تعیین مناطق هدف در پروژه‌های عملیاتی بارورسازی ابرها در ایران به‌کمک شبیه‌سازی پخش مواد باروری با استفاده از مدل پاشندگی HYSPLIT

نوع مقاله : مقاله پژوهشی‌

نویسندگان

1 مرکز ملی تحقیقات و مطالعات باروری ابرها، یزد، ایران

2 گروه فیزیک فضا، مؤسسه ژئوفیزیک دانشگاه تهران، تهران، ایران

چکیده

تعیین مناطق تحت‌تأثیر مواد باروری (مناطق هدف) از موضوعات مهم در اجرای برنامه­های استاندارد بارورسازی ابرها است. مدل پاشندگی HYSPLIT امکان شبیه‌سازی انتقال، پخش و نهشت عوامل باروری و تصویر کردن منطقه تحت‌تأثیر آنها را روی نقشه فراهم می­کند؛ بنابراین در این پژوهش با تأمین داده­های هواشناسی مورد نیاز مدل HYSPLIT به‌کمک اجرای مدل هواشناسی میان‌مقیاس WRF، نقش برخی از پارامترهای مؤثر در شبیه‌‌سازی منطقه هدف بررسی می­شود. برای اطمینان از کیفیت داده­های ورودی، داده­های باد و دمای خروجی مدل WRF با اندازه‌گیری­های ثبت­شده به‌وسیله سنجنده­های هواپیمای مورد استفاده در بارورسازی ابرها اعتبارسنجی و سپس تأثیرپذیری مدل پاشندگی در شبیه‌سازی محدوده پخش مواد باروری به‌ترتیب از دیدگاه پارامترسازی فیزیکی مدل WRF، تفکیک مکانی و زمانی داده­های ورودی و تفکیک مکانی مدل HYSPLIT بررسی می­شود. برای این منظور، مدل WRF با به‌کارگیری هشت ترکیب متفاوت از پارامترسازی فیزیکی برای سی‌وچهار‌‌ پرواز بارورسازی ابرها در سال 2017 به‌اجرا­درآمد و محدوده پخش حاصل از آنها مقایسه شد. سپس مدل HYSPLIT برای چهار پرواز منتخب با داده­های ورودی در سه تفکیک زمانی و دو تفکیک مکانی داده­های هواشناسی ورودی و سه تفکیک مکانی مدل اجرا شد. نتایج، حاکی از حساسیت مدل HYSPLIT در شبیه‌سازی محدوده پخش مواد باروری به همه پارامترهای اعمال­شده در این پژوهش است؛ البته میزان این حساسیت متفاوت است. نتایج مدل پاشندگی در ارتفاع پرواز هواپیمای باروری به‌طور قابل­توجهی تحت‌تأثیر انتخاب ترکیب پارامترسازی فیزیکی مدل WRF است؛ تغییر در پیکربندی پارامترسازی فیزیکی، در برخی موارد، تفاوت­های معنی‌داری در محدوده پخش مواد باروری ایجاد می­کند، اما در بیشتر موارد، افزایش تفکیک افقی داده­های ورودی و افزایش تفکیک مکانی اجرای مدل HYSPLIT، تأثیر شایان­توجهی در افزایش یا کاهش معنی‌دار در محدوده پخش مواد باروری نخواهد داشت و حساسیت مدل به تفکیک زمانی داده­های ورودی نیز قابل­چشم‌پوشی است.

کلیدواژه‌ها


عنوان مقاله [English]

Determining Target and Control Areas in Operational Cloud Seeding Programs over Iran by Dispersion Simulation of Seeding Materials Using HYSPLIT Model

نویسندگان [English]

  • Mansoureh Seyedhasani 1
  • Fatemeh Moradian 1
  • Sarmad Ghader 2
  • Farid Golkar 1
  • Masoud Hatampour 1
1 National Cloud Seeding Research Center, Yazd, Iran
2 Institute of Geophysics, University of Tehran, Tehran, Iran
چکیده [English]

