An Observing System Simulation Experiment (OSSE) to Assess the Impact of Doppler Wind Lidar (DWL) Measurements on the Numerical Simulation of a Tropical Cyclone
The importance of wind observations has been recognized for many years. However, wind observations—especially three-dimensional global wind measurements—are very limited. A satellite-based Doppler Wind Lidar (DWL) is proposed to measure three-dimensional wind profiles using remote sensing techniques. Assimilating these observations into a mesoscale model is expected to improve the performance of the numerical weather prediction (NWP) models. In order to examine the potential impact of the DWL three-dimensional wind profile observations on the numerical simulation and prediction of tropical cyclones, a set of observing simulation system experiments (OSSEs) is performed using the advanced research version of the Weather Research and Forecasting (WRF) model and its three-dimensional variational (3DVAR) data assimilation system. Results indicate that assimilating the DWL wind observations into the mesoscale numerical model has significant potential for improving tropical cyclone track and intensity forecasts. 1. Introduction Although numerical weather prediction (NWP) models have been improved significantly over the past two decades, the forecast accuracy of high-impact weather events, such as tropical cyclones, is still a challenging problem in practical applications. Since most tropical cyclones occur over tropical oceans, where conventional observations are sparse, large uncertainties are presented in the numerical simulations and predictions due to inaccurate initial conditions. Remote sensing techniques provide an opportunity to observe the atmosphere, especially the atmospheric temperature, moisture, and ozone over the oceans either directly or indirectly. However, among all the variables used to represent the state of the atmosphere, wind measurements are the most limited, although the importance of wind observations for meteorological analysis has been recognized for many years [1–3]. Previous studies indicate that wind information plays an important role in improving the tropical and extratropical cyclone forecasts [4–14]. However, the current global observing system does not provide a uniform distribution of tropospheric wind measurements, especially in the tropics, southern hemisphere, and northern hemispheric oceans, where conventional observations are very sparse. During the past two decades there have been several satellites measuring wind over the oceans, such as the Geosat altimeter, the National Aeronautics and Space Administration (NASA) Scatterometer (NSCAT), Quick Scatterometer (QuikSCAT), the Special Sensor Microwave Imager (SSM/I), and
References
[1]
W. E. Baker, G. D. Emmitt, F. Robertson, et al., “Lidar-measured winds from space: a key component for weather and climate prediction,” Bulletin of the American Meteorological Society, vol. 76, no. 6, pp. 869–888, 1995.
[2]
W. A. Lahoz, R. Brugge, D. R. Jackson, et al., “An observing system simulation experiment to evaluate the scientific merit of wind and ozone measurements from the future SWIFT instrument,” Quarterly Journal of the Royal Meteorological Society, vol. 131, no. 606, pp. 503–523, 2005.
[3]
A. Stoffelen, J. Pailleux, E. K?llén, et al., “The atmospheric dynamics mission for global wind field measurement,” Bulletin of the American Meteorological Society, vol. 86, no. 1, pp. 73–87, 2005.
[4]
C. S. Velden, T. L. Olander, and S. Wanzong, “The impact of multispectral GOES-8 wind information on Atlantic tropical cyclone track forecasts in 1995. Part I: dataset methodology, description, and case analysis,” Monthly Weather Review, vol. 126, no. 5, pp. 1202–1218, 1998.
[5]
J. S. Goerss, C. S. Velden, and J. D. Hawkins, “The impact of multispectral GOES-8 wind information on Atlantic tropical cyclone track forecasts in 1995. Part II: NOGAPS forecasts,” Monthly Weather Review, vol. 126, no. 5, pp. 1219–1227, 1998.
[6]
I. Szunyogh, Z. Toth, R. E. Morss, S. J. Majumdar, B. J. Etherton, and C. H. Bishop, “The effect of targeted dropsonde observations during the 1999 Winter Storm Reconnaissance Program,” Monthly Weather Review, vol. 128, no. 10, pp. 3520–3537, 2000.
[7]
P. M. Boorman, R. Swinbank, and D. A. Ortland, “Assimilation of directly measured stratospheric winds into the Unified Model,” Forecasting Research Technical Report 332, Met Office, 2000.
[8]
L. Isaksen and A. Stoffelen, “ERS scatterometer wind data impact on ECMWF's tropical cyclone forecasts,” IEEE Transactions on Geoscience and Remote Sensing, vol. 38, no. 4, pp. 1885–1892, 2000.
[9]
L. Isaksen and P. A. E. M. Janssen, “Impact of ERS scatterometer winds in ECMWF's assimilation system,” Quarterly Journal of the Royal Meteorological Society, vol. 130, no. 600, pp. 1793–1814, 2004.
[10]
S. M. Leidner, L. Isaksen, and R. N. Hoffman, “Impact of NSCAT winds on tropical cyclones in the ECMWF 4DVAR assimilation system,” Monthly Weather Review, vol. 131, no. 1, pp. 3–26, 2003.
[11]
N. ?agar, “Assimilation of equatorial waves by line-of-sight wind observations,” Journal of the Atmospheric Sciences, vol. 61, no. 15, pp. 1877–1893, 2004.
[12]
G. J. Marseille and A. Stoffelen, “Simulation of wind profiles from a space-borne Doppler wind lidar,” Quarterly Journal of the Royal Meteorological Society, vol. 129, no. 594, pp. 3079–3098, 2003.
[13]
G.-J. Marseille, A. D. Stoffelen, and J. A. N. Barkmeijer, “Impact assessment of prospective spaceborne Doppler wind lidar observation scenarios,” Tellus A, vol. 60, no. 2, pp. 234–248, 2008.
