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Quality and Impact of Indian Doppler Weather Radar Wind Profiles: A Diagnostic Study

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Abstract

In the tropics, efficient weather forecasts require high-quality vertical profiles of winds to overcome improper coupling of mass and wind fields and balance relationships in the region. The India Meteorological Department (IMD) operates the network of Doppler Weather Radar (DWR) in microwave frequencies (S-band or C-band) at various locations in India. The National Centre for Medium Range Weather Forecasting (NCMRWF) receives the volume velocity processing (VVP) wind profiles from all DWRs through the Global Telecommunication System (GTS) network in near real time. The radar VVP wind is a mean horizontal wind derived at different heights from radial velocities suitable for numerical weather prediction applications. Three numerical experiments, CNTL (without VVP winds), 3DVAR and HYBRID with the assimilation of VVP winds by means of 3-dimensional variational (3dvar) and hybrid data assimilation systems were conducted using the NCMRWF Global Forecast System (NGFS) model. This study had two objectives: (1) quality assessment of VVP winds and (2) investigation of the impact of VVP wind profiles on NGFS model forecast. The quality of VVP wind profiles was assessed against the NGFS model background and radiosonde wind profiles. The absolute values of zonal and meridional wind observation minus background (O−B) increased with the pressure for all DWRs. All radars exhibited the accepted (rejected) ratio as a decreasing (increasing) function of pressure. The resemblance between the zonal and meridional O−B statistics for 3DVAR and HYBRID experiments is apparently remarkable. The accepted VVP winds and radiosonde winds in both experiments (3DVAR and HYBRID) were consistent. The correlation coefficient (R) was higher at Patna (Patiala) for zonal (meridional) winds in the 3DVAR experiment and at Patna (Jaipur) in the HYBRID experiment. At Chennai, the R value was lower in both the experiments for both wind components. However, because of the assimilation of VVP winds by means of 3dvar, the root-mean-square error (RMSE) of zonal and meridional winds improved by approximately 2–3% up to the day 5 forecast in the analysis performed below 700 hPa. Further improvement in RMSE by approximately 5% was observed in both the wind components because of the hybrid data assimilation. The zonal and meridional wind RMSEs in the HYBRID (3DVAR) experiment improved by ≈5 (3)% compared with the 3DVAR (CNTL) experiment.

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References

  • Abhilash, S., Das, S., Kalsi, S. R., Gupta, M. D., Mohankumar, K., George, J. P., et al. (2007a). Assimilation of Doppler weather radar observations in a mesoscale model for the prediction of intense rainfall events associated with mesoscale convective systems using 3DVAR. Journal of Earth System Science, 116(4), 275–304.

    Article  Google Scholar 

  • Abhilash, S., Das, S., Kalsi, S. R., Gupta, M. D., Mohankumar, K., George, J. P., et al. (2007b). 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. doi:10.1007/s00024-007-0235-2.

    Article  Google Scholar 

  • Abhilash, S., Sahai, A. K., Mohankumar, K., George, J. P., & Das, S. (2012). Assimilation of Doppler weather radar radial velocity and reflectivity observations in WRF-3DVAR system for short range forecasting of convective storms. Pure and Applied Geophysics, 169, 2047–2070. doi:10.1007/s00024-012-0462-z.

    Article  Google Scholar 

  • Alberoni, P. P., Ducrocq, V., Gregoric, G., Hasse, G., Holleman, I., Lindskog, M., Macpherson, B., Nuret, M., & Rossa, A. (2001). Quality and assimilation of radar data for NWP-a review, European Commission Report. EUR. 20600. COST 717 Review Report.

  • Andersson, T. (1998). VAD winds from C band Ericsson Doppler Weather Radars. Meteorology Zeitschrift., 7(6), 309–319.

    Google Scholar 

  • Andersson, E., & Jarvinen, H. (1999). Variational quality control. Quarterly Journal of the Royal Meteorological Society., 125, 697–722. doi:10.1002/qj.49712555416.

    Article  Google Scholar 

  • Boccippio, D. J. (1995). A diagnostic analysis of the VVP single-Doppler retrieval technique. Journal of Atmospheric and Oceanic Technology., 12, 230–248. doi:10.1175/1520-0426(1995)012<0230:ADAOTV>2.0.CO;2.

    Article  Google Scholar 

  • Browning, K. A., & Wexler, R. (1968). The determination of kinematic properties of a wind field using Doppler radar. Journal of Applied Meteorology, 7, 105–113. doi:10.1175/1520-0450(1968)007<0105:TDOKPO>2.0.CO;2.

