Advertisement

Natural Hazards

, Volume 82, Issue 2, pp 845–855 | Cite as

Satellite radiance assimilation using a 3DVAR assimilation system for hurricane Sandy forecasts

  • Tanvir IslamEmail author
  • Prashant K. Srivastava
  • Dinesh Kumar
  • George P. Petropoulos
  • Qiang Dai
  • Lu Zhuo
Original Paper

Abstract

In this article, we present an assimilation impact study for forecasting hurricane Sandy using a three‐dimensional variational data assimilation system (3DVAR). In particular, we employ the 3DVAR component of the Weather Research and Forecasting Model and conduct analysis/forecast cycling experiments for “control” and “radiance” assimilation cases for the hurricane Sandy period. In “control” assimilation experiment, only conventional air and surface observations data are assimilated, while, in “radiance” assimilation experiment, along with the conventional air and surface observations data, the satellite radiance data from the Advanced Microwave Sounding Unit-A (AMSU-A) and the Microwave Humidity Sounder (MHS) sensors are also assimilated. For the radiance assimilation, we employ the community radiative transfer model as the forward operator and perform quality control and bias correction procedure before the radiance data are assimilated. In order to assess the impact of the assimilation experiments, we produce 132-h deterministic forecast starting on 00 UTC October 25, 2012. The results reveal that, in particular, the assimilation of AMSU-A satellite radiances helps to improve the short- to medium-range forecast (up to ~60-h lead time). The forecast skill is degraded in the long-range forecast (beyond 60 h) with the AMSU-A assimilation.

Keywords

Variational data assimilation Numerical weather prediction (NWP) Cyclone forecast Track propagation WRF 3DVAR Radiative transfer ATOVS AMSU-A AMSU-B MHS 

Notes

Acknowledgments

Part of this research was carried out at the Jet Propulsion Laboratory, California Institute of Technology, under a contract with the National Aeronautics and Space Administration (NASA). The data for this study are from NOAA’s National Operational Model Archive and Distribution System (NOMADS) which is maintained at NOAA’s National Climatic Data Center (NCDC). The views expressed here are those of the authors solely and do not constitute a statement of policy, decision, or position on behalf of NOAA, NASA, or the authors’ affiliated institutions.

