بررسی عملکرد سامانه همادی چند‌فیزیکی مدل میان‌مقیاس WRF جهت شبیه‌سازی بارش در مناطق مرکزی ایران

نوع مقاله : مقاله پژوهشی‌

نویسندگان

1 استادیار، دانشگاه هرمزگان، بندرعباس، ایران

2 دانشجوی دکترای هواشناسی، دانشگاه هرمزگان، بندرعباس، ایران

3 دانشیار، گروه فیزیک فضا، موسسه ژئوفیزیک، دانشگاه تهران، تهران، ایران

چکیده

ماهیت آشوب‌ناک جو، موجب بروز عدم‌قطعیت پیش‌بینی عددی وضع هوا می‌شود که ناشی از نقص در شرایط اولیه مدل، خطاهای مدل مانند خطای تقریب برخی معادلات فیزیکی و کم بودن ذاتی پیش‌بینی‌پذیری پدیده‌های فیزیکی است. یکی از روش‌های غلبه بر این عدم‌قطعیت، تولید یک سامانه همادی با ایجاد پریشیدگی در عوامل تولید آن است. ازآنجاکه بارش یکی از دشوارترین پراسنج‌های قابل پیش‌بینی به‌شمار می‌رود، این تحقیق بر آن است تا با توسعه یک سامانه همادی چند‌فیزیکی برای مدل میان‌مقیاس WRF، عملکرد آن را در شبیه‌سازی بارش در مناطق مرکزی ایران بررسی کند. این سامانه با 84 عضو، متشکل از دو طرح‌واره لایه مرزی، پنج طرح‌واره کومولوسی و حالت بدون کومولوس و هفت طرح‌واره میکروفیزیکی، با سه دامنه تودرتو با تفکیک 4، 12 و 36 کیلومتری روی مناطق مرکزی ایران شامل 132 ایستگاه برای دوازده روز بارشی منتخب از زمستان 2016-2015 اجرا شد. با استفاده از داده‌های مشاهداتی همدیدی سازمان هواشناسی، بارش 24 ساعته خروجی سامانه همادی با محاسبه سنجه‌‌های درستی‌سنجی استاندارد منتخب ارزیابی شد. نتایج نشان داد از بین طرح‌واره‌های انتخابی، طرح‌واره لایه مرزی MYJ، طرح‌واره‌های کومولوسی KF و GF و طرح‌واره‌های میکروفیزیکی WSM6، Goddard و New Thompson بهترین عملکرد را داشتند. علاوه بر این بررسی، سنجه‌های درستی‌سنجی برای سه دسته بارشی کمتر از 1 میلی‌متر، بین 1 تا 10 میلی‌متر و بین 10 تا 50 میلی‌متر، نشان دهنده کیفیت مناسب پیش‌بینی‌ بارش سامانه همادی است و عملکرد آن با افزایش تعداد اعضاء بهبود می‌یابد.

کلیدواژه‌ها

موضوعات


عنوان مقاله [English]

Evaluation of a WRF model multi-physics ensemble forecasting system for simulation of precipitation over central region of Iran

نویسندگان [English]

  • maryam rezazadeh 1
  • Fatemeh Moradian 2
  • Sarmad Ghader 3
1 Assistant Professor, Marine Science and Technology faculty, University of Hormozgan, Bandar Abbas, Iran
2 Ph.D Student, Marine Science and Technology faculty, University of Hormozgan, Bandar Abbas, Iran
3 Professor, Space Physics Department, Institute of Geophysics, University of Tehran, Tehran, Iran
چکیده [English]

