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Operational Measurement of Precipitation in Cold Climates

  • Jarmo Koistinen
  • Daniel B. Michelson
  • Harri Hohti
  • Markus Peura
Part of the Physics of Earth and Space Environments book series (EARTH)

Abstract

Over the last 50 years, severe weather such as thunderstorms, squall lines, tornadoes, hurricanes, and extreme precipitation events with ensuing flash floods have dominated both scientific and operational radar meteorology (Atlas, 1990, Collier, 2001). This is natural, as one of the main benefits of weather radars is very dense sampling of precipitating systems in time and space, facilitating real-time warning and nowcasting of mesoscale severe weather which may have huge socioecomonic impacts. Also, an implication has been that some commercial radar manufacturers have paid little attention to the sensitivity of weather radar. Maximal sensitivity of a radar system is required for making snowfall measurements. Even greater sensitivity of operational radars is needed for the production of Doppler winds from clear air. The important topic of wind field estimation from Doppler measurements relates to Gekat et al. (2003), Meischner et al. (2003) and Macpherson (2003), all this book. The major proportion of the frequent boundary layer echoes found in summer, even in the north of Europe, originates from insects. For example, classification of 240 000 vertical profiles of reflectivity (VPR) from a one-year-long period in Finland revealed that 40% of all VPRs originated from clear air echoes reaching the ground, 20% from overhanging precipitation, i.e. ice crystal clouds or snowfall layers aloft, and only 40% involved precipitation reaching the ground level (Pohjola and Koistinen, 2002). The reflectivity of both nonprecipitating echo classes was typically between −20 and 5 dBZ. Both Canadian and Nordic radar networks (NORDRAD) mostly operate sensitive C-band systems. The operational Nordic radars are “standard” systems with sensitivities around −110 dBm, beamwidths of around 0.9° and pulse lengths from 0.5–2 µs. In Canada, the sensitivity requirement to detect snowfall and clear air echoes has led to the specification of a multi-pulse length capability (0.8–5 µs) and of a narrow antenna beamwidth (0.65°) (Joe and Lapczak, 2002).

Keywords

Motion Vector Cold Climate Radar Data Mean Square Deviation Radar Measurement 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

