**1. Introduction**

Flooding is an important hydrometeorological hazard in the world that affects the local population and it has significant consequences for the socioeconomic development of the local region. Flash floods are produced by very heavy localized precipitation affecting urban areas producing vast human and economic impacts. Climate change can further increase the frequency and intensity of floods, and so, it is important to develop measures to manage flood risk. Structural measures, such as dams, river embankments, channels to divert flood water, storage tanks, etc., can help to reduce flood risk, but they can be very expensive to build. Nonstructural measures such as early flood forecasting and warning systems can help to forecast floods several hours ahead allowing a timely emergency response to take place and benefiting the local population during a major flooding event. The economic benefit of early flood warnings in Europe was estimated to be around 400 Euros per 1 Euro invested [1], and so, flood forecasting systems are a fundamental part of

**41**

*Advances in the Measurement and Forecasting of Precipitation with Weather Radar for Flood…*

flood risk management. In the UK, the National Flood Forecasting System (NFFS) provides hydrological forecasts for all the catchments across England and Wales [2]. These flood forecasts are only possible with the help of suitable models (e.g., hydrological models and inundation models) and reliable rainfall forecasts (based on radar rainfall or numerical weather prediction models or a combination of both) to produce reliable flood warnings. So, hydrological models and rainfall forecasts

The hydrological cycle is controlled by different processes such as precipitation, evapotranspiration, groundwater fluxes, and changes in catchment water storage and soil moisture. Understanding the impact of changes in the hydrological cycle due to climate change, urbanization, land use, etc. is an active area of research. Hydrological models allow to simulate the hydrological processes in a catchment. Hydrological models are largely classified into lumped, semidistributed, and distributed models. Lumped models consider the catchment as a single unit, and catchment-averaged values (forcing inputs and model parameters) are used to model the hydrological processes in the catchment. In contrast, distributed models can describe spatial variability within the catchment by using distributed measurements (e.g., rainfall, land use, soil characteristics, terrain elevation, etc.). Semidistributed models take into account some of the spatial variability within the catchment by dividing the catchment in subcatchments and treating each subcatchment as a lumped model. The choice of the model is highly dependent on the task. For instance, distributed models can be useful to study the effects of land use change in the hydrological response of the catchment. However, increasing model complexity does not always guarantee better hydrological simulations [3]. Urbanization has a strong influence in the hydrological response of the catchment by increasing runoff rates and decreasing infiltration due to the presence of impervious surfaces, whereas changes in infiltration and land use can affect evapotranspiration [4]. In fact, urban hydrological processes such as infiltration, evaporation, and storm drainage vary at small spatial and temporal scales, and therefore, the water losses due to these processes need to be accounted for when computing the amount of rainfall that becomes runoff [5]. There are a number of models available in the literature to model the hydrological processes in river catchments and urban areas, each of them with their own complexity, data requirements, and mathematical formulations to estimate the rainfall-runoff processes. These models require calibration of the model parameters to ensure the simulated runoff is close to the observations for a number of storms (or calibration period) that is representative of

Precipitation is one of the key drivers of the hydrological cycle, and so, any errors in the measurement of precipitation have important implications when modeling hydrological processes in river catchments or urban areas. Rainfall can be measured by different instruments such as rain gauges, disdrometers, microwave links, weather radars, and active/passive satellite sensors. Both rain gauges and disdrometers provide point measurements and therefore unable to measure the spatial distribution of precipitation. Typical rain gauge instruments are tipping bucket rain gauges (TBRs) and weighing rain gauges (WGs). The measurements of both instruments are affected by systematic and random errors [6–8]. Typical errors in TBR measurements include gauge malfunctioning, blockages, wetting and evaporation, delayed in rain delivery, underestimation during high rain rates, condensation errors, wind effects, and timing errors [9]. WGs are subject to systematic delays in producing an accurate weight measurement of the precipitation collected in the bucket [7] and the measurements can be affected by evaporation. Disdrometers do not measure the rainfall rates directly, but the drop size distribution (the number of raindrops of different sizes) which can be related to the precipitation rates. The

*DOI: http://dx.doi.org/10.5772/intechopen.83691*

are essential parts in flood forecasting systems.

the climatology of the study area.

