پیش‌بینی عددی چند رخداد مه تابشی و CBL با استفاده از مدل WRF روی برخی مناطق ایران: مطالعه موردی، 27 تا 31 دسامبر سال 2015

نوع مقاله : مقاله پژوهشی

نویسندگان

1 دانشجوی دکتری، پژوهشگاه هواشناسی و علوم جو، تهران، ایران

2 دانشیار، پژوهشگاه هواشناسی و علوم جو، تهران، ایران

3 استادیار، پژوهشگاه هواشناسی و علوم جو، تهران، ایران

چکیده

در این مطالعه رخدادهای مه تابشی و مه ناشی از کاهش ارتفاع کف ابر موسوم به مه CBL‌ (Cloud-Base Lowering) از 27 تا 31 دسامبر 2015 که در ایستگاه‌های هواشناسی فرودگاه‌های ارومیه، اراک، بوشهر، زاهدان، همدان و شهرکرد ثبت شده است، با استفاده از مدل میان مقیاس WRF شبیه‌سازی شده است. برای این منظور، مدل میان مقیاس WRF با 5 پیکربندی متفاوت و 28 تراز قائم اجرا شد. سپس با به‌کارگیری چهار الگوریتم محاسبه دید افقی شامل SW99، FSL، G2009 و RUC روی برونداد مدل، دید افقی محاسبه شد. بررسی نتایج نشان داد که مدل نمی‌تواند مقدار نم نسبی لایه مرزی و در نتیجه رخداد مه را شبیه‌سازی کند. با افزایش تعداد ترازهای قائم مدل در لایه 200 متری مجاور سطح زمین، دیده شد که مهارت مدل در پیش‌بینی رخداد مه افزایش یافت و از 6 مورد مه CBL و 4 مورد مه تابشی به‌ترتیب 5 و 2 مورد رخداد مه پیش‌بینی شد. آزمایش‌های مختلف اهمیت تعداد ترازهای قائم در مجاورت سطح زمین و نقش آن در کیفیت پیش‌بینی مه را مشخص کرد. همچنین نتایج کلی نشان داد که کاریی مدل در پیش‌بینی مه CBL نسبت به مه تابشی بیشتر ‌است.

کلیدواژه‌ها

موضوعات

عنوان مقاله [English]

Numerical prediction of several radiation and CBL fog events over Iran using the WRF model for late December 2015

نویسندگان [English]

  • Razieh Pahlavan 1
  • Mohammad Moradi 2
  • Sahar Tajbakhsh 3
  • Majid Azadi 2
  • Mehdi Rahnama 3
1 Ph.D. Student, Atmospheric Science and Meteorological Research Center (ASMERC), Tehran, Iran
2 Associate Professor, Atmospheric Science and Meteorological Research Center (ASMERC), Tehran, Iran
3 Assistant Professor, Atmospheric Science and Meteorological Research Center (ASMERC), Tehran, Iran
چکیده [English]

In this study it has been attempted to simulate the occurrence of radiation and CBL fog during late December 2015 in Orumiyeh, Arak, Bushehr, Zahedan, Hamedan and Shahrekord airports, using the mesoscale WRF model.
To simulate radiation and CBL fogs, the output of the WRF model with five different configurations as presented in table 1 was used, and then four visibility calculation algorithms were applied to the output of the model to find the best configuration and visibility calculation algorithm for the prediction of the considered fog events. The WRF model version 3.9.1 with Lambert conformal projection, using two nested domains with 16-km and 4-km grid spacing was used for the simulation. Based on the results of some previous studies (e.g. Lin et al., 2017; Van der Velde et al., 2010; Roman-Casc´on et al., 2012), because of the sensitivity of the fog prediction to cloud microphysics, planetary boundary layer and long-wave radiation schemes, five different configurations of the WRF model with varying physical parametrization schemes were implemented.
In the first series of experiments, the five mentioned configurations of the model were run using 28 vertical levels. Then four horizontal visibility calculation algorithms including SW99 (Stoelinga and Warner,1999), FSL (Doran et al., 2009), G2009 (Gultepe et al., 2009) and RUC (Benjamin et al., 2004) were applied on the model output to calculate the horizontal visibility separately for each model output and for six synoptic stations in the domain. It is seen that, since the amount of liquid water content (LWC) of the cloud with all model configurations is zero or nearly zero, the calculated horizontal visibility was much greater than 10 km for all stations. In general, the model showed poor results for the simulation of relative humidity in the boundary layer and thus the occurrence of fog.
Since some previous studies (e.g. Yang and Gao, 2016; Philip et al., 2016; Tardif, 2007) have emphasized the importance of high vertical resolution to resolve the main fog formation processes, in the next series of experiments the number of vertical levels in the model was increased from 28 to 32, such that 11 vertical levels were considered from the ground level up to 200 m above ground. The model was then implemented with the same five configurations as in the first experiments. Examining the results revealed that the model found the skill of recognizing moisture and fog in most cases, and predicted 5 out of 6 cases of CBL and 2 out of 4 cases of radiation fogs.
For the verification the outputs for the predictions of fog events the calculated visibilities were compared with the verifying observational data and hit rate and equitable threat score were calculated. The evaluation indicated that the configuration 2 with 32 vertical levels combined with SW99 and G2009 algorithms performed better for the prediction of visibility for stations considered here. It can also be said that increasing the number of vertical levels close to the surface is of great importance in improving the quality of fog forecasting, because high vertical resolution is required to realistically represent the vertical structure and magnitude of the radiative cooling in the first few meters of the atmosphere, and thus obtain more accurate forecasts of radiation fog. Using high vertical resolution in simulations, results in an acceptable increase of liquid water content (LWC) and thus improves the accuracy of fog prediction. The results also show that the model's skill to predict CBL fog is higher than that of radiation fog in this case study. By comparing the simulated and observational two meter temperature values, it was observed that the simulated air temperature is overestimated, especially for the radiation fog. Previous researches have also shown a positive bias in predicted air temperature at two-meter height in radiation fog conditions, which is probably due to the inability of the model to simulate the actual radiative cooling associated with fog conditions in the first few meters of the atmosphere (Roman-Casc´on et al., 2019).