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References

  1. 1.
    Alberoni, P. P., T. Andersson, P. Mezzasalma, D. B. Michelson, and S. Nanni, 2001: Use of the vertical reflectivity profile for identification of anomalous propagation. Meteorol. Appl., 8 (3), 257–266.CrossRefGoogle Scholar
  2. 2.
    Andrieu, H. and J. D. Creutin, 1995: Identification of vertical profiles of radar reflectivity for hydrological applications using an inverse method: Parts 1 and 2 — Formulation, sensitivity analysis and case study. J. Appl. Meteorol., 34, 225-259.Google Scholar
  3. 3.
    Atlas, D. (ed.), 1990: Radar in Meteorology. AMS, Boston.Google Scholar
  4. 4.
    Barbosa, S., 1994: Brief review of radar-raingauge adjustment techniques. In M. E. Almeida-Teixeira, R. Fantechi, R. Moore, and V. M. Silva (eds.), Advances in Radar Hydrology, European Commission, Brussels, EUR 14334 EN, pp. 148–169.Google Scholar
  5. 5.
    Barnes, S. L., 1973: Mesoscale Objective Map Analysis Using Weighted Time-Series Observations. Technical Report NOAA Technical Memorandum ERL NSSL-62, National Severe Storms Laboratory, Norman, Oklahoma.Google Scholar
  6. 6.
    Battan, L. J., 1973: Radar Observations of the Atmosphere. University of Chicago Press, Chicago.Google Scholar
  7. 7.
    Boudevillain, B., J. Thielen and H. Andrieu, 2000: Definition of the characteristics of an urban hydrological radar: Interest in the vertically integrated liquid water content. Phys. Chem. Earth (B), 25, 1311-1316.Google Scholar
  8. 8.
    Brandes, E. A., 1975: Optimizing rainfall estimates with the aid of radar. J. Appl. Meteorol., 14, 1339-1345.Google Scholar
  9. 9.
    Brandt, R., C. Collier, H.-I. Isemer, J. Koistinen, B. Macpherson, D. Michelson, S. Overgaard, E. Raschke, and J. Svensson, 1996: BALTEX Radar Research–A Plan for Future Action. Publication No. 6, International BALTEX Secretariat, GKSS Research Center, Geesthacht, Germany.Google Scholar
  10. 10.
    Cain, D. E. and P. L. Smith, 1976: Operational adjustment of radar estimated rainfall with rain gage data: A statistical evaluation. Proc. 17th Conf. Radar Meterol., AMS, pp. 533–538.Google Scholar
  11. 11.
    Campos, E. and I. Zawadzki, 2000: Instrumental uncertainties in Z-R relations. J. Appl. Meteorol., 39, 1088-1102.Google Scholar
  12. 12.
    Collier, C. G., 1986: Accuracy of rainfall estimates by radar, Part I: Calibration by Telemetering Raingauges. J. Hydrology, 83, 207-223.Google Scholar
  13. 13.
    Collier, C. G., 1996: Applications of Weather Radar Systems. A Guide to Uses of Radar Data in Meteorology and Hydrology. Praxis/John Wiley and Sons, Chichester/London, 2nd ed.Google Scholar
  14. 14.
    Collier, C. G., 1998: Observations of sea clutter using an S-band weather radar. Meteorol. Appl., 5, 263-270.Google Scholar
  15. 15.
    Collier, C. G. (ed.), 2001: COST Action 75–Advanced Weather Radar Systems -1993-97. Final report. European Commission, Luxembourg, EUR 19546.Google Scholar
  16. 16.
    Daley, R., 1991: Atmospheric Data Analysis. Cambridge University Press, New York.Google Scholar
  17. 17.
    Ebert, E. E. and G. T. Weymouth, 1999: Incorporating satellite observations of “no rain” in an Australian daily rainfall analysis. J. Appl. Meteorol., 38, 44-56.Google Scholar
  18. 18.
    Fabry, F. and I. Zawadzki, 1995: Long term radar observations of the melting layer of precipitation and their interpretations. J. Atmos. Sci., 52, 838-851.Google Scholar
  19. 19.
    Forland, E. J., P. Allerup, B. Dahlström, E. Elomaa, T. Jônsson, H. Madsen, J. Perälä, P. Rissanen, H. Vedin, and F. Vejen, 1996: Manual for operational correction of Nordic precipitation data. Report nr. 24/96, DNMI, P.O. Box 43, Blindern, Oslo, Norway, 66 pp.Google Scholar