The chaotic nature of the atmosphere, inexact equations of motions, and gaps in specifying the initial state of the atmosphere result in some uncertainties in the weather forecasting. One of the operational methods that has been employed to overcome this difficulty and improve the accuracy of weather forecasts is ensemble forecasting which gains popularity as a technique in numerical weather prediction due to its improved forecast results. There are some different approaches to develope an ensemble forecasting system including using multi-numerical weather prediction models with different numerics, utilizing different physical parametrization schemes, and perturbing initial and boundary conditions. The aim of the present work is to develope a multi-physics ensemble forecasting system for the Weather Research and Forecasting (WRF) model and to evaluate its performance in forecasting of precipitation as a low predictability parameter. To construct the ensemble members, seven microphysics, two planetary boundary layers (PBL) and six cumulus parameterization schemes are employed. These choices are used to generate 84 memebers of the ensemble system. For running the model, a three-nested domain consisting of 36, 12, and 4 km spatial resolution was used to simulate the precipitation for 12 days during 2015-2016 winter in central region of Iran. The one degree FNL data of National Center for Environmental Prediction (NCEP) are utilized as the initial condition for running WRF. The model outputs are evaluated against observations using some standard metrics of forecasting verification including categorical, continuous and probabilistic indices. The observed 24-h accumulated precipitation measured at 132 synoptic stations of national meteorological organization of Iran was used for verification of simulations.
    In this study, the general performance of the different members of ensemble system is discussed. The members employing MYJ planetary boundary layer scheme, KF and GF cumulus schemes and WSM6, Goddard and New Thompson schemes are found to be appropriate more than the others for accumulated precipitation simulation in the study region. Furthermore, a multi categorical verification using three threshold of 1, 10, and 50 mm/day showed the suitable appraisal of the total performance of ensemble system. Moreover, increasing in ensemble size results in better outputs. However, it seems that expanding the ensemble members might be limited at an optimum number. It means that the skill of ensemble system doesn’t change significantly when the ensemble members are more than the optimum number. In addition, the results show that using a cumulus parametrization scheme for the spatial resolution of the finest domain of this study (i.e., 4 km) is useful to improve the precipitation simulations. It is worth to mention that this finding is in agreement with reports of other studies.

کلیدواژه‌ها [English]

  • numerical weather prediction
  • WRF model
  • ensemble forecasting
  • precipitation
آزادی، م.، واشانی، س. و حجام، س. ، 1391، پیش‌بینی احتمالی بارش با استفاده از پس‌پردازش (Post Processing) برونداد یک سامانه همادی: مجله فیزیک زمین و فضا، 38(3)، 203-216.
فتحی، م.، آزادی، م.، کمالی، غ. و مشکاتی، ا.، 1397، واسنجی پیش­بینی احتمالاتی بارش برونداد سامانه همادی به روش میانگین‌گیری بایزی بر روی ایران، نشریه هواشناسی و علوم جو، 1(2)، 114-129.
فتحی، م.، آزادی، م.، کمالی، غ. و مشکاتی، ا.، 1398، بهبود پیش­بینی یقینی بارش با استفاده از میانگین وزنی سامانه همادی بر روی ایران: نیوار، 43، 63-67.
قادر، س.، یازجی، د.، سلطانپور، م. و نعمتی، م. ح.، 1394، بکارگیری یک سامانه همادی توسعه داده شده برای مدل WRF جهت پیش­بینی میدان باد سطحی در محدوده خلیج فارس: مجله هیدروفیزیک، 1، 41-54.
قصابی، ز.، کمالی، غ.، مشکوتی، ا. ح.، حجام، ح. و جواهری، ن.، 1393، ارزیابی عملکرد طرح‌واره­های پارامتره‌سازی خردفیزیکی و همرفت مدل WRF در برآورد بارش در حوضه آبریز کارون در جنوب غرب ایران: نشریه پژوهش­های اقلیم­شناسی، 5، 1-10.
نیستانی، ا.، قادر، س. و محب الحجه، ع.، 1396، کاربست داده گواری در مدل WRF برای شبیه­سازی بارش ناشی از یک سامانه همدیدی در غرب ایران: مجله ژئوفیزیک ایران، 11(1)، 101-123.
 