    Article  Google Scholar 

  • Collins, W. G. (2001). The quality control of velocity azimuth display (VAD) winds at the national centres for environmental prediction. 11th Symposium on Meteorological Observations and Instrumentation. Albuquerque. NM. American Meteorological Society. 9.2.

  • Das, S., Abhilash, S., Gupta, M. D., George, J. P., Kalsi, S. R., Banerjee, S. K., Thampi, S. B., Pradhan, D., & Mohankumar, K. (2006). Assimilation of Doppler weather radar wind in a mesoscale model and their impact on simulation of thunderstorms and severe weather systems. NMRF/RR/01/2006.

  • Das, M. K., Chowdhury, M. A. M., & Das, 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. doi:10.1007/s11069-015-1634-9.

    Article  Google Scholar 

  • Desroziers, G. L., Berre, B., & Poli, P. (2005). Diagnosis of observation, background and analysis-error statistics in observation space. Quarterly Journal of Royal Meteorological Society., 131, 3385–3396. doi:10.1256/qj.05.108.

    Article  Google Scholar 

  • George, J. P., Johny, C. J., & Raghavendra, A. (2011). DWR data monitoring and processing at NCMRWF. NMRF/TR/03/2011.

  • Govindankutty, M., Chandrasekar, A., & Pradhan, D. (2010). Impact of 3DVAR assimilation of Doppler weather radar wind data and IMD observation for the prediction of tropical cyclone. International Journal of Remote Sensing, 31(24), 6327–6345. doi:10.1080/01431160903413689.

    Article  Google Scholar 

  • Holleman, I. (2005). Quality control and verification of weather radar wind profiles. Journal of Atmospheric and Oceanic Technology., 22, 1541–1550. doi:10.1175/JTECH1781.1.

    Article  Google Scholar 

  • Holleman, I., Gasteren, H. V., & Bouten, W. (2008). Quality assessment of weather radar wind profiles during bird migration. Journal of Atmospheric and Oceanic Technology., 25, 2188–2198. doi:10.1175/2008JTECHA1067.1.

    Article  Google Scholar 

  • Kiran, P. S., Mohanty, U. C., & Routray, A. (2014). Impact of Doppler weather radar data on thunderstorm simulation during STORM pilot phase-2009. Natural Hazards, 74(3), 1403–1427. doi:10.1007/s11069-014-1250-0.

    Article  Google Scholar 

  • Kleist, D. T., Parrish, D. F., Derber, J. C., Treadon, R., Errico, R. M., & Yang, R. (2009). Improving incremental balance in the GSI 3DVAR analysis system. Monthly Weather Review, 137, 1046–1060. doi:10.1175/2008MWR2623.1.

    Article  Google Scholar 

  • Lindskog, M., Salonen, K., Jarvinen, H., & Michelson, D. B. (2004). Doppler radar wind data assimilation with HIRLAM 3DVAR. Monthly Weather Review, 132, 1081–1092. doi:10.1175/1520-0493(2004)132<1081:DRWDAW>2.0.CO;2.

    Article  Google Scholar 

  • Michelson, S. A., & Seaman, N. L. (2000). Assimilation of NEXRAD-VAD winds in summertime meteorological simulations over the northeastern United States. Journal of Applied Meteorology, 39, 367–383. doi:10.1175/1520-0450(2000)039<0367:AONVWI>2.0.CO;2.

    Article  Google Scholar 

  • Montmerle, T., & Faccani, C. (2009). Mesoscale assimilation of radial velocities from Doppler radars in a preoperational framework. Monthly Weather Review, 137, 1939–1953. doi:10.1175/2008MWR2725.1.

    Article  Google Scholar 

  • Nelson, S., Carr, F. H. & Ruthi, L. J. (1995). Intercomparison of horizontal velocities observed by modern wind-finding systems. 9th Symposium on Meteorological Observations and Instrumentation. Charlotte. NC. American Meteorological Society.

  • Parrett, C. A., Turp, M., Macpherson, B., & Oakley, T. (2004). Quality monitoring of weather radar wind profiles at the met office. Proceeding of ERAD., 2, 168–173.

    Google Scholar 

  • Poli, V., & Alberoni, P. P. (2014). Quality of weather radar wind profiles at APRA Emilia-Romanga. The Eighth European Conference on Radar in Meteorology and Hydrology. ERAD.

  • Prasad, V. S., Mohandas, S., Dutta, S. K., Gupta, M. D., Iyengar, G. R., Rajagopal, E. N., et al. (2014). Improvements in medium range weather forecasting system of India. Journal of Earth System Science, 123(2), 247–258.

