• Bruintjes, R. T., T. L. Clark, and W. D. Hall. 1995. The dispersion of tracer plumes in mountainous regions in central Arizona: Comparisons between observations and modeling results. J. Appl. Meteor. 34:971988.

    • Search Google Scholar
    • Export Citation
  • Bruintjes, R. T., G. L. Kok, D. W. Breed, and V. Salazar. 1999. Hygroscopic seeding: Theory and practice. Proc. Seventh WMO Scientific Conf. on Weather Modification, Chiang Mai, Thailand, WMO, WMO/TD-No. 936, 65–68.

  • Gagin, A. and M. Aroyo. 1985. Quantitative diffusion estimates of cloud seeding nuclei released from airborne generators. J. Wea. Mod. 17:5970.

    • Search Google Scholar
    • Export Citation
  • Holroyd, E. W., J. T. McPartland, and A. B. Super. 1988. Observations of silver iodide plumes over the Grand Mesa of Colorado. J. Appl. Meteor. 27:11251144.

    • Search Google Scholar
    • Export Citation
  • Huang, C. Y. and S. Raman. 1989. Application of the E-ε closure model to simulations of mesoscale topographic effects. Bound.-Layer Meteor. 49:169195.

    • Search Google Scholar
    • Export Citation
  • Langmuir, I. 1961a. Atmospheric Phenomena. Vol. 10, Collected Works of Langmuir, Pergamon Press, 477 pp.

  • Langmuir, I. 1961b. Cloud Nucleation. Vol. 11, Collected Works of Langmuir, Pergamon Press, 619 pp.

  • Levin, Z., S. O. Krichak, and T. Reisin. 1997. Numerical simulation of disposal of inert seeding material in Israel using a three-dimensional mesoscale model. J. Appl. Meteor. 36:474484.

    • Search Google Scholar
    • Export Citation
  • Li, Z. and R. L. Pitter. 1997. Numerical comparison of two ice crystal formation mechanisms on snowfall enhancement from ground-based aerosol generators. J. Appl. Meteor. 36:7085.

    • Search Google Scholar
    • Export Citation
  • Ludwig, F. L. 1982. Effect of a change of atmospheric stability on the growth rate of puffs used in plume simulation models. J. Appl. Meteor. 21:13711374.

    • Search Google Scholar
    • Export Citation
  • Ludwig, F. L., L. S. Gasiorek, and R. E. Ruff. 1977. Simplification of a Gaussian puff model for real-time minicomputer use. Atmos. Environ. 11:431436.

    • Search Google Scholar
    • Export Citation
  • Ludwig, F. L., R. Salvador, and R. Bornstein. 1989. An adaptive volume plume model. Atmos. Environ. 23:127138.

  • Pielke, R. A. and C. L. Martin. 1981. The derivation of a terrain-following coordinate system for use in a hydrostatic model. J. Atmos. Sci. 38:17071713.

    • Search Google Scholar
    • Export Citation
  • Rosenfeld, D. and I. M. Lensky. 1998. Spaceborne sensed insights into precipitation formation processes in continental and maritime clouds. Bull. Amer. Meteor. Soc. 79:24572476.

    • Search Google Scholar
    • Export Citation
  • Rosenfeld, D., X. Yu, and J. Dai. 2005. Satellite-retrieved microstructure of AgI seeding tracks in supercooled layer clouds. J. Appl. Meteor. 44:760767.

    • Search Google Scholar
    • Export Citation
  • Sheih, C. M. 1978. A puff pollutant dispersion model with wind shear and dynamic plume rise. Atmos. Environ. 12:19331938.

  • Shen, Y. M. and J. H. Chen. 1987. Numerical solution to the problem on diffusion on catalytic agent released from an airplane. Acta Meteor. Sin. 1:190197.

    • Search Google Scholar
    • Export Citation
  • Tzivion, S., T. Reizin, Z. Levin, G. Feingold, and A. Manes. 1989. Dispersion of seeding material in clouds. Proc. Fifth WMO Scientific Conf. on Weather Modification and Applied Cloud Physics, Beijing, China, WMO, 171–174.

  • Warburton, J. A., R. H. Stone, and B. L. Marler. 1995. How the transport and dispersion of AgI aerosols may affect detectability of seeding effects by statistical methods. J. Appl. Meteor. 34:19291947.

    • Search Google Scholar
    • Export Citation
  • Yu, X., P. Fan, X. L. Wang, J. Dai, and Z. Y. Li. 1998. Numerical simulation of multiple line source diffusion of seeding agent within stratus. Acta Meteor. Sin. 56:190197.

