A high-resolution radar experiment on the island of Jersey

Transcription

1 METEOROLOGICAL APPLICATIONS Meteorol. Appl. 14: (7) Published online in Wiley InterScience ( DOI: 1.1/met.13 A high-resolution radar experiment on the island of Jersey M. A. Rico-Ramirez, 1 * I. D. Cluckie, 1 G. Shepherd 1 and A. Pallot 1 Water and Environmental Management Research Centre, Department of Civil Engineering, University of Bristol, Lunsford House, Cantocks Close, Bristol BS8 1UP, UK Meteorological Department, Jersey Airport, Jersey JE1 1BY, Channel Islands, UK ABSTRACT: A very high-resolution X-band vertically pointing weather radar was deployed in the island of Jersey, UK, from February to May 4, to study the variation of the vertical reflectivity of precipitation (VPR) in this region. Intercomparison studies were carried out with a C-band scanning weather radar operated by Jersey Met. Department. C-band radar rainfall estimations and raingauge measurements were well correlated at very short ranges (<1 km), but there are clear difficulties in the estimation of precipitation due to ground clutter, and anomalous propagation echoes as well as the variation of VPR. Average VPRs were obtained from the X-band radar that helped reduce the bright band enhancement in C-band scanning weather radar measurements. This paper presents the overall results obtained during this radar experiment. The experiment was one of a series designed to understand the VPR and design removal algorithms for bright band contamination of quantitative radar measurements of precipitation. Copyright 7 Royal Meteorological Society KEY WORDS bright band; radar errors; vertical reflectivity profile; weather radar; X-band radar Received February 6; Revised 19 February 7; Accepted 3 February 7 1. Introduction The quantitative use of radar rainfall estimations in hydrological modelling has been limited due to a variety of sources of uncertainty in the rainfall estimation process. The factors that affect radar measurements are well known and they have been discussed by several authors (Battan, 1973; Austin, 1987; Doviak and Zrnic, 1993; Collier, 1996). This includes factors such as radar calibration, signal attenuation, ground clutter and anomalous propagation variation of vertical reflectivity of precipitation (VPR), range effects, Z-R relationships, variation of the drop size distribution, vertical air motions, beam overshooting the shallow precipitation, and sampling issues among others. The radar reflectivity factor (Z) is a measure of the power reflected back to the radar from precipitation particles. The measured reflectivity is affected by the radar calibration. This requires accurate measurements of transmitted power, beamwidth, antenna gain, wavelength, and pulse width. A well-calibrated radar needs to have a calibration constant known within 1 dbz (Austin, 1987). Signal attenuation along the propagation path by precipitation is considered negligible at 1 cm wavelength, but it is significant at shorter wavelengths (λ 5cm). Corrections for attenuation using only Z, are sensitive to errors in radar calibration (Hitschfeld and Bordan, 1954). * Correspondence to: M. A. Rico-Ramirez, Water and Environmental Management Research Centre, Department of Civil Engineering, University of Bristol, Lunsford House, Cantocks Close, Bristol BS8 1UP, UK. M.A.Rico-Ramirez@bristol.ac.uk The use of dual-polarisation radars at orthogonal polarisations overcomes this problem by using the differential phase measurements to correct for attenuation and differential attenuation (Bringi et al., 1). Water on the radome can also cause attenuation, the extent of which depends upon surface conditions of the radome wavelength and rain rate. The use of a hydrophobic surface produces a substantial improvement in wet transmission loss compared to conventional surfaces. Ground clutter echoes are produced when the main lobe or side lobes of the antenna radiation pattern intercept the ground. The radar usually scans at low elevation angles to obtain measurements close to the ground but echoes from nearby high elevation topography can be misinterpreted as heavy precipitation over that area (Cluckie and Rico-Ramirez, 4). These echoes are called ground clutter and under standard beam propagation conditions their location is well defined and usually techniques such as a map of ground clutter are relatively successful in removing them (Harrison et al., ). However, occasionally the radar beam is bent towards the Earth s surface due to particular vertical temperature and humidity profiles, for example, when warm dry air overlays cooler more moist air (Collier, 1996). This condition is known as anomalous propagation and it represents a serious problem in radar rainfall estimation because its presence and impact are somewhat unpredictable. Doppler radars are able to identify ground clutter echoes because their radial velocity is zero (or close to zero) and their spectrum width is very narrow (Doviak and Zrnic, 1993). Dinku et al. () showed Copyright 7 Royal Meteorological Society

