Abstract
Small scale distributions of water vapor that express convection cells are needed to improve numerical forecasts of heavy rainfall . In this study, temporal variations of refractivity (TVR ), which include information of small scale water vapor variations, were obtained from the phase data of radio waves of the JMA’s operational Doppler radar . The TVR distributions on August 4th 2008 showed that the increased and decreased regions of TVR moved smoothly, corresponding to the movements of sea breeze fronts. These smooth variations indicated that the observed TVR were caused by the atmosphere. The TVR obtained by the operational Doppler radar was assimilated by a nested Local Ensemble Transform Kalman Filter system. The reproduced distributions indicated that the data assimilation of TVR made the rainfall distributions closer to the observed ones by modifying water vapor distributions. This result shows that TVR has the potential to improve rainfall forecasts.
This is a preview of subscription content, log in via an institution to check access.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
References
Dowell DC, Zhang F, Wicker LJ, Snyder C, Crook NA (2004) Wind and temperature retrievals in the 17 May 1981 Arcadia, Oklahoma, Supercell: ensemble Kalman filter experiments. Mon Weather Rev 132:1982–2005
Fabry F, Frush C, Zawadzki I, Kilambi A (1997) On the extraction of near-surface index of fraction using radar phase measurements from ground targets. J Atmos Oceanic Technol 14:978–987
Feng Y, Fabry F, Weckwerth T (2016) Improving radar refractivity by considering the change of vertical refractivity profile and the varying altitudes of ground targets. J Atmos Oceanic Technol doi:10.1175/JRECH-D-15-0224.1, in press
Hunt BR, Kostelich EJ, Szunyogh I (2007) Efficient data assimilation for spatiotemporal chaos: a local ensemble transform Kalman filter. Physica D 230:112–126
Meng Z, Zhang F (2008) Test of an ensemble Kalman filter for mesoscale and regional-scale data assimilation. Part III: comparison with 3DVar in a real-data case study. Mon Weather Rev 136:522–540
Miyoshi T, Aranami K (2006) Applying a four-dimensional local ensemble transform Kalman filter (4D-LETKF) to the JMA nonhydrostatic model (NHM). SOLA 2:128–131
Miyoshi T, Kunii M (2012) The local ensemble transform Kalman filter with the weather research and forecasting model: experiments with real observations. Pure appl Geophys 169:321–333
Nicol JC, Illingworth AJ (2012) The effect pf phase-corrected returns and spatial smoothing on the accuracy of radar refractivity retrievals. J Atmos Ocean Technol 30:22–39
Nicol JC, Illingworth AJ, Darlington T, Kitchen M (2013) Quantifying errors due to frequency changes and target location uncertainty for radar refractivity retrievals. J Atmos Ocean Technol 30:2006–2024
Park S, Fabry F (2010) Simulation and interpretation of the phase data used by the Radar refractivity retrieval algorithm. J Atmos Ocean Technol 27:1287–1301
Saito K, Fujita T, Yamada Y, Ishida J, Kumagai Y, Aranami K, Ohmori S, Nagasawa R, Kumagai S, Muroi C, Kato T, Eito H, Yamazaki Y (2006) The operational JMA nonhydrostatic mesoscale model. Mon Weather Rev 134:1266–1298
Seko H, Miyoshi T, Shoji Y, Saito K (2011) Data assimilation experiments of precipitable water vapor using the LETKF system: intense rainfall event over Japan 28 July 2008. Tellus 63A:402–412
Seko H, Yamauchi H, Suzuki O, Saito K (2009) Estimation of temporal variation of refractive index using c-band doppler radar equipped with magnetron transmitter. SOLA 5:145–148
Seko H, Tsuyuki T, Saito K, Miyoshi T (2013) Development of a two-way nested LETKF system for cloud-resolving model. In: Park SK, Xu L (eds) Data assimilation for atmospheric, oceanic and hydrologic applications, vol 2. Springer, pp 489–507
Snyder C, Zhang F (2003) Assimilation of simulated Doppler radar observations with an ensemble Kalman filter. Mon Weather Rev 131:1663–1677
Tong M, Xue M (2005) Ensemble Kalman filter assimilation of Doppler radar data with a compressible nonhydrostatic model: OSSE experiments. Mon Weather Rev 133:1789–1807
Weckwerth TM, Pettet CR, Fabry F, Park SJ, LeMone MA, Wilson JW (2005) Radar refractivity retrieval: validation and application to short-term forecasting. J Appl Meteorol 44:285–300
Xue M, Tong M, Droegemeier KK (2006) An OSSE framework based on the ensemble square root Kalman filter for evaluating the impact of data from radar networks on thunderstorm analysis and forecasting. J Atmos Ocean Technol 23:46–66
Zhang F, Snyder C, Sun J (2004) Impacts of initial estimate and observation availability with an ensemble Kalman filter. Mon Weather Rev 132:1238–1253
Zhang F, Meng Z, Aksoy A (2006) Test of an ensemble Kalman filter for mesoscale and regional scale data assimilation. Part I: perfect model experiments. Mon Weather Rev 134:722–736
Acknowledgements
The authors would like to thank Mr. Osamu Suzuki, Mr. Yoshihisa Kimata and Mr. Takanori Sakanashi of JMA for many important suggestions on this study. The initial seeds and boundary conditions for Outer LETKF and the conventional observation data, the phase data of Tokyo operational radar were provided from the JMA. This work was supported in part by the research projects of “Tokyo Metropolitan Area Convection Study for Extreme Weather Resilient Cities (TOMACS)” and Grants-in-Aid for Scientific Research “Establishment of extraction methods of water vapor information from phase data of Doppler radar ”.
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2017 Springer International Publishing Switzerland
About this chapter
Cite this chapter
Seko, H., Sato, Ei., Yamauchi, H., Tsuda, T. (2017). Data Assimilation Experiments of Refractivity Observed by JMA Operational Radar. In: Park, S., Xu, L. (eds) Data Assimilation for Atmospheric, Oceanic and Hydrologic Applications (Vol. III). Springer, Cham. https://doi.org/10.1007/978-3-319-43415-5_14
Download citation
DOI: https://doi.org/10.1007/978-3-319-43415-5_14
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-319-43414-8
Online ISBN: 978-3-319-43415-5
eBook Packages: Earth and Environmental ScienceEarth and Environmental Science (R0)