This work assesses the effects of assimilating atmospheric infrared sounder (AIRS) observations on typhoon prediction using the three-dimensional variational data assimilation (3DVAR) and forecasting system of the weather research and forecasting (WRF) model. Two major parameters in the data assimilation scheme, the spatial decorrelation scale and the magnitude of the covariance matrix of the background error, are varied in forecast experiments for the track of typhoon Sinlaku over the Western Pacific. The results show that within a wide parameter range, the inclusion of the AIRS observation improves the prediction. Outside this range, notably when the decorrelation scale of the background error is set to a large value, forcing the assimilation of AIRS data leads to degradation of the forecast. This illustrates how the impact of satellite data on the forecast depends on the adjustable parameters for data assimilation. The parameter-sweeping framework is potentially useful for improving operational typhoon prediction. 1. Introduction The atmospheric infrared sounder (AIRS) is a state-of-the-art hyperspectral infrared sensor that has provided critical observational data for weather and climate analysis since its launch in 2002 (McNally et al. [1], Chahine et al. [2]). The AIRS spectrum consists of 2378 channels from 3.7？μm–15.4？μm with a spectral resolution of . The cross-track swath width is 1650？km and spatial resolution is 13.5？km at the nadir field of view (Aumann et al. [3], Chahine et al. [2]). Due to its high resolution and accuracy (errors are within 1？K for the temperature of a 1？km vertical layer and 20% for the lower tropospheric moisture of a 2？km vertical layer, http://airs.jpl.nasa.gov/), AIRS can potentially provide high-quality temperature and humidity data for applications that would otherwise rely on conventional sounding. Given its uniformly high spatial resolution, the strategy for assimilating the AIRS retrieval data into a weather forecast system may differ from that for assimilating conventional soundings. With the increasing usage of AIRS retrieval products in data assimilation for regional weather prediction (e.g., [4–7]), it remains an outstanding problem to determine the ideal weight given to the AIRS retrieval profiles (and satellite observation in general) in order to optimize its impact on the forecast. As a contribution to this topic, this work will first examine the usefulness of assimilating the AIRS retrieval data for the prediction of typhoon tracks then explore the sensitivity of the forecast error on the adjustable
References
[1]
A. P. McNally, P. D. Watts, J. A. Smith et al., “The assimilation of AIRS radiance data at ECMWF,” Quarterly Journal of the Royal Meteorological Society, vol. 132, no. 616, pp. 935–957, 2006.
[2]
M. T. Chahine, T. S. Pagano, H. H. Aumann et al., “Improving weather forecasting and providing new data on greenhouse gases,” Bulletin of the American Meteorological Society, vol. 87, no. 7, pp. 911–926, 2006.
[3]
H. H. Aumann, M. T. Chahine, C. Gautier et al., “AIRS/AMSU/HSB on the aqua mission: design, science objectives, data products, and processing systems,” IEEE Transactions on Geoscience and Remote Sensing, vol. 41, no. 2, pp. 253–264, 2003.
[4]
S.-H. Chou, B. Zavodsky, G. J. Jedlovec, and W. Lapenta, “Assimilation of atmospheric infrared sounder (AIRS) data in a regional model,” Proceedings of the 14th Conference on Satellite Meteorology and Oceanography, 2006.
[5]
J. Li and H. Liu, “Improved hurricane track and intensity forecast using single field-of-view advanced IR sounding measurements,” Geophysical Research Letters, vol. 36, no. 11, Article ID L11813, 2009.
[6]
H. Liu and J. Li, “An improvement in forecasting rapid intensification of Typhoon Sinlaku (2008) using clear-sky full spatial resolution advanced IR soundings,” Journal of Applied Meteorology and Climatology, vol. 49, no. 4, pp. 821–827, 2010.
[7]
J. Li, H. Liu, and T. J. Schmit, “Advanced IR measurements improve hurricane forecasts,” in Remote Sensing, Proceedings of the SPIE Newsletter, 2010.
[8]
D. M. Barker, W. Huang, Y. R. Guo, A. J. Bourgeois, and Q. N. Xiao, “A three-dimensional variational data assimilation system for MM5: implementation and initial results,” Monthly Weather Review, vol. 132, no. 4, pp. 897–914, 2004.
[9]
T. Zhu, D. L. Zhang, and F. Weng, “Impact of the advanced microwave sounding unit measurements on hurricane prediction,” Monthly Weather Review, vol. 130, no. 10, pp. 2416–2432, 2002.
[10]
X. Zhang, Q. Xiao, and P. J. Fitzpatrick, “The impact of multisatellite data on the initialization and simulation of Hurricane Lili's (2002) rapid weakening phase,” Monthly Weather Review, vol. 135, no. 2, pp. 526–548, 2007.
[11]
C. B. Chou, C. Y. Huang, H. P. Huang, K. H. Wang, and T. C. Yeh, “The analysis of typhoon structures using advanced microwave sounding unit data and its application to prediction,” Journal of Applied Meteorology and Climatology, vol. 47, no. 5, pp. 1476–1492, 2008.
[12]
J. S. Goerss, “Impact of satellite observations on the tropical cyclone track forecasts of the navy operational global atmospheric prediction system,” Monthly Weather Review, vol. 137, no. 1, pp. 41–50, 2009.
[13]
W. C. Skamarock, J. B. Klemp, J. Dudhia et al., “A description of the advanced research WRF Version 3,” NCAR Technical Note, 2008.
[14]
WRF-ARW, Weather Research and Forecasting ARW Version 3 Modeling System User's Guide, National Center for Atmospheric Research, 2009.
[15]
A. C. Lorenc, “Analysis methods for numerical weather prediction,” Quarterly Journal of the Royal Meteorological Society, vol. 112, no. 474, pp. 1177–1194, 1986.
[16]
D. F. Parrish and J. C. Derber, “The National Meteorological Center's spectral statistical- interpolation analysis system,” Monthly Weather Review, vol. 120, no. 8, pp. 1747–1763, 1992.
[17]
G. Desroziers and S. Ivanov, “Diagnosis and adaptive tuning of information error parameters in a variational assimilation,” Quarterly Journal of the Royal Meteorological Society, vol. 127, pp. 1433–1452, 2001.
[18]
W. Sadiki and C. Fischer, “A posteriori validation applied to the 3D-VAR Arpège and Aladin data assimilation systems,” Tellus, vol. 57, no. 1, pp. 21–34, 2005.
[19]
J. Susskind, C. D. Barnet, and J. M. Blaisdell, “Retrieval of atmospheric and surface parameters from AIRS/AMSU/HSB data in the presence of clouds,” IEEE Transactions on Geoscience and Remote Sensing, vol. 41, no. 2, pp. 390–409, 2003.
[20]
J. Susskind, C. D. Barnet, J. Blaisdell et al., “Accuracy of geophysical parameters derived from Atmospheric Infrared Sounder/Advanced Microwave Sounding Unit as a function of fractional cloud cover,” Journal of Geophysical Research D, vol. 111, no. 9, Article ID D09S17, 19 pages, 2006.
[21]
J. Susskind, J. M. Blaisdell, L. Iredell, and F. Keita, “Improved temperature and quality control methodology using AIRS/AMSU data: the AIRS Science team version 5 retrieval algorithm,” IEEE Transactions on Geoscience and Remote Sensing, vol. 49, pp. 833–907, 2011.
[22]
E. T. Olsen and Contributors, AIRS/AMSU/HSB Version 5 data release user guide, Jet Propulsion Laboratory, pp. 68, 2007, http://disc.sci.gsfc.nasa.gov/AIRS/documentation.
[23]
Z. Q. Liu and F. Rabier, “The potential of high-density observations for numerical weather prediction: a study with simulated observations,” Quarterly Journal of the Royal Meteorological Society, vol. 129, no. 594, pp. 3013–3035, 2003.
[24]
P. Bauer, R. Buizza, C. Cardinali, and J. No？l Thépaut, “Impact of singular-vector-based satellite data thinning on NWP,” Quarterly Journal of the Royal Meteorological Society, vol. 137, no. 655, pp. 286–302, 2011.
[25]
N. Bormann and P. Bauer, “Estimates of spatial and interchannel observation-error characteristics for current sounder radiances for numerical weather prediction. I: methods and application to ATOVS data,” Quarterly Journal of the Royal Meteorological Society, vol. 136, no. 649, pp. 1036–1050, 2010.
[26]
N. Bormann, A. Collard, and P. Bauer, “Estimates of spatial and interchannel observation-error characteristics for current sounder radiances for numerical weather prediction. II: application to AIRS and IASI data,” Quarterly Journal of the Royal Meteorological Society, vol. 136, no. 649, pp. 1051–1063, 2010.
[27]
J. Gu, Q. Xiao, Y. H. Kuo, D. M. Barker, J. Xue, and X. Ma, “Assimilation and simulation of typhoon Rusa (2002) using the WRF system,” Advances in Atmospheric Sciences, vol. 22, no. 3, pp. 415–427, 2005.
[28]
Y.-R. Guo, H.-C. Lin, X. X. Ma, X.-Y. Huang, C. T. Terng, and Y.-H. Kuo, “Impact of WRF-Var (3dVar) background error statistics on typhoon analysis and forecast,” in Proceedings of the 7th Annual WRF Users' Workshop, Boulder, Colo, USA, June 2006.
[29]
S. R. H. Rizvi, WRF-Var Tutorial, WRFDA: Background error estimation, 2009, http://box.mmm.ucar.edu/wrf/users/wrfda/Tutorials/2009_July.
