Verification of WRF wind field hindcast forced by different initial and boundary conditions over the Persian Gulf: Comparison with synoptic data and QuikSCAT and ASCAT satellites data


1 Ph.D. Student, Iranian National Institute for Oceanography and Atmospheric Science, Tehran, Iran

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

3 Associate Professor, Iranian National Institute for Oceanography and Atmospheric Science, Tehran, Iran

4 Assistant Professor, Iranian National Institute for Oceanography and Atmospheric Science, Tehran, Iran


The Persian Gulf with subtropical climate is located between latitudes of 23-30 degrees, whit its coast adjoiner to Iraq, Kuwait, Saudi Arabia, Qatar and United Arab Emirates from one side and to Iran from the other side. The Persian Gulf’s width in widest part is 370 km and its length is 990 km. For people living near the sea and the surrounding coastal area as well as construction of onshore and offshore structures, knowledge and understating of the wind field and its variability is essential. In addition, the most common effect of wind is seen in wind driven currents, swells, upwelling and down-welling systems. Moreover wind stress plays a key role in the modeling of air sea interaction phenomenon, e.g., determination of the drag coefficient. Although other factors such as wind palfern and oceanic current are also contributing to the in determination of this coefficient but the wind is the main governing factor. In other words, any inaccuracy in determination of the sea surface wind field could cause a large over or under estimations in atmospheric and oceanic estimations.
This work is devoted to verify the effects of different initial and boundary condition in global data (using reanalysis and analysis datasets) on numerical simulations of WRF (Weather Research and Forecasting) model over the Persian Gulf area. The main obstacle in the verification and evaluation of a model simulation is the lack of observation data with fine spatial and temporal resolution, in particular for offshore areas. Fortunately, in onshore areas, the existence of weather stations has solved the problem somehow, and for offshore areas satellite data are found to be the best dataset considering the spatial coverage. In the present study, simulations of WRF model are compared with different type of observation data. In this research, two types of data are used for verifying the model, i.e. Synoptic stations data and Satellite data (QuikSCAT and ASCAT).
The WRF model version 3.4.1 is employed with ARW dynamical core for simulating of sea surface wind field over the Persian Gulf region. Considering the connection and information exchange between the domains, a two way nesting method is applied in simulations. As the goal was just to verify the effects of different initial and boundary conditions on simulations, therefore, for all the simulations the number of domains and their analogous grid sizes are considered the same. For these simulations three domains are considered the main domain approximately covers the whole are of the Middle East, from West and some parts of Far East with 36km spatial grid spacing. First nested domain covers the southern half of Iran along with marginal countries of Persian Gulf, with a 12km spatial grid resolution and ultimately, the innermost domain of the Persian Gulf that also includes some parts of Oman Sea with 4km grid spacing. The time step for simulations is assumed 216seconds and the time period for each simulation is 30hours, from which the first 6 hours are assumed as spin-up time. To provide the initial and boundary conditions three datasets of ERA-Interim (ECMWF Re-Analysis Interim), NCEP-FNL and NCEP-R2 are employed.
Along with lots of effective factors, results from this research show that one of the sources of error in the WRF model wind simulations is the selection of initial and boundary conditions (input data). The obtained results of this work reveal that for the surface wind hindcast simulations over the Persian Gulf using WRF model, the ECMWF ERA-Interim data is a more suitable dataset to provide the initial and boundary conditions, rather than the NCEP-FNL and NCEP-R2 data. However, the NCEP-FNL is an alternative data set when the ERA-Interim data has some lacks.


Main Subjects

گلشنی، ع. و تائبی، س.، ۱۳۸۷، ارزیابی میدان باد ماهواره‌ای QuikSCAT در دریاهای مجاور ایران با استفاده از داده‌های باد ایستگاه‌های سینوپتیک و مدل‌های عددی جوی، نشریه علمی-پژوهشی مهندسی دریا، ۴ ، ۴۷-۶۳.
Alvarez, I., Gomez-Gesteira, M., deCastro, M. and Carvalho, D., 2014, Comparison of different wind products and buoy wind data with seasonality and interannual climate variability in the southern Bay of Biscay (2000-2009). Deep-Sea Research Part II: Topical Studies in Oceanography 106, 38–48. doi:10.1016/j.dsr2.2013,09.028.
