Radar rainfall estimation of stratiform winter precipitation in the Belgian Ardennes

WATER RESOURCES RESEARCH, VOL. 47, W02507, doi: /2010wr009068, 2011 Radar rainfall estimation of stratiform winter precipitation in the Belgian Ardennes P. Hazenberg, 1 H. Leijnse, 1,2 and R. Uijlenhoet
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WATER RESOURCES RESEARCH, VOL. 47, W02507, doi: /2010wr009068, 2011 Radar rainfall estimation of stratiform winter precipitation in the Belgian Ardennes P. Hazenberg, 1 H. Leijnse, 1,2 and R. Uijlenhoet 1 Received 4 January 2010; revised 8 November 2010; accepted 16 November 2010; published 4 February [1] Radars are known for their ability to obtain a wealth of information about spatial storm field characteristics. Unfortunately, rainfall estimates obtained by this instrument are known to be affected by multiple sources of error. Especially for stratiform precipitation systems, the quality of radar rainfall estimates starts to decrease at relatively close ranges. In the current study, the hydrological potential of weather radar is analyzed during a winter halfyear for the hilly region of the Belgian Ardennes. A correction algorithm is proposed which corrects the radar data for errors related to attenuation, ground clutter, anomalous propagation, the vertical profile of reflectivity (VPR), and advection. No final bias correction with respect to rain gauge data was implemented because such an adjustment would not add to a better understanding of the quality of the radar data. The impact of the different corrections is assessed using rainfall information sampled by 42 hourly rain gauges. The largest improvement in the quality of the radar data is obtained by correcting for ground clutter. The impact of VPR correction and advection depends on the spatial variability and velocity of the precipitation system. Overall during the winter period, the radar underestimates the amount of precipitation as compared to the rain gauges. Remaining differences between both instruments can be attributed to spatial and temporal variability in the type of precipitation, which has not been taken into account. Citation: Hazenberg, P., H. Leijnse, and R. Uijlenhoet (2011), Radar rainfall estimation of stratiform winter precipitation in the Belgian Ardennes, Water Resour. Res., 47, W02507, doi: /2010wr Introduction [2] Weather radars have long been recognized for their ability to obtain spatiotemporal information about storm fields at a much higher resolution than conventional rain gauge networks [Zawadzki, 1975; Joss and Lee, 1995; Smith et al., 2001; Berne et al., 2004a]. Therefore, largescale implementation of these systems during the last decades would, in principle, make this instrument an important tool for rainfall monitoring in the framework of hydrological applications such as (flash) flood forecasting [Collier and Knowles, 1986; Joss and Waldvogel, 1990; Carpenter et al., 2001; Vivoni et al., 2006]. [3] Unfortunately, data obtained by weather radars are known to be affected by multiple sources of error. Interaction with the nearby environment can result in (partial) beam blockage and ground clutter, which especially play a dominant role in mountainous regions. This results either in an underestimation or overestimation of the amount of precipitation [e.g., Delrieu et al., 1995; Gabella and Perona, 1998; Germann and Joss, 2002; Germann et al., 2006; Dinku et al., 2002]. Other sources of error are related to 1 Hydrology and Quantitative Water Management Group, Centre for Water and Climate, Department of Environmental Sciences, Wageningen University, Wageningen, Netherlands. 2 Now at Royal Netherlands Meteorological Institute, De Bilt, Netherlands. Copyright 2011 by the American Geophysical Union /11/2010WR temporal changes of the index of refraction [e.g., Fabry et al., 1997; Steiner and Smith, 2002], variability of the drop size distribution [e.g., Waldvogel, 1974; Berenguer and Zawadzki, 2008], and variability of the vertical profile of reflectivity (VPR) [e.g., Fabry and Zawadzki, 1995; Smyth and Illingworth, 1998; Cluckie et al., 2000]. [4] During the past decades, different techniques have been developed to correct for these types of errors, resulting in a serious improvement in the radar data quality [e.g., Kitchen and Jackson, 1993; Ciach et al., 1997; Pellarin et al., 2002; Gourley et al., 2009]. Joss and Lee [1995] implemented a stepwise algorithm identifying clutter and correcting for beam occultation, radar calibration errors, and VPR effects using either a climatological or real-time profile estimate. Anagnostou and Krajewski [1999a, 1999b] developed a similar system for an environment where topography causes no serious problems. In all of these studies, rain gauge measurements were used to correct for any final bias. A different approach was taken by Delrieu et al. [2009] for a series of extreme precipitation events within a mountainous environment in the southern part of France. Besides correcting for the errors mentioned above, the type of precipitation (convective versus