2 118 M. A. RICO-RAMIREZ ET AL. that the local topography surrounding the radar can complicate some error correction schemes. The VPR is an important source of uncertainty in the estimation of precipitation using radars. The variation is largely due to factors such as the growth or evaporation of precipitation, the thermodynamic phase of the hydrometeors, or melting, and wind effects. As the range increases the radar beam is at some height above the ground while the radar sampling volume increases and is unlikely to be homogeneously filled by hydrometeors. As an example, the lower part of the volume could be in rain whereas the upper part of the same volume could be filled with snow or even be without an echo. This variability affects reflectivity measurements and the estimation of precipitation may not represent the rainfall rate at the ground. Snowflakes are generally low-density aggregates and when they start to melt they look like big raindrops to the radar, resulting in larger values of reflectivities compared to the expected reflectivity below the melting layer (Battan, 1973). This phenomenon is called bright band and the interception of the radar beam with melting snowflakes can cause significant overestimates of precipitation by up to a factor of five, and when the radar beam is above the bright band it can cause underestimates of precipitation by up to a factor of four per kilometre above the bright band (Joss and Waldvogel, 199). Several techniques have been developed to correct for the variation of the VPR. Fabry et al. (1998) proposed to extract the VPR from vertical pointing radars to correct scanning radar data. Hardaker et al. (1995) proposed a melting layer model to correct for the bright band using scanning radar data. Gray et al. () developed an approach using a set of typical VPR to estimate the ground reflectivity. Rico-Ramirez et al. (5a) proposed a fuzzy classifier to identify the bright band and the use of an idealised VPR to correct it. Kitchen et al. (1994) proposed an algorithm to convolve an idealised VPR with beam power profile to obtain the expected reflectivity on the ground. In this algorithm, VPR is considered to have three parts: low level orographic growth, a triangular bright band shape with a constant depth of 7 m, and a decrease of reflectivity with height above the bright band. The VPRs are also adjusted using infrared satellite data, and the freezing level is estimated from the UK Met Office s mesoscale model. Vignal et al. (1999) proposed a VPR correction using volume scan radar measurements to estimate the VPR in real-time and Vignal et al. () concluded that even a crude estimate of VPR allows reducing the difference between radar and raingauge measurements. In addition to this, the range effect contributes to the degradation of the VPR. The radar sample volume increases with distance from the radar (beam broadening) and the antenna radiation pattern acts as a low-pass filter smoothing the VPR (Rico-Ramirez, 4). This smoothing is range dependent and it has been shown to degrade the estimated VPR at long ranges. The estimation of rain rates is calculated by using a Z-R relationship such as Z = ar b. Its parameters can be calculated by using measured or simulated drop size distributions (DSD) or by statistical methods (logarithmic regression between measured Z and R). Doviak and Zrnic (1993) concluded that the average deviation in the rain rate estimation from reflectivity measurements due to DSD variability is around 3 to 35%. Joss and Waldvogel (199) suggested that after averaging over space and time the errors in rainfall estimates due to the variability of the DSD rarely exceed a factor of two. Austin (1987) pointed out that a Z-R relationship can be selected according to rain type and geographic location. The use of dualpolarisation techniques has enabled the coefficient of the Z-R relationship to be adjusted in real-time (Bringi et al., ). Sampling issues result from the fact that raingauge observations are point measurements integrated over a period of time whereas radar observations are volume measurements sampled with a given temporal resolution. In addition to this, raingauge measurements are subject to error (Upton and Rahimi, 3), but despite these shortcomings they are probably the most reliable surface point rainfall measurements employed to compare with radar rainfall estimations. However, the problem of comparing point and volume measurements of precipitation remains an inherent difficulty when trying to define ground truth. Several methods address the issue of correcting the mean field bias between radar and raingauge estimations (Smith and Krajewski, 1991; Seo et al., 1999). This paper presents an analysis of some of the above mentioned sources of error in radar rainfall estimates. Particular attention has been given to the variation of the VPR and a methodology is presented to correct scanning weather radar measurements using high-resolution VPRs obtained from a vertically pointing radar. Section presents the characteristics of the radar devices used in this paper as well as an overview of data processing. Section 3 shows some of the persistent sources of error found in scanning radar rainfall estimates and the comparisons with raingauge data are presented in Section 4. Section 5 shows some of the VPRs observed during this experiment and presents a methodology to use high resolution VPRs to correct scanning weather radar measurements.. Observing systems and data processing A weather radar experiment was carried out on the island of Jersey during the period February to May 4, to study the variation of the VPR in this region and to apply this knowledge to improve the estimation of precipitation when using scanning weather radar measurements. The infrastructure of this experiment involved a very high resolution X-band vertically pointing weather radar (hereinafter referred to as X-band radar) deployed by the WEMRC, and a C-band scanning weather radar (hereinafter referred to as C-band radar) operated by the Jersey Met. Department (Figure 1). The X-band radar was deployed to study the variation of the VPR (Figure ). This device measures echoes from Copyright 7 Royal Meteorological Society Meteorol. Appl. 14: (7) DOI: 1.1/met