[14]
G.-J. Marseille, A. D. Stoffelen, and J. Barkmeijer, “A cycled sensitivity observing system experiment on simulated Doppler wind lidar data during the 1999 Christmas storm “Martin”,” Tellus A, vol. 60, no. 2, pp. 249–260, 2008.
[15]
S.-H. Chen, “The impact of assimilating SSM/I and QuikSCAT satellite winds on Hurricane Isidore simulations,” Monthly Weather Review, vol. 135, no. 2, pp. 549–566, 2007.
[16]
Z. Pu, X. Li, C. Velden, S. Aberson, and W. T. Liu, “Impact of aircraft dropsonde and usatellite wind data on the numerical simulation of two landfalling tropical storms during TCSP,” Weather and Forecasting, vol. 23, pp. 62–79, 2008.
[17]
D. G. H. Tan and E. Andersson, “Simulation of the yield and accuracy of wind profile measurements from the Atmospheric Dynamics Mission (ADM-Aeolus),” Quarterly Journal of the Royal Meteorological Society, vol. 131, no. 608, pp. 1737–1757, 2005.
[18]
M. Weissmann and C. Cardinali, “Impact of airborne Doppler lidar observations on ECMWF forecasts,” Quarterly Journal of the Royal Meteorological Society, vol. 133, no. 622, pp. 107–116, 2007.
[19]
C. J. Grund, R. M. Banta, J. L. George, et al., “High-resolution doppler lidar for boundary layer and cloud research,” Journal of Atmospheric and Oceanic Technology, vol. 18, no. 3, pp. 376–393, 2001.
[20]
C. P. Arnold Jr. and C. H. Dey, “Observing-systems simulation experiments: past, present and future,” Bulletin of the American Meteorological Society, vol. 67, no. 6, pp. 687–695, 1986.
[21]
R. M. Atlas, “Observing system simulation experiments: methodology, examples and limitations,” in CGC/WMO Workshop, Geneva, Switzerland, April 1997, WMO TD No. 868.
[22]
S. J. Lord, E. Kalnay, R. Daley, G. D. Emmitt, and R. Atlas, “Using OSSEs in the design of future generation integrated observing systems,,” in Proceedings of the 1st Symposium on Integrated Observing Systems, pp. 45–47, AMS, Long Beach, Calif, USA, 1997, preprints.
[23]
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.
[24]
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.
[25]
D. M. Barker, M. S. Lee, Y.-R. Guo, W. Huang, Q.-N. Xiao, and R. Rizvi, “WRF variational data assimilation development at NCAR,” in Proceedings of the 5th WRF/14th MM5 Users' Workshop, p. 5, NCAR, Boulder, Colo, USA, 2004.
[26]
M. Masutani, J. S. Woollen, S. J. Lord, et al., “Observing system simulation experiments at the National Centers for Environmental Prediction,” Journal of Geophysical Research D, vol. 115, no. 7, Article ID D07101, 2010.
[27]
O. Reale, J. Terry, M. Masutani, E. Andersson, L. P. Riishojgaard, and J. C. Jusem, “Preliminary evaluation of the European Centre for Medium-Range Weather Forecasts' (ECMWF) Nature Run over the tropical Atlantic and African monsoon region,” Geophysical Research Letters, vol. 34, no. 22, Article ID L22810, 6 pages, 2007.
[28]
Z. Pu, L. Zhang, B. Gentry, and B. Demoz, “Potential impact of lidar wind measurements on high-impact weather forecasting: a regional OSSEs study,” in Proceedings of the 13th AMS Conference on Integrated Observing Systems for Atmosphere, Ocean, and Land Surface (IOAS-AOLS '09), Phoenix, Ariz, USA, January 2009.
[29]
W. C. Skamarock, J. B. Klemp, J. Dudhia, et al., “A description of the advanced research WRF version 2,” NCAR Technical Note NCAR/TN-468+STR, NCAR, Boulder, Colo, USA, 2005.
[30]
W. Baker, “Concept for a US space-based wind lidar: status and current activities,” in Joint Center for Satellite Data Assimilation (JCSDA) Seminar, July 2009.
[31]
W. M. Frank and E. A. Ritchie, “Effects of vertical wind shear on the intensity and structure of numerically simulated hurricanes,” Monthly Weather Review, vol. 129, no. 9, pp. 2249–2269, 2001.
[32]
W. A. Nuss and R. A. Anthes, “A numerical investigation of low-level processes in rapid cyclogenesis,” Monthly Weather Review, vol. 115, pp. 2728–2743, 1987.
[33]
E. Rogers and L. F. Bosart, “A diagnostic study of two intense oceanic cyclones,” Monthly Weather Review, vol. 119, no. 4, pp. 965–996, 1991.
[34]
G. H. Crescenti and R. A. Weller, “Analysis of surface fluxes in the marine atmospheric boundary layer in the vicinity of rapidly intensifying cyclones,” Journal of Applied Meteorology, vol. 31, no. 8, pp. 831–848, 1992.
[35]
P. J. Neiman and M. A. Shapiro, “The life cycle of an extratropical marine cyclone. Part I: frontal-cyclone evolution and thermodynamic air-sea interaction,” Monthly Weather Review, vol. 121, no. 8, pp. 2153–2176, 1993.
[36]
D.-L. Zhang, E. Radeva, and J. Gyakum, “A family of frontal cyclones over the western Atlantic Ocean. Part II: parameter studies,” Monthly Weather Review, vol. 127, no. 8, pp. 1745–1760, 1999.
[37]
N. Papasimakis, G. Cervone, F. Pallikari, and M. Kafatos, “Multifractal character of surface latent heat flux,” Physics A, vol. 372, no. 2, pp. 703–718, 2006.