  20. 20.
    Fulton, R. A., J. P. Breidenbach, D.-J. Seo, and D. A. Miller, 1998: The WSR-88D rainfall algorithm. Weather and Forecasting, 13, 337–395.CrossRefGoogle Scholar
  21. 21.
    Golding, B.W., 1998: NIMROD: A system for generating automated very short range forecasts. Meterol. App., 5, 1-16.Google Scholar
  22. 22.
    Häggmark, L., S. Gollvik, K.-I. Ivarsson, and P.-O. Olofsson, 2000: Mesan, an operational mesoscale analysis system. Tellus, 52A (1), 2–20.CrossRefGoogle Scholar
  23. 23.
    Hand, W.H., 1996: An object-oriented technique for nowcasting heavy showers and thunderstorms. Meterol. App., 3, 31-41.Google Scholar
  24. 24.
    Handwerker, J., J. Ressing and K.D. Beheng, 2000: Tracking convective cells in the upper Rhine valley. Phys. Chem. Earth (B), 25, 1317–1322.CrossRefGoogle Scholar
  25. 25.
    Hardaker, P. J., B. Macpherson, and P. R. A. Brown, 1999: Weather radar and Numerical Weather Prediction models. In COST 75 Advanced weather radar systems. International seminar, European Commission, EUR 18567 EN, pp. 451–459.Google Scholar
  26. 26.
    Harrison, D. L., S. J. Driscoll, and M. Kitchen, 2000: Improving precipitation estimates from weather radar using quality control and correction techniques. Meteorol. Appl., 7, 135-144.Google Scholar
  27. 27.
    Holmlund, K., 1998: The utilization of statistical properties of satellite-derived atmospheric motion vectors to derive quality indicators. Weather and Forecasting, 13, 1093–1104.CrossRefGoogle Scholar
  28. 28.
    Holmlund, K., 1999: The use of observation errors as an extension to Barnes interpolation scheme to derive smooth instantaneous vector fields from satellite-derived atmospheric motion vectors. In Proc. 1999 EUMETSAT Meteorol. Satellite Data Users Conf., EUMETSAT, EUM P26, pp. 633–637.Google Scholar
  29. 29.
    Joe, P. and S. Lapczak, 2002: Evolution of the Canadian operational radar network. In Proc. ERAD (2002), EMS, Copernicus GmbH, pp. 370–382.Google Scholar
  30. 30.
    Joss, J. and R. Lee, 1995: The application of radar-gauge comparisons to operational precipitation profile corrections. J. Appl. Meteorol., 34, 2612-2630.Google Scholar
  31. 31.
    Joss, J. and A. Waldvogel, 1990: Precipitation measurement and hydrology: A review. In In Battan Memorial and Radar Conference. Radar in Meteorology, AMS, Boston, Chap. 29a, pp. 577–606.Google Scholar
  32. 32.
    Källén, E., 1996: HIRLAM Documentation Manual, system 2.5. Technical report, SMHI, S-601 76 Norrköping, Sweden.Google Scholar
  33. 33.
    Keenan, T.D., J.W. Wilson, P.I. Joe, C.G. Collier, B. Golding, D.W. Burgess, R. Carbone, A. Seed, P.T. May, L. Berry, J. Bally and C.E. Pierce, 2001: The World Weather Research Programme (WWRP) Sydney 2000 Forecast Demonstration Project: Overview. Proc. 30th Conf. Radar Meteorol., AMS, pp. 474476.Google Scholar
  34. 34.
    Kerr, D. E. (ed.), 1951: Propagation of Short Radio Waves. Dover Publications, New York.Google Scholar
  35. 35.
    Kitchen, M. and R. B. Blackall, 1992: Representiveness errors in comparisons between radar and gauge measurements of rainfall. J. Hydrology, 134, 13–33.CrossRefGoogle Scholar
  36. 36.
    Koistinen, J., 1991: Operational correction of radar rainfall errors due to vertical reflectivity profile. Proc. 25th Conf. Radar Meteorol., AMS, pp. 91–94.Google Scholar
  37. 37.
    Koistinen, J., 1997: Clutter cancellation and the capabilities of modern Doppler radars. Proc. COST 75 Workshop on Doppler Weather Radar. European Commission, Luxembourg, pp. 7–11.Google Scholar
  38. 38.
    Koistinen, J. and D. B. Michelson, 2002: BALTEX weather radar-based precipitation products and their accuracies. Boreal Env. Res., 7 (3), 253–263.Google Scholar