3 A RADAR EXPERIMENT IN JERSEY 119 Northing [km] Radars (triangle) and raingauge (circles) locations Jersey airport Xband radar Maison St. Louis Obs. Cband radar Easting [km] Figure 1. Locations of radars (triangles) and raingauges (circles) on the island of Jersey. precipitation along the vertical in the atmosphere at specific time intervals. The echo intensity is related directly to reflectivity. Attenuation due to heavy precipitation and the melting layer is more severe at this frequency (Joss and Germann, ). In this experiment, the X-band radar was configured to average 56 samples with an approximate temporal resolution of 1 s, vertical resolution of 7.5 m, and the pulse width of the transmitter was set to 5 ηs to improve the signal-to-noise ratio of the weakest precipitation echoes. (NB: the acquisition system was set up to acquire data every 7.5 m even though the pulse length gives a resolution of 37.5 m). The C-band radar has a beamwidth of 1 o. The dataset were encoded in BUFR format. There were four elevations employed in the scan strategy (Figure 3) which were.5, 1., 1.5, and.5 (hereinafter referred to as b5, b1, b15 and b5 respectively). There was also a composite precipitation scan (hereinafter referred to as bs) which is a modified version of the lowest scan with higher elevation scans inserted to remove ground clutter. The clutter treatment is performed using clutter maps, which are obtained by accumulating scans with the same elevation during days without precipitation. Those range bins, which contain significant signals in non-precipitation conditions, are flagged as ground clutter cells and their value replaced by ones interpolated radially from nearby unaffected cells. The spatial resolution of the data was km with a temporal resolution of 15 min. The radar scan typically presents a square with a side of km mapped onto a rectangular grid. The reflectivity was transformed to an estimate of rain rate using the Marshall and Palmer equation, that is, Z = R 1.6 (Marshall et al., 1955) which is valid for stratiform precipitation. This equation seems to work well for stratiform precipitation in the UK (Harrison et al., ), but variations in the general shape of the DSD may produce changes in the Z-R climatology. Without additional information on the shape of the DSD, it would be an additional source of discrepancy to choose a different Z-R relationship. The radar scans were originally transformed to rain rates without corrections. In addition, raingauges were located at Jersey airport and at Maison St. Louis observatory providing groundtruth measurements (Figure 1). Daily rainfall accumulations were gathered from the official 5-inch funnel gauges at both locations. At Jersey airport, there was one additional tipping bucket raingauge (TBR) with a 1-inch Aerial system Antenna diameter Beam width at half power Antenna relative gain Polarisation m parabolic reflector degrees 38 db Linear horizontal Transmitter Radar frequency Wavelength Peak power Pulse widths Pulse repetition frequency MHz 3. cm (X-band) 44 dbw (5 kw) 5 ηs 5 ηs and 1 µs 13 and 65 Hz Receiver Type Tuning Noise factor IF IF bandwidth Log with balanced mixer Manual 1.5 db Centred on 6 MHz MHz and 4 MHz Acquisition system Sampling frequency Spatial resolution Temporal resolution Averaged samples MHz 7.5 m 1 s 56 pulses with 48 data points Figure. Specifications of the X-band vertically pointing radar (Cluckie et al., ). Copyright 7 Royal Meteorological Society Meteorol. Appl. 14: (7) DOI: 1.1/met

4 1 M. A. RICO-RAMIREZ ET AL. 3. Sources of error in radar rainfall estimations The dominant sources of error in radar rainfall estimations can be identified with a long-term accumulation of radar data. This procedure removes small spatial and temporal variations of precipitation between individual scans and highlights the biases in the rainfall estimation. If the height of the bright band varies throughout the accumulation period, the accumulated rainfall may capture an average enhancement on those ranges where the bright band took place, compared to the surrounding regions. The accumulation of rainfall was performed by adding all the C-band radar scans with the same elevation Clutter echoes Figure 3. C-band radar scan strategy. funnel which recorded rainfall to a resolution of. mm every 6 min. Temporal averaging over 4 h tends to smooth the variability in radar and raingauge measurements. However, for hydrological applications of radar data, shorter averaging intervals are required. Therefore, hourly and daily rain accumulations were calculated using the TBR data. The accumulated rainfall from February to May 4 is shown in Figure 4. This was calculated using data which had been clutter corrected as described in Section. The scans, b5 and b1, are heavily contaminated with ground clutter and anomalous propagation echoes from the north of the French coastline and also some of the Channel Islands. Some weak ground clutter echoes are still present in scans b15 and b5. There is no obvious ground clutter echo on the island of Jersey even though the north and Figure 4. C-band radar rainfall accumulation from February to May 4 for scans b5 b5. This figure is available in colour online at Copyright 7 Royal Meteorological Society Meteorol. Appl. 14: (7) DOI: 1.1/met

