Rainfall Rate Measurement. Encyclopedia. Available at: https://encyclopedia.pub/entry/50298. Accessed May 18, 2024.
Dunkerley, David. "Methods Suitable for Wide-Area Rainfall Rate Measurement" Encyclopedia, https://encyclopedia.pub/entry/50298 (accessed May 18, 2024).
Dunkerley, D. (2023, October 14). Methods Suitable for Wide-Area Rainfall Rate Measurement. In Encyclopedia. https://encyclopedia.pub/entry/50298
Dunkerley, David. "Methods Suitable for Wide-Area Rainfall Rate Measurement." Encyclopedia. Web. 14 October, 2023.
Methods Suitable for Wide-Area Rainfall Rate Measurement
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This entry is adapted from the peer-reviewed paper 10.3390/w15193383

Many design principles for rain gauges that have the capacity to record rainfall intensity have been proposed or developed. For catchment-based studies, data distributed spatially is needed, particularly for localised convective storms. One approach to gathering such data are to install multiple gauges; however, it is also possible to adopt measurement methods that have the capability of recording area-wide intensity data.

rainfall rate rainfall intensity rain gauge

1. Introduction

The intensity of rainfall varies from moment to moment. As Rodda & Dixon [1] observed, even recording ‘… the true rainfall at a point remains an elusive variable’. Intensity is a rain integral parameter, reflecting the combined arrival rate (water flux) of the range of drop diameters present in rain, falling at speeds that vary with diameter. At a single point on the ground, the intensity is actually zero in the moments between the arrival of successive drops and only becomes measurable as a (virtually) continuous variable when there are multiple drop arrivals across the substantial collecting area of a sensing device, such as the ~700 cm2 area of a typical rain gauge collecting funnel. At the landscape scale, marked spatial variability in rainfall intensities and amounts arises over km scales (Fiener & Auerswald [2]) in part because wind and rain are steered by topography (Sharon [3], Hirose & Okada [4]), with small drops being more easily deflected by wind than larger drops. Though well-known (Blumen [5]), local-scale wind effects on rainfall have not been widely explored insofar as they affect intensity or storm pattern (intensity profile through the duration of a rainfall event) because rain gauge networks are rarely sufficiently dense to resolve these influences, especially in rugged or complex terrain where wind effects on rainfall might be most important. Gauges themselves of course perturb the local wind field near and above their collecting orifice, and this typically results in an under-catch as drops are swept past the gauge (Constantinescu et al. [6], Pollock et al. [7], Muchan & Dixon [8], Cauteruccio & Lanza [9]). The influence of topography further suggests that rainfall characteristics, including intensity, are likely to depend on wind direction, and topographic steering will also influence the angle of rainfall arrival at the ground. Oblique rainfall can further affect rain gauge data (Crockford et al. [10]). The limited spatial and temporal resolution of satellite-derived rainfall products has been shown to result in biased data on rainfall intensities, over-estimating moderate and high intensity falls, and underestimating no- or low-intensity falls. This problem has been documented, especially in mountainous terrain where wind effects are complex (Yu et al. [11][12]).
Complicating the measurement of intensity are the rapidity of its fluctuations and the characteristic temporal intermittency of rainfall. Rain commonly stops and re-starts at intervals during a rainfall event (intra-event intermittency), and the temporary cessations may have durations extending from minutes to hours. If rain ceases for longer than a nominated interval, often 6 h, then when rain recommences, it is considered to mark the beginning of a new event. The rainless period is then classified as inter-event intermittency. Intermittency is linked to the measurement of rainfall intensity because if rainfall data are aggregated, for instance, to hourly totals, then it is likely that some raining time and some non-raining times are included, as well as rain of various different intensities. In the presence of intermittency, the actual intensity when raining would then be underestimated if the mean rainfall rate RR (mm h−1) was calculated from RR = P/T, where P is the rainfall depth (mm) and T is the duration (h) through which P was tallied (15 min, hour, etc.). Consequently, time-aggregated rainfall data are generally unsuitable for the estimation of intensity, and data with high temporal resolution (seconds to no more than minutes) are needed.