Beljaars, A., 1995, The parametrization of surface fluxes in large-scale models under free convection. Quarterly Journal of the Royal Meteorological Society 121, 255–270.
Bentamy, A., Croize-Fillon, D. and Perigaud, C., 2008, Characterization of ASCAT measurements based on buoy and QuikSCAT wind vector observations. Ocean Science 4, 265–274. doi:10.5194/os-4-265-2008.
Bentamy, A., Grodsky, S. A., Carton, J. A., Croizé-Fillon, D. and Chapron, B., 2012, Matching ASCAT and QuikSCAT winds. Journal of Geophysical Research: Oceans 117, 1–15. doi:10.1029/2011JC007479.
Bjerknes, V., 1904, The problem of weather prediction, as seen from the standpoints of mechanics and physics. Meteorologische Zeitschrift.
Blanke, B., 2005, Modeling the structure and variability of the southern Benguela upwelling using QuikSCAT wind forcing. Journal of Geophysical Research 110, C07018. doi:10.1029/2004JC002529.
Caires, S., Sterl., A., Bidlot, J.-R., Graham, N. and Swail, V., 2004, Intercomparison of Different Wind – Wave Reanalyses. Journal of Climate 17, 1893–1913.
Capps, S. B. and Zender, C. S., 2010, Estimated global ocean wind power potential from QuikSCAT observations, accounting for turbine characteristics and siting. Journal of Geophysical Research Atmospheres 115, 1–13. doi:10.1029/2009JD012679.
Carvalho, D., Rocha, A. and Gómez-Gesteira, M., 2012, Ocean surface wind simulation forced by different reanalyses: Comparison with observed data along the Iberian Peninsula coast. Ocean Modelling 56, 31–42. doi:10.1016/j.ocemod.2012,08.002.
Carvalho, D., Rocha, A., Gómez-Gesteira, M., Alvarez, I. and Silva Santos, C., 2013, Comparison between CCMP, QuikSCAT and buoy winds along the Iberian Peninsula coast. Remote Sensing of Environment 137, 173–183. doi:10.1016/j.rse.2013,06.005.
Carvalho, D., Rocha, A., Gomez-Gesteira, M.
and Santos, C. S., 2016, Offshore winds
and wind energy production estimates derived from ASCAT, OSCAT, NWP models
and buoys - a comparative study for the Iberian Peninsula Atlantic coast 1–12. doi:10.1016/j.renene.2016,10.063.
Carvalho, D., Rocha, A., Gómez-Gesteira, M. and Silva Santos, C., 2014a, Offshore wind energy resource simulation forced by different reanalyses: Comparison with observed data in the Iberian Peninsula. Applied Energy 134, 57–64. doi:10.1016/j.apenergy.2014,08.018.
Carvalho, D., Rocha, A., Gómez-Gesteira, M.
and Silva Santos, C., 2014b, WRF wind simulation and wind energy production estimates forced by different reanalyses: Comparison with observed data for Portugal. Applied Energy 117, 116–126. doi:10.1016/j.apenergy.2013,12.001.
Cheristina, T. and Lars, I., 2011, Data usage and quality control in ERA-40, ERA-Interim and th e operational DA system, Journal of Chemical Information and Modeling. doi:10.1017/CBO9781107415324.004.
Chou, M.-D. and Suarez, M. J., 1999, A solar radiation parameterization for atmospheric studies. NASA Tech. Memo 104606, 40.
Chou, M.-D., Suarez, M. J., Liang, X.-Z., Yan, M. M. -H. and Cote, C., 2001, A thermal infrared radiation parameterization for atmospheric studies.
Dee, D. P., Uppala, S. M., Simmons, A. J., Berrisford, P., Poli, P., Kobayashi, S., Andrae, U., Balmaseda, M. A., Balsamo, G., Bauer, P., Bechtold, P., Beljaars, A. C. M., van de Berg, L., Bidlot, J., Bormann, N., Delsol, C., Dragani, R., Fuentes, M., Geer, A. J., Haimberger, L., Healy, S. B., Hersbach, H., Holm, E. V., Isaksen, L., Kallberg, P., Kohler, M., Matricardi, M., Mcnally, A. P., Monge-Sanz, B. M., Morcrette, J. J., Park, B. K., Peubey, C., de Rosnay, P., Tavolato, C., Thepaut, J. N. and Vitart, F., 2011, The ERA-Interim reanalysis: Configuration and performance of the data assimilation system. Quarterly Journal of the Royal Meteorological Society 137, 553–597. doi:10.1002/qj.828.
