4.12 Improving wind profiler data recovery in non-uniform precipitation using a modified consensus algorithm

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1 4.12 Improving wind profiler data recovery in non-uniform precipitation using a modified consensus algorithm Raisa Lehtinen 1, Daniel Gottas 2, Jim Jordan 3, Allen White 2 1 Vaisala Inc, Boulder, Colorado, USA 2 Cooperative Institute for Research and Environmental Sciences, University of Colorado, NOAA Earth System Research Laboratory, Boulder, Colorado, USA 3 NOAA Earth System Research Laboratory, Boulder, Colorado, USA 1. Introduction A Doppler beam swinging (DBS) radar wind profiler is capable of producing good quality wind estimates in uniform precipitation. The contribution of the fall velocity of hydrometeors is determined and removed using either a vertical beam or the sum of two oblique beams. Problems arise when the precipitation echo is detected only in some beam directions, or only a part of the consensus averaging time. These changing conditions often cause the consensus average to fail or to produce erroneous results 1. The objective of this study is to compare the quality of winds, as derived by the standard consensus algorithm and a consensus algorithm modified for precipitation detection. The motive for this modification is to increase the timeheight coverage of wind data during periods of non-uniform precipitation. The algorithm was developed by the NOAA Earth System Research Laboratory (ESRL), and has been implemented within Vaisala s LAP-XM wind profiler control and signal-processing software. The algorithm was tested on precipitation data collected at an ESRL 915-MHz profiler site and validated with collocated radiosonde soundings. 2. Method description The precipitation detection algorithm consists of two parts: thresholding and modified consensus averaging. Thresholds are applied to the spectral moments of one averaging period. The signal-to-noise ratio (SNR) values are recalculated to correct for potential receiver saturation and range effects 2. Radar dwells from the vertical beam are analyzed to find spectral peaks with range-corrected SNR above 60 db and radial fall velocities exceeding 1.0 m s -1. These signals are flagged as originating from precipitation. The oblique beams of the measurement are then compared to the nearest-in-time vertical beam. If the SNR of a spectral peak exceeds 60 db and the corresponding height in the nearest vertical beam has a precipitation flag, the oblique beam signal is flagged as precipitation. The radial velocity is also corrected for the vertical velocity component using the nearest vertical beam. If the averaging period contains both precipitation and clear-air signals, the data points are divided into two populations accordingly, and the consensus algorithm 3 is applied to each group. The consensus threshold criteria are set less strict for the precipitation signals which are assumed to be stronger and more consistent. For each range gate, the reported wind is obtained from the population that passed consensus. During intermittent precipitation both groups may produce a valid consensus result. In this case, the group size is used as a factor for a weighted average. 3. Comparison of results A set of 71 wind profiles from a NOAA operated 915-MHz wind profiler in Pittsburgh, Pennsylvania, were selected for comparison. Each profile consisted of 56 range gates, with the lowest gate at 165 m above the ground and a vertical resolution of 97 m. A nearby NOAA National Weather Service radiosonde launching station provided simultaneous vertical profiles of wind to assist in validating the results. The profiles include all available data from the period of December 2004 to May 2005 where visual inspection indicated that precipitation was present. The precipitation events may include snow, mixed snow/rain and rain with different intensity levels during the 30- minute consensus averaging time. The data were divided into two precipitation intensities, heavy and moderate, using vertical velocity thresholds of -2.0 m s -1 and -0.6 m s -1 respectively, and a 60 db SNR threshold. Correlation diagrams similar to those shown by Ralph et al. 4 were used in defining these thresholds. Further inspection of the data indicated that the heavy precipitation cases were often associated with non-steady precipitation regimes (e.g., convection). Differences of 10 m/s or more between the wind profiler and radiosonde wind speeds were considered outliers and removed from the analysis. Such values constituted less than 2% of the data points.

