The Local Ensemble Transform Kalman Filter with the Weather Research and Forecasting Model: Experiments with Real Observations
- 740 Downloads
The local ensemble transform Kalman filter (LETKF) is implemented with the Weather Research and Forecasting (WRF) model, and real observations are assimilated to assess the newly-developed WRF-LETKF system. The WRF model is a widely-used mesoscale numerical weather prediction model, and the LETKF is an ensemble Kalman filter (EnKF) algorithm particularly efficient in parallel computer architecture. This study aims to provide the basis of future research on mesoscale data assimilation using the WRF-LETKF system, an additional testbed to the existing EnKF systems with the WRF model used in the previous studies. The particular LETKF system adopted in this study is based on the system initially developed in 2004 and has been continuously improved through theoretical studies and wide applications to many kinds of dynamical models including realistic geophysical models. Most recent and important improvements include an adaptive covariance inflation scheme which considers the spatial and temporal inhomogeneity of inflation parameters. Experiments show that the LETKF successfully assimilates real observations and that adaptive inflation is advantageous. Additional experiments with various ensemble sizes show that using more ensemble members improves the analyses consistently.
KeywordsData assimilation numerical weather prediction ensemble Kalman filter
The authors thank the members of the UMD Weather-Chaos Group for fruitful discussions. The NCEP PREPBUFR observation data were obtained from the UCAR data server, while several missing files were kindly provided by Daryl Kleist of NCEP. This study was supported by the Office of Naval Research (ONR) Grant N000141010149 under the National Oceanographic Partnership Program (NOPP).
- Anderson, J. L., 2009: Spatially and temporally varying adaptive covariance inflation for ensemble filters. Tellus, 61A, 72–83.Google Scholar
- Anderson, J. L., T. Hoar, K. Raeder, H. Liu, N. Collins, R. Torn, and A. Avellano, 2009: The Data Assimilation Research Testbed A Community Facility. Bull. Amer. Meteor. Soc., 90, 1283–1296.Google Scholar
- Anderson, J. L. and S. L. Anderson, 1999: A Monte Carlo implementation of the nonlinear filtering problem to produce ensemble assimilations and forecasts. Mon. Wea. Rev., 127, 2741–2758.Google Scholar
- Bishop, C. H., B. J. Etherton, and S. J. Majumdar, 2001: Adaptive sampling with the ensemble transform Kalman filter. Part I: Theoretical aspects. Mon. Wea. Rev., 129, 420–436.Google Scholar
- Chou, K.-H., C.-C. Wu, P.-H. Lin, S. D. Aberson, M. Weissmann, F. Harnisch, T. Nakazawa, 2011: The Impact of Dropwindsonde Observations on Typhoon Track Forecasts in DOTSTAR and T-PARC, Mon. Wea. Rev., 139, 1728–1743. doi: 10.1175/2010MWR3582.1.
