Abstract
This chapter describes the authors’ effort on ensemble data assimilation of satellite precipitation measurements. The Local Ensemble Transform Kalman Filter (LETKF) was implemented with the Nonhydrostatic Icosahedral Atmospheric Model (NICAM), and JAXA’s GSMaP (Global Satellite Mapping of Precipitation) data were assimilated at 112-km resolution.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
References
Campbell, W. F., Satterfield, E., Ruston, B., & Baker, N. (2017). Accounting for correlated observation error in a dual formulation 4D-variational data assimilation system. Monthly Weather Review, 145, 1019–1032. https://doi.org/10.1175/MWR-D-16-0240.1.
Huffman, G. J., Bolvin, D. T., Nelkin, E. J., Wolff, D. B., Adler, R. F., Gu, G., Hong, Y., Bowman, K. P., & Stocker, E. F. (2007). The TRMM Multisatellite Precipitation Analysis (TMPA): Quasi-global, multiyear, combined-sensor precipitation estimates at fine scales. Journal of Hydrometeorology, 8, 38–55. https://doi.org/10.1175/JHM560.1.
Hunt, B. R., Kostelich, E. J., & Szunyogh, I. (2007). Efficient data assimilation for spatiotemporal chaos: A local ensemble transform Kalman filter. Physica D: Nonlinear Phenomena, 230, 112–126. https://doi.org/10.1016/j.physd.2006.11.008.
Kotsuki, S., Terasaki, K., & Miyoshi, T. (2014). GPM/DPR precipitation compared with a 3.5-km-resolution NICAM simulation. SOLA, 10, 204–209. https://doi.org/10.2151/sola.2014-043.
Kotsuki, S., Miyoshi, T., Terasaki, K., Lien, G.-Y., & Kalnay, E. (2017a). Assimilating the global satellite mapping of precipitation data with the Nonhydrostatic Icosahedral Atmospheric Model (NICAM). Journal of Geophysical Research, 122, 631–650. https://doi.org/10.1002/2016JD025355.
Kotsuki, S., Ota, Y., & Miyoshi, T. (2017b). Adaptive covariance relaxation methods for ensemble data assimilation: Experiments in the real atmosphere. Quarterly Journal of the Royal Meteorological Society, 143, 2001–2015. https://doi.org/10.1002/qj.3060.
Kotsuki, S., Terasaki, K., Yashiro, H., Tomita, H., Satoh, M., & Miyoshi, T. (2018). Online model parameter estimation with ensemble data assimilation in the real global atmosphere: A case with the Nonhydrostatic Icosahedral Atmospheric Model (NICAM) and the global satellite mapping of precipitation data. Journal of Geophysical Research, 123, 7375–7392. https://doi.org/10.1029/2017JD028092.
Lien, G.-Y., Kalnay, E., & Miyoshi, T. (2013). Effective assimilation of global precipitation: Simulation experiments. Tellus A, 65, 1–16. https://doi.org/10.3402/tellusa.v65i0.19915.
Lien, G.-Y., Kalnay, E., Miyoshi, T., & Huffman, G. J. (2016a). Statistical properties of global precipitation in the NCEP GFS model and TMPA observations for data assimilation. Monthly Weather Review, 144, 663–679. https://doi.org/10.1175/MWR-D-15-0150.1.
Lien, G.-Y., Miyoshi, T., & Kalnay, E. (2016b). Assimilation of TRMM multisatellite precipitation analysis with a low-resolution NCEP global forecasting system. Monthly Weather Review, 144, 643–661. https://doi.org/10.1175/MWR-D-15-0149.1.
Miyoshi, T. (2005). Ensemble Kalman filter experiments with a primitive-equation global model. Ph.D. dissertation, University of Maryland, College Park, 197 pp. Available at https://drum.lib.umd.edu/handle/1903/3046, last accessed 23 Nov 2018.
Miyoshi, T. (2011). The Gaussian approach to adaptive covariance inflation and its implementation with the local ensemble transform Kalman filter. Monthly Weather Review, 139, 1519–1535. https://doi.org/10.1175/2010MWR3570.1.
Miyoshi, T., & Yamane, S. (2007). Local ensemble transform Kalman filtering with an AGCM at a T159/L48 resolution. Monthly Weather Review, 135, 3841–3861. https://doi.org/10.1175/2007MWR1873.1.