کلیدواژه‌ها [English]

  • Numerical Prediction
  • Radiation fog
  • CBL fog
  • WRF Model
  • Vertical levels

مراجع

آزادی، م.، رضازاده، پ.، میرزایی، ا. و وکیلی، غ.، 1382، پیش‌بینی عددی سیستم‌های زمستانی روی ایران، مطالعه مقایسه‌ای پارامتری‌سازی‌های فیزیکی، هشتمین کنفرانس دینامیک شاره‌ها، تبریز، ایران.
تاج‌بخش، س.، مرادی، م.، محمدپور پنچاه، م. و رشیدزاد، م.، 1397، پیش‌بینی رخداد مه به‌کمک برخی روش‌های تجربی (فرودگاه‌های تهران و مشهد)، م. فیزیک زمین و فضا، 44، 395-379.
جابری. پ.، ثابت قدم، س. و قادر، س.، 1397، پیش‌بینی عددی کاهش دید افقی در منطقه تهران: مطالعه موردی، هجدهمین کنفرانس ژئوفیزیک ایران، 18 تا 20 اردیبهشت 1397، تهران.
لایقی، ب.، قادر، س.، بیدختی، ع. ع. و آزادی، م. 1396. حساسیت‌سنجی شبیه‌سازی‌های مدل WRF به پارامتری‌سازی‌های فیزیکی در محدوده خلیج فارس و دریای عمان در زمان مونسون تابستانی، م. ژئوفیزیک ایران، 11(1)، 1-19.
Ahrens, C. D. and Henson, R., 2018, Meteorology today: An Introduction to Weather, Climate, and the Envirinment, 12th Edition. Cengage Learning.
Bang, C., Lee, J. W. and Hong, S.Y., 2009, Predictability experiments of fog and visibility in local airports over Korea using the WRF model. Journal of Korean society for atmospheric environment., 24, 92-101.
Ballard, S. P., Golding, B. W. and Smith, R. N. B., 1991, Mesoscale model experimental forecasts of the Haar of northeast Scotland. Monthly Weather Review., 119, 2107–2123.
Bartok, J., Bott, A. and Gera, M., 2012, Fog prediction for road traffic safety in a coastal desert region. Boundary-Layer Meteorology., 145(3), 485–506.
Benjamin, S. G., Devenyi, D., Weygandt, S. S., Brundage, K. J., Brown, J. M., Grell, G. A., Kim, D., Schwartz, B. E., Smirnova, T. G., Smith, T. L. and Manikin, G. S., 2004, An hourly assimilation–forecast cycle: The RUC. Mon. Wea. Rev., 132, 495–518.
Bergot, T., 2007, Quality assessment of the Cobel-Isba numerical forecast system of fog and low clouds. Pure appl. geophys., 164 (2007), 1265–1282.
Byers, H. R., 1959, General Meteorology. McGraw-Hill.
Cuxart, J. and Jiménez, M.A., 2011, Deep radiation fog in a wide closed valley: study by numerical modeling and remote sensing. Pure Appl Geophys., 169, 911–926.
Doran, J. A., Rohr, P. J., Beberwyk, D. J., Brooks, G. R., Gayno, G. A., Williams, R. T., Lewis, J. M. and Lefevre, R. J., 1999, TheMM5at the AF Weather Agency—New products to support military operations. Preprints, Eighth Conf. on Aviation, Range, and Aerospace Meteorology, Dallas, TX, Amer. Meteor. Soc., 115–119.
Dudhia, J., 1989, Numerical study of convection observed during the Winter Monsoon Experiment using a mesoscale two–dimensional model. J. Atmos. Sci., 46, 3077–3107.
Fultz, A. J. and Ashley, W. S., 2016, Fatal weather related general aviation accidents in the United States. Physical Geography., 37(5), 291-312, DOI: 10.1080/02723646.2016.1211854
George, J. J., 1951, Fog. Compendium of Meteorology, T. F. Malone, Amer. Meteor. Soc., 1179–1189.
Gultepe, I. and Milbrandt, J., 2007, Microphysical observations and mesoscale model simulation of a warm fog case during FRAM project. Pure Appl. Geophys., 164, 1161–1178.
Gultepe, I., Hansen, B., Cober, S. G., Pearson, G., Milbrandt, J. A., Platnick, S. and Oakley, J. P., 2009, The fog remote sensing and modeling field project. Bulletin of the American Meteorological Society, 90(3), 341-359.