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, doi:10.1175/2008WAF2007101.1.
Anderson, J. L., 1996, A method for producing and evaluating probabilistic forecasts from ensemble model integrations: Journal of Climate, 9, 1518–1530.
Arakawa, A., 2004, The cumulus parameterization problem: Past, present, and future: Journal of Climate, 17, 2493-2525.
Argüeso, D., Hidalgomuoz, J. M., Gámizfortis, S. R., Estebanparra, M. J., Dudhia, J., and Castrodiez, Y., 2011, Evaluation of WRF parameterizations for climate studies over Southern Spain using a multistep regionalization: Journal of Climate, 24, 5633–5651, doi:10.1175/JCLI-D-11-00073.1.
Bruno, F., Cocchi, D., Greco, F., and Scardovi, E., 2014, Spatial reconstruction of rainfall fields from rain gauge and radar data: Stochastic Environmental Research and Risk Assessment, 28, 1235–1245, doi:10.1007/s00477013-0812-0.
Buizza, R., 1997, Potential Forecast Skill of ensemble prediction and spread and skill distributions of the ECMWF ensemble prediction system: Monthly Weather Review, 125, 99-119.
Buizza, R., and Palmer, T. N., 1998, Impact of ensemble size on ensemble prediction: Monthly Weather Review, 126, 2503–2518.
Buizza, R., Miller, M., and Palmer, T. N., 1999, Stochastic representation of model uncertainties in the ECMWF Ensemble Prediction System: Quarterly Journal of the Royal Meteorological Society, 125, 2887–2908.
Cardoso, R. M., Soares, P. M., Miranda, P. M. A., and Belo-Pereira, M., 2013, WRF high resolution simulation of Iberian mean and extreme precipitation climate: International Journal of Climatology, 33, 2591–2608, doi:10.1002/joc.3616.
Chambon, P., Zhang, S. Q., Hou, A. Y., Zupanski, M., and Cheung, S., 2014, Assessing the impact of pre-GPM microwave precipitation observations in the Goddard WRF ensemble data assimilation system: Quarterly Journal of the Royal Meteorological Society, 140, 1219–1235, doi:10.1002/qj.2215.
Chen, J., Brissette, F. P., and Li, Z., 2014, Postprocessing of ensemble weather forecasts using a stochastic weather generator: Monthly Weather Review, 142, 1106–1124.
Chen, F., Liu, C., Dudhia, J., and Chen, M., 2014, A sensitivity study of high-resolution regional climate simulations to three land surface models over the western United States: Journal of Geophysical Research, 119, 7271–7291, doi: 10.1002/2014JD021827.
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.
Collischonn, W., Haas, R., Andreolli, I., and Tucci, C. E. M., 2005, Forecasting River Uruguay flow using rainfall forecasts from a regional weather-prediction model: Journal of Hydrology, 305, 87–98, doi: 10.1016/j.jhydrol.2004.08.028.
Du, J. and M. S. Tracton, 2001, Implementation of a real-time short-range ensemble forecasting system at NCEP: an update: Preprints, 9th Conference on Mesoscale Processes,  Ft.Lauderdale, Florida, American Meteorological Society, 355-356.
Du, J., DiMego, G., Tracton, M. S., and Zhou, B., 2003, NCEP short-range ensemble forecasting (SREF) system: multi-IC, multi-model and multi-physics approach: Research Activities in Atmospheric and Oceanic Modelling (edited by J. Cote), Report 33, CAS/JSC Working Group Numerical Experimentation (WGNE), WMO/TD-No. 1161, 5.09-5.10.
Du, J., DiMego, G., Zhou, B., Jovic, D., Ferrier, B., Pyle, M., Manikin, G., Yang, B., Wolff, J., and Etherton, B., 2012, “New 16km NCEP Short-Range Ensemble Forecast (SREF)system: what we have and what we need?”: SREF.v6.0.0, implementation date: Aug. 21. 2012; http://www.emc.ncep.noaa.gov/mmb/SREF/SREF.html
