A real-time procedure for adjusting radar data using raingauge information II: Initial performance of the PMM procedure

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A real-time procedure for adjusting radar data using raingauge information II: Initial performance of the PMM procedure C. G. Collier 1, J. Black 1,2, J. Powell 2, R.

uk 2 Hydro-Logic Ltd, Old Grammar School, Church Street, Bromyard, Herefordshire, HR7 4DP (Dated: 15 May 2012) Presenting author Chris. G. Collier 1.

outlined in Part I of the accompanying paper.

1 A real-time procedure for adjusting radar data using raingauge information II: Initial performance of the PMM procedure C. G. Collier 1, J. Black 1,2, J. Powell 2, R. Mason 2 l National Centre for Atmospheric Science, School of Earth & Environment, University of Leeds, Leeds, Yorkshire, LS2 9JT, United Kingdom, c.g.collier@leeds.ac.uk 2 Hydro-Logic Ltd, Old Grammar School, Church Street, Bromyard, Herefordshire, HR7 4DP (Dated: 15 May 2012) Presenting author Chris. G. Collier 1. Introduction This paper sets out the results of the first case study for 6 th July 2011 in part of the Yorkshire Water region produced by the City RainNet Knowledge Transfer Partnership (KTP) project outlined in Part I of the accompanying paper. The City RainNet KTP project is a 27-month, joint project involving Hydro-Logic Ltd and the University of Leeds to develop a demonstrator of a real-time system to improve the quality and accuracy of radar rainfall data. Yorkshire Water, Scottish Water and Northumbrian Water are also partners in the project. The project began in August 2010 and finishes in November The procedure adopted to calibrate the radar rainfall data with raingauge data uses a modified version of the Probability Matching Method (PMM) algorithm (Rosenfeld et al 1993, 1994). Networks of OTT Pluvio2 weighing-principle raingauges together with Isodaq Frog loggers have or are being installed in the areas of each of the three Water Company partners. The rainfall data is transmitted by the Frog Loggers using GPRS over the mobile phone data networks. In this paper Met Office radar rainfall data is used and rain fall data from the Yorkshire Water City RainNet raingauge network is used. 2. Case study This case study focuses on the 6th July 2011 during which a slow moving waving frontal system was located north of Yorkshire with a convective trough situated over the south Yorkshire coast (Fig. 1). This day was chosen because there was an extended period of rainfall lasting most of the day. Indeed the Met Office radar rainfall data registers rain throughout the whole day in the area covered by the Yorkshire Water City RainNet raingauge network. Fig. 2 shows a screen shot of the prototype Geographic Information System (GIS) which shows the extent of the Met Office radar composite data used in this study. The round circles mark the location of six raingauges, which show the extent of the subset of the Yorkshire Water City RainNet raingauge network which was used in the case study. The colours on the raingauge locations represent the difference between radar rain intensity and radar rainfall intensity.. Fig. 1: Synoptic situation 12:00 6 July 2011 Mean sea level pressure: Met Office Fig. 2: Screen shot of the prototype GIS 03:50 6 July2011

2 3. Applying the PMM technique to locations where there are raingauges Results were generated by modifying radar rainfall data adjusted using the PMM algorithm in radar squares where raingauges are located. A subset of 22 raingauges was chosen from the Yorkshire Water City RainNet raingauge network. Two approaches were examined. The first was to use the radar data as supplied by the Met Office. The second was to use geographically interpolated radar data. In the second approach, the radar data for radar squares where there are raingauges was geographically interpolated to the location of the raingauge in that square. There are a total of 1304 samples of radar and rainfall data. However results are presented for only input radar values which are over 0.55mm/hr as these are the only ones which are modified using the modified PMM algorithm. The means of analysis is to count the differences between the radar data and the corresponding raingauge values, which was carried out in four bands. To assess the improvement that the modified PMM algorithm makes, the analysis was applied to input radar rainfall data and radar rainfall data adjusted using the modified PMM algorithm described in the previous section. The four bands relate to the absolute difference between the radar and rainfall data and are given the following "traffic light" colours: Green: absolute difference 0.5 mm/hr Amber: absolute difference > 0.5 mm/hr and 1.0 mm/hr Red: absolute difference > 1.0 mm/hr and 2.0 mm/hr Purple: absolute difference > 2.0 mm/hr Table 1 shows the results of using Met Office cutout radar rainfall data. Note that results are only presented for input radar samples which are equal to or greater 0.55mm/hr as these are values which are adjusted. The threshold means that are 778 samples out of the 1304 samples were considered. Also shown is the performance of the modified PMM procedure. Traffic Light Band Met Office Radar Data Modified PMM Adjusted Data Modified PMM Adjusted Data % Improvement for band Green (diff <=0.5mm/hr) % Amber (diff >0.5 <=1mm/hr) % Red (diff >1 <=2 mm/hr) % Purple (diff > 2 mm/hr) % Total Count Table 1: Results of traffic light analysis to input Met Office radar data and Modified PMM adjusted Met Office radar data 18% 36% 8% 10% 21% 14% 68% 25% Fig. 3: Pie charts showing result of traffic light analysis to input Met Office radar data (left) and modified PMM adjusted Met Office radar data (right)