Miyoshi, T., Yamane, S., & Enomoto, T. (2007). Localizing the error covariance by physical distances within a local ensemble transform Kalman filter (LETKF). SOLA, 3, 89–92. https://doi.org/10.2151/sola.2007-023.
Miyoshi, T., Sato, Y., & Kadowaki, T. (2010). Ensemble Kalman filter and 4D-Var intercomparison with the Japanese operational global analysis and prediction system. Monthly Weather Review, 138, 2846–2866. https://doi.org/10.1175/2010MWR3209.1.
Miyoshi, T., Kalnay, E., & Li, H. (2013). Estimating and including observation-error correlations in data assimilation. Inverse Problems in Science and Engineering, 21, 387–398. https://doi.org/10.1080/17415977.2012.712527.
Otsuka, S., Kotsuki, S., & Miyoshi, T. (2016). Nowcasting with data assimilation: A case of global satellite mapping of precipitation. Weather and Forecasting, 31, 1409–1416. https://doi.org/10.1175/WAF-D-16-0039.1.
Satoh, M., Tomita, H., Yashiro, H., Miura, H., Kodama, C., Seiki, T., Noda, A. T., Yamada, Y., Goto, D., Sawada, M., Miyoshi, T., Niwa, Y., Hara, M., Ohno, T., Iga, S., Arakawa, T., Inoue, T., & Kubokawa, H. (2014). The non-hydrostatic icosahedral atmospheric model: Description and development. Progress in Earth and Planetary Science, 1, 18. https://doi.org/10.1186/s40645-014-0018-1.
Saunders, R., Hocking, J., Rundle, D., Rayer, P., Matricardi, M., Geer, A., Lupu, C., Brunel, P., & Vidot, J. (2013). RTTOV-11: Science and validation report. NWP-SAF Rep., UK Met Office, 62 pp. Available at https://nwpsaf.eu/oldsite/deliverables/rtm/docs_rttov11/rttov11_svr.pdf, last accessed 23 Nov 2018.
Terasaki, K., & Miyoshi, T. (2014). Data assimilation with error-correlated and non-orthogonal observations: Experiments with the Lorenz-96 model. SOLA, 10, 210–213. https://doi.org/10.2151/sola.2014-044.
Terasaki, K., & Miyoshi, T. (2017). Assimilating AMSU-A radiances with the NICAM-LETKF. Journal of the Meteorological Society of Japan, 95, 433–446. https://doi.org/10.2151/jmsj.2017-028.
Terasaki, K., Sawada, M., & Miyoshi, T. (2015). Local ensemble transform Kalman filter experiments with the nonhydrostatic icosahedral atmospheric model NICAM. SOLA, 11, 23–26. https://doi.org/10.2151/sola.2015-006.
Ushio, T., Sasashige, K., Kubota, T., Shige, S., Okamoto, K., Aonashi, K., Inoue, T., Takahashi, N., Iguchi, T., Kachi, M., Oki, R., Morimoto, T., & Kawasaki, Z.-I. (2009). A Kalman filter approach to the global satellite mapping of precipitation (GSMaP) from combined passive microwave and infrared radiometric data. Journal of the Meteorological Society of Japan, 87A, 137–151. https://doi.org/10.2151/jmsj.87A.137.
Weston, P. P., Bell, W., & Eyre, J. R. (2014). Accounting for correlated error in the assimilation of high-resolution sounder data. Quarterly Journal of the Royal Meteorological Society, 140, 2420–2429. https://doi.org/10.1002/qj.2306.
Yashiro, H., Terasaki, K., Miyoshi, T., & Tomita, H. (2016). Performance evaluation of a throughput-aware framework for ensemble data assimilation: The case of NICAM-LETKF. Geoscientific Model Development, 9, 2293–2300. https://doi.org/10.5194/gmd-9-2293-2016.
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2020 Springer Nature Switzerland AG
About this chapter
Cite this chapter
Miyoshi, T. et al. (2020). Precipitation Ensemble Data Assimilation in NWP Models. In: Levizzani, V., Kidd, C., Kirschbaum, D., Kummerow, C., Nakamura, K., Turk, F. (eds) Satellite Precipitation Measurement. Advances in Global Change Research, vol 69. Springer, Cham. https://doi.org/10.1007/978-3-030-35798-6_25
Download citation
DOI: https://doi.org/10.1007/978-3-030-35798-6_25
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-030-35797-9
Online ISBN: 978-3-030-35798-6
eBook Packages: Earth and Environmental ScienceEarth and Environmental Science (R0)