References

  1. Barker DM, Huang W, Guo YR, Bourgeois AJ, Xiao QN (2004) A three-dimensional variational data assimilation system for MM5: implementation and initial results. Mon Weather Rev 132(4):897–914. doi: 10.1175/1520-0493(2004)132<0897:atvdas>2.0.co;2 CrossRefGoogle Scholar
  2. Chambon P, Zhang SQ, Hou AY, Zupanski M, Cheung S (2014) Assessing the impact of pre-GPM microwave precipitation observations in the Goddard WRF ensemble data assimilation system. Q J R Meteorol Soc 140(681):1219–1235. doi: 10.1002/qj.2215 CrossRefGoogle Scholar
  3. Chen F, Dudhia J (2001) Coupling an advanced land surface-hydrology model with the Penn State-NCAR MM5 modeling system. Part I: model implementation and sensitivity. Mon Weather Rev 129(4):569–585. doi: 10.1175/1520-0493(2001)129<0569:caalsh>2.0.co;2 CrossRefGoogle Scholar
  4. Chou CB, Huang HP (2011) The impact of assimilating Atmospheric Infrared Sounder observation on the forecast of typhoon tracks. Adv Meteorol. doi: 10.1155/2011/803593 Google Scholar
  5. Dai Q, Han DW, Rico-Ramirez MA, Islam T (2013) The impact of raindrop drift in a three-dimensional wind field on a radar-gauge rainfall comparison. Int J Remote Sens 34(21):7739–7760. doi: 10.1080/01431161.2013.826838 CrossRefGoogle Scholar
  6. Dong HP, Li XW, Guo WD, Gao TC (2013) A study on satellite data assimilation with different ATOVS in typhoon numerical experiments. J Trop Meteorol 19(3):242–252Google Scholar
  7. Hong SY, Dudhia J, Chen SH (2004) A revised approach to ice microphysical processes for the bulk parameterization of clouds and precipitation. Mon Weather Rev 132(1):103–120. doi: 10.1175/1520-0493(2004)132<0103:aratim>2.0.co;2 CrossRefGoogle Scholar
  8. Hong SY, Noh Y, Dudhia J (2006) A new vertical diffusion package with an explicit treatment of entrainment processes. Mon Weather Rev 134(9):2318–2341. doi: 10.1175/mwr3199.1 CrossRefGoogle Scholar
  9. Ishak A, Remesan R, Srivastava P, Islam T, Han DW (2013) Error correction modelling of wind speed through hydro-meteorological parameters and mesoscale model: a hybrid approach. Water Resour Manag 27(1):1–23. doi: 10.1007/s11269-012-0130-1 CrossRefGoogle Scholar
  10. Islam T, Rico-Ramirez MA, Han DW, Bray M, Srivastava PK (2013) Fuzzy logic based melting layer recognition from 3 GHz dual polarization radar: appraisal with NWP model and radio sounding observations. Theor Appl Climatol 112(1–2):317–338. doi: 10.1007/s00704-012-0721-z CrossRefGoogle Scholar
  11. Islam T, Rico-Ramirez MA, Han DW, Srivastava PK (2014) Sensitivity associated with bright band/melting layer location on radar reflectivity correction for attenuation at C-band using differential propagation phase measurements. Atmos Res 135:143–158. doi: 10.1016/j.atmosres.2013.09.003 CrossRefGoogle Scholar
  12. Islam T, Srivastava PK, Rico-Ramirez MA, Dai Q, Gupta M, Singh SK (2015) Tracking a tropical cyclone through WRF–ARW simulation and sensitivity of model physics. Nat Hazards 76(3):1473–1495. doi: 10.1007/s11069-014-1494-8 CrossRefGoogle Scholar
  13. Jones TA, Stensrud DJ (2012) Assimilating AIRS temperature and mixing ratio profiles using an ensemble Kalman filter approach for convective-scale forecasts. Weather Forecast 27(3):541–564. doi: 10.1175/waf-d-11-00090.1 CrossRefGoogle Scholar
  14. Liu QH, Weng FZ (2013) Using advanced matrix operator (AMOM) in community radiative transfer model. IEEE J Sel Top Appl Earth Observ Remote Sens 6(3):1211–1218. doi: 10.1109/jstars.2013.2247026 CrossRefGoogle Scholar
  15. Liu ZQ, Schwartz CS, Snyder C, Ha SY (2012) Impact of assimilating AMSU-A radiances on forecasts of 2008 Atlantic tropical cyclones initialized with a limited-area ensemble Kalman filter. Mon Weather Rev 140(12):4017–4034. doi: 10.1175/mwr-d-12-00083.1 CrossRefGoogle Scholar
  16. Liu QH, Xue Y, Li C (2013) Sensor-based clear and cloud radiance calculations in the community radiative transfer model. Appl Opt 52(20):4981–4990. doi: 10.1364/ao.52.004981 CrossRefGoogle Scholar
  17. Singh R, Kishtawal CM, Pal PK, Joshi PC (2012) Improved tropical cyclone forecasts over north Indian Ocean with direct assimilation of AMSU-A radiances. Meteorol Atmos Phys 115(1–2):15–34. doi: 10.1007/s00703-011-0165-5 CrossRefGoogle Scholar
  18. Skamarock WC, Klemp JB (2008) A time-split nonhydrostatic atmospheric model for weather research and forecasting applications. J Comput Phys 227(7):3465–3485. doi: 10.1016/j.jcp.2007.01.037 CrossRefGoogle Scholar
  19. Srivastava PK, Han DW, Ramirez MAR, Islam T (2013) Comparative assessment of evapotranspiration derived from NCEP and ECMWF global datasets through Weather Research and Forecasting model. Atmos Sci Lett 14(2):118–125. doi: 10.1002/asl2.427 CrossRefGoogle Scholar
  20. Subramani D, Chandrasekar R, Ramanujam KS, Balaji C (2014) A new ensemble-based data assimilation algorithm to improve track prediction of tropical cyclones. Nat Hazards 71(1):659–682. doi: 10.1007/s11069-013-0942-1 CrossRefGoogle Scholar
  21. Wu WS, Purser RJ, Parrish DF (2002) Three-dimensional variational analysis with spatially inhomogeneous covariances. Mon Weather Rev 130(12):2905–2916. doi: 10.1175/1520-0493(2002)130<2905:tdvaws>2.0.co;2 CrossRefGoogle Scholar
  22. Xu JJ, Powell AM (2012) Dynamical downscaling precipitation over Southwest Asia: impacts of radiance data assimilation on the forecasts of the WRF-ARW model. Atmos Res 111:90–103. doi: 10.1016/j.atmosres.2012.03.005 CrossRefGoogle Scholar
  23. Xu DM, Liu ZQ, Huang XY, Min JZ, Wang HL (2013) Impact of assimilating IASI radiance observations on forecasts of two tropical cyclones. Meteorol Atmos Phys 122(1–2):1–18. doi: 10.1007/s00703-013-0276-2 CrossRefGoogle Scholar
  24. Zhang SQ, Zupanski M, Hou AY, Lin X, Cheung SH (2013) Assimilation of precipitation-affected radiances in a cloud-resolving WRF ensemble data assimilation system. Mon Weather Rev 141(2):754–772. doi: 10.1175/mwr-d-12-00055.1 CrossRefGoogle Scholar
  25. Zou XL, Qin ZK, Weng FZ (2013) Improved quantitative precipitation forecasts by MHS radiance data assimilation with a newly added cloud detection algorithm. Mon Weather Rev 141(9):3203–3221. doi: 10.1175/mwr-d-13-00009.1 CrossRefGoogle Scholar
  26. Zupanski D, Zhang SQ, Zupanski M, Hou AY, Cheung SH (2011) A prototype WRF-based ensemble data assimilation system for dynamically downscaling satellite precipitation observations. J Hydrometeorol 12(1):118–134. doi: 10.1175/2010jhm1271.1 CrossRefGoogle Scholar

Copyright information

© Springer Science+Business Media Dordrecht 2016

Authors and Affiliations

  • Tanvir Islam
    • 1
    • 2
    • 3
    Email author
  • Prashant K. Srivastava
    • 4
    • 5
  • Dinesh Kumar
    • 6
  • George P. Petropoulos
    • 7
  • Qiang Dai
    • 8
  • Lu Zhuo
    • 9
  1. 1.Jet Propulsion LaboratoryCalifornia Institute of TechnologyPasadenaUSA
  2. 2.NOAA/NESDIS Center for Satellite Applications and ResearchCollege ParkUSA
  3. 3.Cooperative Institute for Research in the AtmosphereColorado State UniversityFort CollinsUSA
  4. 4.NASA Goddard Space Flight CenterGreenbeltUSA
  5. 5.Earth System Science Interdisciplinary CenterUniversity of MarylandCollege ParkUSA
  6. 6.Central University of JammuJammuIndia
  7. 7.Department of Geography and Earth SciencesAberystwyth UniversityAberystwythUK
  8. 8.School of Geographic ScienceNanjing Normal UniversityNanjingChina
  9. 9.Department of Civil EngineeringUniversity of BristolBristolUK

Personalised recommendations