Dudhia, J., 1996, A multi-layer soil temperature model for MM5, in: Preprints, The Sixth PSU/NCAR Mesoscale Model Users’ Workshop. pp. 22–24.
Dunbar, R. S., Lungu, T., Weiss, B., Stiles, B., Huddleston, J., Callahan, P. S., Shirtliffe, G., Perry, K., Hsu, C., Mears, C. and Wentz, F., 2006, QuikSCAT Science Data Product User Manual, Version 3.0, JPL Document D-18053 - Rev A. Pasadena, CA: Jet Propulsion Laboratory.
Emery, K. O., 1956, Sediments and water of Persian Gulf. AAPG Bulletin 40, 2354–2383.
Figa-Saldana, J., Wilson, JJ.W., Attema, E., Gelsthorpe, R., Drinkwater, M. R. and Stoffelen, A., 2002, The advanced scatterometer (ASCAT) on the meteorological operational( MetOp) platform: A follow on for European wind scatterometers. Canadian Journal of Remote Sensing 28. 404-412.
Furevik, B. R., Sempreviva, A. M., Cavaleri, L., Lefèvre, J.-M. and Transerici, C., 2011, Eight years of wind measurements from scatterometer for wind resource mapping in the Mediterranean Sea. Wind Energy 14.
Ghader, S., Yazgi, D., Haghshenas, S. A., Arab, A. R., Attari, M. J., Bakhtiari, A. and Zinsazboroujerdi, H., 2016a, Hindcasting Tropical Storm Events in the Oman Sea. Journal of Coastal Research 1087–1091.
Ghader, S., Yazgi, D., Soltanpour, M. and Nemati, M. H, 2016b, On the use of an ensemble forecasting system for prediction of surface wind over the Persian Gulf. In proceedings of the 12th International Conference on Coasts, Ports and Marin355–372. doi:10.1002/wee Structure (ICOPMAS 2016), Tehran, Iran, 31 Oct - 2 Nov. 2016.
Grima, N., Bentamy, A., Katsaros, K., Quilfen, Y., Delecluse, P. and Levy, C., 1999, Sensitivity of an oceanic general circulation model forced by satellite wind stress fields. Journal of Geophysical Research-Oceans 104, 7967–7989. doi:10.1029/1999JC900007.
Grodsky, S. A. and Carton, J. A., 2001, Coupled land/atmosphere interactions in the West African monsoon 28, 1503–1506.
Hasager, C. B., Mouche, A., Badger, M., Bingöl, F., Karagali, I., Driesenaar, T., Stoffelen, A., Peña, A. and Longépé, N., 2015, Offshore wind climatology based on synergetic use of Envisat ASAR, ASCAT and QuikSCAT. Remote Sensing of Environment 156, 247–263. doi:10.1016/j.rse.2014,09.030.
Hersbach, H., Stoffelen, A. and de Haan, S., 2007, An improved C-band scatterometer ocean geophysical model function: CMOD5. Journal of Geophysical Research: Oceans 112.
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.
Jiang, D., Zhuang, D., Huang, Y., Wang, J. and Fu, J., 2013, Evaluating the spatio-temporal variation of China’s offshore wind resources based on remotely sensed wind field data. Renewable and Sustainable Energy Reviews 24, 142–148. doi:10.1016/j.rser.2013,03.058.
Jimenez, B., Moennich, K., Rey, J. and Durante, F., 2012, Use of different globally available long-term data sets and its influence on expected wind farm energy yields.
Kain, J. S., 2004, The Kain-Fritsch convective parameterization: an update. Journal of Applied Meteorology 43, 170–181.
Kalnay, E., Kanamitsu, M., R, K., Collins, W., Deaven, D., Gandin, L., Iredell, M., Saha, S., White, G., Woollen, J. and Zhu, Y., 1996, The NCEP/NCAR 40-year reanalysis project. Bulletin of the American Meteorological Society 77, 437–471. doi:10.1175/1520-0477(1996)077<0437:TNYRP>2.0.CO; 2.
