Optimal run-time selection of convection scheme based on radar data in the WRF model for short-range precipitation prediction


1 Ph.D. Graduated of Meteorology, Department of Space Physics, Institute of Geophysics, University of Tehran, Iran

2 Associate Professor, Department of Space Physics, Institute of Geophysics, University of Tehran, Iran


One of the challenges facing meteorologists in recent years is to improve the quality and accuracy of weather nowcasting for limited areas and in this regard various methods based on the statistics principles, such as data assimilation and ensemble forecasting methods, have been used in numerical weather prediction models. In the data assimilation methods, by transferring and collecting different data including atmospheric measurements by observational stations, satellites and radars, the process of rectifying the results of numerical models is performed statistically. The aim of this research is to investigate and address the question of whether it is possible to facilitate a cycle of numerical weather prediction and improve the prediction accuracy using remote sensing data, without involving very large computational effort required in the data assimilation techniques.
To reach the objectives of this research, we first designed and developed a software for radar data analysis. Second, based on both the output data provided by the radar model and the innovative changes in the relevant part of the main model for controlling convection, the Weather Research and Forecasting (WRF) model was modified in such a way that the best convection scheme is chosen during the execution of the model. In fact, the appropriate convection scheme is chosen automatically by the capability of the radar system in detecting convection within the execution of the model. To evaluate the results, the modified model was used for a region of Iran in such a way that the site of Tehran weather radar was located in the center of the simulation domain. Before carrying out the simulations, two necessary actions were taken. First, the sensitivity of the results provided by the WRF model to the initial input data was examined. In this stage, using two reanalysis datasets of the NCEP-FNL (Final) on 1°×1° grid prepared operationally every six hours and the ECMWF dataset gridded to a horizontal resolution of approximately 80 km at 6-h intervals, a selected case was studied and the results were compared with the observational data. Then, the processing algorithms necessary to identify and remove radio electromagnetic interference (RFI) noise from radar returns were prepared.
The modified WRF model was run for 12 hours to evaluate its ability and the quality of prediction for very short time periods. In total, forty experiments were carried out using eight configurations of physical parameterization schemes as well as inclusion of the radar data. In addition, the modified model was implemented for a severe squall line that occurred in Tehran’s area at 2330UTC on the 30th of March 2009 and was detected by the Tehran weather radar.
Results for the root mean square error index and correlation between the forecasted and the observed precipitation showed that the accuracy of precipitation forecast in the study area for very short time, e.g. 6 hours, was increased when the modified model was carried out. The comparison between the forecasted precipitation patterns and the observations confirms higher consistency for the modified model’s results. Also, evaluating the results by the statistical methods, it is seen that the correlation between the forecasted precipitation and the observed values is increased significantly and the root mean square error is decreased. In addition, the time series of the precipitation data for Mehrabad synoptic station in Tehran was investigated for which the root mean square error in the precipitation time series was decreased when the Tehran radar data was included in the working of the WRF model.