5 A RADAR EXPERIMENT IN JERSEY 11 northeast of the island are illuminated by the radar beam at the lowest elevation. Some other ground returns are produced during anomalous propagation (anaprop) conditions. These echoes are always a problem particularly on days with strong low-altitude inversions. Fortunately, from a forecasting perspective, these are usually days when rain is not expected and can, therefore, be safely ignored. It is not always possible to separate the influence of anaprop from conventional ground returns, and the echo pattern is often quite complex. The composite scan bs (not shown) which uses higher elevation scans at short range in an attempt to reduce the impact of ground clutter echoes which are not rejected by the clutter correction scheme still contains some residual ground clutter echoes. To minimise this contamination it is necessary to update periodically the clutter maps. In this case, the variation in the VPR at the higher elevations has to be considered. Another interesting feature shown in the accumulated radar rainfall is a half-moon artefact which is present only in scan b5, and it was caused by sea clutter echoes. These echoes are produced because the radar is located close to the coast and it has a clear view of the sea. This was corroborated with a ground clutter predictor model which is based on simple principles using the terrain elevation and assuming standard atmospheric propagation conditions (Gonzalez Ramirez, 5). This model predicts echoes from the sea between 1 and 3 approximately, in azimuth, which is consistent with the observations. The scan, b5, shows that on average the sea clutter returns produce an overestimation of precipitation by upto a factor of three compared to the surrounding pixels during the whole accumulation period. However the instantaneous intensity of the sea clutter returns depends upon the state of the sea and the radar cross-section has been found to increase with increasing wind speed, wave height, and grazing angle (angle with respect to the tangent to the surface), and to vary in magnitude with the wave direction relative to the radar beam (Collier, 1998). The intensity of the sea echoes also depends upon radar frequency polarisation and radar antenna height above datum (Skolnik, 198). There are also some other extraneous echoes forming a trail from Jersey towards the north of the French coast (scan b5 Figure 4). These are probably due to shipping movements between the French port of St Malo and the Channel Islands. 3.. Variation of the VPR In the C-band radar s Plan Position Indicator (PPI) scans, the bright band looks like an annular feature with large intensity values compared to the surrounding regions. Its intensity is highly dependent on the rain rates associated with it, the range of the measurements, and beamwidth of the radar. The annular feature can be clearly observed on the accumulated scans b15 and b5, and it is also present in scans b5 and b1 although the annular feature associated with it is more spread and distorted (Figure 4). Beyond the bright band, the reflectivity shows a decrease to the point of overshooting the precipitation. Further 4 hr rain accumulation (mm) Total accumulated rainfall (mm) (a) Daily raingauge raingauge comparisons (1 3): r=.89, rmse=1.97 mm, error=16%, bias= 7% (1 ): r=.94, rmse= mm, error=35%, bias= 7% ( 3): r=.89, rmse=.3 mm, error=46%, bias= % 3//4 3//4 14/3/4 3/4/4 3/4/4 13/5/ (1) Jersey airport () Maison St. Louis Observatory (3) Jersey airport (TBR) (1) Jersey airport () Maison St. Louis Observatory (3) Jersey airport (TBR) (b) Total rainfall accumulations from raingauges 3//4 3//4 14/3/4 3/4/4 3/4/4 13/5/4 Figure 5. Raingauge-raingauge comparisons at Jersey airport and Maison St Louis Observatory. Copyright 7 Royal Meteorological Society Meteorol. Appl. 14: (7) DOI: 1.1/met

6 1 M. A. RICO-RAMIREZ ET AL. discussion on VPRs gathered from the X-band radar is presented in detail in Section Radar and raingauge comparisons at short ranges Daily raingauge comparisons are shown in Figure 5. The most marked difference was on 6 February 4 with the TBR. The daily rainfall calculated with the TBR registered 5. mm on the 6th and mm on the 7th, whereas the daily totals from the official 5-inch funnel gauges registered 1.1 mm at Jersey airport and 18.4 mm at Maison St Louis observatory during the 6 February. This was because it snowed on those two days (Figure 1, and the VPR which shows the absence of bright band). The 5-inch funnel gauge readings were, during this period, actually an estimate made using the equivalent snow depth, whereas the automatic TBR would have had problems recording snowfall once the funnel was blocked by snow. Daily accumulated rain estimated with the C- band radar at pixel locations of Jersey airport and Maison St Louis Observatory is shown in Figure 6. The rain time series from the different scans are compared with scan b5. As shown in both locations, the