  39. 39.
    Koistinen, J. and T. Puhakka, 1981: An improved spatial gauge-radar adjustment technique. Proc. 20th Conf. Radar Meteorol., AMS, pp. 179–186.Google Scholar
  40. 40.
    Koistinen, J. and T. Puhakka, 1986: Can we calibrate radar with raingauges? Geophysica (Helsinki), 22, 119–129.Google Scholar
  41. 41.
    Koistinen, J. and E. Saltikoff, 1999: Experience of customer products of accumulated snow, sleet and rain. In COST 75 Advanced Weather Radar Systems. International seminar, European Commission, EUR 18567 EN, pp. 397–406.Google Scholar
  42. 42.
    Li, L., W. Schmid and J. Joss, 1995: Nowcasting of motion and growth of precipitation with radar over a complex orography. J. Appl. Meteorol., 34, 1286-1300.Google Scholar
  43. 43.
    Liao, L. and R. Meneghini, 1999: A study of effective dielectric constant of ice-water spheres where fractional water content is prescribed as function of radius. Proc. 29th Conf. Radar Meteorol., AMS, pp. 699–702.Google Scholar
  44. 44.
    Marzano, F. S., E. Picciotti, and G. Vulpiani, 2002: Reconstruction of rainrate fields in complex orography from C-band radar volume data. In Proc. ERAD (2002), EMS, Copernicus GmbH, pp. 227–232.Google Scholar
  45. 45.
    Matrosov, S. Y., 1992: Radar reflectivity in snowfall. IEEE Trans. Geoscience Remote Sensing, 30, 454–461.CrossRefGoogle Scholar
  46. 46.
    Michelson, D. B., T. Andersson, J. Koistinen, C. G. Collier, J. Riedl, J. Szturc, U. Gjertsen, A. Nielsen, and S. Overgaard, 2000: BALTEX Radar Data Centre Products and their Methodologies. Reports Meteorology and Climatology RMK 90, SMHI, SE-601 76 Norrköping, Sweden.Google Scholar
  47. 47.
    Michelson, D. B. and J. Koistinen, 2000: Gauge-radar network adjustment for the Baltic Sea Experiment. Phys. Chem. Earth (B), 25(10-12), 915–920.Google Scholar
  48. 48.
    Michelson, D. B., J. Koistinen, R. Bennartz, C. Fortelius, and A. Thoss, 2002: BALTEX radar achievements at the end of the main experiment. In Proc. ERAD (2002), EMS, Copernicus GmbH, pp. 357–362.Google Scholar
  49. 49.
    Pamment, J. A. and B. J. Conway, 1998: Objective identification of echoes due to anomalous propagation in weather radar data. J. Atmos. Oceanic Technol., 15, 98–113.CrossRefGoogle Scholar
  50. 50.
    Peura, M., 2002: Computer vision methods for anomaly removal. In Proc. ERAD (2002), EMS, Copernicus GmbH, pp. 312–317.Google Scholar
  51. 51.
    Pohjola, H. and J. Koistinen, 2002: Diagnostics of reflectivity profiles at the radar sites. In Proc. ERAD (2002), EMS, Copernicus GmbH, pp. 233–237.Google Scholar
  52. 52.
    Raschke, E., J. Meywerk, K. Warrach, U. Andræ, S. Bergström, F. Beyrich, F. Bosveld, K. Bumke, C. Fortelius, L. P. Graham, S.-E. Gryning, S. Halldin, L. Hasse, M. Heikinheimo, H.-J. Isemer, D. Jacob, I. Jauja, K.-G. Karlsson, S. Keevallik, J. Koistinen, A. van Lammeren, U. Lass, J. Launianen, A. Lehmann, B. Liljebladh, M. Lobmeyr, W. Matthäus, T. Mengelkamp, D. B. Michelson, J. Napiôrkowski, A. Omstedt, J. Piechura, B. Rockel, F. Rubel, E. Ruprecht, A.-S. Smedman, and A. Stigebrandt, 2001: The Baltic Sea Experiment (BALTEX): A European contribution to the investigation of the energy and water cycle over a large drainage basin. Bull. Amer. Meteorol. Soc., 82(11),2389-2413.Google Scholar
  53. 53.
    Rosenfeld, D., E. Amitai, and D. B. Wolf, 1995: Improved accuracy of radar WPMM estimated rainfall upon application of objective classification criteria. J. Appl. Meteorol., 34, 212–223.CrossRefGoogle Scholar
  54. 54.
    Saltikoff, E., J. Koistinen, and H. Hohti, 2000: Experience of real time spatial adjustment of the Z-R relation according to water phase of hydrometeors. Phys. Chem. Earth (B), 25(10-12), 1017–1020.Google Scholar