- Elsberry, R. L. and P. A. Harr, 2008: Tropical Cyclone Structure (TCS08) field experiment science basis, observational platforms, and strategy. Asia-Pacific J. Atmos. Sci., 44, 209–231.Google Scholar
- Gaspari, G. and S. E. Cohn, 1999: Construction of correlation functions in two and three dimensions. Quart. J. Roy. Meteor. Soc., 125, 723–757.Google Scholar
- Greybush, S., 2011: Mars Weather and Predictability: Modeling and Ensemble Data Assimilation of Spacecraft Observations. Ph.D. Dissertation, University of Maryland, College Park.Google Scholar
- Hamill, T. M., J. S. Whitaker, and C. Snyder, 2001: Distance-dependent filtering of background error covariance estimates in an ensemble Kalman filter. Mon. Wea. Rev., 129, 2776–2790.Google Scholar
- Hoffman, M. J., S. J. Greybush, R. J. Wilson, G. Gyarmati, R. N. Hoffman, E. Kalnay, K. Ide, E. J. Kostelich, T. Miyoshi, and I. Szunyogh, 2010: An Ensemble Kalman Filter Data Assimilation System for the Martian Atmosphere: Implementation and Simulation Experiments. Icarus, 209, 470–481.Google Scholar
- Huang, X.-Y., and Coauthors, 2009: Four-Dimensional Variational Data Assimilation for WRF: Formulation and Preliminary Results. Mon. Wea. Rev., 137, 299–314.Google Scholar
- Hunt, B. R., E. Kalnay, E. J. Kostelich, E. Ott, D. J. Patil, T. Sauer, I. Szunyogh, J. A. Yorke, and A. V. Zimin, 2004: Four-dimensional ensemble Kalman filtering. Tellus, 56A, 273–277.Google Scholar
- Hunt, B. R., E. J. Kostelich and I. Szunyogh, 2007: Efficient Data Assimilation for Spatiotemporal Chaos: A Local Ensemble Transform Kalman Filter. Physica D, 230, 112–126.Google Scholar
- Kang, J.-S., 2009: Carbon cycle data assimilation using a coupled atmosphere-vegetation model and the local ensemble transform Kalman filter. Ph.D. dissertation, University of Maryland, College Park, 164 pp.Google Scholar
- Kang, J.-S., E. Kalnay, J. Liu, I. Fung, T. Miyoshi, and K. Ide, 2011: “Variable localization” to improve carbon cycle data assimilation in an Ensemble Kalman Filter. J. Geophys. Res., 116, D09110. doi: 10.1029/2010JD014673
- Keyser, D., 2010: PREPBUFR PROCESSING AT NCEP, http://www.emc.ncep.noaa.gov/mmb/data_processing/prepbufr.doc/document.htm.
- Lorenz, E., 1996: Predictability: a problem partly solved. Proceeding of the ECMWF Seminar on Predictability, vol. 1, Reading, UK.Google Scholar
- Lorenz, E. and K. Emanuel, 1998: Optimal Sites for Supplementary Weather Observations: Simulation with a Small Model. J. Atmos. Sci., 55, 399–414.Google Scholar
- Miyoshi, T., 2005: Ensemble Kalman filter experiments with a primitive-equation global model. Ph.D. dissertation, University of Maryland, College Park, 197 pp.Google Scholar
- Miyoshi, T., 2011: The Gaussian Approach to Adaptive Covariance Inflation and its Implementation with the Local Ensemble Transform Kalman Filter. Mon. Wea. Rev., 139, 1519–1535. doi: 10.1175/2010MWR3570.1.
- Miyoshi, T. and K. Aranami 2006: Applying a Four-dimensional Local Ensemble Transform Kalman Filter (4D-LETKF) to the JMA Nonhydrostatic Model (NHM). SOLA, 2, 128–131.Google Scholar
- Miyoshi, T. and Y. Sato, 2007: Assimilating Satellite Radiances with a Local Ensemble Transform Kalman Filter (LETKF) Applied to the JMA Global Model (GSM). SOLA, 3, 37–40.Google Scholar
- Miyoshi, T. and S. Yamane, 2007: Local ensemble transform Kalman filtering with an AGCM at a T159/L48 resolution. Mon. Wea. Rev., 135, 3841–3861.Google Scholar
- Miyoshi, T., S. Yamane, and T. Enomoto, 2007: Localizing the Error Covariance by Physical Distances within a Local Ensemble Transform Kalman Filter (LETKF). SOLA, 3, 89–92. doi: 10.2151/sola.2007-023.
