**4.3 Classification of precipitation particles**

DP weather radars are sensitive to size, shape, orientation, and thermodynamic phase of the precipitation particles, and hence, this has enabled the identification of rain, snow, melting snow, ice, and hail. The identification of precipitation particles (also known as hydrometeors) can help to improve our understanding of the microphysics of precipitation [21] and it can also benefit the weather forecasting research community by improving the initial conditions of numerical weather prediction models to produce better forecasts. The classification of precipitation particles can be achieved using fuzzy logic classifiers. This is because DP radar measurements share some similarities for different types of precipitation particles. For instance, heavy rain is associated with large values of reflectivity and differential reflectivity (due to large oblate raindrops), whereas hail is associated with large values of reflectivity and small values of differential reflectivity (due to spherical shapes). Similarly, ρ*hv* and LDR are sensitive to melting snow. **Figure 4** shows an example of hydrometeor classification using DP weather radar.

**47**

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

DP radars measure the differential reflectivity *Zdr*, which is related to the size of raindrops and when combined with *Z* has the potential to improve the measurement of precipitation. *K*dp is almost linearly related to the liquid water content and it also provides the possibility of better estimates of rainfall rates (*R*) in heavy precipitation [14]. Therefore, in addition to the *Z-R* algorithm, other algorithms of the form *R* = *f*(*Z*, *Zdr*) and *R = f*(*K*dp) have been proposed to estimate precipitation with radar. The *R-K*dp algorithm is useful in heavy precipitation and it has the advantage that *K*dp is immune to attenuation. The parameters of these algorithms can be computed using measurements of drop size distributions. Therefore, given the advantages of each rainfall algorithm in different rainfall conditions, a composite algorithm was developed that uses the *Z-R* equation in light rain, the algorithm *R* = *f*(*Z*, *Zdr*) in moderate rain and the algorithm *R = f*(*K*dp) in heavy rain [23]. Composite algorithms have shown to provide more accurate rainfall rates (See **Figure 5**). **Figure 5** demonstrates how the total rainfall measured by a DP weather radar (shown in colour) matches the

*Total rain accumulation between July 19 and July 24, 2007 using a composite rain rate algorithm. The numbers* 

*on the figure are the rain accumulations measured by a network of rain gauges [23].*

rain gauge observations on the ground (shown with the numbers).

Some approaches to reduce the errors in radar rainfall are based on merging radar rainfall with rain gauge measurements in order to bring the benefits of both instruments, such as the accuracy of point observations from rain gauges and better representation of the spatial distribution of precipitation from radar [24, 25]. **Figure 6** shows a rainfall product that blends radar rainfall with point rain gauge observations using a geostatistical technique known as kriging with external drift (KED). The figure also shows the use of this merged rainfall product to predict a flood in Kingston

**5. Precipitation and hydrological forecasting**

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

**4.4 Improvements in rainfall estimation**

**Figure 5.**

**Figure 4.** *Classification of precipitation particles [22].*

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

**Figure 5.**

*Technology, Science and Culture - A Global Vision*

storm, which caused a large attenuation of reflectivity (see circled region on the left figure) that can be observed by the large phase shifts in Φdp (see circled region on the right figure). The figure shows a circled region where the reflectivity values decreased (in some areas by more than 20dBZ) due to the signal attenuation

*Reflectivity and differential phase measured by a C-band radar during an extreme rain event [20].*

DP weather radars are sensitive to size, shape, orientation, and thermodynamic phase of the precipitation particles, and hence, this has enabled the identification of rain, snow, melting snow, ice, and hail. The identification of precipitation particles (also known as hydrometeors) can help to improve our understanding of the microphysics of precipitation [21] and it can also benefit the weather forecasting research community by improving the initial conditions of numerical weather prediction models to produce better forecasts. The classification of precipitation particles can be achieved using fuzzy logic classifiers. This is because DP radar measurements share some similarities for different types of precipitation particles. For instance, heavy rain is associated with large values of reflectivity and differential reflectivity (due to large oblate raindrops), whereas hail is associated with large values of reflectivity and small values of differential reflectivity (due to spherical shapes). Similarly, ρ*hv* and LDR are sensitive to melting snow. **Figure 4** shows an example of

produced by the large heavy rainfall cell shown in red.

hydrometeor classification using DP weather radar.