error tends to increase with elevation even though the differences in height between the lowest and the highest elevations are around 5 m. These differences could be largely due to the displacement of precipitation cells between 4 hr rain accumulation (mm) 4 hr rain accumulation (mm) (a) Daily radar accumulations at Jersey airport (scan b5 scan b1) r=.98, rmse=.7 mm, error=14%, bias= 3% (scan b5 scan b15) r=.95, rmse= mm, error=4%, bias= % (scan b5 scan b5) r=.91, rmse=1.44 mm, error=37%, bias= 4% 3//4 3//4 14/3/4 3/4/4 3/4/4 13/5/ Scan b5 Scan b15 Scan b1 Scan b5 Scan bs Scan b5 Scan b15 Scan b1 Scan b5 Scan bs (b) Daily radar accumulations at Maison St. Louis Observatory (scan b5 scan b1) r=.93, rmse=1.58 mm, error=43%, bias=1% (scan b5 scan b15) r=.93, rmse=1.78 mm, error=41%, bias=16% (scan b5 scan b5) r=.9, rmse=.1 mm, error=43%, bias=4% 3//4 3//4 14/3/4 3/4/4 3/4/4 13/5/4 Figure 6. Daily C-band radar rainfall estimations at different elevations at Jersey airport and Maison St Louis Observatory. Radar accumulations [mm] hr radar raingauge comparisons r =.9, rmse = 1.56 mm error = 4%, bias = 6.1% Radar accumulations [mm] hr radar raingauge comparisons r =.68, rmse =.53 mm error = 8%, bias = 3.7% Raingauge accumulations [mm] Raingauge accumulations [mm] Figure 7. Daily and hourly C-band radar and raingauge comparisons during stratiform precipitation events (using radar data from scan bs). Copyright 7 Royal Meteorological Society Meteorol. Appl. 14: (7) DOI: 1.1/met

7 A RADAR EXPERIMENT IN JERSEY 13 4 hr rain accumulation (mm) Total Rainfall (mm) (1) Jersey airport () Maison St. Louis Obs. (3) Averaging 1 (4) Jersey airport (TBR) (5) X band radar X band radar raingauge comparisons (1 5) r=.87, rmse=. mm, error=49%, bias= 8% ( 5) r=.9, rmse=.1 mm, error=49%, bias= 3% (3 5) r=.9, rmse=1.96 mm, error=46%, bias= 5% (4 5) r=.81, rmse=.1 mm, error=57%, bias= % 3//4 4/3/4 14/3/4 4/3/4 3/4/4 13/4/4 3/4/4 3/5/4 13/5/4 (1) Jersey airport () Maison St. Louis Obs. (3) Averaging 1 (4) Jersey airport (TBR) (5) X band radar X band radar raingauge comparisons 3//4 4/3/4 14/3/4 4/3/4 3/4/4 13/4/4 3/4/4 3/5/4 13/5/4 Figure 8. Daily X-band radar and raingauge comparisons during stratiform and convective rainfall events. elevations causing the sampling of slightly different volumes of precipitation, the VPR effect, missing radar scans in some of the elevations and time delays. The latter effect is due to the fact that the time interval between each radar scan was approximately one minute, so that between the lowest and the highest elevations there would be a time difference of approximately three minutes. In order to compare radar rainfall estimations with raingauge measurements, the period between and 8 February 4 was removed because there were no radar data available during this period due to operational difficulties. Daily and hourly radar and raingauge comparisons are shown in Figure 7 with a root mean square error (RMSE) of 1.56 and.54 mm respectively. The hourly comparisons only include the TBR, whereas the daily comparisons included all the three raingauges. It is interesting to note that the hourly comparisons present more scatter than the daily comparisons, very likely due to variations of the DSD, differences in the radar and raingauge sampling techniques and raingauge-related errors. These variations are smoothed out to some extent when accumulating during longer periods (e.g. 4 h). However, for hydrological applications of weather radar, radar rainfall accumulations over shorter time scales are required (e.g. less than 1 h). A similar radar experiment carried out in Poland produced an RMSE of around mm and 1 mm for daily and hourly radar and raingauge comparisons respectively (Rico Ramirez et al., 5b). It would be interesting to assess the impact of this radar error characteristic in hydrological modelling. 5. The VPR as observed by the X-band radar The radar constant of the X-band radar was calibrated using only raingauge measurements during stratiform precipitation (using Z = R 1.6 ) during 4 h accumulation intervals. The result is shown in Figure 8. As shown, the radar slightly underestimates the rain accumulations, particularly during heavy precipitation. This may be due to the fact that Rayleigh scattering is not appropriate for large raindrops (i.e. heavy precipitation) and therefore the reflectivity is no longer a function of the sixth power of the drop diameters. Typical VPR patterns in Height Time Indicator (HTI) format associated with stratiform precipitation events are shown in Figure 9. The change of reflectivity intensity with height is indicative of changing the particle size phase growth and evaporation. Melting of snow particles produces an enhanced reflectivity known as the bright band, whereas hailstones and very large raindrops also produce high reflectivities. The bright band is clearly shown in these stratiform precipitation events. For instance, Figure 9(a) exhibits an intense bright band centred on 1.7 km. The reflectivities beneath this height are caused by raindrops and, higher up, by snowflakes. Figure 9(b) shows the bright band centred at.9 km on 17 April 4, at, and then ascends to around km on 18 April 4, at due to the passage of a Copyright 7 Royal Meteorological Society Meteorol. Appl. 14: (7) DOI: 1.1/met

