ERAD THE SIXTH EUROPEAN CONFERENCE ON RADAR IN METEOROLOGY AND HYDROLOGY

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1 Real-time adjustment of radar data for water management systems using a PDF technique: The City RainNet Project Chris. G. Collier 1, Rod Hawnt 2 and John Powell 2 1. National Centre for Atmospheric Science, School of Earth & Environment, University of Leeds, Leeds, Yorkshire, LS2 9JT, UK; 2. Hydro-Logic Ltd, Old Grammar School, Church Street, Bromyard, Herefordshire, HR7 4DP UK Chris. G. Collier c.g.collier@leeds.ac.uk 1. Introduction (Dated: 7 June 2010) In this paper we outline a Knowledge Transfer Partnership (KTP) R & D Project involving the University of Leeds and Hydro-Logic Ltd aimed at combining radar and raingauge data in real-time within urban areas. The project also involves three Water Companies, Yorkshire Water, Northumbria Water and Scottish Water, who will purchase three dense raingauge networks for three urban areas Bridlington, Filey and Scarborough, Newcastle and Glasgow (Figure 1). The motivation for the Water Companies is linked to their Asset Management Programmes (AMP) 5 concerned with developing storm intensity alarms before sewer flooding occurs, and post-event analysis through improved storm frequency statistics. For each incident of sewer flooding the UK Environment Agency may impose a fine of around 20k upon a Water Company. At present in the UK urban areas suffer from a lack of dense raingauge networks. Those tipping bucket raingauge networks that are available are costly to maintain, and often have uncertain calibrations. Also radar data in cities is sometimes of poor quality, particularly in short-period convective storms, Hence, the raingauge and radar data will be merged in real-time, with the raw data and merged product being stored in a Web-based archive. Prototype facilities will be provided for the Water Companies to access the quality controlled radar and raingauge data. 2. Raingauge networks The requirements for the raingauge networks in the cities were specified as, low maintenance annual visits only lower cost of ownership than tipping bucket raingauges wireless and battery powered each connection to GSM/GPRS telemetry logger proven measurement technology a focus on medium to high intensity storms good resolution and uncertainty readily available sites having easy access ease of installation These requirements have been satisfied by the use of the OttPluvio2 weighing gauge, which was best in its class in the 2009 WMO raingauge trials. This raingauge is used with the Isodaq Frog telemetry unit. The Water Companies are funding the cost of the raingauge equipment, and installation / maintenance of a minimum of 36 gauges which will deployed in each city on a grid of minimum size area 25 km 2. Work in the Project will ascertain the most appropriate grid configuration. 1

2 3. The UK radar network That part of the UK weather radar network relevant to the Project comprises radar the sites shown in Figure 1. The radars operate at 5.4 GHz (C-band) with an antenna diameter of 3.7 m giving a beam width of nominally 1 degree. Radar does not measure rainfall (R) (or snowfall) directly but measures reflectivity (Z), which is then converted to rainfall (or snowfall) using a Z-R relationship. There are a number of errors associated with this process described by many authors see for example Collier (1996). For the data to be used in this Project many of these errors are removed or reduced by processing at the radar site and/or at the Met Office network centre at Exeter (Kitchen et al., 1994). The Project will be working first with radar data over the Scarborough, Filey and Bridlington, Yorkshire area from the Ingham, Lincolnshire radar. These data will be available over this town on a 2 km x 2 km grid. Figure 1: Part of the UK radar network relevant to this project showing the three urban districts within which the dense raingauge networks are to be installed (blue squares): dark blue 1 km x 1km grid, lighter blue 2 km x 2km grid and light blue 5km x 5km grid (after Met Office) Figure 2 Example of thunderstorm rainfall observed by radar (courtesy Met Office) High resolution (1 km) radar imagery 7 May :35 mm/hr < > Ground clutter removal Although most of the radar echoes arising from the intersection of the radar beam with the ground should be removed by the Met Office processing, it is possible that some spurious echoes, perhaps caused by the anomalous beam propagation conditions, may remain. A first task therefore is to identify radar pixels which might on occasions be contaminated in this way. A number of dry days will be selected, and a detailed analysis of the occurrence of any ground echoes will be carried out at all radar elevations. Each radar grid box within which one of the network raingauges is located will be examined to select which elevation should be used to ensure no ground clutter contamination is present. 5. Raingauge adjustment procedure Many attempts have been made to remove the observed radar bias and reduce the Mean Absolute Error in radar measurements of rainfall using additional measurements provided by the raingauges (see for example Collier et al., 1983). The motivation for much of this work arose from the demands of hydrologists for improved reliable, accurate rainfall estimates. Whilst considerable progress has been made in improving the quality of radar estimates of rainfall, including, most recently, via the use of 2

