General References

  1. Nishizawa, S., H. Yashiro, Y. Sato, Y. Miyamoto, and H. Tomita, 2015, Influence of grid aspect ratio on planetary boundary layer turbulence in large-eddy simulations. Geosci. Model Dev., 8, 3393-3419, doi:10.5194/gmd-8-3393-2015.

  2. Sato, Y., S. Nishizawa, H. Yashiro, Y. Miyamoto, Y. Kajikawa, and H. Tomita, 2015, Impacts of cloud microphysics on trade wind cumulus: which cloud microphysics processes contribute to the diversity in a large eddy simulation?. Prog. Earth Planet. Sci., 2, 23, doi:10.1186/s40645-015-0053-6.

Publications

2023

  1. Sato, Y., M. Kajino, S. Hayashi, and R. Wada, 2023, A numerical study of lightning-induced NOx and formation of NOy observed at the summit of Mt. Fuji using an explicit bulk lightning and photochemistry model. Atmos. Env.: X, 18, 100218, doi:10.1016/j.aeaoa.2023.100218 .

  2. Kawai, Y. and H. Tomita, 2023, Numerical accuracy necessary for large-eddy simulation of planetary boundary layer turbulence using discontinuous Galerkin gethod. Mon. Wea. Rev., EOR, doi:10.1175/MWR-D-22-0245.1 .

  3. Taylor, J., T. Honda, A. Amemiya, S. Otsuka, Y. Maejima, and T. Miyoshi, 2023, Sensitivity to localization radii for an ensemble filter numberical weather prediction system with 30-second update. Wea. Forecasting, 38, 611-632, doi:10.1175/WAF-D-21-0177.1 .

2022

  1. Honda, T., A. Amemiya, S. Otsuka, G.-Y. Lien, J. Taylor, Y. Maejima, S. Nishizawa, T. Yamaura, K. Sueki, H. Tomita, S. Satoh, Y. Ishikawa, and T. Miyoshi, 2022, Development of the real-time 30-s-update big data assimilation system for convective rainfall prediction with a phased array weather rader: description and preliminary evaluation. J. Adv. Model. Earth Syst., 14, e2021MS002823, doi:10.1029/2021MS002823 .

  2. Honda, T., A. Amemiya, S. Otsuka, J. Taylor, Y. Maejima, S. Nishizawa, T. Yamaura, K. Sueki, H. Tomita, and T. Miyoshi, 2022, Advantage of 30-s-updating numerical weather prediction with a phased-array weather radar over operationl nowcast for a convective precipitation system. Geophys. Res. Lett., 49, e2021GL096927, doi:10.1029/2021GL096927 .

  3. Maejima, Y., T. Kawabata, H. Seko, and T. Miyoshi, 2022, Observing system simulation experiments of a rich phased array weather radar network covering Kyushu for the July 2020 heavy rainfall event. SOLA, 18, 25-32, doi:10.2151/sola.2022-005 .

  4. Sueki, K., S. Nishizawa, T. Yamaura, and H. Tomita, 2022, Precision and convergence speed of the ensemble Kalman filter-based parameer estimation: stting parameter uncertainty for reliable and efficient estimation. Prog. Earth Planet. Sci., 9, 47, doi:10.1186/s40645-022-00504-4 .

  5. Yanase, T., S. Nishizawa, H. Miura, and H. Tomita, 2022, Characteristic form and distance in high-level hierarchical structure of self-aggregeated clouds in radiative-convective equilibrium. Geophys. Res. Lett., 49, e2022GL100000, doi:10.1029/2022GL100000 .

  6. Yanase, T., S. Nishizawa, H. Miura, T. Takemi, and H. Tomita, 2022, Low-level circulation and its coupling with free-tropospheric variability as a mechanisim of spontaneous aggregation of moist convection. J. Atmos. Sci., 79, 3429-3451, doi:10.1175/JAS-D-21-0313.1 .

2021

  1. Honda, T. and T. Miyoshi, 2021, Predictability of the July 2018 heavy rain event in Japan associated with typhoon Prapiroon and southern convective disturbances. SOLA, 17, 113-119, doi:10.2151/sola.2021-018 .

  2. Honda, T., Y. Sato, and T. Miyoshi, 2021, Potential impacts on lightning flash observations on numerical weather prediction with explicit lightning processes. J. Geophys. Res. Atmos., 126, e2021JD034611, doi:10.1029/2021JD034611 .

  3. Kawai, Y. and H. Tomita, 2021, Numerical accuracy of advection scheme necessary for large-eddy simulation of planetary boundary layer turbulence. Mon. Wea. Rev., 149, 2993-3012, doi:10.1175/MWR-D-20-0362.1 .

  4. Kondo, M., Y. Sato, M. Inatsu, and Y. Katsuyama, 2021, Evaluation of cloud microphysical schemes for winter snowfall events in Hokkaido: A case study of snowfall by winter monsoon. SOLA, 17, 74-80, doi:10.2151/sola.2021-012 .

  5. Miyamoto, Y., 2021, Effects of number of concentrationof cloud condensation nuclei on moist convection formation. J. Atmos. Sci., 78, 3401-3413, doi:10.1175/JAS-D-21-0058.1 .

  6. Nakata, M. and Y. Sato, 2021, Effects of mountains on aerosols determined by AERONET/DRAGON/J-ALPS measurements and regional model simulations. Earth and Space Science, 8, e2021EA001972, doi:10.1029/2021EA001972 .

  7. