Advertisement

Assimilation of atmospheric infrared sounder radiances with WRF-GSI for improving typhoon forecast

  • Yan-An Liu
  • Zhibin Sun
  • Maosi Chen
  • Hung-Lung Allen Huang
  • Wei Gao
Research Article
  • 3 Downloads

Abstract

The Atmospheric Infrared Sounder (AIRS) can provide the profile information on atmospheric temperature and humidity in high vertical resolution. The assimilation of its radiances has been proven to improve the Numerical Weather Prediction (NWP) in global models. In this study, regional assimilation of AIRS radiances was carried out in a community assimilation system, using Gridpoint Statistical Interpolation (GSI) coupled with the Weather Research and Forecasting (WRF) model. The AIRS channel selection, quality control, and radiances bias correction were examined and illustrated for optimized assimilation. The bias correction scheme in the regional model showed that corrections on most of the channels produce satisfactory results except for several land surface channels. The assimilation and forecast experiments were carried out for three typhoon cases (Saola, Damrey, and Haikui in 2012) with and without including AIRS radiances. Results show that the assimilation of AIRS radiances into the WRF/GSI model improves both the typhoon track and intensity in a 72-hour forecast.

Keywords

AIRS WRF/GSI model radiance assimilation typhoon forecast 

Preview

Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.

Notes

Acknowledgements

This work was supported by the National Natural Science Foundation of China (Grant No. 41601469) and Fundamental Research Funds for the Central Universities in China (East China Normal University). The experiments were run on the Supercomputer located at the Computing Center of East China Normal University.