stratiform) was identified as well. This resulted in a radar product of which the quality is comparable to that of rain gauge measurements. [5] The impact of radar correction steps for stratiform precipitation systems occurring within a winter period has received less attention. During such situations, the upper part of the atmosphere consists of snow and ice particles. W of15 W02507 HAZENBERG ET AL.: RADAR RAINFALL ESTIMATION OF STRATIFORM PRECIPITATION The melting of these particles results in a stronger return signal, known as the bright band, and causes the amount of precipitation to be overestimated by the weather radar. For the snow-ice region above the bright band, a significant decrease in the returned reflectivity signal can be observed. Especially at farther ranges, reflectivity samples originate from these two regions, which has a detrimental impact on the quality of the radar product [Fabry et al., 1992; Kitchen and Jackson, 1993; Bellon et al., 2005]. Long-term investigations of the influence of different correction mechanisms for such stratiform situations have been presented by Vignal and Krajewski [2001], Borga [2002], Germann et al. [2006], and Bellon et al. [2007]. Unfortunately, they did not attempt to verify the quality of the adjusted radar product by using it as an input to a hydrological model. [6] The hydrological potential of weather radar has been investigated both for individual precipitation events [e.g., Hossain et al., 2004; Berne et al., 2005; Vieux and Bedient, 1998; Ogden et al., 2000; Smith et al., 2007] and on a longer-term basis [Borga, 2002; Neary et al., 2004]. Most of these identify the benefits of using radar (i.e., the ability to obtain spatial-temporal properties of the precipitation field at a high resolution). Unfortunately, only few of them corrected for all significant types of measurement errors. As a consequence, obtained results using weather radar rainfall information as an input to a hydrological model are highly dependent on the quality of the data and the environment of application. [7] This paper addresses the importance of correcting volumetric radar reflectivity data and the applicability of these correction steps for long-term real-time hydrological purposes. The region studied is situated in the Belgian Ardennes mountain range and focuses on a winter half-year during which most of the precipitation has a stratiform character. Volumetric radar data are corrected for errors associated with attenuation, ground clutter and anomalous propagation conditions, VPR, and advection. It was decided not to correct for the remaining final bias between the amount of precipitation estimated by the radar and a rain gauge network. This decision was made to get a better W02507 understanding of the quality of the radar and because of inherent scale problems between both devices [e.g., Austin, 1987; Kitchen and Blackall, 1992; Steiner et al., 1999; Ciach and Krajewski, 1999; Morin et al., 2003]. [8] This paper is organized as follows. Section 2 will give an overview of the study area and data availability. Section 3 focuses on the different radar correction steps that have been implemented, followed by a comparison with rain gauge measurements for a series of events (section 4). Next, a whole winter period is analyzed (section 5). The different implementations are discussed in section 6, in which we will also present an application to real-time hydrological modeling at the catchment scale. 2. Study Area and Data Availability [9] The hilly plateaus of the Ardennes, part of the Meuse basin, are situated in the eastern part of Belgium (see Figure 1) and display maximum elevations of around 650 m above sea level (asl). The hydrologic response can be classified as rain fed with some snow in winter. This results in a runoff regime that can be classified as highly variable, giving rise to low discharges in summer and high discharges in winter [Leander et al., 2005]. [10] In 2001, a C-band Doppler radar was installed at an elevation of 600 m asl near the village of Wideumont, close to the border with Luxembourg. The radar has two scan sequences: one every 5 min at five different elevations and a second scan at another 10 elevations every 15 min. In this study the 5-min data were used to obtain areal information about the precipitation field. The reflectivity data from the second scan serves only to obtain an initial estimate of the VPR. A summary of the characteristics of the weather radar is presented in Table 1. [11] Directly to the north of the radar lies the 1600 km2 Ourthe catchment, which has been the focus of previous studies [Berne et al., 2005; Driessen et al., 2010]. Its outlet is situated in the north near Tabreux, at approximately 60 km from the radar. Rain gauge information in the region is available for 42 rain gauges, of which 10 are directly Figure 1. (left) Location of the study area, with a km box indicating the area shown in Figure 1 (right). (right) Topographic map of the Belgian Ardennes, where the solid lines represent the channel network. The white line indicates the Ourthe catchment (1600 km 2). Also shown are the position of the radar (solid circle), catchment outlet (triangle), the meteorological station (open circle), and the position of the rain gauges (crosses). 