Impact of Assimilation of Radar Data on the Simulation of Squall Line Event

Document Type : Research Article

Authors

1 Ph.D Student, Department of earth science, Science and Research Branch, Islamic Azad University, Tehran, Iran

2 Associate Professor, Atmospheric Science and Meteorological Research Center (ASMERC), Tehran, Iran

3 Associate Professor, Department of earth science, Science and Research Branch, Islamic Azad University, Tehran, Iran

Abstract

Accurate prediction of squall lines that accompany thunderstorms is a challenging task. A squall line was recorded in Dayyer port station over the southwest of Iran in Bushehr province, on 19 March 2017. In this study, the properties associated with the mentioned squall line including the time of formation, growth and destruction of the convective cells in terms of intensity and vertical growth, as well as the associated precipitation and other meteorological features are simulated using the WRF-ARW model with 3-dimensional variational (3DVAR) assimilation and control experiment (CTRL) for 18UTC 18 March 2017 with two domains of 27 and 9-km horizontal resolution. Radial winds and reflectivity of Bushehr Doppler Weather Radar (DWR) along with surface and upper-level observational synoptic data are used to simulate the above mentioned squall line event with the aim of updating initial and boundary conditions through 3DVAR assimilation in WRF model. In order to verify the simulated properties associated with the squall line event, the horizontal wind speed, mean sea-level pressure, surface temperature and surface relative humidity, as well as time series of reflectivity and vertical growth in the squall line location on Bushehr DWR were compared with the corresponding observational data. To assess the performance of accumulated precipitation forecasts, the fraction skill score (FSS) curves are plotted for different rainfall thresholds 0.5, 5, 10 and 15 mm/day. In general, the results showed that the radar data assimilation has a significant effect on the simulation of the characteristics accompanied with the squall line event such that without data assimilation, the WRF model is not capable of simulating the squall line thoroughly. The results of 3DVAR simulation are also much closer to the observational data in predicting the features along the squall line. The absolute value of the mean errors in simulations with assimilation for surface horizontal wind speed, surface temperature, mean sea level pressure, and surface relative humidity were decreased by 27%, 7%, 18%, and 10%, respectively, compared to those without assimilation. The formation time and growth of convective cells, their horizontal distributions, vertical structure, and their destruction time in 3DVAR simulation are closer to the verifying observational data. The 3DVAR simulation also achieved significant success in predicting 6-h accumulated precipitation with a threshold value of above 10 mm. Also, the value of error in 3DVAR simulation in 6-h accumulated precipitation at synoptic stations in Bushehr province was decreased by 33%, compared to those without assimilation.
 

Keywords

Main Subjects

References

آزادی، م.، صوفیانی، م.، وکیلی، غ.، قائمی، ه.، 1395، مطالعه موردی اثر گوارد داده­های ایستگاه­های دیدبانی و جوّ بالا بر برون‌داد بارش مدل WRF روی منطقه ایران: مجله ژئوفیزیک ایران، 10، 110-119.
ذاکری، ز.، آزادی، م.، قادر، س.، 1397، بررسی اثر داده‌گواری داده­های ماهواره prepbufr و GPSro در پیش­بینی باد و بار گرد و خاک در دو مورد گرد و خاک در مدل WRF-Chem: مجله ژئوفیزیک ایران، 12، 1-22.
صفر، م.، احمدی، ف.، محب‌الحجه، ع.، 1391، بررسی اثر گوارد داده­های رادار در مدل عددی ARPS در شبیه­سازی حاصل از سامانه همدیدی 31 مارس 2009 در منطقه تهران: مجله ژئوفیزیک ایران، 6، 94-112.
کریم­خانی، م.، جمشیدی خزلی، ت.، آزادی، م.، فتاحی، ا.، 1396، تأثیر تفکیک افقی بر دقت پیش‌بینی بارش با استفاده از مدل WRF منطقه مورد مطالعه: حوضه‌های آبریز کرخه و کارون: مجله علمی- پژوهشی اکوبیولوژی تالای، 9، 55-74.
لایقی، ب.، قادر، س.، علی اکبری بیدختی، ع.، آزادی، م.، 1396، حساسیت­سنجی شبیه‌سازی­های مدل WRF به پارامترسازی­های فیزیکی در محدوده خلیج فارس و دریای عمان در زمان مونسون تابستانی: مجله ژئوفیزیک ایران، 11، 1-19.
Abhilash, S., Das, S., Kalsi, S. R., Das, G. M., Mohankumar, K., George, J. P., Banerjee, S. K., Thampi, S. B., and Pradhan, D., 2007, Impact of Doppler radar wind in simulating the intensity and propagation of rainbands associated with mesoscale convective complexes using MM5-3DVAR system: Pure and Applied Geophysics, 164, 1491–1509.
Akter, F., and Ishikawa, H., 2014, Synoptic features and environmental conditions of the tornado outbreak on March 22, 2013 at Brahmanbaria in the east-central region of Bangladesh: Natural Hazards, 74(3), 1309–1326.
 