  55. 55.
    Schmid, W., S. Mecklenburg, and J. Joss, 2000: Short-term risk forecasts of severe weather. Phys. Chem. Earth (B), 25, 1335–1338.CrossRefGoogle Scholar
  56. 56.
    Seed, A. W., J. Nicol, G. L. Austin, C. D. Stow, and S. G. Bradley, 1996: The impact of radar and raingauge sampling errors when calibrating a weather radar. Meteorol. Appl., 3, 43–52.CrossRefGoogle Scholar
  57. 57.
    Sekhon, R. S. and R. C. Srivastava, 1970: Snow size spectra and radar reflectivity. J. Atmos. Sci., 27, 299–307.CrossRefGoogle Scholar
  58. 58.
    Smith, P. L., 1984: Equivalent Radar Reflectivity Factors for Snow and Ice Particles. J. Climate Appl. Meteorol., 23, 1258–1260.CrossRefGoogle Scholar
  59. 59.
    Sonka, M., V. Hlavac, and R. Boyle, 1993: Image Processing, Analysis and Computer Vision. Chapman and Hall Computing.Google Scholar
  60. 60.
    Steiner, M. and J. A. Smith, 2002: Use of three-dimensional reflectivity structure for automated detection and removal of nonprecipitating echoes in radar data. J. Atmos. Oceanic Technol., 19, 673-686.Google Scholar
  61. 61.
    Sugier, J., J. Parent du Châtelet, P. Roquain, and A. Smith, 2002: Detection and removal of clutter and anaprop in radar data using a statistical scheme based on echo fluctuation. In Proc. ERAD (2002), EMS, Copernicus GmbH, pp. 17–24.Google Scholar
  62. 62.
    Super, A. B. and E. W. Holroyd, 1997: Snow accumulation algorithm for the WSR88D radar. Second Annual Report of the Bureau of Reclamation R-97-5, Denver.Google Scholar
  63. 63.
    Toussaint, M., B. Jacquemin, I. Donet, A. Carlier, and M. Malkomes, 2000: GSF — A Doppler weather radar based tracking tool. Phys. Chem. Earth (B), 25, 1339–1442.Google Scholar
  64. 64.
    Vignal, B., G. Galli, J. Joss, and U. Germann, 2000: Three methods to determine profiles of reflectivity from volumetric radar data to correct precipitation estimates. J. Appl. Meteorol., 39 (10), 1715–1726.CrossRefGoogle Scholar
  65. 65.
    Watson, R. J., 1996: Data comparisons for spatially separated meteorological radars. Ph.D. Thesis, Dept. Electronic Systems Engineering, University of Essex, UK.Google Scholar
  66. 66.
    Wessels, H. R. A. and J. H. Beekhuis, 1992: Automatic suppression of anomalous propagation clutter for noncoherent weather radars. Scientific reports; WR 92-06, kNMI, PO Box 201, 3730 AE De Bilt, The Netherlands.Google Scholar
  67. 67.
    Wilson, C., 2001: Review of current methods and tools for verification of numerical forecasts of precipitation. COST 717 Working Document WDF_02_200109_1.Google Scholar
  68. 68.
    Wilson, J. W. and E. A. Brandes, 1979: Radar measurement of rainfall — A summary. Bull. Am. Meteorol. Soc., 60 (9), 1048–1058.CrossRefGoogle Scholar
  69. 69.
    Yang, D., B. E. Goodison, J. R. Metcalfe, P. Louie, G. Leavesley, D. Emerson, C. I. Hanson, V. S. Golubev, E. Elomaa, T. Gunther, T. Pangburn, E. Kang and J. Milkovic, 1999: Quantification of precipitation measurement discontinuity induced by wind shields on national gauges. Water Resource Res., 35, 491–508.CrossRefGoogle Scholar
  70. 70.
    Zawadzki, I., 1984: Factors affecting the precision of radar measurements of rain. Proc. 22nd Conf. Radar Meteorol., AMS, pp. 251–256.Google Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2004

Authors and Affiliations

  • Jarmo Koistinen
    • 1
  • Daniel B. Michelson
    • 2
  • Harri Hohti
    • 1
  • Markus Peura
    • 1
  1. 1.Finnish Meteorological Insitute (FMI)HelsinkiFinland
  2. 2.Swedish Meteorological and Hydrological Institute (SMHI)NorrköpingSweden

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