2 of 15 Table 1. Characteristics of the C-Band Doppler Radar at Wideumont Used in This Study Parameter situated inside the watershed. In addition, hourly temperature and potential evaporation data are available from the weather station near St. Hubert (see Figure 1). [12] This study analyzes the spatial and temporal characteristics of rainstorms and the resulting catchment response of the Ourthe for the period from 1 October 2002 until 31 March During this winter half-year, most storms had a stratiform character, for which bright bands could already be observed within 1000 m from the surface. Radar data are not available for the second week of November and for one day at the end of March. These periods are left out of the analysis. For the hydrological analysis they are substituted by rain gauge data. 3. Radar Reflectivity Analysis [13] The general measurement equation of the radar can be stated as follows: PðrÞ ¼ CZ mðrþ r 2 ; ð1þ where P(r) is the received power (W) for a given elevation at a range r (m) from the radar, C (W m 5 mm -6 ) is the radar constant, and Z m (r) is the measured reflectivity (mm 6 m -3 ). Both the radar reflectivity and rainfall intensity R (mm h -1 ) are dependent on the raindrop size distribution. The relationship between both parameters is generally assumed to follow a power law [Marshall and Palmer, 1948; Marshall et al., 1955; Battan, 1973]: Z ¼ ar b ; Value Latitude, longitude (deg) 49.91, 5.51 Height (m asl) 600 Frequency (GHz) 5.64 Pulse repetition 600 frequency (Hz) Beam width (deg) 1 Antenna diameter (m) 4.2 Maximum range (m) 240 Scanning sequences 2 Pulse length (m) 250 (scan 1), 500 (scan 2) Recurrence interval (min) 5 (scan 1), 15 (scan 2) Elevations (deg) 0.3, 0.9, 1.8, 3.3, 6.0 (scan 1); 0.5, 1.2, 1.9, 2.6, 3.3, 4.0, 4.9, 6.5, 9.4, 17.5 (scan 2) where the parameters a and b are a function of the raindrop size distribution and vary with precipitation type [Ulbrich, 1983]. Before radar data can be used for hydrological purposes, errors related to the environment and spatiotemporal atmospheric variations should be accounted for [Andrieu et al., 1997]. On the basis of the characteristics of the weather radar (see Table 1) and the Ardennes mountain range, it was therefore decided to correct the data for losses due to attenuation, artifacts due to clutter, range effects due to VPR, and potential errors due to finite sampling of rainfall. Although the radar data may be affected by other types ð2þ of errors (e.g., radome attenuation, radar calibration, partial beam filling, blockage, and overshooting), these four are considered to be the main sources of error for the environment under study. Sections present an in-depth overview of the different steps taken to correct for these four sources of error Signal Attenuation [14] Signal attenuation can become a source of error for operational C band weather radars, especially during high rainfall intensities, and depends on both the raindrop size distribution and temperature [e.g., Delrieu et al., 1991, 1997; Berenguer et al., 2002]. One method to correct for attenuation was developed by Hitschfeld and Bordan [1954] (HB algorithm). The measured radar reflectivity is the product of two terms: Z m ðrþ ¼Z a ðrþaðrþ; where Z a (r) (mm 6 m 3 ) is the apparent reflectivity at a given height not subject to any attenuation. In this study, it is assumed that the radar is well calibrated and there are no signal losses due to wet radome effects. Then, the amount of two-way path-integrated attenuation (PIA) (db) is given by AðrÞ ¼exp 2ln10 10 Z r 0 ð3þ kðsþds ; ð4þ where k(s) (db km 1 ) is the specific attenuation at a distance s (km). The relation between the reflectivity and specific attenuation can also be stated as a power law: Z ¼ ck d : [15] On the basis of equations (3), (4), and (5), the apparent reflectivity can be expressed as ð5þ Z m ðrþ Z a ðrþ ¼ R : ð6þ 1 2ln10 r Z mðsþ 1=dds d 10d 0 c [16] In case of severe attenuation the denominator of equation (5) becomes small, causing the HB algorithm to become unstable. Another algorithm (originally developed for spaceborne radar [see Marzoug and Amayenc, 1994]), which is not prone to this source of error, makes use of a mountain reference [Delrieu et al., 1997; Bouilloud et al., 2009]. Unfortunately, in the current study mountainous returns are limited to a region close to the radar and cannot be applied for attenuation correction. Although it can be expected that the amount of PIA is limited for the stratiform precipitation encountered during a winter period [Delrieu et al., 1999, 2000; Uijlenhoet and Berne, 2008], the maximum amount of PIA was set to 10 db to prevent the algorithm from becoming unstable. The parameter values of the Z-k relation (equation (5)) were estimated on the basis of drop size distributions sampled in the Netherlands [Uijlenhoet, 2008], with c ¼ and d ¼ These were assumed to originate from similar storm systems as those observed in the Ardennes region. 