8 14 M. A. RICO-RAMIREZ ET AL (b) (a) /4/4 17/4/4 dbz :: 3::7 :44:57 :7:9 3:3: 4:5:33 6:15:3 7:37:3 5. (d) /4/4 7/4/4 8/4/4 8/4/4 8/4/4 8/4/4 8/4/4 8/4/4 dbz 3:: 3:59:58 :59:56 1:59:56 :59:56 3:59:58 4:59:58 6:: (c) //4 1//4 1//4 1//4 1//4 1//4 1//4 1//4 dbz :: :5: 1:4: :3: 3:: 4:9:59 4:59:59 5:5:. 1/3/4 1/3/4 1/3/4 1/3/4 1/3/4 1/3/4 1/3/4 1/3/4 dbz 16:6: 16:9:44 16:13:9 16:17:15 16:1: 16:4:45 16:8:31 16:3:16 Figure 9. VPR during stratiform precipitation. This figure is available in colour online at //4 6//4 6//4 6//4 6//4 6//4 6//4 6//4 dbz 18:9:59 18:59:58 19:9:58 19:59:58 :9:58 :59:58 1:9:58 1:59: (a) 5. VERTICAL REFLECTIVITY PROFILE OF PRECIPITAION 5..6 (b). 8//4 8//4 8//4 8//4 8//4 8//4 8//4 8//4 dbz :41: :5:14 3:3:9 3:14:44 3:5:59 3:37:15 3:48:3 3:59:46 Figure 1. VPR during convective precipitation. This figure is available in colour online at warm front. Figure 9(c) shows a stratiform rainfall event with evaporation of precipitation at low levels. Sometimes, strong convection around the melting level disrupts the typical bright band feature as shown in Figure 9(d). This is caused by strong turbulence around the melting level making it difficult to identify the bright band. Figure 1 shows convective rainfall events associated with cells with high reflectivities. The VPR during the convective rain does not present a typical form as in stratiform precipitation. High reflectivities are extended along the vertical and it is often difficult to discern at what level the melting owing takes place to the constant mixing induced by convective processes. An interesting feature is observed when there are temperature inversions. As ice particles fall through an upper C isotherm layer they start melting then re-freezing until melting begins again in another layer Copyright 7 Royal Meteorological Society of sub-zero temperature. The result, when observed by a vertically pointing radar, is a double bright band (Figure 11). Double bright bands are common in fronts which cause temperature inversions (Fabry, 1994) VPR correction scheme A bright band detection algorithm developed by RicoRamirez and Cluckie (7) was applied to highresolution VPRs from stratiform rainfall events. These events were manually selected when there was a clear bright band. Temporal averaging of VPRs was performed to smooth the variations between consecutive profiles. Some of the results are shown in Figure 1 using different averaging intervals. The variations in detection of the bright band boundaries are smoothed when large averaging intervals are employed (e.g. 5 min), however, this Meteorol. Appl. 14: (7) DOI: 1.1/met

9 A RADAR EXPERIMENT IN JERSEY (a) /3/4 :5: /3/4 :5:18 /3/4 :54:33 /3/4 :56:47 /3/4 :59: /3/4 3:1:17 /3/4 3:3:31. /3/4 dbz :5:46 REFLECTVITY [dbz] (b) 1/4/4 7:59:59 1/4/4 8:1:3 1/4/4 8::3 1/4/4 8:3: 1/4/4 8:4: 1/4/4 8:5:1 1/4/4 9::. 1/4/4 dbz :1:1 REFLECTVITY [dbz] Figure 11. VPR with two bright bands. This figure is available in colour online at VERTICAL REFLECTIVITY PROFILE OF PRECIPITATION 5. VERTICAL REFLECTIVITY PROFILE OF PRECIPITATION (a) 17/4/4 :: 17/4/4 3::7 :44:57 :7:9 3:3: 4:5:33 6:15:3 7:37:3. dbz (b) 17/4/4 :: 17/4/4 3::7 :44:57 :7:9 3:3: 4:5:3 6:15: 7:37:31. dbz VERTICAL REFLECTIVITY PROFILE OF PRECIPITATION 5. VERTICAL REFLECTIVITY PROFILE OF PRECIPITATION (c) 17/4/4 :: 17/4/4 3::7 :44:57 :7:9 3:3: 4:5:33 6:15:3. dbz 7:37:3 (d) 17/4/4 :: 17/4/4 3::7 :44:57 :7:9 3:3: 4:5:3 6:15: 7:37:31. dbz Figure 1. Bright band detection using (a) 5 s (b) 6 s (c) 1 s and (d) 3 s temporally averaged VPR data. This figure is available in colour online at does not mean that the detection of the bright band is incorrect when using short averaging intervals. There are large variations between consecutive VPRs mainly due to noise, and this is reflected in the detection of the bright band boundaries. Conversely, large averaging intervals produce a smoother detection but with a reduction in the maximal bright band reflectivity. A compromise must be made between having enough data to enable the VPRs to be smoothed whilst maintaining the bright band intensities. For this analysis, an average interval of 5 s was selected. The reflectivity measurements above (Z e(snow) ) within (Z e(peak) ) and below (Z e(rain) ) the bright band and the bright band thickness were extracted and the scatter plots are shown in Figure 13. It may be seen that Z e(peak) is strongly dependent on the value of Z e(rain) which was also shown by Fabry and Zawadzki (1995) in Montreal. Z e(peak) having a value dbz above Z e(rain) and that this value decreases with the rain reflectivity at arateof.9z e(rain). The reflectivity enhancement due to the bright band is therefore Z = Z e(rain) [dbz]. The reflectivity measurements above the bright band and the bright band depth also depend on the rain reflectivity (Figure 13(b) and (d) respectively). There is a trend of increasing bright band thickness when increasing the rain reflectivity which is also congruent with the observations obtained by Fabry and Zawadzki (1995). They pointed out that large aggregates associated with heavy precipitation will take more time to melt because they fall faster increasing the bright band thickness. Additional factors such as the lapse rate and the relative humidity also contribute to the bright band thickness (Hardaker, 1993). All the VPRs extracted were grouped according to the maximal bright band intensity and their heights were normalised with respect to the height of bright band peak. Copyright 7 Royal Meteorological Society Meteorol. Appl. 14: (7) DOI: 1.1/met