Bachmann, K., Keli, C., Craig, G. C., Weissman, M., and Welzbacher, C. A., 2019, Predictability of deep convection in idealized and operational forecasts: Effects of radar data assimilation, orography and synoptic weather regime: Monthly Weather Review, Early Online Release-Posted online on 11 Sep 2019.
Barker, D. M., Huang, W., Guo, Y. G., and Bourgeois, A., 2003, A Three-Dimensional Variational (3DVAR) Data Assimilation System For Use With MM5, NCAR Tech. Note, NCAR/TN-453+STR, UCAR Communications: Boulder, CO., 68 pp.
Barker, D. M., Huang, W., Guo, Y. R., Bourgeois, A., and Xiao, Q., 2004, A three dimensional variational (3DVAR) data assimilation system for use with MM5: Implementation and Initial Results: Monthly Weather Review, 132, 897–914.
Basnayake, B., Das, M. K., Nessa, F. F., and Rahman, M. M., 2010, Nor'easters over Bangladesh and neighborhood during pre-monsoon season of 2009: observations and WRF model simulations: SAARC Meteorological Research Centre, 36, 58-70.
Biggerstaff, M., and Listemaa, A., 1999, An improved scheme for convective/stratiform echo classification using radar reflectivity: Journal of Applied Meteorology, 34, 2129-2162.
Brewster, K., Hu, M., Xue, M., and Gao, J., 2005, Efficient assimilation of radar data at high resolution for short-range numerical weather prediction: Preprints, WWRP Int. Symp. on Nowcasting and Very Short Range Forecasting: Toulouse, France, WWRP, 3.06.
Carlin, J. T., Ryzhkov, A. V., Snyder, J. C., and Khain, A., 2016, Hydrometeor mixing ratio retrievals for storm-scale radar data assimilation: Utility of current relations and potential benefits of polarimetry: Monthly Weather Review, 144, 2981-3001.
Courtier, P., Thépaut, J. N., and Hollingsworth, A., 1994, A strategy for operational implementation of 4D-Var using an incremental approach: Quarterly Journal of the Royal Meteorological Society, 120, 1367–1387.
Das, M. K., Chowdhury, M. D., Das, S., Debsarma, S. K., and Karmakar, S., 2015, Assimilation of Doppler weather radar data and their impacts on the simulation of squall events during pre-monsoon season: Natural Hazards, 77, 901-931.
Das, M. K., Das, S., Chowdhury, M., Debsarma, S. K., 2010, Simulation of a severe thunderstorm event using WRF-ARW model during the SAARC STORM Pilot field experiment 2009, in Proceedings of the thirteenth Asian congress of fluid mechanics (13acfm-2010), Bangladesh, 2, 733–736.
Das, S., Ashrit, R., and Moncrieff, M. W., 2006, Simulation of a Himalayan Cloudburst event: Journal of Earth System Science, 115(3), 299–313.
Das, S., Basnayake, B., Das, M. K., Akand, M., Rahman, M. M., Sarker, M. A., and Islam, M. N., 2009, Composite characteristics of Nor’westers observed by TRMM and simulated by WRF model: SAARC Meteorological Research Centre, 25, 44-51.
Das, S., Dutta, S. K., Debsarma, S. K., Ferdousi, N., Nessa, F. F., 2012, Assimilation of STORM 2009 field observation in WRF model and their impact on the simulation of thunderstorm: SAARC Meteorological Research Centre, 43, 42-53.
Fritsch, J. M., and Carbone, R. E., 2004, Research and development of improved quantitative precipitation forecasts in the warm season: Bulletin of the American Meteorological Society, 85, 955–965.