3 The numbers of counts in each traffic light band can also be visualised by means of pie charts. These are shown in Fig. 3. Examining Table 1 and Fig. 3 indicates that the overall error is reduced with significant increases to the green band and significant reductions to the red and purple and red bands, with a smaller reduction in the amber band. This indicates a shuffling down of the differences from the amber, red and purple bands into the green band. The modified PMM algorithm was also applied to geographically interpolated Met Office radar data. Because the data are modified there are a different number of samples over 0.55 mm/hr. There are 789 samples out of 1304 over 0.55 mm/hr. The outcome of the traffic light analysis applied to the results of the modified PMM algorithm using geographically interpolated radar data are shown in Table 2. Pie charts of the counts in the colour bands for the input radar data and the results of applying the modified PMM algorithm are shown in Fig. 4. It is seen that the percentage of the number of points for the input data indicates that the geographically interpolated radar data is closer in value on average to the raingauge data, which is to be expected. The results of applying the modified PMM algorithm again result in an increase in accuracy. The percentage in points in each band is similar to those shown in Table 1, for the Met Office data, i.e. radar data which is not geographically interpolated. There is a smaller percentage in the green band by 6.6%, but this is small. The percentage improvement is not as large as in the previous analysis. This is to be expected as the input data are closer to the raingauge data on average to start with. Traffic Light Band Geographically Interpolated Met Office radar Data Modified PMM Modified PMM Adjusted Data Adjusted Data using Geographically Interpolated Radar Data Green (diff <=0.5mm/hr) % Amber (diff >0.5 <=1mm/hr) % Red (diff >1 <=2 mm/hr) % Purple (diff > 2 mm/hr) % Total Count % Improvement for band Table 2: Results of traffic light analysis to input spatially interpolated Met Office radar data and the input Met Office radar data 17% 11% 38% 10% 21% 14% 65% 24% Fig. 4: Pie charts showing result of traffic light analysis to input geographically interpolated Met Office radar data (left) and modified PMM adjusted geographically interpolated Met Office radar data (right) Overall the results indicate that the use of the modified PMM algorithm produces an overall reduction in error between the radar rainfall data and raingauge data, when using both the input Met Office modified PMM adjusted radar data and the geographically interpolated radar data using the modified PMM procedure. The counts in green band (smallest difference) is increased and the counts in the other bands are decreased.

4. Error statistics using 22 raingauges The mean absolute error [1/n * sum( abs(radar-raingauge) )] for the input radar data compared to raingauge data is 1.

4 4. Error statistics using 22 raingauges The mean absolute error [1/n * sum( abs(radar-raingauge) )] for the input radar data compared to raingauge data is 1.49, whereas the value for the radar data adjusted using the modified PMM algorithm compared to raingauge data is The mean bias [1/n*sum(radar)/sum(raingauge)] is and respectively. The mean absolute error [1/n * sum(radarraingauge)] for the input radar data is , whereas for the radar data using the modified PMM algorithm is The average difference, referred to as the ERROR, was used by Atencia et al (2010) who also used the BIAS defined as log Σ R i / Σ G i. They also used the Root Mean Square Error (RMSE) = [Σ(R i G i ) 2 / N ] ½. In the four case studies using an advection correction, values of BIAS ranged from to -0.14, ERROR ranged from to 4.20 and RMSE ranged from 0.92 to Note that the Met Office radar data does include an advection correction. The root mean square error is a measure of the quantitative agreement between the time series of raingauge and radar values and has been widely used by the Met Office. However, as pointed out by Lewis and Harrison (2007) this is highly correlated with the magnitude of the surface rain-rate such that poorly performing radars in light rain could appear more successful than radars in heavy rain. Hence a better statistic which gets around this problem is the Root Mean Square Factor (RMSF) (Golding, 1998, 2000), where N is the number of radar (R) gauge (G) pairs. Clearly there is a problem when the rain gauge is zero. Golding (1998, 2000) and Lewis and Harrison (2007) use a lower threshold of 1/8 mm/hr for rainfall rates and 0.2 mm for hourly accumulations i.e. data less than these thresholds are ignored. We will have to do something similar i.e. use a threshold equal to the minimum that the OTT Pluvio2 gauge can measure namely 0.01 mm in 5 minutes (0.12 mm/hr) if we are to produce comparable statistics. Values of this quantity were reported by Lewis and Harrison (2007) for hilly areas after rain gauge calibration between 3.13 and Golding (2000) reported a mean value of about 2.4 for hourly accumulations noting this value is within the range of representativity errors reported by Kitchen and Blackall (1992). Using the mean error measure, then the root mean square factor in the present study for the input radar data compared to raingauge data is 2.54, whereas the value for the radar data adjusted using the modified PMM algorithm compared to raingauge data is Assessing the performance of the modified PMM algorithm where there are no raingauges In order to assess the performance of the modified PMM algorithm it is necessary to derive pseudo raingauge values at locations where there are no raingauges. A common and accurate software technique for geographical interpolation was used to interpolate from a subset of initially 11 raingauges (Source Subset) to a second subset of 11 gauges (Target Subset) as outlined in Part I of this paper. The raingauge locations from the Yorkshire Water raingauge network are shown in Fig. 5. Fig. 5: The arrangement of the source and target raingauge locations. Target gauges are marked with a red circle; source gauges are marked with a square