10 16 M. A. RICO-RAMIREZ ET AL. Figure 13. Scatter plots among the reflectivity measurements above the bright band (Z e(snow) ) within the bright band (Z e(peak) ) and below the bright band (Z e(rain) ) at X-band frequencies over Jersey. Height from bright band top [km] Z [dbz] Figure 14. VPR climatology for different bright band peak intensities. All the VPRs with the same bright band peak intensity were averaged to produce a VPR climatology. This was carried out in steps of 1 dbz and the resulting VPRs are shown in Figure 14. Their heights are shown with respect to the height of the top of the bright band, which can be approximated to the height of the wet-bulb freezing level. All the VPR shown in Figure 14 were obtained averaging at least 5 individual profiles. These VPRs are proposed to correct C-band scanning radar reflectivity measurements for the variation of VPR. The correction procedure is similar to that performed by Kitchen et al. (1994) with the major difference that the VPR is not an idealised profile with a triangular bright band with a fixed thickness of 7 m. An initial VPR climatology shown in Figure 14 is selected and its height is adjusted according to the height of the freezing level which can be obtained from a numerical weather prediction model or radiosonde measurements. The beam power profile is convolved with the VPR climatology and an estimate of reflectivity at the height of the measurement of the scanning radar is calculated (Ẑ m ). Following the correction proposed by Brown et al. (1991) and adopted by Kitchen et al. (1994), the reflectivity observed by a scanning radar is the result of weighting the beam power profile with the observed VPR, and it is calculated by: Ẑ m = β α Z(θ)f(θ)dθ (1) where α and β are the lower and upper elevation angles corresponding to the half power points Z(θ) is the VPR relative to the beam centre and f(θ)dθ is a fraction of the beam power at the angle θ. This fraction is calculated by: f(θ)dθ = β α P(θ)dθ P(θ)dθ () where P(θ) is the beam power profile which is proposed according with the characteristics of the radar antenna. Copyright 7 Royal Meteorological Society Meteorol. Appl. 14: (7) DOI: 1.1/met

11 A RADAR EXPERIMENT IN JERSEY 17 The two-way response of the beam power profile relative to the beam centre is given by (Brown et al., 1991): [ ] sin(kθ) 4 P(θ) = (3) kθ where k is a function of the beamwidth (θ 1 ) of the radar that is k = /θ 1. The reflectivity Ẑ m is compared with the actual reflectivity measured with the scanning radar (Z m ). This procedure is applied to all the VPR climatologies and that profile with the smallest difference Ẑ m Z m is selected. This difference has to be relatively small to be valid (e.g.<1 dbz). The measured reflectivity Z m is replaced with the expected reflectivity in rain (Z rain ) from that VPR with the smallest difference. 5.. VPR correction results The proposed VPR correction was applied to three widespread precipitation events with clear bright bands (i.e. annular features). The correction was performed to scans b5 and b15 in order to compare with the lowest scan b5. The heights of the VPRs were adjusted according to an estimate of the freezing level (i.e. bright band top) as measured with the X-band radar. However, in an operational environment, the height of the wet-bulb freezing level has to be used instead. The freezing level was assumed to be constant at any point of the scan. A particular VPR correction is shown in Figure 15. For this particular period, the X-band radar collected VPRs with maximum bright band intensities of 3 dbz, a bright band thickness of 5 m, and a bright band height of 1.5 km. The bright band enhancement was removed and the result was compared with the lowest scan b5. The performance of this method is calculated by correcting all the scans within a given precipitation event, and all the corrected and uncorrected reflectivity measurements are extracted and grouped according to the height of the freezing level. The RMSE are calculated and the results are shown in Figure 16. Kitchen et al. s (1994) algorithm was also implemented (Correction B) assuming a decrease in reflectivity in snow of 5 dbzkm 1. The performance of the VPR correction within the bright band is comparable with Kitchen et al. s (1994) algorithm at least for these precipitation events. The proposed VPR correction reduces the bright band enhancement but it has a lower performance in correcting the VPR above the bright band. This is because the spread of reflectivities above the bright band from different VPRs is very narrow, and a similar value of snow reflectivity can lead to different bright band intensities, and, therefore, there is more uncertainty in selecting the correct VPR. Although Kitchen et al. s (1994) algorithm is intended to correct the whole VPR they also pointed out that systematic errors persist at ranges when the radar beam is above the bright band. The analysis of VPRs presented in this Section show that a set of averaged VPRs obtained at X-band frequencies (i.e. attenuated frequencies) can help to reduce the bright band enhancement. However the use of X-band VPRs is limited to bright band peak intensities less than about 4 dbz because of the lack of a large number of samples to obtain reliable VPRs, and also because of attenuation