2. Methods Suitable for Wide-Area Rainfall Rate Measurement

For catchment-based studies, data distributed spatially is needed, particularly for localised convective storms. One approach to gathering such data are to install multiple gauges; however, it is also possible to adopt measurement methods that have the capability of recording area-wide intensity data. Three such approaches are considered next.

2.1. Radar-Based Approaches

There are many approaches to the recording of rainfall arrivals using some form of radar detection of droplets. Radar systems may be operated from the ground or from satellite platforms. Essentially, they detect the strength of the backscattered part of the radar signal, Z, which increases with the volume occupied by droplets in the area scanned, essentially linked to the rainfall rate R. Thus, interpretation depends on establishing, for different kinds of rainfall (stratiform, convective, etc.), the Z-R relationship (e.g., Kirsch et al. [13]). There are many different radar configurations (frequencies used, beam dimensions and polarization, scan frequencies, and other parameters), and it is neither possible nor appropriate to cover these in this review. Reviews of radar methods for precipitation measurement include Wilson & Brandes [14], Atlas [15], Sauvageot [16], Krajewski & Smith [17], Nanding & Rico-Ramirez [18], Borga et al. [19] and for satellite-based methods, the two volumes edited by Levizzani et al. [20]. Radar applications include large systems capable of scanning hundreds of km2, small systems such as the micro-rain radar (MRR), and relatively new devices, including the Lufft WS100 low-power radar sensor that is smaller than many conventional rain gauges (see https://www.lufft.com, accessed on 2 April 2023). A recent test of this device by Vokoun & Moravec [21] found that the device reported rainfall that was much larger than the result from conventional gauges. Some implementations of radar systems provide a disdrometer capability. Prodi et al. [22] for instance, explored the Pludix X-band doppler disdrometer, which senses over an area of a few square metres above the radome.
Mansheim et al. [23] reported the performance of a microwave rain radar. This was in fact an adaptation of a radar unit intended for the measurement of farm vehicle ground speed but oriented upward from the ground instead of downwards as intended for use on agricultural machinery. Using the Marshall-Palmer model of the rain DSD, this configuration yielded a signal proportion to the mean speed of droplets within the field of view, independent of the volume being sensed (see this relationship plotted in Figure 4 in Mansheim et al. [23]), and this was then related to the rain rate in mm h−1.
Chang et al. [24] describe the conversion of MRR estimated drop diameters to fall speed using the Gunn & Kinzer [25] relationship. They deployed 16 instruments, including five vertically pointing MRRs, within an area of 400 m2. Measurements over several weeks were collected in order to compare the representation of DSDs. The MRR showed higher concentrations of small drops (<1.0 mm) than did 2DVD or JWD, and also more large drops (>5.2 mm). Chang et al. [24] attributed this to the larger sampling volume of the MRR and the resulting better representation of the less frequent arrival of large drops and of the more difficult detection of small drops. The JWD was found to be the least accurate, and the MRR had the lowest uncertainty owing to its larger sample volume and accurate Doppler measurement principle.
Though weather radar systems are widely used to record precipitation and now cover the range from wide-area measurement to virtual at-a-point measurement, the approach is an indirect one, and calibration is required using existing forms of rain gauge, commonly the TBRG. The great advantage offered by radar-based methods, however, is their ability to provide area-wide data with high temporal resolution, such that the progression of storm cells across the landscape can be monitored. Quantitative rainfall measurements are still problematic and affected by many influences other than rainfall itself, including ground clutter (Krajewski et al. [26]); performance in heavy rainfall remains relatively poor (Pastorek et al. [27]).