5 The results are in the range where the radar is adjusted i.e. from (equal to or greater than) 0.5mm/hr to (less than) 480mm/hr. They are computed by comparing raingauge values to radar values, where the original radar values are 0.55mm/hr and < 480mm/hr. Results are calculated for the entire day of 6th July 2011 for 11 gauges by interpolating the raingauge values from the Source Subset for a given 5 minute period to the target locations.. This gives a total of 3168 values (12 readings in an hour, 24 hours and 11 rain gauges). However for the target gauge sites there are only 422 values where the original radar is 0.55mm/hr and < 480mm/hr. Figure 6 shows the results of a traffic light analysis for the original radar data (upper frame) and the modified PMM radar data (lower frame). Note that the maximum of the original radar data is 31.8 mm/hr, and the maximum source raingauge intensity is 36.4 mm/hr at Wilsthorpe SPS (near Bridlington) at 15:15. The maximum target gauge intensity is 34.4 mm/hr at Leisure World (in Bridlington) at 15:20. The number of five minute periods with rain in them for the source data is 114 out of a total of 288, or 39.6%. The number of 5 minute periods for the target data with rain in them is 108 or 37.5%. The overall data set for 22 gauges together has 118 out of 288 times with rain or 41.0%. The interpolation of raingauge values was also applied by reversing the source and target gauges, so the gauges that were previously considered target gauges were treated as source gauges and raingauge values were interpolated to the locations that were previously considered source gauge locations. The results for the reverse interpolation are similar to the originals. This is reassuring, and the analyses shown in Fig. 6 indicate that the modified PMM improves the accuracy of the radar data as supplied by the Met Office. The performance of the modified PMM algorithm where there are not raingauges is not as great compared to where there are raingauges. But this is to be expected as the raingauge values are estimated. Fig. 6: Applying the PMM technique to locations where there are no raingauges interpolating from only 11 gauges

6 Acknowledgements The authors are very grateful to the following organisations for supplying data or allowing it to be used on this project: Yorkshire Water for use of raingauge data from their OTT Pluvio2 Network The UK Meteorological Office for supplying rainfall radar data used in the project The UK Environment Agency for supply of the raingauge data from their network of tipping bucket raingauges Adrian Boar is also thanked for his work in developing the prototype GIS system that is shown in Fig. 2. References Atencia A., Llasat M.C., Garrote L., Mediero, L. 2010: Effect of radar rainfall time resolution on the predictive capability of a distributed hydrologic model, Hydrol. Earth Syst. Sci. Discuss., 7, Golding B.W. 1998: Nimrod: a system for generating automated very short range forecasts, Meteor. Appl., 5, 1-16 Golding B.W Quantitative precipitation forecasting in the UK, J. Hydrology, 239, Kitchen M. and Blackall R.M. 1992: Representativeness errors in comparisons between radar and gauge measurements of rainfall, J. Hydrology, 134, Lewis H.W., Harrison D.L. (2007) Assessment of radar data quality in upland catchments, Meteor. Appl., 14,

A real-time procedure for adjusting radar data using raingauge information I: System description

A real-time procedure for adjusting radar data using raingauge information I: System description J. Black 1,2, C. G. Collier 3, J. Powell 2, R. Mason 2 1 National Centre for Atmospheric Science, School