- Miyoshi, T., Y. Sato, and T. Kadowaki, 2010: Ensemble Kalman filter and 4D-Var inter-comparison with the Japanese operational global analysis and prediction system. Mon. Wea. Rev., 138, 2846–2866.Google Scholar
- Molteni, F., 2003: Atmospheric simulations using a GCM with simplified physical parametrizations. I: Model climatology and variability in multi-decadal experiments. Climate Dynamics, 20, 175–191.Google Scholar
- Ohfuchi, W., H. Nakamura, M. K. Yoshioka, T. Enomoto, K. Takaya, X. Peng, S. Yamane, T. Nishimura, Y. Kurihara, and K. Ninomiya, 2004: 10-km mesh meso-scale resolving simulations of the global atmosphere on the Earth Simulator-Preliminary outcomes of AFES (AGCM for the Earth Simulator). J. Earth Simulator, 1, 8–34.Google Scholar
- Ott, E., B. R. Hunt, I. Szunyogh, A. V. Zimin, E. J. Kostelich, M. Corazza, E. Kalnay, D. J. Patil, and J. A. Yorke, 2004: A local ensemble Kalman filter for atmospheric data assimilation. Tellus, 56A, 415–428.Google Scholar
- Parsons, D., P. Harr, T. Nakazawa, S. Jones, and M. Weissmann, 2008: An overview of the THORPEX-Pacific Asian Regional Campaign (T-PARC) during August-September 2008. Extended Abstracts, 28th Conf. on Hurricanes and Tropical Meteorology, Orlando, FL, AMS, 1–6.Google Scholar
- Penny, S., 2011: Data Assimilation of the Global Ocean Using the 4D Local Ensemble Transform Kalman Filter (4D-LETKF) and the Modular Ocean Model (MOM2). Ph.D. Dissertation, University of Maryland, College Park.Google Scholar
- Saito, K., M. Kunii, M. Hara, H. Seko, T. Hara, M. Yamaguchi, T. Miyoshi and W. Wong, 2010: WWRP Beijing 2008 Olympics Forecast Demonstration/Research and Development Project (B08FDP/RDP). Tech. Rep. MRI, 62, 210 pp. (http://www.mri-jma.go.jp/Publish/Technical/DATA/VOL_62/62_en.html).
- Saito, K., H. Seko, M. Kunii, M. Hara and T. Miyoshi, 2009: Influence of lateral boundary perturbations on the mesoscale EPS using BGM and LETKF. CAS/JSC WGNE Research Activities in Atmospheric and Oceanic Modelling, 39, 5.21–5.22.Google Scholar
- Shchepetkin, A. and J. C. McWilliams, 2005: The Regional Oceanic Modeling System: A split-explicit, free-surface, topography-following-coordinate ocean model. Ocean Modell., 9, 347–404.Google Scholar
- Skamarock, W. C., J. B. Klemp, J. Dudhia, D. O. Gill, D. M. Barker, W. Wang, and J. G. Powers, 2005: A description of the Advanced Research WRF version 2. NCAR Tech. Note TN-468 + STR, 88 pp.Google Scholar
- Szunyogh, I., E. J. Kostelich, G. Gyarmati, D. J. Patil, B. R. Hunt, E. Kalnay, E. Ott, and J. A. Yorke, 2005: Assesing a local ensemble Kalman filter: perfect model experiments with the National Centers for Environmental Prediction global model. Tellus, 57A, 528–545.Google Scholar
- Szunyogh, I., E. J. Kostelich, G. Gyarmati, E. Kalnay, B. R. Hunt, E. Ott, E. Satterfield, and J. A. Yorke, 2008: A local ensemble transform Kalman filter data assimilation system for the NCEP global model. Tellus, 60A, 113–130.Google Scholar
- Whitaker, J. S. and T. M. Hamill, 2002: Ensemble data assimilation without perturbed observations. Mon. Wea. Rev., 130, 1913–1924.Google Scholar
- Wilson, R. J. and K. P. Hamilton, 1996: Comprehensive model simulation of thermal tides in the martian atmosphere. J. Atmos. Sci., 53, 1290–1326.Google Scholar
- Zhang, F., C. Snyder, and J. Sun: 2004: Impacts of Initial Estimate and Observation Availability on Convective-Scale Data Assimilation with Ensemble Kalman Filter. Mon. Wea. Rev., 132, 1238-1253.Google Scholar