Kämpf, J. and Sadrinasab, M., 2005, The circulation of the Persian Gulf: a numerical study. Ocean Science Discussions 2, 129–164. doi:10.5194/osd-2-129-2005.
Kanamitsu, M., Ebisuzaki, W., Woollen, J., Yang, S. K., Hnilo, J. J., Fiorino, M. and Potter, G. L., 2002, NCEP-DOE AMIP-II reanalysis
(R-2). Bulletin of the American Meteorological Society 83, 1631–1643+1559. doi:10.1175/BAMS-83-11-1631.
Karagali, I., Badger, M., Hahmann, A. N., Peña, A., B. Hasager, C. and Sempreviva, A. M., 2013, Spatial and temporal variability of winds in the Northern European Seas. Renewable Energy 57, 200–210. doi:10.1016/j.renene.2013,01.017.
Karagali, I., Peña, A., Badger, M. and Hasager, C. B., 2014, Wind characteristics in the North and Baltic Seas from the QuikSCAT satellite. Wind Energy 17, 123–140.
Liléo, S. and Petrik, O., 2011, Investigation on the use of NCEP / NCAR , MERRA and NCEP / CFSR reanalysis data in wind resource analysis. European Wind Energy Conference and Exhibition (EWEC) 10.
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.
Liu, C. X., Wang, J., Qi, Y. Q. and Wan, Q. L., 2004, A Preliminary study on QuikSCAT wind data assimilation using model WRF. J. Trop. Oceanogr 23, 69–74.
Liu, W. T., Tang, W. and Xie, X., 2008, Wind power distribution over the ocean. Geophysical Research Letters 35. doi:10.1029/2008GL034172.
Mass, C. and Ovens, D., 2011, Fixing WRF’s high speed wind bias: A new subgrid scale drag parameterization and the role of detailed verification, in: 24th Conference on Weather and Forecasting and 20th Conference on Numerical Weather Prediction, Preprints, 91st American Meteorological Society Annual Meeting.
Mazaheri, S., Kamranzad, B. and Hajivalie, F., 2013, Modification of 32 years ECMWF wind field using QuikSCAT data for wave hindcasting in Iranian Seas. Journal of Coastal Research 344-349.
Meissner, T., Smith, D. and Wentz, F., 2001, A 10 year intercomparison between collocated Special Sensor Microwave Imager oceanic surface wind speed retrievals and global analyses. Journal of Geophysical Research 106, 11731. doi:10.1029/1999JC000098.
Menendez, M., Tomas, A., Camus, P., Garcia-Diez, M., Fita, L., Fernandez, J., Mendez, F. J. and Losada, I. J., 2011, A methodology to evaluate regional-scale offshore wind energy resources. OCEANS 2011 IEEE - Spain 1–8. doi:10.1109/Oceans-Spain.2011,6003595.
Michael Reynolds, R., 1993, Physical oceanography of the Gulf, Strait of Hormuz, and the Gulf of Oman-Results from the Mt Mitchell expedition. Marine Pollution Bulletin 27, 35–59. doi:10.1016/0025-326X(93)90007-7.
Mlawer, E. J., Taubman, S. J., Brown, P. D., Iacono, M. J. and Clough, S. A., 1998, Radiative transfer for inhomogeneous atmospheres: RRTM, a validated correlated-k model for the longwave. Journal of Geophysical Research: Atmospheres 102, 16663–16682.
NCEP-FNL, 2016, operational model global tropospheric analyses, continuing from July 1999, Research Data Archive at the National Center for Atmospheric Research, Computational and Information Systems Laboratory, Boulder, CO. Available: http://rda. ucar. edu/datasets/ds083
Onogi, K., Tsutsui, J., Koide, H., Sakamoto, M., Kobayashi, S., Hatsushika, H., Matsumoto, T., Yamazaki, N., Kamahori, H., Takahashi, K., Kadokura, S., Wada, K., Kato, K., Oyama, R., Ose, T., Mannoji, N. and Taira, R., 2007, The JRA-25 Reanalysis. Journal of the Meteorological Society of Japan. Ser. II 85, 369–432. doi:10.2151/jmsj.85.369.