Determination of affected area by seeding agents, the so-called target area, is an essential requirement for evaluation of cloud seeding projects. The most conservative and credible estimates of seeding effects were obtained from control matches drawn from outside the operational target within 2 hours of the time that each unit was seeded initially (DeFelice et al., 2014). A coupled modeling system consisting of the mesoscale WRF model and the Hybrid Single-Particle Lagrangian Integrated Trajectory Model (HYSPLIT), provides capability to simulate the transportation and dispersion of seeding materials and to characterize target area on the map.
This study is devoted to sensitivity analysis of simulated dispersion patterns to several parameters including different configuration based on physical parameterizations used in WRF model, horizontal and temporal resolution of WRF and spatial resolution of HYSPLIT, to determine the most probable dispersion patterns.
Since temperature and wind parameters are the most important parameters in cloud seeding operations, they are measured instantaneously at 1-second intervals at the flight height of the airplane during each flight and therefore, they are very valuable data to assess the performance of the WRF model in simulating these fields. Hence, at first the WRF model outputs such as temperature and wind are validated by data measured by the airplane. Results indicate that there is an acceptable agreement between field data and WRF outputs that are going to be used as input data for dispersion model.
In this study, eight configurations of the WRF model based on different physical parameterization schemes are used for 34 flights in cloud seeding project in 2015 and HYSPLIT model is run by these types of input data and resulting target area are compared on the map. Then, HYSPLIT model is run for four selected seeding operations according to three temporal and two horizontal resolutions of input data in addition to three spatial resolutions of HYSPLIT model and the transport of seeding plumes is characterized on the geographical map.
The results indicate that dispersion model is sensitive to all mentioned parameters. Also, in most cases, dispersion model results at the flight height of cloud seeding aircraft are significantly influenced by the input data provided by the WRF model. In addition, the dispersion model results are less sensitive to other parameters. Furthermore, when the spatial resolution of the HYSPLIT model is close to the horizontal resolution of the input meteorological data provided by the WRF model, affected area of seeding agents is more integrated and therefore there is a greater degree of certainty in determining the target area.

کلیدواژه‌ها [English]