**4.3 Classification of precipitation particles**

**Figure 3.**

**46**

**Figure 4.**

*Classification of precipitation particles [22].*

*Total rain accumulation between July 19 and July 24, 2007 using a composite rain rate algorithm. The numbers on the figure are the rain accumulations measured by a network of rain gauges [23].*

#### **4.4 Improvements in rainfall estimation**

DP radars measure the differential reflectivity *Zdr*, which is related to the size of raindrops and when combined with *Z* has the potential to improve the measurement of precipitation. *K*dp is almost linearly related to the liquid water content and it also provides the possibility of better estimates of rainfall rates (*R*) in heavy precipitation [14]. Therefore, in addition to the *Z-R* algorithm, other algorithms of the form *R* = *f*(*Z*, *Zdr*) and *R = f*(*K*dp) have been proposed to estimate precipitation with radar. The *R-K*dp algorithm is useful in heavy precipitation and it has the advantage that *K*dp is immune to attenuation. The parameters of these algorithms can be computed using measurements of drop size distributions. Therefore, given the advantages of each rainfall algorithm in different rainfall conditions, a composite algorithm was developed that uses the *Z-R* equation in light rain, the algorithm *R* = *f*(*Z*, *Zdr*) in moderate rain and the algorithm *R = f*(*K*dp) in heavy rain [23]. Composite algorithms have shown to provide more accurate rainfall rates (See **Figure 5**). **Figure 5** demonstrates how the total rainfall measured by a DP weather radar (shown in colour) matches the rain gauge observations on the ground (shown with the numbers).

### **5. Precipitation and hydrological forecasting**

Some approaches to reduce the errors in radar rainfall are based on merging radar rainfall with rain gauge measurements in order to bring the benefits of both instruments, such as the accuracy of point observations from rain gauges and better representation of the spatial distribution of precipitation from radar [24, 25]. **Figure 6** shows a rainfall product that blends radar rainfall with point rain gauge observations using a geostatistical technique known as kriging with external drift (KED). The figure also shows the use of this merged rainfall product to predict a flood in Kingston

#### **Figure 6.**

*Merged radar-rain gauge rainfall product (top) used to simulate a flood (bottom) in Hull, England [26].*

upon Hull located in the north of England (from [26]). The results show that the simulated flooded areas produced by the inundation model agree with the flooded areas reported by the Environment Agency and Hull City Council. The results show the enormous potential of weather radar for real-time flood forecasting.

Although the above simulation was performed using measured radar and rain gauge data, it highlights the potential of weather radar to predict flood inundation in real time. In fact, precipitation forecasts can be produced either by numerical weather prediction (NWP) models or by using a sequence of radar rainfall scans. NWP models have a better performance over longer timescales as they dynamically resolve the large-scale atmospheric processes. Radar-based precipitation forecasting is often known as precipitation nowcasting. Nowcasting models are based on the extrapolation of radar rainfall scans to track the motion of precipitation cells with a forecasting lead time of a few hours. Radar-based precipitation nowcasting has a higher performance than NWP forecasts for the first few hours of the forecasts, but NWP forecasts have a better performance at longer forecasting lead times. However, radar-based precipitation nowcasting can be very useful for flash flood forecasting in urban areas (e.g., [27]) or hydrological forecasting for river catchments (e.g., [2]). **Figure 7** shows river flow forecasts obtained in the Upper Medway catchment using a combination of radar nowcasts and NWP forecasts coupled with a hydrological model of the catchment. The results show that the

**49**

**Figure 7.**

*initialized at 14:15 (left) and 16:15 (right).*

**6. Concluding comments**

**Author details**

United Kingdom

Miguel A. Rico-Ramirez

and they can help to manage flood risk.

potential for real-time flood forecasting applications.