3of15 3.2. Anomalous Propagation and Clutter Identification [17] Radar data in mountainous environments can be contaminated by ground clutter (GC) due to sidelobe reflections from topography and/or (partial) blockage by topography [e.g., Delrieu et al., 1995; Gabella and Perona, 1998; Pellarin et al., 2002]. Anomalous propagation (AP) occurs in situations where the vertical gradient of refractivity is large in the lower part of the atmosphere, causing the radar signal to bend down toward the surface, resulting in ground echoes [e.g., Alberoni et al., 2001; Steiner and Smith, 2002; Cho et al., 2006; Berenguer et al., 2006]. Especially at longer ranges, AP-induced GC can result in serious overestimates of the amount of precipitation [Andrieu et al., 1997]. [18] In literature, multiple GC identification techniques have been proposed, using either pulse to pulse reflectivity fluctuations [Wessels and Beekhuis, 1994], radial Doppler velocity information [Joss and Lee, 1995], spatial reflectivity information [Alberoni et al., 2001], or dual-polarization data [Giuli et al., 1991]. Other sources of data have also been used to identify GC, such as digital elevation models, temperature, or satellite information [e.g., Delrieu et al., 1995; Michelson and Sunhede, 2004; Fornasiero et al., 2006]. Currently, most GC identification algorithms make use of a classification scheme using multiple information criteria [e.g., Joss and Pittini, 1991; Joss and Lee, 1995; Steiner and Smith, 2002; Grecu and Krajewski, 2000; Berenguer et al., 2006; Cho et al., 2006]. In the framework of this study, it was decided to use the identification tree as proposed by Steiner and Smith [2002] because no radial velocity information was available. The original algorithm was extended to all elevations to identify GC pixels for the higher radar elevations as well. In the first step, a polar pixel is identified as GC if it has a minimum vertical extent less then 500 m. Next, radar pixels for which spatial variability and vertical variability both exceed a threshold value are identified as clutter. Further details of this method were presented by Steiner and Smith [2002]. Within mountainous environments, beam occultation causes a decrease in the total beam power, resulting in an underestimation by the radar [Delrieu et al., 1995]. For this type of error, no corrections were implemented because the Wideumont radar is situated at a relatively high altitude within the region. It is assumed that most of the observed GC is caused by sidelobe interception instead of direct blockage of the main beam and that therefore no beam occultation occurs Identification of the Vertical Profile of Reflectivity [19] As explained in section 1, variation in the vertical structure of the precipitation field can be a serious source of error, especially for stratiform precipitation [Andrieu et al., 1997;Seo et al., 2000]. Kitchen et al. [1994] applied a correction method which updates the shape of a theoretical stratiform VPR using local meteorological characteristics. Results showed significant improvement in the estimated amount of precipitation. Germann and Joss [2002] estimate a spatially variable apparent VPR on the basis of measured volumetric weather radar data for regions up to 70 km from the radar (the meso- scale). What these investigators and others [e.g., Dinku et al., 2002;Jordan et al., 2003] did not consider is the fact that the radar sampling volume increases with range. [20] A method that does take this aspect into account is the inverse VPR identification technique of Andrieu and Creutin [1995], which was extended for volumetric radar data by Vignal et al. [1999]. The main assumptions behind this method are a spatially uniform VPR over a certain region and the decomposition of the spatial variation of the apparent reflectivity Z a (r) (equation (3)) into a horizontal and vertical component: Z a ðrþ ¼Z REF ðxþz a ðyþ: [21] Here Z REF (x) is the reflectivity at a certain reference level at distance x from the radar, and z a (y) is the apparent vertical profile of reflectivity, which is influenced by the increase of the radar beam volume as a function of range. This latter effect can be written in a simplified way as Z z a ðyþ ¼ f 2 ð 0 ; yþzðyþdy; ð8þ where f is the power distribution of the radar signal, 0 is the radar beam width, and z(y) represents the actual average vertical reflectivity signal. The numerical solution discretizes z(y) into finite intervals of a few hundred meters. For each of these increments at a given range from the radar, its contribution to the total power distribution of the transmitted signal is calculated. In order to estimate the discretized profile of z(y), two types of information are needed. First, an initial estimate of the VPR, for which either a climatological profile or one estimated from the sampled volumetric data can be used. The second type of informati
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