Gultepe, I., Milbrandt, J. and Zhou, B., 2017, Marine Fog: A review on microphysics challenges and advancements in observations, modeling, and forecasting, )Chapter 7). In: Koracin, D. and Dorman, C., Marine Fog: Challenges and Advancements in Observations, Modeling, and Forecasting, Springer Atmospheric Science., 7.345–7.394.
Gultepe, I., Heymsfield, A, J. and Gallagher, M., 2020, Arctic ice fog: its microphysics and prediction (Chapter 4). In: Kokhanovsky, A. and Tomasi, C., Physics and Chemistry of the Arctic Atmosphere, Springer Polar Sciences., 6.361–6.414.
Hong, S. Y., Noh, Y. and Dudhia, J., 2006, A new vertical diffusion package with an explicit treatment of entrainment processes, Mon. Wea. Rev., 134, 2318–2341.
Iacono, M. J., Delamere, J. S., Mlawer, E. J., Shephard, M. W., Clough, S. A. and Collins, W. D., 2008, Radiative forcing by long–lived greenhouse gases: Calculations with the AER radiative transfer models. J. Geophys. Res., 113, D13103.
Lester, J. F., 2007, Aviation weather, Jepson Pub. U.S.A.
Lin, Y.L., Farley, R. D. and Orville, H. D., 1983, Bulk parameterization of the snow field in a cloud model. J. Appl. Meteor., 22, 1065–1092.
Lin, C.Y., Zhang, Z.F., Pu, Z.X. and Wang, F., 2017, Numerical simulations of an advection fog event over Shanghai Pudong International Airport with the WRF model. J. Meteor. Res., 31(5), 874–889.
Lim, K. S. S. and Hong, S. Y., 2010, Development of an effective double–moment cloud microphysics scheme with prognostic cloud condensation nuclei (CCN) for weather and climate models. Mon. Wea. Rev., 138, 1587–1612.
Mlawer, E. J., Taubman, S. J., Brown, P. D., Iacono, M. J. and Clough, S. A., 1997, Radiative transfer for inhomogeneous atmospheres: RRTM, a validated correlated–k model for the longwave. J. Geophys. Res., 102, 16663–16682.
Muller, M. D., Masbou, M. and Bott, A., 2010, Three-dimensional fog forecasting in complex terrain. Q J R Meteorol Soc., 136, 2189–2202.
Nakanishi, M. and Niino, H., 2004, An improved Mellor–Yamada level-3model with condensation physics: Its design and verification. Boundary-Layer Meteorol., 112, 1–31.
Obukhov, A. M., 1971, Turbulence in an atmosphere with a non-uniform temperature. Boundary-Layer Meteorology., 2 (1), 7-29.
Oztaner, Y.B., Unal, A. and Kindap, T., 2014, Comparative analysis of fog prediction algorithms via use of WRF model over istanbul, 14th EMS Annual Meeting & 10th European Conference on Applied Climatology (ECAC), 06 – 10 October 2014, Prague, Czech Republic.
Philip, A., Bergot, T., Bouteloup, Y. and Bouyssel, F., 2016, The impact of vertical resolution on fog forecasting in the kilometric-scale model AROME: A case study and statistics. Weather and Forecasting., 31(1), 1655–1671.
Pleim, J. E., 2007, A combined local and nonlocal closure model for the atmospheric boundary layer. Part I: Model description and testing. J. Appl. Meteor. Climatol., 46, 1383–1395.
Roman-Cascón, C., Yag¨ue, C., Sastre, M., Maqueda, G., Salamanca, F. and Viana, S., 2012, Observations and WRF simulations of fog events at the Spanish northern plateau. Adv. Sci. Res. 8: 11–18.
Román-Cascón, C., Steeneveld, G. J., Yague, C., Sastre, M., Arrillaga, J. A. and Maqueda, G., 2016, Forecasting radiation fog at climatologically contrasting sites: Evaluation of statistical methods and WRF. Quart. J. Roy. Meteor. Soc., 142, 1048–1063, doi: 10.1002/qj.2016.142.issue- 695.