  • cloud seeding
  • HYSPLIT model
  • WRF model
  • target area
  • temporal and spatial resolution
  • physical parameterization
American Society of Civil Engineers (ASCE), 2004, Standard Practice for the Design and Operation of Precipitation Enhancement Project, ASCE/EWRI Standard 42-04, Reston, VA, 63p.
American Society of Civil Engineers (ASCE), 2016, Guidelines for Cloud Seeding to Augment Precipitation, Third Edition, ISBN 978-0-7844-1411-8 (print).
Aligo, E. A., Gallus, W. A., and Segal, M., 2009, On the impact of WRF model vertical grid resolution on midwest summer rainfall forecasts: Weather Forecasting, 24, 575–594.
Betts, A. K., and Miller, M. J., 1986, A new convective adjustment scheme. Part II: Single column tests using GATE wave, BOMEX, and arctic air-mass data sets: Quarterly Journal of Royal Meteorological Society, 112, 693–709.
Breed, D., Rasmussen, R., Weeks, C., Boe, B., and Deshler, T., 2014, Evaluating winter orographic cloud seeding: Design of the Wyoming Weather Modification Pilot Project (WWMPP): Journal of Applied Meteorology and Climatology, 53, 282–299.
Breed, D., Axisa, D., Liu, C., and Feng, X., 2015, An evaluation of seeding effectiveness in the central Colorado Mountains River Basins weather modification program: Research Applications Laboratory of the National Center for Atmospheric Research (RAL/NCAR).
Bruintjes, R. T., Clark, T. L., and Hall, W. D., 1995, The dispersion of tracer plumes in mountainous regions of central Arizona: Comparisons between observations and modeling results: Journal of Applied Meteorology, 34, 971–988.
Challa, V. S., Indrcanti, J., Baham, J. M., Patrick, C., Rabarison, M. K., Young, J. H., Hughes, R., Swanier, S. J., Hardy, M. G., and Yerramilli, A., 2008, Sensitivity of atmospheric dispersion simulations by HYSPLIT to the meteorological predictions from a meso-scale model, Published online: 24 September 2008 © Springer Science+Business Media B.V. 2008.
Chen, F., and Dudhia, J., 2001, Coupling an advanced land-surface/ hydrology model with the Penn State/ NCAR MM5 modeling system. Part I: Model description and implementation: Monthly Weather Review, 129, 569–585.
Clark, A. J., Gallus, W. A., Xue, M., and Kong, F., 2009, A comparison of precipitation forecast skill between small convection-allowing and large convection-parameterizing ensembles: Weather Forecasting, 24, 1121–1140.
DeFelice, T. P., Golden, J., Griffith, D., Woodley, W., Rosenfeld, D., Breed, D., Solak, M., and Boe, B., 2014, Extra area effects of cloud seeding - An updated assessment: Atmospheric Research, 135–136 (2014) 193–203.
Dennis, A. S., 1980, Weather modification by cloud seeding: International Geophysics Series, 24, Academic Press, New York.
Draxler, R. R., and Hess, G. D., 1998, An overview of the Hysplit_4 modelling system for trajectories, dispersion, and deposition: Australian Meteorological Magazine, 47, 295-308.
Draxler, R. R., Stunder, B., Rolph, G., Stein, A., and Taylor, A., 2013, HYSPLIT4 USER's GUIDE, Version 4, file:///C/Home/Hysplit4/html/index.htm [4/19/2013 9:29:29 AM].
Dudhia, J., 1996, A multi-layer soil temperature model for MM5: Preprints, 6th Annual MM5 Users Workshop, Boulder, CO.
Dudhia, J., 1989, Numerical study of convection observed during the winter monsoon experiment using a mesoscale two-dimensional model: Journal of Atmospheric Sciences, 46, 3077–3107.
Fleming, Z. L., Monks, P. S., and Manning, A. J., 2012, Review: Untangling the influence of air-mass history in interpreting observed atmospheric composition: Atmospheric Research, 104105, 1–39, doi: 10.1016/j.atmosres.2011.09.009.
Gallus, W. A., and Bresch, J., 2006, Comparison of impacts of wrf dynamic core, physics package, and initial conditions on warm season rainfall forecasts: Monthly Weather Review, 134, 2632–2641.
Grell, G. A., and Devenyi, D., 2002, A generalized approach to parameterizing convection combining ensemble and data assimilation techniques: Geophysal Research Letters, 29(14), Article 1693.
Griffith, D. A., Thompson, J. R., and Risch, D. A., 1991, A winter cloud seeding program in Utah. WMA: Journal of Weather Modification, 23(1), 27-34.
Griffith, D. A., Yorty, D. P., and Beall, S. D., 2015, Target/control analyses for Santa Barbara County’s operational winter cloud seeding program.WMA: Journal of Weather Modification, 47, 10-25.
Griffith, D. A., Beall, S. D., Yorty, D. P., 2016, Feasibility/design study for a winter cloud seeding program in the Upper Cuyama River Drainage, California, North American Weather Consultants, Inc. Report No. WM 16-8, Project No. 15-376.