2.2. Microwave Attenuation (Cellular Phone Links, Satellite Links, etc.)

Path-based approaches to the estimation of rainfall rates have been widely explored. These include attenuation on commercial microwave links (CMLs) such as those that form cellular telephone networks (Roversi et al. [28]), as well as links that carry data to and from satellites. Lian et al. [29]) provided a recent review. Essentially, the more intense the rainfall along the microwave path from transmitter to receiver (mm h−1), the greater the signal attenuation (dB). Figure 2 in Lian et al. [29] illustrates this relationship with data from two days with intermittent rainfall. The potential utility of these CML-based methods is that there is a large network of devices already installed, and the network is particularly dense in urban areas where rapidly acquired rainfall data could be of great value in the prediction of flash flooding. A well-known issue is that attenuation additional to that caused by the rainfall along the link path can arise from water adhering to the antennas; this can result in the overestimation of rainfall rates. However, devices and procedures to correct for this are being developed. Nebuloni et al. [30] conducted a field test in Italy, in which CML data were compared with rain gauge and disdrometer data. Their results suggest that the use of CML data are successful in detecting the occurrence of rainfall, but less so in quantifying rainfall amounts or intensities.
Giannetti et al. [31] and Giannetti & Reggiannini [32] explored the determination of rainfall rate from the attenuation of satellite downlink data, which involves transmission frequencies in the range ~10–40 GHz. They reviewed the difficulties that arise from rain occupying only a part of the beam path, resolving rain start and end times, and other factors that affect the method.
Additional explorations of the use of CML attenuation data in the recording of rainfall include Kumah et al. [33], Pudashine et al. [34], and Zheng et al. [35]. Zheng et al. [35] focus particularly on the capability of CML data to provide good area rainfall data in urban areas. Recent studies in general confirm that with careful processing, CML data can provide distributed rainfall data that can be applied with confidence to studies of urban hydrology and flooding problems (Liu et al. [36], Pastorek et al. [27]).

2.3. Seismic Methods for Recording Rainfall

The impact of rainfall on the ground creates sound waves that travel through solid earth materials and can be recorded by seismic geophones. Indeed, rain is a source of environmental seismic noise that is known to have the potential to interfere with the intended purpose of seismic work in areas such as geological investigations (Dean [37]).
Bakker et al. [38] explored the seismic recording of rainfall in a catchment in France, using Parsivel disdrometer data to provide 1 min rainfall data. They found that most seismic energy recorded at a geophone arrives from a radial distance of up to ~25 m from the sensor. Because up to 90% of the seismic power was found to arise from drops of >3 mm diameter, Bakker et al. [38] suggest that seismic monitoring is best suited to the study of intense rainfall, during which large drops arrive more frequently.
Diaz et al. [39] showed that at frequencies above 40 Hz, most seismic signals at their field site in Spain were generated by rainfall. This then suggested that frequencies above this threshold could be used to monitor rainfall. They were able to collect seismic data at 6 min intervals and monitor the passage of rainfall cells across the landscape using a spatially distributed network of seismic stations.
Like microwave attenuation and radar methods, seismic monitoring of rainfall has the potential advantage of providing spatial coverage and sensing areas much larger than those of gauges, disdrometers, or other point-located devices. However, further exploration of these methods will be necessary to explore their suitability on geological substrates of varying properties and within the range of vegetation canopies, which may alter drop speeds and kinetic energy, as well as drop sizes.

References

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  5. Blumen, W. Atmospheric Processes over Complex Terrain; Springer: New York, NY, USA, 1990.
  6. Constantinescu, G.S.; Krajewski, W.F.; Ozdemir, C.E.; Tokyay, T. Simulation of airflow around rain gauges: Comparison of LES with RANS models. Adv. Water Resour. 2007, 30, 43–58.
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  35. Zheng, X.; Messer, H.; Wang, Q.; Xu, T.; Qin, Y.; Yang, T. On the potential of commercial microwave link networks for high spatial resolution rainfall monitoring in urban areas. Atmos. Res. 2022, 277, 106289.
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  39. Diaz, J.; Ruiz, M.; Udina, M.; Polls, F.; Martí, D.; Bech, J. Monitoring storm evolution using a high-density seismic network. Sci. Rep. Nat. Publ. Group 2023, 13, 1853.
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