کاربست داده‌گواری در مدل WRF برای شبیه‌سازی بارش ناشی از یک سامانه همدیدی در غرب ایران

نوع مقاله : مقاله پژوهشی‌

نویسندگان

موسسه ژئوفیزیک دانشگاه تهران، تهران، ایران

چکیده

استفاده از مدل‌های پیش‌بینی عددی وضع هوا برای پیش‌بینی عددی توزیع مکانی و زمانی بارش با تفکیک بالا توجه زیادی را به خود جلب کرده است. کاهش دقت پیش‌بینی‌ها عمدتاً وابسته به میزان خطا در شرایط اولیه و انتخاب نامناسب طرحواره های فیزیکی می­باشد. در این تحقیق تاثیر داده­گواری در بهبود پیش­بینی بارش در منطقه غرب ایران مورد بررسی قرار گرفته است. آزمایش­های داده­گواری مختلف با استفاده از روش داده­گواری وردشی سه­بعدی (3DVAR) با اجرای مدل پیشرفته تحقیقاتی و پیش­بینی وضع هوا WRF و کاربست بسته داده­گواری مدل طراحی شده است. یک مورد رخداد بارش سنگین ناشی از یک سامانه همدیدی قوی در منطقه غرب ایران به منظور بررسی تاثیر داده­گواری بر پیش­بینی بارش انتخاب گردید. شبیه­سازی­ها شامل اجراهای کنترلی با سه مجموعه داده شرایط اولیه از سه منبع مختلف و اجراهای داده­گواری با استفاده از میدان ­زمینه GFS همراه با مشاهدات سطحی اندازه­گیری شده در ایستگاه­های همدیدی سازمان هواشناسی ایران و مشاهدات مرکز NCEP می­باشد. استفاده از داده­های مشاهداتی برای اصلاح میدان زمینه، تاثیر قابل توجهی در شرایط اولیه دمای تراز 2 متر و مولفه­های باد مداری و نصف­النهاری تراز 10 متر نشان داد. برای مثال، در بعضی از مناطق شبیه­سازی، دما در میدان زمینه 3+ درجه سلسیوس دارای فروتخمین نسبت به تحلیل به دست آمده بوده است و میدان باد نیز در بعضی مناطق به میزان 3± متر بر ثانیه تصحیح شد. همچنین مقایسه نمودارهای پراکنش میدان ­زمینه و تحلیل نسبت به مشاهدات مؤید کاهش پراکندگی و کاهش خطا در تحلیل به­دست آمده از روش 3DVAR می­باشد. نتایج نشان داد که دقت پیش­بینی­ها بسته به نوع داده به­کار رفته در شرایط اولیه مدل و طرحواره­های فیزیکی انتخابی دارای تفاوت­های اساسی است. در تحلیل اریبی بارش در ایستگاه­های منتخب در غرب ایران، داده­گواری در یکی از پیکربندی­های فیزیکی با داده­های سطحی سازمان هواشناسی ایران باعث کاهش 73% در میزان اریبی پیش‌بینی بارش تجمعی 24 ساعته گردید و در پیش­بینی­های بارش تجمعی 48 ساعته تاثیر داده­گواری کاهش یافت. تحلیل همبستگی به منظور مقایسه الگوهای بارش پیش‌بینی شده و مشاهدات نشان داد که داده­گواری تاثیری مثبت ولی محدود دارد. همچنین بیشینه تاثیر داده­گواری بر الگوی همبستگی پیش­بینی­های بارش نسبت به حالت کنترلی حدود 8%  به­دست آمد.

کلیدواژه‌ها

عنوان مقاله [English]

Application of data assimilation using WRF model to simulate precipitations caused by synoptic systems in the western regions of Iran

نویسندگان [English]

  • Abolfazl Neyestani
  • Sarmad Ghader
  • alireza Mohebalhojeh
Institute of Geophysics, University of Tehran, Tehran, Iran
چکیده [English]

Ever increasing attention is paid to numerical weather prediction (NWP) models  with the purpose of providing high-resolution precipitation forecasts. In such applications, which are based on both the theoretical analysis and numerical experiments, the prediction accuracy is  closely related to the errors in the initial conditions and in the physical parametrization schemes. In  the present research, the potential of data assimilation in improving precipitation forecasts was investigated in a case study on an active weather system in the western regions of Iran. Various data assimilation experiments were designed by running the weather research and forecasting (WRF) model and its data assimilation package (WRF-DA). In each data assimilation experiment, we applied the three-dimensional variational data assimilation (3DVAR) method. A heavy rainfall event  caused by a strong synoptic system in western Iran was selected in order to  study the  influence of data assimilation on precipitation forecast.
So as to carry out this study, the initial atmospheric and lateral boundary conditions were taken from three data categories: NCEP global forecast system (GFS), real-time forecasts at 3-h intervals, which are gridded to horizontal resolutions of 1̊×1̊ and 0.5̊×0.5̊, NCEP FNL (Final) Operational Global Analysis data on 1̊×1̊ grids prepared operationally every six hours and ERA-Interim reanalysis dataset of ECMWF, gridded to horizontal resolution of approximately 80 km at 6-h intervals.
Simulations were divided into control runs and data assimilation runs, with the former runs being based on three sets of data as initial conditions. The data assimilation runs were conducted  utilizing GFS data as the background and two sets of obeservations, namely the surface observations of Iran Meteorological Organization (IRIMO) and the NCEP observations. The observation data showed a significant impact on the initial conditions of 2m temperature and 10m zonal and meridional wind components, such that in  certain parts of the simulation domain, the background temperature  was estimated to be up to +3C°relative to the analysis and the wind field was revised by up to ±3 meters per second in some areas.
The comparison between the scatter plots of the background and observations relative to the analysis  corroborates the fact that the scatter and errors were decreased after using 3DVAR. The  findings indicated that the accuracy of forecasts depends directly on the type of data  employed as initial conditions for WRF model and the physical parametrization schemes, hence the fact that the simulations demonstrate significant differences. The bias analysis of precipitation for stations  with precipitation records in the west  illustrated that the assimilation of IRIMO surface data in one of the physical configurations decreased the forecast bias to a minimum of  73% of the cumulative 24-hour precipitation forecast. The impact of data assimilation, on the other hand, decreased in the cumulative 48-hour precipitation forecasts.
Correlation analysis of the forecasted precipitation patterns and the observed values demonstrated that data assimilation  generates a higher correlation coeffcient, implying that  it  had a discernible, though limited, positive impact  on the case examined. In addition, the maximum impact of data assimilation on the correlation between data assimilation runs  and control runs for precipitation  was approximately 8%. Specifying a precipitation threshold for quantitative precipitation forecasts (QPF), the binary analysis was  done,  while the proportion correct score (PC)  of each threshold was  employed in order to investigate the forecasts quality.  In conclusion, using the skill score of binary analysis is not a proper method to compare forecasts quality in different experimental runs when the number of forecasts and observational stations are limited.

کلیدواژه‌ها [English]

  • Numerical Prediction
  • Data assimilation
  • WRF
  • 3DVAR
  • quantitative precipitaion forecast

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