    Article  Google Scholar 

  • Prasad, V. S., Mohandas, S., Gupta, M. D., Rajagopal, E. N., & Dutta, S. K. (2011). Implementation of upgraded global forecasting systems (T382L64 and T574L64) at NCMRWF. NCMR/TR/5/2011.

  • Rinne, J. (2000). The use of radar wind observations in numerical assimilation. Physics and Chemistry of Earth Part B: Hydrology, Oceans and Atmosphere., 25B, 1273–1276. doi:10.1016/S1464-1909(00)00192-1.

    Article  Google Scholar 

  • Routray, A., Mohanty, U. C., Rizvi, S. R. H., Niyogi, D., Osuri, K. K., & Pradhan, D. (2010). Impact of Doppler weather radar data on numerical forecast of Indian monsoon depressions. Quarterly Journal of Royal Meteorological Society., 136, 1836–1850. doi:10.1002/qj.678.

    Article  Google Scholar 

  • Roybhowmik, S. K., Roy, S. S., Srivastava, K., Mukopadhay, B., Thampi, S. B., Reddy, Y. K., et al. (2011). Processing of Indian Doppler weather radar data for mesoscale applications. Meteorology and Atmospheric Physics, 111(3), 133–147. doi:10.1007/s00703-010-0120-x.

    Article  Google Scholar 

  • Simonin, D., Ballard, S. P., & Li, Z. (2014). Doppler radar radial wind assimilation using an hourly cycling 3D-Var with a 1.5 km resolution version of the Met Office Unified Model for nowcastings. Quarterly Journal of Royal Meteorological Society., 140, 2298–2314. doi:10.1002/qj.2298.

    Article  Google Scholar 

  • Srivastava, K., & Bhardwaj, R. (2014). Assimilation of Doppler weather radar data in WRF model for simulation of tropical cyclone alia. Pure and Applied Geophysics, 171, 2043–2072. doi:10.1007/s00024-013-0723-5.

    Article  Google Scholar 

  • Srivastava, K., Bhowmik, S. K., Roy, S. S., Thampi, S. B., & Reddy, Y. K. (2010). Simulations of high impact convective events over Indian region by ARPS model with assimilation of Doppler weather radar radial velocity and reflectivity. Atmosfera., 23(1), 53–73.

    Google Scholar 

  • Waldteufel, P., & Corbin, H. (1979). On the analysis of single Doppler radar data. Journal of Applied Meteorology, 18, 532–542. doi:10.1175/1520-0450(1979)018<0532:OTAOSD>2.0.CO;2.

    Article  Google Scholar 

  • Waller, J. A., Simonin, D., Dance, S. L., Nichols, N. K., & Ballard, S. P. (2016). Diagnosing observation error correlations for Doppler radar radial winds in the Met Office UKV model using observation-minus-background and observation-minus-analysis statistics. Monthly Weather Review, 144, 3533–3551. doi:10.1175/MWR-D-15-0340.1.

    Article  Google Scholar 

  • Xiao, Q., Kuo, Y. H., Sun, J., Lee, W. C., Lim, E., Guo, Y., & Barker, D. M. (2005). Assimilation of Doppler radar observations with a regional 3DVAR system: impact of Doppler velocities on forecasts of a heavy rainfall case. Journal of Applied Meteorology, 44, 768–788. doi:10.1175/JAM2248.1.

    Article  Google Scholar 

  • Xiao, Q., Sun, J., Lee, W. L., Kuo, Y. H., Barker, D. M., Lim, E., et al. (2008). Doppler radar data assimilation in KMA’s operational forecasting. Bulletin of American Meteorological Society., 89, 39–43. doi:10.1175/BAMS-89-1-39.

    Article  Google Scholar 

  • Zhao, Q., Cook, J., Xu, Q., & Harasti, P. R. (2006). Using radar wind observations to improve mesoscale numerical weather prediction. Weather Forecast., 21, 502–522. doi:10.1175/WAF936.1.

    Article  Google Scholar 

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Acknowledgements

The authors would like to thank anonymous reviewers for their valuable comments that improved the quality of paper. The authors are grateful to the IMD for providing VVP datasets and to the Head, NCMRWF, Dr. E. N. Rajagopal for his valuable suggestions.

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  1. National Centre for Medium Range Weather Forecasting (NCMRWF), MoES, A-50, Institutional Area, Sector-62, Noida, 201309, UP, India

    A. Sandeep, V. S. Prasad & C. J. Johny

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  1. A. Sandeep
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Sandeep, A., Prasad, V.S. & Johny, C.J. Quality and Impact of Indian Doppler Weather Radar Wind Profiles: A Diagnostic Study. Pure Appl. Geophys. 174, 2847–2862 (2017). https://doi.org/10.1007/s00024-017-1544-8

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