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

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1 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 of Earth & Environment, University of Leeds, Leeds, Yorkshire, LS2 9JT, United Kingdom, j.v.black@leeds.ac.uk 2 Hydro-Logic Ltd, Old Grammar School, Church Street, Bromyard, Herefordshire, HR7 4DP 3 National Centre for Atmospheric Science, School of Earth & Environment, University of Leeds, Leeds, Yorkshire, LS2 9JT, United Kingdom, c.g.collier@leeds.ac.uk (Dated: 25 May 2012) John Black 1. Introduction This paper outlines the software system that has been developed in the City RainNet Knowledge Transfer Partnership (KTP) project. This is a 27-month, joint project involving Hydro-Logic Ltd and the University of Leeds exploring real-time improvement of 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 improvement which has been achieved by calibrating radar rainfall data received in real-time every 5 minutes from the Met Office with raingauge data, using the Probability Matching Method (PMM) algorithm (Rosenfeld et al 1993, 1994) is outlined in Part II of this paper. The Met Office has a network of 15 radars located throughout the UK. There are also two radars in the Republic of Ireland and one in Jersey. Fig. 1 shows the weather radar located near Ingham in Lincolnshire, which is the nearest to the Yorkshire Water study area considered in this paper. Fig. 1: Ingham radar Networks of OTT Pluvio2 weighing-principle raingauges have or are being installed in the areas of each of the three Water Company partners, one for each Water Company. The rainfall data is logged using Isodaq Frog loggers which transmit data via mobile phone data networks using GPRS. A raingauge network of 35 OTT Pluvio2 raingauges has been installed in the Yorkshire coastal area for Yorkshire Water. The most northerly is at Ravenscar Sewage Treatment Works (STW), the most southerly at Skipsea STW, the most westerly at Sherburn STW and the most easterly at Flamborough North Landing. The majority of the raingauges are located in the towns of Scarborough, Filey and Bridlington and the village of Flamborough, with some 20 raingauges being located in these areas. The overall area the raingauges cover some 30km east-west by 46 km north-south. A map showing the locations of the OTT raingauges is shown in Fig. 2. A picture of the OTT Pluvio2 raingauge located at Scalby Bottom (from the Yorkshire Water City RainNet raingauge network) is shown in Fig. 3.

Fig. 2: Map of the locations of the OTT Pluvio2 raingauges in the Yorkshire Water City RainNet raingauge network Fig. 3: OTT Pluvio2 at Scalby Bottom in Yorkshire 2.

2 Fig. 2: Map of the locations of the OTT Pluvio2 raingauges in the Yorkshire Water City RainNet raingauge network Fig. 3: OTT Pluvio2 at Scalby Bottom in Yorkshire 2. Comparison of Pluvio2 and tipping bucket raingauge data In this section a comparison of the rainfall recorded by the OTT Pluvio2 located at Filey School (from the Yorkshire Water raingauge network) and a United Kingdom Environment Agency (EA) tipping bucket raingauge (TBR) located within a few meters of each other at Filey School in the same compound, together with a EA check gauge. The data supplied by the EA for their TBR is the number of 0.2mm tips in a 15 minute period. Therefore a single tip 0.2mm has been accumulating since the previous tip. The data obtained from the Pluvio2s is accumulations in a given 5 minute period. Pluvio2 data has processed to combine sets of readings from 3 consecutive, 5 minute periods to coincide with the 15 minute periods for the TBR. A graph is presented in Fig. 4 which shows the Pluvio2 data processed into 15 minute intervals and the corresponding TBR data. Only data from 00:00 to 15:00 is shown as there was no rainfall was recorded by either raingauge after 15: A c c u m u l a ti o n s (m m ) EA TBR Pluvio : : : : : : : : : : : : : : : : : : : : 3 0 Time Fig. 4: Comparison of Pluvio2 and EA TBR readings on 6th July 2011 at Filey School for 15 minute intervals In the fifteen minute period up to 00:15 a 0.2mm tip was recorded by the TBR, but no apparent corresponding reading from the Pluvio2. However there was a Pluvio2 reading of 0.206mm at 23:45 on 5th July, i.e. about 30 minutes previously. There was no corresponding rainfall recorded by the TBR. So it appears that the TBR tip at 00:15 is an accumulation. After 00:15