3 polarisation radar techniques, there remains a need to combine radar and raingauge data. However, particularly in highly variable convective rainfall, the use of raingauge adjustment can be detrimental depending upon the density of the raingauge network. Therefore many operational radars are only adjusted using an average over many raingauges close to the radar site with running daily (or longer) radar raingauge comparisons. Procedures have been developed which derive individual radar (averaged over a number of grid squares) raingauge ratios, and extrapolate the value of the grid providing correction factors (see for example the IRIS Product & Display Manual (February 2006) offered by Vaisala FM). The success of this approach depends very much on the density of the raingauge network. Even if the network spacing is 1 km (say) in convective rainfall this can be insufficient. Figure 2 shows an example of radar rainfall on a 1 km x 1 km grid. Note the very large rainfall gradients in some locations which change rapidly in this type of rainfall. Rosenfeld et al (1994) proposed the combination of radar reflectivity and raingauge data using an approach called the Probability Matching Method (PMM). An adjustment function is derived from an analysis of historic radar and raingauge data. This can be updated and modified as more data are acquired in real-time and the rainfall type changes. One advantage of this approach is that a stable relationship between the raingauge measured rainfall and the radar reflectivity can be derived covering the full range of measured reflectivity values particularly if there are many raingauges available. A further advantage is that the relationship can be used successfully when raingauges fail to operate in real-time, or are unavailable in specific locations because the raingauge network is not uniform due to siting difficulties (a likely situation in practice as shown by the example in Figure 3). In such a circumstance Rosenfeld et al (1995) noted that the adjustment function can be derived from relatively small reflectivity raingauge samples. This system has not been fully tested in a real-time operational system. The current Project has chosen to implement this system, the basis of which is described next. Figure 3: A raingauge site on a roof in Bridlington (courtesy IETG Ltd Leeds) Raingauge 6. The Probability Matching Method (PMM) of adjusting radar estimates of rainfall The PMM has been developed as a specific series of processing steps as follows: 6.1 Conversion of raingauge (G) and radar (R) values to decibels Z (dbz) Z G (dbz) = 10 log 10 G or Z R (db) = 10 log 10 R (1) Note that G = 0.2 mm is equivalent to about 7 dbz 6.2 Default cumulative frequency graph 3

4 For a network of 36 raingauges giving running hourly rainfall totals every 5 minutes, the total number of values available is 36 x 12 = 432 which are used to construct the cumulative frequency versus G and Z graph as shown in Figure 4. For each raingauge location the running hourly radar rainfall totals (corresponding grid box) are extracted then, Step 1: Convert the corresponding radar grid rainfall values to reflectivity (Z R ) values, and the raingauge values to equivalent reflectivity (Z R ) values (equation (1)). Step 2: Construct the graph (Probability Density Function, PDF) using (say) at least three month s data this is the default or climatological graph. Check for, and remove, any significant outliers which may arise from raingauge siting problems. Step3: From the graph derive equation (2) as follows. graph Figure 4: Example of the cumulative frequency Z R and Z G 6.3 Derivation of radar adjusted (calibration) rainfall Note that the crossover point C in Figure 4 will vary depending upon the range of the study area from the radar, the topography, the type of precipitation and the radar calibration. Z C = A/B (C Z R ) (2) C is defined as where Z R = Z G Example from Figure 4 taking the likely values of A, B and C: Z C = 10/20 (32 Z R ) Z cal = Z c + Z R (3) R cal = radar adjusted (calibrated) rainfall R cal = Antilog (Z cal / 10) (4) It is possible that Z R and Z G do not intersect, and therefore the radar is either very much over- or under-estimating at all rainfall rates i.e. the radar is very poorly calibrated. A case of this type is most likely when the radar very much under-estimates the rainfall. In such a case the two curves are likely to come closest together at the highest raingauge equivalent reflectivity observed, and 4