effects and the Mie scattering effects for large particles. The VPR climatologies shown in Figure 14 have a variable bright band thickness, and the height of the peak of the bright band varies with its intensity. In contrast Kitchen et al. s (1994) VPR climatology has a constant bright band thickness of 7 m and the peak of the bright band is always at the same height (with respect to freezing level). One of the reasons of the success of Kitchen et al. s (1994) algorithm is the fact that the bright band thickness of 7 m is large enough to cover all the range of possibilities of bright band thicknesses (Figure 13). Additionally, this type of bright band correction is sensitive to the height of the freezing level (Kitchen et al., 1994; Borga et al., 1997). Kitchen et al. (1994) pointed out that the bright band correction requires the height of the bright band to be known within m and according to Mittermaier and Illingworth (3) the UK operational forecast model can provide the height of the bright band with a RMSE of 15 m. Figure 15. Correction of the bright band using the method outlined in Section 5.1. The dashed line indicates the height of the wet-bulb freezing level. The event was recorded on 4 March 4, 1:15 hr. This figure is available in colour online at Copyright 7 Royal Meteorological Society Meteorol. Appl. 14: (7) DOI: 1.1/met

12 18 M. A. RICO-RAMIREZ ET AL. RMSE scans b5 & b5 [dbz] Event: 4/3/4 8: 1:15 scans b5 without correction scans b5 with correction A scans b5 with correction B RMSE scans b15 & b5 [dbz] Event: 4/3/4 8: 1:15 scans b15 without correction scans b15 with correction A scans b15 with correction B Height from freezing level [m] Height from freezing level [m] RMSE scans b5 & b5 [dbz] Event: 8/4/4 :15 6: scans b5 without correction scans b5 with correction A scans b5 with correction B RMSE scans b15 & b5 [dbz] Event: 8/4/4 :15 6: scans b15 without correction scans b15 with correction A scans b15 with correction B Height from freezing level [m] Height from freezing level [m] 1 Event: 16/4/4 1: :15 1 Event: 16/4/4 1: :15 RMSE scans b5 & b5 [dbz] scans b5 without correction scans b5 with correction A scans b5 with correction B RMSE scans b15 & b5 [dbz] scans b15 without correction scans b15 with correction A scans b15 with correction B Height from freezing level [m] Height from freezing level [m] Figure 16. VPR correction using the method outlined in Section 5.1 (Correction A) and using Kitchen et al. s (1994) algorithm (Correction B). 6. Conclusions This study carried out in the island of Jersey, highlights some of the difficulties in the estimation of precipitation using weather radars. The long-term data accumulation from the C-band radar highlights problems associated with ground clutter echoes from the north of the French coast and some of the Channel Islands sea clutter echoes, and echoes very likely due to shipping moving between the French coast and the Channel Islands. To minimise the impact of these unwanted echoes in rainfall estimation, it is necessary to extend the areas prone to clutter contamination in the clutter map and to use higher clutter-free elevations to fill these gaps. The long-term C-band radar data accumulations also show that there are problems associated with the variation of the VPR of precipitation, particularly in the higher elevations. This turns out to be very important because higher elevations may be used to fill the gaps in lower elevations prone to ground clutter contamination. This is one of the reasons for the importance of studying the variation of the VPR and to propose corrections to minimise its impact in rainfall estimation algorithms. The analysis of VPRs from the X-band radar shows that averaged VPRs can be used to correct scanning weather radar measurements. The proposed VPR correction reduces the bright band enhancement but it has a lower performance in estimating rain reflectivities using measurements above the bright band. Comparisons between radar and raingauges have been carried out at very short ranges. For hourly comparisons, Copyright 7 Royal Meteorological Society Meteorol. Appl. 14: (7) DOI: 1.1/met

13 A RADAR EXPERIMENT IN JERSEY 19 the RMSE is approximately.54 mm with a correlation of.71 and for daily comparisons the RMSE is approximately 1.56 mm with a correlation of.9. These errors are a combination of DSD variations differences in the radar and raingauge sampling techniques and raingaugerelated errors. This experimental study was the first to be made in relation to the Jersey radar and contributes to the early investigation of the potential quantitative use of the data for hydrological modelling purposes as well as to operational rainfall forecasting. Acknowledgements The authors would like to thank the EPSRC Flood Risk Management Research Consortium (GR/S7634/1), the EU FP5 FLOODRELIEF project (EVKI-CT-- 117), and CONACYT (scholarship ) for partial support in the development of this radar experiment. We also acknowledge the State of Jersey, Public Services Department, for the use of their pumping station site at Archirondel to locate the vertically pointing radar. 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Three methods to determine profiles of reflectivity from volumetric radar data to correct precipitation estimates. Journal of Applied Meteorology 39: Copyright 7 Royal Meteorological Society Meteorol. Appl. 14: (7) DOI: 1.1/met