Roman-Cascón, C., Yagüe, C., Steeneveld, G. J., Morales, G., Arrillaga, J.A., Sastre, M. and Maqueda, G., 2019, Radiation and cloud-base lowering fog events: Observational analysis and evaluation of WRF and HARMONIE. Atmospheric Research., 229, 190–207.
Singh, A., George, J. P. and Iyengar, G. R., 2018, Prediction of fog/visibility over India using NWP Model. Journal of Earth System Science., 127-153.
Skamarock, W. C., Klemp, J. B., Dudhia, J., Gill, D. O., Barker, D. M., Duda, M. G., Huang, X., Wang, W. and Powers, J. G., 2008, A description of the advanced research WRF version 3; NCAR Tech. Note (NCAR/TN-475+STR); National Center for Atmospheric Research: Boulder, CO, USA., 125.
Steeneveld, G. J., Ronda, R. J. and Holtslag, A. A. M., 2015, The challenge of forecasting the onset and development of radiation fog using mesoscale atmospheric models. Bound. Layer Meteor., 154, 265–289, doi: 10.1007/s10546-014-9973-8.
Stoelinga, M. T. and Warner T. T., 1999, Nonhydrostatic, mesobeta-scale model simulations of cloud ceiling and visibility for an East Coast winter precipitation event. J. Appl. Meteor., 38, 385–404, doi: 10.1175/1520-0450(1999)038 2.0.CO;2.
Tardif, R., 2007, The impact of vertical resolution in the explicit numerical forecasting of radiation fog: A case study. Pure Appl. Geophys., 164, 1221-1240.
Tardif, R. and Rasmussen, R. M., 2007, Event-based climatology and typology of fog in the New York City region. J. Appl. Meteor. And Climatology., 46, 1141-1168, doi:10.1175/JAM2516.1.
Tardif, R. and Rasmussen, R. M., 2008, Process-oriented analysis of environmental conditions associated with precipitation fog events in the New York city region. Journal of Applied Meteorology and Climatology., 47, 1681–1703.
Tewari, M., Chen, J., Wang, W., Dudhia, J., LeMone, M.A., Mitchell, K., Ek, M., Gayno, G., Wegiel, J. and Cuenca, R. H., 2004, Implementation and verification of the unified NOAH land surface model in the WRF model. 20th conference on weather analysis and forecasting/16th conference on numerical weather prediction, 11–15.
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. Mon. Wea. Rev., 136, 5095–5115.
Van der Velde, I., Steeneveld, G., 2010, Wichers Schreur, B. and Holtslag, A., Modeling and forecasting the onset and duration of severe radiation fog under frost conditions. Monthly Weather Review., 138, 4237–4253.
Willett, H. C., 1928, Fog and haze, their causes, distribution, and forecasting. Mon. Wea. Rev., 56, 435–468.
Yang, Y. and Gao, S., 2016, Sensitivity study of vertical resolution in WRF numerical simulation for sea fog over the Yellow Sea. Acta Meteorol. Sin., 74 (6), 974–988, https://doi.org/10.11676/qxxb2016.062.
Yang, Y., Hu, X. M., Gao, S. and Wang, Y., 2019, Sensitivity of WRF simulations with the YSU PBL scheme to the lowest model level height for a sea fog event over the Yellow Sea. Atmospheric Research., 215 (2019), 253–267.
Yuan, X. and Chen, Z. H., 2013, Statistics and monitoring analysis of advection fog at Shanghai Pudong Airport. J. Meteor. Sci., 33, 95–101, DOI:10.3969/2012jms.0149
Zhou, B., Du, J., Gultepe, I. and DiMego, G., 2012, Forecast of low visibility and fog from NCEP: Current status and efforts. Pure and Applied Geophysics., 169, 895–909.
فیزیک زمین و فضا
دوره 46، شماره 3
آبان 1399
صفحه 561-582

فایل ها

هم رسانی

ارجاع به این مقاله

آمار