#### *Advances in the Measurement and Forecasting of Precipitation with Weather Radar for Flood… DOI: http://dx.doi.org/10.5772/intechopen.83691*

flood risk management. In the UK, the National Flood Forecasting System (NFFS) provides hydrological forecasts for all the catchments across England and Wales [2]. These flood forecasts are only possible with the help of suitable models (e.g., hydrological models and inundation models) and reliable rainfall forecasts (based on radar rainfall or numerical weather prediction models or a combination of both) to produce reliable flood warnings. So, hydrological models and rainfall forecasts are essential parts in flood forecasting systems.

The hydrological cycle is controlled by different processes such as precipitation, evapotranspiration, groundwater fluxes, and changes in catchment water storage and soil moisture. Understanding the impact of changes in the hydrological cycle due to climate change, urbanization, land use, etc. is an active area of research. Hydrological models allow to simulate the hydrological processes in a catchment. Hydrological models are largely classified into lumped, semidistributed, and distributed models. Lumped models consider the catchment as a single unit, and catchment-averaged values (forcing inputs and model parameters) are used to model the hydrological processes in the catchment. In contrast, distributed models can describe spatial variability within the catchment by using distributed measurements (e.g., rainfall, land use, soil characteristics, terrain elevation, etc.). Semidistributed models take into account some of the spatial variability within the catchment by dividing the catchment in subcatchments and treating each subcatchment as a lumped model. The choice of the model is highly dependent on the task. For instance, distributed models can be useful to study the effects of land use change in the hydrological response of the catchment. However, increasing model complexity does not always guarantee better hydrological simulations [3]. Urbanization has a strong influence in the hydrological response of the catchment by increasing runoff rates and decreasing infiltration due to the presence of impervious surfaces, whereas changes in infiltration and land use can affect evapotranspiration [4]. In fact, urban hydrological processes such as infiltration, evaporation, and storm drainage vary at small spatial and temporal scales, and therefore, the water losses due to these processes need to be accounted for when computing the amount of rainfall that becomes runoff [5]. There are a number of models available in the literature to model the hydrological processes in river catchments and urban areas, each of them with their own complexity, data requirements, and mathematical formulations to estimate the rainfall-runoff processes. These models require calibration of the model parameters to ensure the simulated runoff is close to the observations for a number of storms (or calibration period) that is representative of the climatology of the study area.

Precipitation is one of the key drivers of the hydrological cycle, and so, any errors in the measurement of precipitation have important implications when modeling hydrological processes in river catchments or urban areas. Rainfall can be measured by different instruments such as rain gauges, disdrometers, microwave links, weather radars, and active/passive satellite sensors. Both rain gauges and disdrometers provide point measurements and therefore unable to measure the spatial distribution of precipitation. Typical rain gauge instruments are tipping bucket rain gauges (TBRs) and weighing rain gauges (WGs). The measurements of both instruments are affected by systematic and random errors [6–8]. Typical errors in TBR measurements include gauge malfunctioning, blockages, wetting and evaporation, delayed in rain delivery, underestimation during high rain rates, condensation errors, wind effects, and timing errors [9]. WGs are subject to systematic delays in producing an accurate weight measurement of the precipitation collected in the bucket [7] and the measurements can be affected by evaporation. Disdrometers do not measure the rainfall rates directly, but the drop size distribution (the number of raindrops of different sizes) which can be related to the precipitation rates. The