2 The precipitation algorithm improved the total data recovery by 4.8%, see Table 1 and Figure 1. The new algorithm showed most improvement in heavy precipitation with 88.3% data coverage compared to 64.3%. In addition, there was some improvement in data recovery for the moderate precipitation cases (4%). When compared to the radiosonde data, the mean and RMS differences were fairly identical for both cases. The overall speed bias in both cases (~ -1.0 m/s) is due to unknown reason and may require further investigation, which is not the topic of this study. The new method did not appear to distort results in clear air conditions. Table 1. Comparison of standard and precipitation methods. Data Parameter Standard Precip all data recovery % heavy data recovery % moder. data recovery % all speed RMS / bias m/s 2.4 / / -1.0 heavy speed RMS / bias m/s 3.0 / / -1.5 moder. speed RMS / bias m/s 2.3 / / -0.9 all dir. RMS / bias deg / / -0.2 heavy dir RMS / bias deg / / -2.4 moder. dir RMS / bias deg / / -0.1 and a minor decrease in bias in stronger echoes. A more detailed study could classify individual profiles as uniform or non-uniform precipitation, with the assumption that most benefit will be obtained in non-uniform cases. The method has also been implemented for radio acoustic sounding system (RASS) measurements for improved vertical air motion correction. The simple thresholds used for classification may need to be adjusted and refined 5. The described signal processing will be installed for operational use in the NOAA wind profiler network in Summary The main result of this study is to show that the new threshold and modified consensus processing is capable of improving UHF wind profiler measurements during precipitation. Overall increase in data recovery was approximately 5%, and the strongest echo cases showed over 20% improvement. The results compared well with radiosonde data, with a minor increase in RMS error Figure 1. Comparison of the data recovery. for standard and precipitation methods. 5. References 1. Wuertz, D.B., B.L. Weber, R.G. Strauch, A.S. Frisch, C.G. Little, D.A. Merritt, K.P. Moran, and D.C. Welsh, 1988: Effects of precipitation on UHF wind profiler measurements. J. Atmos. Oceanic Technol., 5, White, A.B., D.J. Gottas, E.T. Strem, F.M. Ralph, and P.J. Neiman, 2002: An automated brightband height detection algorithm for use with Doppler radar spectral moments. J. Atmos. Oceanic Technol., 19, Strauch, R.G., D.A. Merritt, K.P. Moran, K.B. Earhshaw, and D. Van de Kamp, 1984: The Colorado Wind Profiling Network, J. Atmos. Oceanic Technol., 1, Ralph, F.M., P.J. Neiman, and D. Ruffieux, 1996: Precipitation identification from radar wind profiler spectral moment data: Vertical velocity histograms, velocity variance, and signal power vertical velocity correlations. J. Atmos. Oceanic Technol., 13, Williams, C.R., W.L. Ecklund, P.E. Johnston, and K.S. Gage, 2000: Cluster analysis techniques to separate air motion and hydrometeors in vertical incident profiler observations, J. Atmos. Oceanic Technol., 17,

3 Appendix The following figures illustrate some characteristics of the compared data, and show additional results for the precipitation algorithm. Figure 2. Scatterplot of consensus averaged vertical beam velocities and range-corrected SNR values from all 71 profiles and 56 range gates as given by the modified consensus processing. The SNR is an average over the consensus averaged SNR values from all three radar beams. Data that did not pass the consensus threshold are excluded from the figure, but were used in calculating recovery values. For the analysis purposes the data were divided into moderate precipitation and heavy precipitation sub-populations using vertical velocity thresholds of -2.0 m s -1 and -0.6 m s -1 respectively, and a 60 db SNR threshold. Figures 2 and 3 indicate that heavy precipitation is likely to correspond to radar echoes from intense rain. The moderate precipitation class is a more mixed set containing some light rain, snow, and some clear air echoes. For contrast, solid lines show the 1.0 m s -1 and 60 db thresholds used to classify raw spectral moments as precipitation in the modified consensus processing. This threshold method will exclude very light rain/drizzle and some light snow. We are less concerned about the correct classification for these signals, since these hydrometeor fall velocities tend to be uniformly well behaved.

4 Figure 3. Scatterplot of consensus averaged vertical beam velocities as a function of height from all 71 profiles and 56 range gates.

5 Figure 4. Comparison of the data recovery for the modified precipitation processing and the standard consensus processing. Data is sorted by the range-corrected SNR values of the modified algorithm. The SNR values are averaged over the consensus SNR values from all three radar beams. The precipitation algorithm provides some improvement in data recovery for moderate echo intensities, and significantly for strong echoes around 90 db. The strongest echoes near 100 db consisted of only few cases, and in this case both algorithms provided perfect recovery.

6 Figure 5. Data recovery as a function of altitude for data points classified as heavy precipitation. This data set consisted of 462 points out of the total of The modified precipitation processing provides a significant improvement over most range gates, compared to the standard algorithm. The improvement is more pronounced than the improvement for all data points shown in Figure 1. Table 1 indicates that there are no notable changes to RMS and bias values compared to the standard algorithm.

7 Figure 6. A case study of non-uniform precipitation on 2 April 2005, 1100 to 1130 UTC. The top left and right images show velocity-height contours of power spectra from the South beam a) in the beginning and b) in the end of the 30- minute averaging period. The bottom images show power spectra from the West beam c) in the beginning and d) in the end of the same averaging period. During this time the precipitation echo intensifies and the top extends from 3 km to above 5.5 km altitude.

8 Figure 7. Wind speed and direction profiles from the time period described in Figure 6. The standard consensus method provides a consensus result at 16 range gates less than the precipitation method. The missing range gates are at above 3 km altitude where the weather conditions changed from clear air to precipitation.

9 Figure 8. A case study of non-uniform precipitation on 16 February 2005, 1100 to 1130 UTC. The top left and right images show velocity-height contours of power spectra from the South beam a) in the beginning and b) in the end of the 30-minute averaging period. The bottom images show power spectra from the West beam c) in the beginning and d) in the end of the same averaging period. This case shows a similar intensification of the precipitation echo as the previous figures.

10 Figure 9. Wind speed and direction profiles from the time period described in Figure 8. The standard consensus method has failed to pass the consensus threshold at several altitudes, especially in the m range where there is a strong wind speed shear, and in the highest altitude ranges. The lowest altitudes match poorly with the very high wind speeds measured by the radiosonde.

11 Figure 10. Wind profiles from 2 April 2005, calculated with a) standard consensus algorithm and b) modified precipitation detection algorithm. The modified algorithm has filled in several gaps in the data, although several missing altitudes still remain. Precipitation during the day was very variable.