5 therefore the radar curve should be taken as crossing the raingauge curve at this value. An example where this is almost the case was discussed by Telford et al (2002) for the Hameldon Hill radar in North West England. It is unlikely that the radar would consistently over-estimate the rainfall at all rainfall rates, including very small rainfall rates as at such values the radar is more likely not to see the rain at all. The above equations are derived for each raingauge network, and applied to the radar data using an initial (three months say) data set designated the climatological data set. In the case of the three urban areas in Yorkshire (Figure 5) separate equations will be applied to each at first to investigate the importance of spatial variations in the adjustment. If the equations vary significantly between the three areas they will be applied separately, otherwise they will be averaged. Figure 5: Approximate locations of the raingauges in the three Yorkshire Water urban areas. Note the orographic contours and the wooded areas shown green. The sea area is shown blue. The grid squares are 1 km x 1km. (partly after Yorkshire Water) 6.4 Real-time adjustment Step 1: Every 5 minutes derive Z R and Z G for each raingauge location (corresponding radar grid box), and add to the database for the graph. If no rain has occurred leave the graph as it is, and use the previously derived value of equation (1) calculated using the existing data set. Step 2: Re-derive equation (2) from the updated database. Step 3: For each radar grid box value of Z R derive equations (3) and (4) using the new equation (2). Applying this methodology beyond the location of the raingauge network should be done cautiously. It would not be expected to deliver substantial improvements in accuracy of the radar estimates of rainfall at significant distances from the raingauge network. 7. Assessing the performance of the adjustment procedure The performance of the PMM technique will be assesses following the procedures outlined by Krajewski et al (2010). Taking raingauge values as G i, which have not been used to derive the PMM relationship, and the corresponding radar values as R i then, G av = Σ G i / N and R av = Σ R i / N (5) 5

6 E [G/R] = Σ [G i / R i ] / N (6) where σ [G / R] = standard deviation of the ratios between raingauge measurements G and radar rainfall estimates R; the relative dispersion about E[G / R] = 100 [σ [G / R] / E [ G / R]; the average differences = 100 Σ[ ІG i R i І / G i ] /N; and average difference (storm bias removed), random component of the error = 100 І[(G i R i ) E[G / R]]/G i І / N. These parameters will be evaluated separately in summer, autumn, winter and spring. Comparison will be made with the procedure for merging radar and raingauge data currently used in the Met Office Nimrod system similar to that described by Seo and Breidenbach (2002) and Seo et al (2000). The performance of the PMM procedure will also be assessed against the analysis of the quality of the Nimrod radar data in upland areas reported by Lewis and Harrison (2007), and the procedure described by Vignal et al (2000) in Switzerland. References Collier, C. G. (1996) Applications of Weather Radar Systems. A guide to uses of radar data in meteorology and hydrology, 2 nd Edition, John Wiley & Sons, Chichester, 390pp Collier, C.G., Larke, P. R. and May, B. R. (1983) A weather radar correction procedure for real-time estimation of surface rainfall, Quart. J. R. Met. Soc., 109, Kitchen, M., Brown, R. And Davies, A. G. (1994) Real-time correction of weather radar data for the effects of bright-band, range and orographic growth in widespread precipitation, Quart. J. R. Met. Soc., 120, Krajewski, W. F., Villarini, G. and Smith, J. A. (2010) Radar-rainfall uncertainties. Where are we after thirty years of effort?, Bull. Am. Met. Soc., 91, Lewis,M. W. and Harrison, D. L. (2007) Assessment of radar data quality in upland catchments, Meteor. Apps., 14, Rosenfeld, D., Atlas, D., Wolff, D.B. and Amitai, E. (1994) The window probability matching method for rainfall measurement with radar, J. Appl. Met., 33, Seo, D-J. and Breidenbach, J. P. (2002) Real-time correction of spatially nonuniform bias in radar rainfall data using rain gauge measurements, J. Hydromet, 3, Seo, D-J., Breidenbach, J., Fulton, R. and Miller, D. (2000) Real-time adjustment of range-dependent biases in WSR-88D rainfall estimates due to nonuniform vertical profile of reflectivity, J. Hydromet., 1, Tilford, K.A., Fox, N.I. and Collier, C.G. (2002) Application of weather radar data for urban hydrology, Meteor. Apps., 9, Vignal, B., Galli, G., Joss, J. And Germann, U. (2000) Three methods in determine profiles of reflectivity from volumetric radar data to correct precipitation estimates, J. Appl. Met., 39,