Griffith, D. A., and Solak, M., 2016, San Gabriel mountains cloud seeding draft program report, North American weather consultants, Inc, Report No. WM 16-1, Los Angeles Department of Public Works Contract No. 003343.
Griffith, D. A., Ward, S. M., and Yorty, D. P., 2016, Analysis of ice detector observations at Mount Crested Butte, Colorado during the 2014-2015 winter season: Journal of Weather Modification, 48.
Hong, S. Y., Dudhia, J., and Chen, S. H., 2004, A revised approach to ice microphysical processes for the bulk parameterization of clouds and precipitation: Monthly Weather Review, 132, 103–120.
Hong, S. Y., Noh, Y., and Dudhia, J., 2006, A new vertical diffusion package with an explicit treatment of entrainment processes: Monthly Weather Review, 134, 2318–2341.
Janjic, Z. I., 1994, The step-mountain eta coordinate model: further developments of the convection, viscous sublayer and turbulence closure schemes: Monthly Weather Review, 122, 927–945.
Janjic, Z. I., 1996, The surface layer in the NCEP Eta Model: Eleventh conference on numerical weather prediction, Norfolk, VA, 19–23 August, American Meteorological Society, Boston, MA, 354–355.
Janjic, Z. I., 2000, Comments on” Development and evaluation of a convection scheme for use in climate models”: Journal of Atmospheric Sciences, 57, 3686.
Janjic, Z. I., 2002, Nonsingular implementation of the Mellor–Yamada level 2.5 scheme in the NCEP Meso model: NCEP Office Note, No. 437, 61 pp.
Jankov, I., Gallus, W. A., Segal M., Shaw, B., Koch, S. E., 2005, The impact of different WRF model physical parameterizations and their interactions on warm season MCS rainfall: Weather Forecasting, 20, 1048–1060.
Jankov, I., Gallus, W. A., Segal, M., Shaw, B., and Koch, S. E., 2007, Influence of initial conditions on the WRF–ARW model QPF response to physical parameterization changes: Weather Forecasting, 22, 501–519.
Kalnay, E., 2003, Atmospheric Modeling, Data Assimilation and Predictability: Cambridge, Cambridge University Press, 341 pp.
Keyes, C. G., Bomar, G. W., DeFelice, T. P., Griffith, D. A., and Langerud, D. W., 2016, Guidelines for Cloud Seeding to Augment Precipitation, Third Edition, American Society of Civil Engineering (ASCE) Manuals and Reports on Engineering Practice No. 81. ISBN. 9780784414118.
Kain, J. S., and Fritsch, J. M., 1990, A one-dimensional entraining/detraining plume model and its application in convective parameterization: Journal of Atmospheric Sciences, 47, 2784–2802.
Kain, J. S., and Fritsch, J. M., 1993, Convective parameterization for mesoscale models: The Kain-Fritcsh scheme, in Emanuel, K. A., and Raymond, D. J., eds., The representation of cumulus convection in numerical models: American Meteorology Society, 246 pp.
Kain, J. S., 2004, The Kain-Fritsch convective parameterization: An update: Journal of Applied Meteorology, 43, 170–181.
Lin, Y. L., Farley, R. D., and Orville, H. D., 1983, Bulk parameterization of the snow field in a cloud model: Journal of Climate and Applied Meteorology, 22, 1065–1092.
Mellor, G. L., and Yamada, T., 1982, Development of a turbulence closure model for geophysical fluid problems: Reviews of Geophysics and Space Physics, 20, 851–875.
Mlawer, E. J., Taubman, S. J., Brown, P. D., Iacono, M. J., and Clough, S. A., 1997, Radiative transfer for inhomogeneous atmosphere: RRTM, a validated correlated-k model for the long wave: Journal of Geophysical Research, 102(D14), 16663–16682.
Milly, G. H., Ball, J. T., and Spiegler, D. B., 1969, A numerical experiment on the spatial distribution of cloud seeding nuclei: Journal of Applied Meteorology, 8(1), 83-91.
Morrison, A., Siems, S., Manton, M., Nazarov, A., Denholm, J., 2007, An Overview of Current Cloud Seeding Research in Australia and an Analysis of the Tasmanian Cloud Seeding Operations from 1964 to 2005 and An Analysis of Cloud Seeding Operations Over Tasmania from 1964 to 2005: Applied Meteorology and Climate, 48, 1267‐1280.
Mosca, S, Graziani, G., Klug, W., Bellasio, R., and Bianconi, R., 1998, A statistical methodology for the evaluation of long-range dispersion models: An application to the ETEX exercise: Atmospheric Environment, 32, 4307–4324, doi:10.016/S1352-2310(98)00179-4.
Ngan, F., Stein, A., and Draxler, R., 2015, Inline coupling of WRF–HYSPLIT: Model development and evaluation using tracer experiments: Journal of Applied Meteorology and Climatology, 54, 1162–1176, doi:10.1175/JAMC-D-14-0247.1.
Pielke, R. A., and Uliasz, M., 1998, Use of meteorological models as input to regional and mesoscale air quality models—limitations and strengths: Atmospheric Environment, 32, 1455–1466, doi:10.1016/ S1352-2310(97)00140-4.
Ruiz, J. J., Saulo, C., and Nogués-Paegle, J., 2010, WRF model sensitivity to choice of parameterization over South America: validation against surface variables: Monthly Weather Review, 138, 3342–3355.