3 readings between the two raingauges correspond closely, allowing for the fact that the TBR readings are accumulations and multiples of 0.2mm. So corresponding TBR readings will appear delayed until an accumulation of 0.2mm is reached. The correspondence between the two sets of data is further underlined by processing the data into hourly accumulations. This is shown in Table 1. Data is presented only in hours where there was rainfall recorded. If the 0.2mm TBR accumulation in the first hour is ignored then it can be seen that subsequent 0.8mm up until 02:00 for the TBR is in close agreement to the total for the Pluvio2, for 00:00 to 02:00, which is 0.872mm. Similar effects can be seen for the rest of the period by comparing the readings from both gauges. Considering totals for the entire day, if the 0.2mm TBR tip at 00:15 is ignored then the accumulations for the day are 6.108mm for the Pluvio2 and 6.2mm for the TBR. These totals are within half a TBR tip and shows close correspondence. Time Pluvio2 (mm) EA TBR (mm) 01: : : : : : : : : Total Table 1: Comparison of Pluvio2 and EA TBR readings on 6th July 2011 at Filey School for hourly accumulations 3. PMM algorithm implementation The PMM algorithm involves converting both the radar and raingauge data to dbz using the generic formula where: dbz = 10*log 10 (f*v p ) V is the value of the rain intensity in mm/hr measured by radar or raingauge f is a factor and the value used is 200 p is a power and the value used is 1.6 There is much noise in the data due to the fact that only certain values can occur. For instance the radar rainfall intensity values are multiples of mm/hr. This can be seen in Fig. 5 which shows a scatter plot of dbzg (dbz for raingauge rainfall intensity values) vs. dbzr (dbz for radar rainfall intensity values) for 1st March 2011 to 31st August 2011, i.e. the spring and summer months. Fig. 6 shows a scatter plot of dbzg vs. dbzr for geographically interpolated radar data for 1st March 2011 to 31st August Comparing Fig. 5 to Fig. 6 shows that there is a reduction of scatter. It should be remembered that this data used in this project has a 5 minute sampling interval. It should be noted that previous implementations of the PMM algorithm have been averaged over a longer period, for example 9 minutes in Rosenfeld et al (1994) and 30 minutes in Atencia et al (2010). The scatter is due to the matching radar data (which is areal) to raingauge data (which is point data) (Kitchen and Blackall 1992).

Fig. 5: Graph of dbzg vs. dbzr Fig. 6: Graph of dbzg vs. geographically interpolated dbzr Thresholds were applied as follows to the data: Radar 0.55 mm/hr, which gives dbzr = 18.9 Raingauge 0.

Following the work of Atencia et al (2010), an exponential distribution is fitted to the raingauge data converted to dbz and a Gamma distribution to the radar data converted to dbz.

4 Fig. 5: Graph of dbzg vs. dbzr Fig. 6: Graph of dbzg vs. geographically interpolated dbzr Thresholds were applied as follows to the data: Radar 0.55 mm/hr, which gives dbzr = 18.9 Raingauge 0.6 mm/hr, which gives dbzg = 19.5 This eliminates very low intensity rain which is of little interest. The different thresholds give gives equal numbers of data points for radar and raingauge data. Following the work of Atencia et al (2010), an exponential distribution is fitted to the raingauge data converted to dbz and a Gamma distribution to the radar data converted to dbz. Different options were experimented with. For the exponential distribution, the data fitting gave a mean of 8.2 dbz. The mean is the characteristic parameter of the exponential distribution. However when comparing exponential distribution with a mean of 8.2 dbz to a histogram of the raingauge data converted to dbz, the fit was not close. This can be observed in Fig. 7, where the exponential with a mean of 8.2 dbz is marked by a red line and the histogram of raingauge values converted to dbz in green. Note that the histogram shown contains raingauge values from the network for 1st March to 31st August Three different exponential distributions were plotted against the histogram. These are also shown in Fig. 7 in purple. The means of the exponential distributions are as follows: 5dBZ (lowest), 6dBZ (middle) and 7 dbz (highest). The 5dBZ mean exponential distribution was chosen as was judged closest visually to higher dbz values, which are of most interest. Probability dbzg dbzr Fig. 7: Graph of histogram of raingauge intensity Fig. 8: Graph of histogram of radar rain intensity values converted to dbz and several exponential distributions converted to dbz and several gamma distributions Two different options were tried for fitting a gamma distribution to the histogram of radar rainfall intensity values converted to dbz, as well as an exponential distribution. These are shown in Fig. 8, plotted against a histogram of radar rainfall intensity data converted to dbz, for 1st March to 31st August 2011, plotted in red. The two different gamma distributions are