**40**

**Chapter**

**Abstract**

polarimetric radar

**1. Introduction**

Management

*Miguel A. Rico-Ramirez*

Advances in the Measurement and

Forecasting of Precipitation with

Precipitation is the main driver of the hydrological cycle, and therefore, the measurement and forecasting of precipitation is a key element in hydrological and meteorological applications such as rainfall-runoff modeling, precipitation forecasting, flood forecasting, flood risk management, and hydrological and climate studies. Flooding is one of the most vulnerable natural hazards in the world. It has vast impacts, including loss of life, damage to property and goods, and negative health, social and economic impacts. Reliable and accurate meteorological and hydrological forecasting is therefore a major priority to minimize such impacts. Significant progress has been made to improve the forecasting of extreme rainfall events for flood prediction in large rural catchments. However, accurate, reliable, and timely flood forecasting in urban areas is a challenging task that it is now crucial for the reduction of hazard and the preservation of life and property. This chapter discusses some of the latest advances in the measurement and forecasting of precipitation with weather radars, including applications for river catchments and small urban areas.

**Keywords:** weather radar, rainfall, flooding, nowcasting, hydrological forecasting,

Flooding is an important hydrometeorological hazard in the world that affects the local population and it has significant consequences for the socioeconomic development of the local region. Flash floods are produced by very heavy localized precipitation affecting urban areas producing vast human and economic impacts. Climate change can further increase the frequency and intensity of floods, and so, it is important to develop measures to manage flood risk. Structural measures, such as dams, river embankments, channels to divert flood water, storage tanks, etc., can help to reduce flood risk, but they can be very expensive to build. Nonstructural measures such as early flood forecasting and warning systems can help to forecast floods several hours ahead allowing a timely emergency response to take place and benefiting the local population during a major flooding event. The economic benefit of early flood warnings in Europe was estimated to be around 400 Euros per 1 Euro invested [1], and so, flood forecasting systems are a fundamental part of

Weather Radar for Flood Risk

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

**43**

*Advances in the Measurement and Forecasting of Precipitation with Weather Radar for Flood…*

in the microwave frequency range resulting in more compact systems that can be installed on aircraft [13]. At the time, radar operators realized that radar systems were sensitive enough to detect precipitation, and so, there was a huge potential for radar in weather forecasting. Nowadays, weather radars are used for meteorological services around the world to estimate precipitation over large regions at high spatial and temporal resolutions for hydrological and meteorological purposes. Weather radar measurements can be used to produce short-term precipitation forecasts up to several hours ahead (typically 3–6 h of forecasting lead time) for real-time flood forecasting and warning. Weather radar measurements can be used with other atmospheric observations to improve the initial conditions of numerical weather prediction models through data assimilation to advance weather forecasting. The following section briefly describes how radar operates and the latest advances in

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 non-

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.),

, where *a* and *b* are parameters that depend on

*DOI: http://dx.doi.org/10.5772/intechopen.83691*

weather radar technology.

**2. How weather radar works?**

linear *Z-R* equation of the form *Z* = *aR <sup>b</sup>*

**Figure 1.** *Real-time radar rainfall mosaic over the UK.*

*Advances in the Measurement and Forecasting of Precipitation with Weather Radar for Flood… DOI: http://dx.doi.org/10.5772/intechopen.83691*

in the microwave frequency range resulting in more compact systems that can be installed on aircraft [13]. At the time, radar operators realized that radar systems were sensitive enough to detect precipitation, and so, there was a huge potential for radar in weather forecasting. Nowadays, weather radars are used for meteorological services around the world to estimate precipitation over large regions at high spatial and temporal resolutions for hydrological and meteorological purposes. Weather radar measurements can be used to produce short-term precipitation forecasts up to several hours ahead (typically 3–6 h of forecasting lead time) for real-time flood forecasting and warning. Weather radar measurements can be used with other atmospheric observations to improve the initial conditions of numerical weather prediction models through data assimilation to advance weather forecasting. The following section briefly describes how radar operates and the latest advances in weather radar technology.