Du, J., Berner, J., Buizza, R., Charron, M., Houtekamer, P., Hou, D., Isidora, J., Mu, M., Wang, X., Wei, M., Yuan, H., 2018, Ensemble methods for meteorological predictions: U.S. Department of Commerce National Oceanic and Atmospheric Administration (NOAA), National Weather Service National Centers for Environmental Prediction (NCEP), 5830 University Research Court, College Park, MD 20740.
Evans, J. P., Ekstrِm, M., and Ji, F., 2011, Evaluating the performance of a WRF physics ensemble over South-East Australia: Climate Dynamics, 39, 1241–1258, doi:10.1007/s00382-011-1244-5.
Flaounas, E., Bastin, S., and Janicot, S., 2011, Regional climate modelling of the 2006 West African monsoon: sensitivity to convection and planetary boundary layer parameterization using WRF: Climate Dynamics, 36, 1083–1105, doi:10.1007/s00382-010-0785-3.Gallus, W. A. and Bresch, J. F., 2006, Comparison of impacts of WRF dynamic core, physics package, and initial conditions on warm season rainfall forecasts: Monthly Weather Review, 134, 2632–2641.
Ghelli, A., ECMWF, Verification of categorical predictands: 4IWVM - Tutorial Session, June 2009.
Givati, A., Lynn, B., Liu, Y., and Rimmer, A., 2012, Using the WRF model in an operational streamflow forecast system for the Jordan River: Journal of Applied Meteorology and Climatology, 51, 285–299, doi:10.1175/JAMC-D-11-082.1.
Grell, G. A., and Freitas, S. R., 2014, A scale and aerosol aware stochastic convective parameterization for weather and air quality modeling: Atmospheric Chemistry and Physics, 14, 5233–5250, doi: 10.5194/acp-145233-2014.
Hanssen, A.W. and Kuipers, W.J.A., 1965: On the relationship between the frequency of rain and various meteorological parameters: Mededeelingen enVerhandelingen, Royal Netherlands Meteorological Institute, 81.
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.
Hu, X. M., Nielsengammon, J. W., and Zhang, F., 2010, Evaluation of three planetary boundary layer schemes in the WRF model: Journal of Applied Meteorology and Climatology, 49, 1831–1844, doi:10.1175/2010JAMC2432.1.
Inness P., Dorling S., 2013, Operational weather forecasting: John Wiley & Sons, 231pp.
Janjic, I. Z., 1994, The step-mountain eta coordinate model: Further developments of the convection, viscous sublayer, and turbulence closure schemes: Monthly Weather Review, 122, 927–945, doi:10.1175/1520-493(1994)1222.0.CO;2.
Jankov, I., Gallus, W. A., Swgal, M., and 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, doi:10.1175/WAF888.1.
Jankov, I., Gallus, W. A., Segal, M., 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.
Jankov, I., Grasso, L. D., Senguota, M., Neiman, P. J., Zupanski, D., Zupanski, M., Lindsey, D., Hillger, D. W., Birkenheuer, D. L., Brummer, R., and Yuan, H., 2011, An evaluation of five ARW-WRF microphysics schemes using synthetic GOES imagery for an atmospheric river event affecting the California coast: Journal of Hydrometeorology, 12, 618–633, doi:10.1175/2010JHM1282.1.
Kalnay, E., 2003, Atmospheric Modeling, Data Assimilation and Predictability: Cambridge, Cambridge University Press, 341pp.
Kenneth, R., 2002, Decision-making from probability forecasts based on forecast value: Meteorological Applications, 9, 307–315.
Kessler, E., 1969, On the distribution and continuity of water substance in atmoshperic circulations: Meteorological Monographs, 32: American Meteorological Society.
Klein, C., Heinzeller, D., Bliefernicht, J., and Kunstmann, H., 2015, Variability of West African monsoon patterns generated by a WRF multi-physics ensemble: Climate Dynamics, 45, 1–23, doi:10.1007/s00382-015-2505-5.