Perrone, T. J., 1979, Winter Shamal in the Persian Gulf, Naval Environmental Prediction Research Facility, Monterey.
Pimenta, F., Kempton, W. and Garvine, R., 2008, Combining meteorological stations and satellite data to evaluate the offshore wind power resource of Southeastern Brazil. Renewable Energy 33, 2375–2387. doi:10.1016/j.renene.2008,01.012.
Rani, S. I., Das Gupta, M., Sharma, P. and Prasad, V. S., 2014, Intercomparison of Oceansat-2 and ASCAT Winds with In Situ Buoy Observations and Short-Term Numerical Forecasts. Atmosphere-Ocean 52, 92–102. doi:10.1080/07055900.2013,869191.
Risien, C. M. and Chelton, D. B., 2008, A Global Climatology of Surface Wind and Wind Stress Fields from Eight Years of QuikSCAT Scatterometer Data. Journal of Physical Oceanography 38, 2379–2413. doi:10.1175/2008JPO3881.1.
Sempreviva, A. M., Barthelmie, R. J. and Pryor, S. C., 2008, Review of methodologies for offshore wind resource assessment in European seas. Surveys in Geophysics 29, 471–497. doi:10.1007/s10712-008-9050-2.
Simmons, A., Uppala, S., Dee, D. and Kobayashi, S., 2007, ERA-Interim: New ECMWF reanalysis products from 1989 onwards. ECMWF Newsletter 110, 25–35. doi:ECMWF Newsletter n.110.
Singh, R., Kishtawal, C. M., Pal, P. K. and Joshi, P. C., 2011, Assimilation of the multisatellite data into the WRF model for track and intensity simulation of the Indian Ocean tropical cyclones. Meteorology and Atmospheric Physics 111, 103–119. doi:10.1007/s00703-011-0127-y.
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. Technical Report 113. doi:10.5065/D6DZ069T.
Stopa, J. E. and Cheung, K. F., 2014, Intercomparison of wind and wave data from the ECMWF Reanalysis Interim and the NCEP Climate Forecast System Reanalysis. Ocean Modelling 75, 65–83. doi:10.1016/j.ocemod.2013,12.006.
Tang, W. and Liu, W. T., 1996, Equivalent neutral wind. Jet Propulsion Laboratory, National Aeronautics and Space Administration.
Uppala, S. M., KÅllberg, P. W., Simmons, A. J., Andrae, U., Bechtold, V. D. C., Fiorino, M., Gibson, J. K., Haseler, J., Hernandez, A., Kelly, G. A., Li, X., Onogi, K., Saarinen, S., Sokka, N., Allan, R. P., Andersson, E., Arpe, K., Balmaseda, M. A., Beljaars, A. C. M., Berg, L. Van De, Bidlot, J., Bormann, N., Caires, S., Chevallier, F., Dethof, A., Dragosavac, M., Fisher, M., Fuentes, M., Hagemann, S., Hólm, E., Hoskins, B. J., Isaksen, L., Janssen, P. A. E. M., Jenne, R., Mcnally, A. P., Mahfouf, J.-F., Morcrette, J.-J., Rayner, N. A., Saunders, R. W., Simon, P., Sterl, A., Trenberth, K. E., Untch, A., Vasiljevic, D., Viterbo, P. and Woollen, J., 2005, The ERA-40 re-analysis. Quarterly Journal of the Royal Meteorological Society 131, 2961–3012. doi:10.1256/qj.04.176.
Wang, W., Bruyére, C., Duda, M., Dudhia, J., Gill, D., Kavulich, M., Keene, K., Lin, H. C., Michalakes, J., Rizvi, S., 2014, ARW Version 3 Modeling System User’s Guide January 2014, National Center for Atmospheric Research, Boulder, CO, http://www2. mmm. ucar. edu/wrf/users/docs/user_guide_V3 5.
Yamartino, R. J., 1984, A Comparison of
Several “Single-Pass” Estimators of the Standard Deviation of Wind Direction. Journal of Climate and Applied Meteorology 23, 1362–1366. doi:10.1175/1520-0450(1984)023<1362:ACOSPE>2.0.CO; 2.