Main Subjects

صفر، م.، احمدی گیوی، ف. و گلستانی، ی.، 1395، کنترل کیفی داده های رادار هواشناسی با استفاده از ساختار افقی و قائم برگشت پذیری: مجله ژئوفیزیک ایران، 10(2)، 120-131.
صفر، م.، احمدی‌گیوی، ف. و محب‌الحجه، ع.، 1391، بررسی اثر گوارد داده‌های رادار در مدل عددی ARPS در شبیه‌سازی بارش حاصل از سامانه همدیدی 31 مارس 2009 در منطقه تهران: مجله ژئوفیزیک ایران، 6 (3)، 94-112.
 Albers, S., McGinley, J. M., Birkenheuer, D. and Smart, J., 1996, The local analysis and prediction system (LAPS): Analyses of clouds, precipitation and temperature. Wea. Forecasting, 11, 273–287.
Barclay, P. E. and Wilk, K. E., 1970, Sever thunderstorm radar echo motion and related weather events hazardous to aviation operation. ESSA Tech. Mem., NSSL, 63 pp.
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.
Benjamin, S. G., Grell, G. A., Brown, J. M., Smirnova, T. G. and Bleck, R., 2004, Mesoscale weather prediction with the RUC hybrid isentropic-terrain-following coordinate model. Mon. Wea. Rev., 132, 473-494.
Betts, A. and Miller, M. J., 1986, A new convective adjustment scheme. Part II: Single column tests using GATE wave, BOMEX, ATEX, and arctic airmass data sets. Quart. J. Roy. Meteor. Soc., 112, 693-709.
Brewster, K., 1996, Implementation of a Bratseth analysis scheme including Doppler radar. Pages 19–23 in Proceeding of the 15th Conference on Weather Analysis and Forecasting, American Meteorological Society, Boston, USA.
Browning, K. A., Collier, C. G., Larke, P. R., Menmuir, P., Monk, G. A. and Owens, R. G., 1982, On the forecasting of frontal rain using a weather radar network. Mon. Wea. Rev., 110, 534-552.
Caya, A., Sun, J. and Snyder, C., 2005, A comparison between the 4DVAR and the ensemble Kalman filter techniques for radar data assimilation. Mon. Wea Rev., 133, 3081-3094.
Daley, R., 1991, Atmospheric Data Analysis. Cambridge Atmospheric and Space Science Series, Cambridge University Press, 457pp.
Dixon, M. and Wiener, G., 1993, TITAN: Thunderstorm identification, tracking, analysis, and nowcasting, A radar-based methodology. J. Atmos. Oceanic Technol., 10, 785–797.
Ebert, E. and McBride, L., 2000, Verification of precipitation in weather systems: determination of systematic errors. J. Hydrol., 239, 179-202.
Einfalt, T., Denoeux, T. and Jacquet, G., 1990, A radar rainfall forecasting method designed for hydrological purposes. J. Hydrol., 114, 229-224.
Ghil, M., 1989, Meteorological Data Assimilation for Oceanographers. Part I: Description and Theoretical Framework. Dyn. Atmos. Oceans, 13, 171-218.
Janjic, Z. I., 1994, The step-mountain Eta coordinate model: Further developments of the convection, viscous, sublayer, and turbulence colosure schemes. Mon. Wea. Rev., 122, 927-945.
Janjic, Z. I., 2002, Nonsingular implementation of the Mellor-Yamada Level 2.5 Scheme in the NCEP Meso model. NCEP Office Note, No. 434, 61pp.
Kain, J. S., 2004, The Kain-Fritsch convective parameterization: An update. J. Appl. Meteor., 43, 170- 181.
Kain, J. S. and Fritsch, J. M., 1993, Convective parameterization for mesoscale models: The Kain-Fritsch scheme. The Representation of Cumulus Convection in Numerical Models, Meteor. Monogr., No. 46, Amer. Meteor. Soc., 165-170.
Kain, J. S. and Fritsch, J. M., 1990, A one-dimensional entraining/detraining plume model and its application in convective parameterization. J. Atmos. Sci., 47, 2784-2802.
Koskinen, J. T., Poutiainen, J., Schultz, D. M., Joffre, S., Koistinen, J., Saltikoff, E., Gregow, E., Turtiainen, H., Dabberdt, W. F., Damski, J., Eresmaa, N., Goke, S., Hyvarinen, O., Jarvi, L., Aarppinen, A., Kotro, J., Kuitunen, T., Kukkonen, J., Kulmala, M., Moisseev, D., Nurmi, P., Ponjola, H., Pylkko, P., Vesala, T., and Viisanen, Y., 2011, The Helsinki Testbed: A Mesoscale Measurement, Research and Service Platform. Bull. Amer. Met. Soc., 32, 325-342.
Lin, Y., Ray, P. and Johnson, K., 1993, Initialization of a modeled convective storm using Doppler radar derived fields. Mon. Wea. Rev., 121, 2757–2775.
Liu, Y., 2008, The operational mesogamma-scale analysis and forecast system of the U.S. army test and evaluation command’. Part I: overview of the modeling system, the forecast products, and how the products are used. J. Appl. Meteor. Climatol., 47, 1077–1092.
Lorenc, A., 1986, Analysis methods for numerical weather prediction. Quart. J. Roy. Meteor. Soc., 112, 1177-1194.
Lushine, J. B., 1976, Picture of the Month. Convective growth and movement as seen from GOES-1. Mon. Wea. Rev., 104, 1449-1450.
Mueller, C., Saxen, T., Roberts, R., Wilson, J., Betancourt, T., Dettling, S., Oien, N. and Yee, J., 2003, NCAR auto-nowcast system. Wea. Forecasting, 18, 545–561.
Noel, T. M. and Fleisher, A., 1960, The linear predictability of weather radar signals. MIT Dept. of Meteorology Research Rep. 34, 46 pp.
Pierce, C. E., 2004, The nowcasting of precipitation during Sydney 2000: an appraisal of the QPF algorithms. Wea. Forecasting, 19, 7–21.
Schroeder, A. J., Stauffer, D. R., Seaman, N. L., Deng, A., Gibbs, A. M., Hunter, G. K. and Young, G. S., 2006, An automated high-resolution, rapidly relocatable meteorological nowcasting and prediction system. Mon. Wea. Rev., 134, 1237–1265.
Sugimoto, S., Crook, N., Sun, J. and Xiao, Q., 2008, An examination of WRF 3DVAR radar data assimilation on its capability in retrieving unobserved variables and forecasting precipitation through observing system simulation experiments. Mon. Wea. Rev., 137, 4011-4029.
Sun, J. Z., Wang, H. L., Tong, W. X., Zhang, Y., Lin, C.-Y. and Xu, D. M., 2016, Comparison of the impacts of momentum control variables on high-resolution variational data assimilation and precipitation forecasting. Mon. Wea. Rev., 144, 149-169.
Sun, J. Z. and Crook, N. A., 1998, Dynamical and microphysical retrieval from Doppler radar observations using a cloud model and its adjoint. Part II: Retrieval experiments of an observed Florida convective storm. J. Atmos. Sci., 55(5), 835-852.
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., 2014, User’s Guide for the Advanced Research WRF (ARW) Version 3.6, NCAR.
Wang, Z., Droegemeier, K. and White, L., 1998, The adjoint truncated Newton algorithm for large-scale unconstrained optimization. Comput. Optimization Appl., 10(3), 283–320.
Weygandt, S., Shapiro, A. and Droegemeier, K., 2002, Retrieval of model initial fields from single-Doppler observations of a supercell thunderstorm. I: Single-Doppler velocity retrieval. Mon. Wea. Rev., 130, 433–453.
Wilson, J. W., Feng, Y., Chen, M. and Roberts, R. D., 2010, Nowcasting challenges during the Beijing Olympics: successes, failures, and implications for future nowcasting systems. Wea. Forecasting, 25, 1691-1714.
Xu, M., Liu, Y., Davis, C. and Warner, T., 2002, Sensitivity of nudging parameters on the performance of a mesoscale FDDA system: A case study. 15th Conference on Numerical Weather Prediction, 12–16 August, 2002, San Antonio, Texas, 127–130.