Lee, J., Shin, H. H., Hong, S., and Hong, J., 2015, Impacts of subgrid-scale orography parameterization on simulated surface layer wind and monsoonal precipitation in the highresolution WRF Model: Journal of Geophysical Research, 120, 644–653, doi: 10.1002/2014JD022747.
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.
Madala, S., Satyanarayana, A. N. V., and Rao, T. N., 2014, Performance evaluation of PBL and cumulus parameterization schemes of WRF ARW model in simulating severe thunderstorm events over Gadanki MST radar facility – Case study: Atmospheric Research, 139, 1–17, doi: 0.1016/j.atmosres.2013.12.017.
Mailier, P.J., Jolliffe, I.T., Stephenson, D.B., 2006, Quality of Weather Forecasts: Royal Meteorological Society.
Mason, S. J., and Graham, N. E., 1999, Conditional probabilities, relative operating characteristics, and relative operating levels: Weather Forecasting, 14, 713–725.
McBride, J. L. and Ebert, E. E., 2000, Verification of quantitative precipitation forecasts from operational numerical weather prediction models over Australia: Weather Forecast., 15, 103–121.
Mullen, S. L., Du, J., and Sanders, F., 1999, The dependence of ensemble dispersion on analysis forecast system: implications to short-range ensemble forecasting of precipitation, Monthly Weather Review, 127, 1674-1686.
Pei, L., Moore, N., Zhong, S., Luo, L., Hyndman, D. W., Heilman, W. E., and Gao, Z., 2014, WRF model sensitivity to land surface model and cumulus parameterization under short-term climate extremes over the Southern Great Plains of the United States: Journal of Climate, 27, 7703–7724, doi:10.1175/JCLI-D-14-00015.1.
Pennelly, C., Reuter, G., and Flesch, T., 2014, Verification of the WRF model for simulating heavy precipitation in Alberta: Atmospheric Research, 135–136, 172–179, doi: 10.1016/j.atmosres.2013.09.004.
Qie, X., Zhu, R., Yuan, T., Wu, X. K., Li, W., and Liu, D., 2014, Application of total-lightning data assimilation in a mesoscale convective system based on the WRF model: Atmospheric Research, 145–146, 255–266, doi: 10.1016/j.atmosres.2014.04.012.
Remesan, R., Bellerby, T., Holman, I., and Frostick, L., 2015, WRF model sensitivity to choice of parameterization: a study of the “York Flood 1999”: Theoretical And Applied Climatology, 122, 229–247, doi: 10.1007/s00704-014-1282-0.
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 Technical Procedures Bulletin, 488, 15 pp.
Rutledge, S. A., and Hobbs, P., 1983, The mesoscale and microscale structure and organization of clouds and precipitation in midlatitude cyclones. VIII: A model for the “seeder-feeder” process in warm-frontal rainbands: Journal of Atmospheric Sciences, 40, 1185–1206, doi:10.1175/1520-0469(1983)0402.0.CO;2, 1983.
Schaefer, J.T., 1990, The critical success index as an ind ica t or of forecasting skill: Weather Forecasting, 5, 570–575.
Shepherd, T. J. and Walsh, K. J., 2016, Sensitivity of hurricane track to cumulus parameterization schemes in the WRF model for three intense tropical cyclones: impact of convective asymmetry: Meteorology and Atmospheric Physics, 16, 1–30, doi:10.1007/s00703-016-0472-y.
Sivillo, J. K., Ahlquist, J. E., Toth, Z., 1997, An ensemble forecasting primer: Weather and Forecasting, 12, 809-818.
Skamarock, W. C., Klemp, J. B., Dudhi, J., Gill, D. O., Barker, D. M., Duda, M. G., Huang, X. Y., Wang, W., and Powers, J. G., 2008, A Description of the Advanced Research WRF Version 3: NCAR Technical note 475+STR. Tao, W. K., Simpson, J., and McCumber, M., 1989, An ice-water saturation adjustment: Monthly Weather Review, 117, 231–235.