    • Search Google Scholar
    • Export Citation
  • Yu, X., J. Dai, W. M. Jiang, and P. Fan. 2000. A three-dimensional model of transport and diffusion of seeding material within stratus. Adv. Atmos. Sci. 4:617635.

    • Search Google Scholar
    • Export Citation
All Time Past Year Past 30 Days
Abstract Views 0 0 0
Full Text Views 61 40 0
PDF Downloads 28 19 0

Comparison of Model-Predicted Transport and Diffusion of Seeding Material with NOAA Satellite-Observed Seeding Track in Supercooled Layer Clouds

View More View Less
  • a Meteorological Institute of Shaanxi Province, Xi’an, China
  • | b Institute of Earth Sciences, The Hebrew University of Jerusalem, Jerusalem, Israel
  • | c Institute of Atmospheric Physics, Chinese Academy of Sciences, Beijing, China
  • | d Meteorological Institute of Shaanxi Province, Xi’an, China
  • | e Center of Weather Modification of Shaanxi Province, Xi’an, China
Restricted access

Abstract

From 0615 to 0749 UTC 14 March 2000, an operation of cloud seeding for precipitation enhancement by aircraft was carried out in the middle part of Shaanxi Province, China. National Oceanic and Atmospheric Administration (NOAA)-14 satellite imagery was received at 0735 UTC for the study region. A vivid cloud track appeared on the satellite imagery; its length was about 350 km, and its average width and width maximum were 9 and 14 km, respectively. Through application of a three-dimensional numerical model of the transport and diffusion of the seeding material, the simulated plume shape, the turning points, and the width and length of seeding lines agree with that of the cloud pattern indicated by the satellite imagery. The track is consistent with the transport and diffusion of the seeding line. All of these factors suggest that the cloud track that is detected by satellite imaging is the direct physical evidence of cloud seeding near the cloud top, with the cloud responding to the transport and diffusion of the seeding material and/or the propagation of the glaciation by secondary effects. The track is indeed caused by the cloud seeding, and the model can predict the evolution of the response zone of cloud seeding. For this seeding case, the duration for segments of the seeding line varies between 20 and 80 min, and the time period for each segment of the seeding line diffusing to the maximum width is about from 40 to 70 min. One hour after cloud seeding, the dispersion rate of the cloud track is 7.0 km h−1, and the predicted expansion rates of the seeding material concentrations of 1 and 4 L−1 are 7.6 and 4.6 km h−1, respectively. The comparison demonstrates that the numerical model of transport and diffusion can predict the main characteristics of transport and diffusion of the seeding effect, and the simulation results are reasonable.

Corresponding author address: Mr. Xing Yu, Meteorological Institute of Shaanxi Province, No. 36 Beiguanzhengjie, Xi’an 710015, China. yuxing23@163.com

Abstract

From 0615 to 0749 UTC 14 March 2000, an operation of cloud seeding for precipitation enhancement by aircraft was carried out in the middle part of Shaanxi Province, China. National Oceanic and Atmospheric Administration (NOAA)-14 satellite imagery was received at 0735 UTC for the study region. A vivid cloud track appeared on the satellite imagery; its length was about 350 km, and its average width and width maximum were 9 and 14 km, respectively. Through application of a three-dimensional numerical model of the transport and diffusion of the seeding material, the simulated plume shape, the turning points, and the width and length of seeding lines agree with that of the cloud pattern indicated by the satellite imagery. The track is consistent with the transport and diffusion of the seeding line. All of these factors suggest that the cloud track that is detected by satellite imaging is the direct physical evidence of cloud seeding near the cloud top, with the cloud responding to the transport and diffusion of the seeding material and/or the propagation of the glaciation by secondary effects. The track is indeed caused by the cloud seeding, and the model can predict the evolution of the response zone of cloud seeding. For this seeding case, the duration for segments of the seeding line varies between 20 and 80 min, and the time period for each segment of the seeding line diffusing to the maximum width is about from 40 to 70 min. One hour after cloud seeding, the dispersion rate of the cloud track is 7.0 km h−1, and the predicted expansion rates of the seeding material concentrations of 1 and 4 L−1 are 7.6 and 4.6 km h−1, respectively. The comparison demonstrates that the numerical model of transport and diffusion can predict the main characteristics of transport and diffusion of the seeding effect, and the simulation results are reasonable.

Corresponding author address: Mr. Xing Yu, Meteorological Institute of Shaanxi Province, No. 36 Beiguanzhengjie, Xi’an 710015, China. yuxing23@163.com

Save