5 shown in black and blue in Fig. 8 as well as an Exponential distribution in green, in Fig. 8. The black distribution was chosen, because it matches most closely, visually, to higher dbz values, which are of most interest. The chosen distributions were used as inputs to the PMM algorithm, which was modified to take account of the variability shown in Figs. 7 and 8, and which is used to calculate adjustment factors for radar rainfall data. 4. Results from Interpolating from a subset of gauge locations to a second subset of gauge locations 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. It was decided to use a common and accurate software technique for geographical interpolation method to interpolate from a subset of initially 11 raingauges (Source Subset) to a second subset of 11 gauges (Target Subset). An analysis of using the standard geographic interpolation method to interpolate from the Target Subset to Source Subset has also been carried out. The data examined is for 6 th July There are three subareas in the Yorkshire Water raingauge areas, with the following numbers of raingauges: Scarborough: 6 Filey: 8 Bridlington and Flamborough: 8 The measurements from the Source Subset for a given 5 minute period were interpolated to the target locations. Data for the whole of 6th July 2011 was considered. There are a total 3,168 data values for the whole day (12 readings in an hour, 24 hours and 11 rain gauges). However there is no recorded rain after 15:35. There are also periods without recorded rain in the 22 gauges, from 07:00 to 10:00 and 11:45 to 14:15. The maximum source gauge intensity is mm/hr at Wilsthorpe SPS (near Bridlington) at 15:15. The maximum target gauge intensity is 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%. Table 2 below considers the traffic light analysis for values where both the true values at the target gauges or interpolated values at the target gauges are greater than 0 mm/hr. This results in 717 values or about 22.6% of the day's data. Traffic Light Colour Condition: Absolute Difference Number Percentage Green 0.5mm/hr % Amber > 0.5 mm/hr, 1.0mm/hr % Red > 1.0 mm/hr, 2.0mm/hr % Purple >2.0 mm/hr % Total % Table 2: Absolute difference traffic light analysis where true target or interpolated values are both above 0 mm/hr A second traffic light analysis was carried out where instances are considered if both the true target values or interpolated values are greater than 0.6mm/hr are considered. The value of 0.6mm/hr was chosen as the threshold as it was the threshold considered for the modified PMM algorithm presented at the last stakeholder meeting. This results in the number of values being reduced to 670 or 11.5 % of the total number of values. The traffic light analysis is presented in Table 3 below. Traffic Light Colour Condition: Absolute Difference Number Percentage Green 0.5mm/hr % Amber > 0.5 mm/hr, 1.0mm/hr % Red > 1.0 mm/hr, 2.0mm/hr % Purple >2.0 mm/hr % Total % Table 3: Absolute difference traffic light analysis where true target or interpolated values are both above 0.6 mm/hr 5. Testing the modified PMM algorithm The interpolation procedure has been used to provide pseudo raingauge data with which to test the modified PMM algorithm. This is described in Part II of this paper.

6 Acknowledgments 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 rainfall data from their OTT Pluvio2 raingauge network The UK Meteorological Office for supplying radar rainfall data used in the project The UK Environment Agency for supply of the rainfall data from their network of tipping bucket raingauges Contains Environment Agency information Environment Agency and database right References Atencia A., Llasat M. C., Garrote L., Mediero L. 2010: Effect of radar rainfall time resolution on the predictive capability of a distributed hydrological model, Hydrology and Earth Systems Science Discussion, 7, Kitchen M., Blackall R. M. 1992: Representativeness errors in comparisons between radar and gauge measurements of rain fall, Journal Hydrology, 134, Rosenfeld D., Wolff D. B., Atlas D. 1993: General probability-matched relation between radar reflectivity and rain rate, Journal Applied Meteorology, 32, Rosenfeld D., Wolff D. B., Amitai E. 1994: The window probability matching method for rainfall measurements with radar, Journal Applied Meteorology, 33,

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

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