Rogers, E., Black, T., Ferrier, B., Lin, Y., Parrish, D. , and DiMego, G., 2001, Changes to the NCEP Meso Eta Analysis and Forecast System: Increase in resolution, new cloud microphysics, modified precipitation assimilation, modified 3DVAR analysis. NWS Tech. Procedures Bull. 488, 15 pp.
Skamarock, W. C., Klemp, J. B., Dudhia, J., Gill, D. O., Barker, D. M., Duda, M. G., Huang, X., Wang, W., and Powers, J., 2008, A description of the advanced research WRF version 3, NCAR/TN–475+STR.
Schwarzkopf, M. D., and Fels, S. B., 1991, The simplified exchange method revisited: An accurate, rapid method for computations of infrared cooling rates and fluxes. J. Geophys. Res., 96, 9075-9096.
Stein, A. F., Draxler, R., Stunder, B. J. B., Cohen, M. D., and Ngan, F., 2015, NOAA’S HYSPLIT Atmospheric Transport and Dispersion Modeling System: American Meteorological Society.
Super, A. B., and Reynolds, D. W., 1991, The feasibility of enhancing stream flow in the Sevier River Basin of Utah by seeding winter mountain clouds: Bureau of Reclamation, USDI, Denver Federal Center, Denver, CO.
Super, A. B., 1999, Summary of the NOAA/Utah atmospheric modification, program: 1990–1998: Journal of Weather Modification, 31, 51–75.
Super, A. B., and Heimbach, J. A., 2005a, Randomized propane seeding experiment: Wasatch Plateau, Utah: Journal of Weather Modification, 37, 35-66.
Super, A. B., and Heimbach, J. A., 2005b, Final Report on Utah cloud seeding experimentation using propane during the 2003/04 winter: Utah Division of Water Resources report to Bureau of Reclamation, March, 2005, 114 pp.
Tessendorf, S. A., Arnold, C., Bruintjes, R. T., Axisa, D., Peter, J., Wilson, L., Siems, S., Manton, M., May, P. T., and Stone, R., 2009, A characterization of cloud base aerosol and associated microphysics in southeast Queensland: American Geophysical Union, Fall Meeting 2009.
Tessendorf, S. A., 2010, Overview of the Queensland cloud seeding research program: Journal of Weather Modification, 42, 33–48.
Tessendorf, S. A., 2013, Aerosol characteristics observed in southeast Queensland and implications for cloud microphysics: Journal of Geophysical Research: atmosphere, 118, 2858–2871, doi:10.1002/jgrd.50274.
Thompson, G., Rasmussen, R. M., and Manning, K., 2004, Explicit forecasts of winter precipitation using an improved bulk microphysics scheme. Part I: Description and sensitivity analysis: Monthly Weather Review, 132, 519–542.
Tian, J., Liu, J., Yan, D., Li, C., Yu, F., 2017, Numerical rainfall simulation with different spatial and temporal evenness by using a WRF multiphysics ensemble: Natural Hazards and Earth System Science, 17, 563–579.
Tilley, J. S., David, R., and McDonough, F., 2015, On the utility of HYSPLIT trajectories driven by operational NWP analyses and forecasts for evaluating and forecasting cloud seeding plume pathways and targeting effectiveness: 95th American Meteorological Society Annual Meeting.
Yorty, D., Weston, W., Solak, M., and Griffith, D., 2012, Low-level atmospheric stability during icing periods in Utah, and implications for winter ground-based cloud seeding: Journal of Weather Modification, 44, 48-68.
Yorty, D., Weston, W., Solak, M., and Griffith, D., 2013, Low-level stability during winter storms in the UINTA basin of Utah: Potential impacts on ground-based cloud seeding, North American Weather Consultants, David Yorty, Warren Weston: Journal of Weather Modification, 45.
Wang, W., Bruyère, C., Duda, M., Dudhia, J., Gill, D., Lin, H., Michalakes, J., Rizvi, S., Zhang, X., Beezley, J. D., Coen, J. L., and Mandel, J., 2010, User’s guide for the advanced research WRF (ARW) version 3.2, NCAR (http://www.mmm.ucar.edu/wrf/users/docs/arw_v3.pdf).
Xue, L., Tessendorf, S., Nelson, E., Rasmussen, R., Breed, D., Parkinson, S., Holbrook, P., and Blestrud, D., 2013a, AgI cloud seeding effects as seen in WRF simulations. Part I: Model description and idealized 2D sensitivity tests: Journal of Applied Meteorology and Climatology, 52, 1433–1457.
Xue, L., Tessendorf, S., Nelson, E., Rasmussen, R., Breed, D., Parkinson, S., Holbrook, P., and Blestrud, D., 2013b, AgI cloud seeding effects as seen in WRF simulations. Part II: 3D real case simulations and sensitivity tests: Journal of Applied Meteorology and Climatology, 52, 1458–1476.
Xue, L., Chu, X., Rasmussen, R., Breed, D., Boe, B., and Geerts, B., 2013c, The dispersion of silver iodide particles from ground-based generators over complex terrain, Part 2: WRF large-eddy simulations versus observations: Journal of Applied Meteorology and Climatology, 1342–1361.