Golding, B. W., 1998, Nimrod: A system for generating automated very short range forecasts: Meteorological Applications, 5, 1–16.
Harrison, D. L., Driscoll, S. J., and Kitchen, M., 2000, Improving precipitation estimates from weather radar using quality control and correction techniques: Meteorological Applications, 7, 135–144.
Hu, M., Xue, M., and Brewster, K., 2006, 3DVAR and cloud analysis with WSR-88D level-II data for the prediction of the Fort Worth, Texas, tornadic thunderstorms. Part I: Cloud analysis and its impact: Monthly Weather Review, 134, 675–698.
Kain, J. S., and Fritsch, J. M., 1990, A one-dimensional entraining/ detraining plume model and its application in convective parameterization: Journal of the Atmospheric Sciences, 47, 2784-2802.
Kain, J. S., and Fritsch, J. M., 1993, Convective parameterization for mesoscale models: The Kain-Fritcsh scheme. The representation of cumulus convection in numerical models, Emanuel, K. A., and Raymond, D.J., eds.: American Meteorological Society, 246 p.
Li, Y., Wang, X., and Xue, M., 2012, Assimilation of radar radial velocity data with the WRF hybrid ensemble-3DVAR system for the prediction of Hurricane Ike (2008): Monthly Weather Review, 140, 3507-3524.
Li, X., Mecikalski, J. R., and Posselt, D., 2017, An ice-phase microphysics forward model and preliminary results of polarimetric radar data assimilation: Monthly Weather Review, 145, 683-708.
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.
Lindskog, M., Salonen, K., Jarvinen, H., and Michelson, D. B., 2003, Doppler radar wind data assimilation with HIRLAM 3DVAR: Monthly Weather Review, 132, 1081-1092.
Litta, A. J., Mohanty, U. C., Das, S., and Idicula, S. M., 2012, Numerical simulation of severe local storms over east India using WRF–NMM mesoscale model: Atmospheric Research, 116, 161–184.
Maiello, R., Ferretti, R., Gentile, S., Montopoli, M., Picciotti, E., Marzano, F.S., and Faccani, C., 2014, Impact of radar data assimilation for the simulation of a heavy rainfall case in Central Italy using WRF-3Dvar: Atmospheric Measurement Techniques, 7, 2919-2935.
Meng, Z., Yan, D., and Zhang, Y., 2012, General features of Squall lines in East China: Monthly Weather Review, 141, 1629-1647.
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, 16663-16682.
Mohan, K., Das, M. D., Chowdhury, A. M., Sujit, D., Debsarma, K., and Karmakar, S., 2015, Assimilation of Doppler Weather Radar Data and their Iimpacts on the simulation of squall line events during pre-Monsoon season: Natural Hazards, 77, 901-931.
Murphy, A. H., and Epstein, E. S., 1989, Skill scores and correlation coefficients in model verification: Monthly Weather Review, 117, 572–581.
Murphy, A. H., 1993, What is a good forecast? An essay on the nature of goodness in weather forecasting: Weather Forecasting, 8, 281-293.
Parrish, D. F., and Derber, J. C., 1992, The national meteorological center’s spectral statistical-interpolation analysis system: Monthly Weather Review, 120, 1747–1763.
Ridal, M., and Dahlbom, M., 2017, Assimilation of Multinational Radar Reflectivity Data in a mesoscale model: A proof of concept: Journal of Applied Meteorology and Climatology, 56, 1739-1751.