Stephenson, D.B., 2000, Use of the ‘odds ratio’ for diagnosing forecast skill. Weather Forecasting, 15, 221–232.
Thompson, G., Field, P. R., Rasmussen, R. M., and Hall, W. D., 2008, Explicit forecasts of winter precipitation using an improved bulk microphysics scheme. Part II: Implementation of a new snow parameterization: Monthly Weather Review, 136, 5095–5115, doi:10.1175/2008MWR2387.1.
Tian, J., Li, J., Yan, D., Li, H., and Yu, F., 2017, Numerical rainfall simulation with different spatial and temporal evenness by using a WRF multiphysics ensemble: Natural Hazards and Earth System Sciences, 17, 563–579, doi:10.5194/nhess-17-563-2017.
Tietdke, M., 1989, A comprehensive mass flux scheme for cumulus parameterization in large-scale models: Monthly Weather Review, 117, 177-1800.Toth, Z., and Kalnay, E., 1997, Ensemble forecasting at NCEP and the breeding method: Monthly Weather Review, 125, 3297–3319.
Tracton, M. S., and Kalnay, E.,1993, Operational ensemble prediction at the National Meteorological Center: Practical aspects: Weather Forecasting, 8, 379–398.
Vasconi, M., Montani, A., and Paccagnella, T., 2018, Sensitivity of forecast skill to the parameterisation of moist convection in a Limited-area ensemble forecast system: Master’s Thesis in Physics of the Earth System, University of Bologna, Italy, Manuscript under review for journal Nonlin. Processes Geophys., Discussion started: 27 March 2018 Nonlinear Processes in Geophysics. available at http://amslaurea.unibo.it/14566/1/Tesi_Magistrale_Vasconi.pdf.
Wandishin, M. S., Mullen, S. L., Stensrud, D. J., and Brooks, H. E., 2001, Evaluation of a short-range multimodel ensemble system: Monthly Weather Review, 129, 729–747.
Wang, S., Yu, E., and Wang, H., 2012, A simulation study of a heavy rainfall process the Yangtze River valley using the two-way nesting approach: Advances in Atmospheric Sciences, 29, 731–743, doi:10.1007/s00376012-1176-y.
Wang, X., Hamill, T. M., Whitaker, J. S., and Bishop, C. H., 2009, A comparison of the hybrid and EnSRF analysis schemes in the presence of model error due to unresolved scales: Monthly Weather Review, 137, 3219-3232.
Wilks, D., 1995, Statistical methods in the atmospheric sciences: International Geophysics Series, 59, Academic Press.
Wilks, D. S., and Hamill, T. M., 2007, Comparison of ensemble-MOS methods using GFS reforecasts: Monthly Weather Review, 135, 2379–2390.
 
World Meteorological Organization, 2012, Guidelines on Ensemble Prediction Systems and Forecasting: WMO-No. 1091.
World Meteorological Organization, 2014, Draft Document on Standardized Surface Verification of Deterministic NWP Products: 4.1_v3.
Xue, L., Chu, X., Rasmussen, R., Breed, D., Boe, B., and Geerts, B., 2014, The dispersion of silver iodide particles from ground-based generators over complex terrain. Part II: WRF large eddy simulations versus Observations: Journal of Applied Meteorology and Climatology, 53,1342–1361.
Yang, B., Zhang, Y., and Qian, Y., 2012, Simulation of urban climate with high-resolution WRF model: A case study in Nanjing, China, Asia-Pac: Journal of the Atmospheric Sciences, 48, 227–241, doi:10.1007/s13143-0120023-5, 2012.

Zhang, H., Pu, Zh., 2010, “Review Article, Beating the Uncertainties: Ensemble Forecasting and Ensemble-Based Data Assimilation in Modern Numerical Weather Prediction”., Advances in Meteorology, Hindawi Publishing Corporation Advances in Meteorology, Volume 2010, Article ID 432160, 10 pages, doi:10.1155/2010/432160