References

  1. Aumann H H, Chahine M T, Gautier C, Goldberg M D, Kalnay E, Mcmillin L M, Revercomb H, Rosenkranz P W, Smith W L, Staelin D H, Strow L L, Susskind J (2003). AIRS/AMSU/HSB on the aqua mission: design, science objectives, data products, and processing systems. IEEE Trans Geosci Remote Sens, 41(2): 253–264CrossRefGoogle Scholar
  2. Bauer P, Thorpe A, Brunet G (2015). The quiet revolution of numerical weather prediction. Nature, 525(7567): 47–55CrossRefGoogle Scholar
  3. Benjamin S G, Weygandt S S, Brown J M, Hu M, Alexander C R, Smirnova T G, Olson J B, James E P, Dowell D C, Grell G A, Lin H, Peckham S E, Smith T L, Moninger W R, Kenyon J S, Manikin G S (2016). A North American hourly assimilation and model forecast cycle: the rapid refresh. Mon Weather Rev, 144(4): 1669–1694CrossRefGoogle Scholar
  4. Bernardet L, Tallapragada V, Bao S, Trahan S, Kwon Y, Liu Q, Tong M, Biswas M, Brown T, Stark D, Carson L, Yablonsky R, Uhlhorn E, Gopalakrishnan S, Zhang X, Marchok T, Kuo B, Gall R (2015). Community support and transition of research to operations for the hurricane weather research and forecasting model. Bull Am Meteorol Soc, 96(6): 953–960CrossRefGoogle Scholar
  5. Carrier M J, Zou X, Lapenta W M (2008). Comparing the vertical structures of weighting functions and adjoint sensitivity of radiance and verifying mesoscale forecasts using AIRS radiance observations. Mon Weather Rev, 136(4): 1327–1348CrossRefGoogle Scholar
  6. Carrier M, Zou X, Lapenta W M (2007). Identifying cloud-uncontaminated AIRS spectra from cloudy FOV based on cloud-top pressure and weighting functions. Mon Weather Rev, 135(6): 2278–2294CrossRefGoogle Scholar
  7. Chahine M T, Pagano T S, Aumann H H, Atlas R, Barnet C, Blaisdell J, Chen L, Divakarla M, Fetzer E J, Goldberg M, Gautier C, Granger S, Hannon S, Irion F W, Kakar R, Kalnay E, Lambrigtsen B H, Lee S Y, Le MARSHALL J, McMillan W W, McMillin L, Olsen E T, Revercomb H, Rosenkranz P, Smith W L, Staelin D, Strow L L, Susskind J, Tobin D, Wolf W, Zhou L (2006). Improving weather forecasting and providing new data on greenhouse gases. Bull Am Meteorol Soc, 87: 911–926CrossRefGoogle Scholar
  8. De PondecaMS F V, Manikin G S, DiMego G, Benjamin S G, Parrish D F, Purser R J, Wu W S, Horel J D, Myrick D T, Lin Y, Aune R M, Keyser D, Colman B, Mann G, Vavra J (2011). The real-time mesoscale analysis at NOAA’s National Centers for Environmental Prediction: current status and development. Weather Forecast, 26(5): 593–612CrossRefGoogle Scholar
  9. Derber J C, Wu W S (1998). The use of TOVS cloud-cleared radiances in the NCEP SSI analysis system. Mon Weather Rev, 126(8): 2287–2299CrossRefGoogle Scholar
  10. Eyre J R, Kelly G A, Mcnally A P, Andersson E, Persson A (1993). Assimilation of TOVS radiance information through one-dimensional variational analysis. Q J R Meteorol Soc, 119(514): 1427–1463CrossRefGoogle Scholar
  11. Fourrié N, Thépaut J J (2002). Validation of the NESDIS Near Real Time AIRS channel selection. ECMWF Technical Memorandum, 1–14Google Scholar
  12. Goldberg M D, Kilcoyne H, Cikanek H, Mehta A (2013). Joint polar satellite system: the United States next generation civilian polarorbiting environmental satellite system. J Geophys Res Atmos, 118(24): 13,463–13,475CrossRefGoogle Scholar
  13. Klaes K D, Cohen M, Buhler Y, Schlüssel P, Munro R, Luntama J P, von Engeln A, Clérigh E Ó, Bonekamp H, Ackermann J, Schmetz J (2007). An introduction to the EUMETSAT polar system. Bull Am Meteorol Soc, 88(7): 1085–1096CrossRefGoogle Scholar
  14. Le Marshall J, Jung J, Derber J, Chahine M, Treadon R, Lord S J, Goldberg M, Wolf W, Liu H C, Joiner J, Woollen J, Todling R, van Delst P, Tahara Y (2006). Improving global analysis and forecasting with AIRS. Bull Am Meteorol Soc, 87(7): 891–894CrossRefGoogle Scholar
  15. Li J, Liu H (2009). Improved hurricane track and intensity forecast using single field-of-view advanced IR sounding measurements. Geophys Res Lett, 36(11): L11813CrossRefGoogle Scholar
  16. Lim A H N, Jung J A, Huang H A, Ackerman S A, Otkin J A (2014). Assimilation of clear sky Atmospheric Infrared Sounder radiances in short-term regional forecasts using community models. J Appl Remote Sens, 8(1): 083655CrossRefGoogle Scholar
  17. Liu Y A, Huang H L A, Gao W, Lim A H N, Liu C, Shi R (2015). Tuning of background error statistics through sensitivity experiments and its impact on typhoon forecast. J Appl Remote Sens, 9(1): 096051CrossRefGoogle Scholar
  18. Liu Y, Huang H A, Lim A H N, Gao W(2018). Adaptive bias correction of advanced infrared sounding radiance assimilation in a regional model and its impact on typhoon forecast. J Appl Remote Sens, 12: 1Google Scholar
  19. McCarty W, Jedlovec G, Miller T L (2009). Impact of the assimilation of Atmospheric Infrared Sounder radiance measurements on short-term weather forecasts. J Geophys Res Atmos, 114: D18122CrossRefGoogle Scholar
  20. McNally A P, Watts P D (2003). A cloud detection algorithm for highspectral- resolution infrared sounders. Q J R Meteorol Soc, 129(595): 3411–3423CrossRefGoogle Scholar
  21. McNally P, Watts P D, Smith J, Engelen R, Kelly G, Thépaut J N, Matricardi M (2006). The assimilation of AIRS radiance data at ECMWF. Q J R Meteorol Soc, 132(616): 935–957CrossRefGoogle Scholar
  22. Menzel W P, Schmit T J, Zhang P, Li J (2018). Satellite based atmospheric infrared sounder development and applications. Bull Am Meteorol Soc, 99(3): 583–603CrossRefGoogle Scholar
  23. Miyoshi T, Kunii M (2012). Using AIRS retrievals in the WRF-LETKF system to improve regional numerical weather prediction. Tellus, Ser A, Dyn Meterol Oceanogr, 64(1): 18408CrossRefGoogle Scholar
  24. Pu Z, Zhang L (2010). Validation of atmospheric infrared sounder temperature and moisture profiles over tropical oceans and their impact on numerical simulations of tropical cyclones. J Geophys Res Atmos, 115(D24): 1–13CrossRefGoogle Scholar
  25. Rabier F, Fourrie N, Chafai D, Prunet P (2002). Channel selection methods for infrared atmospheric sounding interferometer radiances. Q J R Meteorol Soc, 128(581): 1011–1027CrossRefGoogle Scholar
  26. Skamarock W C, Klemp J B, Dudhia J, Gill D O, Barker D M, Duda M G, Huang X Y, Wang W, Powers J G (2008). A Description of the Advanced Research WRF Version 3. NCAR Tech. Notes NCAR/TN-475 + STRT, 1–113Google Scholar
  27. Xu D, Liu Z, Huang X Y, Min J, Wang H (2013). Impact of assimilating IASI radiance observations on forecasts of two tropical cyclones. Meteorol Atmos Phys, 122(1–2): 1–18CrossRefGoogle Scholar
  28. Zheng J, Li J J, Schmit T J, Li J J, Liu Z (2015). The impact of AIRS atmospheric temperature and moisture profiles on hurricane forecasts: Ike (2008) and Irene (2011). Adv Atmos Sci, 32(3): 319–335CrossRefGoogle Scholar
  29. Zhou Y P, Lau K M, Reale O, Rosenberg R (2010). AIRS impact on precipitation analysis and forecast of tropical cyclones in a global data assimilation and forecast system. Geophys Res Lett, 37: L02806CrossRefGoogle Scholar

Copyright information

© Higher Education Press and Springer-Verlag GmbH Germany, part of Springer Nature 2018

Authors and Affiliations

  1. 1.Key Laboratory of Geographic Information Science (Ministry of Education)East China Normal UniversityShanghaiChina
  2. 2.School of Geographic SciencesEast China Normal UniversityShanghaiChina
  3. 3.ECNU-CSU Joint Research Institute for New Energy and the EnvironmentEast China Normal UniversityShanghaiChina
  4. 4.Joint Laboratory for Environmental Remote Sensing and Data AssimilationEast China Normal UniversityShanghaiChina
  5. 5.Natural Resource Ecology LaboratoryColorado State UniversityFort CollinsUnited States
  6. 6.Cooperative Institute for Meteorological Satellite StudiesUniversity of Wisconsin-MadisonMadisonUnited States

Personalised recommendations