Department of Civil Engineering, University of Bristol, Bristol,

\*Address all correspondence to: m.a.rico-ramirez@bristol.ac.uk

provided the original work is properly cited.

© 2019 The Author(s). Licensee IntechOpen. This chapter is distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/ by/3.0), which permits unrestricted use, distribution, and reproduction in any medium,

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

*Hydrological forecasts in the Upper Medway catchment in the UK using radar nowcasts [28]. The forecasts were* 

forecasts are able to predict the peak flow several hours in advance. The blue area shows the uncertainty in the predictions. These types of forecasts can benefit the local population and emergency services before and during a major flooding event

Weather radar can measure precipitation over large areas, in real time and has the advantage of providing rainfall measurements with high spatial and temporal resolutions. Although radar rainfall measurements can be affected by different error sources, there are many algorithms in the literature to control the quality of radar rainfall. Polarimetric weather radars bring several benefits including improvements in radar data quality, identification of hydrometeors, attenuation correction, and radar rainfall estimation. Weather radars have demonstrated a huge

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

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

#### **Figure 7.**

*Technology, Science and Culture - A Global Vision*

upon Hull located in the north of England (from [26]). The results show that the simulated flooded areas produced by the inundation model agree with the flooded areas reported by the Environment Agency and Hull City Council. The results show

*Merged radar-rain gauge rainfall product (top) used to simulate a flood (bottom) in Hull, England [26].*

Although the above simulation was performed using measured radar and rain gauge data, it highlights the potential of weather radar to predict flood inundation in real time. In fact, precipitation forecasts can be produced either by numerical weather prediction (NWP) models or by using a sequence of radar rainfall scans. NWP models have a better performance over longer timescales as they dynamically resolve the large-scale atmospheric processes. Radar-based precipitation forecasting is often known as precipitation nowcasting. Nowcasting models are based on the extrapolation of radar rainfall scans to track the motion of precipitation cells with a forecasting lead time of a few hours. Radar-based precipitation nowcasting has a higher performance than NWP forecasts for the first few hours of the forecasts, but NWP forecasts have a better performance at longer forecasting lead times. However, radar-based precipitation nowcasting can be very useful for flash flood forecasting in urban areas (e.g., [27]) or hydrological forecasting for river catchments (e.g., [2]). **Figure 7** shows river flow forecasts obtained in the Upper Medway catchment using a combination of radar nowcasts and NWP forecasts coupled with a hydrological model of the catchment. The results show that the

the enormous potential of weather radar for real-time flood forecasting.

**48**

**Figure 6.**

*Hydrological forecasts in the Upper Medway catchment in the UK using radar nowcasts [28]. The forecasts were initialized at 14:15 (left) and 16:15 (right).*

forecasts are able to predict the peak flow several hours in advance. The blue area shows the uncertainty in the predictions. These types of forecasts can benefit the local population and emergency services before and during a major flooding event and they can help to manage flood risk.

## **6. Concluding comments**

Weather radar can measure precipitation over large areas, in real time and has the advantage of providing rainfall measurements with high spatial and temporal resolutions. Although radar rainfall measurements can be affected by different error sources, there are many algorithms in the literature to control the quality of radar rainfall. Polarimetric weather radars bring several benefits including improvements in radar data quality, identification of hydrometeors, attenuation correction, and radar rainfall estimation. Weather radars have demonstrated a huge potential for real-time flood forecasting applications.

## **Author details**

Miguel A. Rico-Ramirez Department of Civil Engineering, University of Bristol, Bristol, United Kingdom

\*Address all correspondence to: m.a.rico-ramirez@bristol.ac.uk

© 2019 The Author(s). Licensee IntechOpen. This chapter is distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/ by/3.0), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