Roberts, N. M., and Lean, H. W., 2008, Scale-selective verification of rainfall accumulations from high-resolution forecasts of convective events: Monthly Weather Review, 136, 78-97.
Rosenfeld, D., and Amitai, E., 1992, Classification of rain regimes by the three-dimensional properties of reflectivity fields: Journal of Applied Meteorological, 34, 198-219.
RoyBhowmik, S. K., Roy, S. S., Srivastava, K., Mukhopadhay, B., Thampi, S., Reddy, Y. K., Sing, H., Venkateswarlu, S., and Adhikary, S., 2011, Processing of Indian Doppler Weather Radar data for mesoscale applications: Meteorology and Atmospheric Physics, 111, 134–147.
Skamarock, W. C., Klemp, J. B., Dudhia, 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 reasearch WRF Version 3. NCAR Technical Note, TN 475+STR, 113 pp., available at: www.mmm.ucar.edu/wrf/users/docs/arwv3.pdf (last access: January 2012), 2008.
Srivastava, K., Gao, J., Brewster, K., Bhowmik, S. K. R., Xue, M., and Gadi, R., 2011, Assimilation of Indian radar data with ADAS and 3DVAR techniques for simulation of a small scale tropical cyclone using ARPS model: Natural Hazards, 58, 15–29.
Stainer, M., and Houze, R., 1994, Climatological characterization of three-dimensional storm structure from operational radar and rain gauge data: Journal of Applied Meteorology, 34, 1978-2006.
Sun, J., and Zhang, Y., 2007, Analysis and prediction of a squall line observed during IHOP using multiple WSR-88D observation: Monthly Weather Review, 136, 2364-2387.
Theis, S. E., Hense, A., and Damrath, U., 2005, Probabilistic precipitation forecasts from a deterministic model: A pragmatic approach: Meteorological Applications., 12, 257–268.
Vendrasco, E. P., Sun, J., Herdies, D. L., and Angelis, C. F. D., 2016, Constraining a 3DVAR radar data assimilation system with large-scale analysis to improve short-range precipitation forecasts: Journal of Applied Meteorology and Climatology, 55, 673-690.
Wilson, B, W., 1972, Seiches: Advances in Hydroscience Elsevier, 8, 1–94.
WMO (World Meteorological Organization) (1962; 2014) Abridged final report of the third session of the commission for instruments and methods of observation. WMO-no. 116 R.P. 48, Geneva WMO (World Meteorological Organization) (2014) Manual on codes international codes. I.1, Part A—alphanumeric codes. WMO-no. 306, Geneva.
Xiao, Q., and Sun, J., 2006, Multiple radar assimilation and short range quantitative precipitation forecasting of squall line observed during IHOP_2002: Monthly Weather Review, 135, 3311-3404.
Zhang, F., Zhang, M., and Poterjoy, J., 2013, E3DVar: coupling an ensemble Kalman filter with three-dimensional variational data assimilation in a limited-area weather prediction model and comparison to E4DVar: Monthly Weather Review, 141, 900–917.
Zhang, M., Zhang, F., Huang, X. Y., and Zhang, X., 2011, Intercomparison of an ensemble Kalman filter with three and four-dimensional variational data assimilation methods in a limited-area model over the month of June 2003: Monthly Weather Review, 139, 566–572.
Iranian Journal of Geophysics
Volume 14, Issue 2
August 2020
Pages 63-82

Files

Share

How to cite

Statistics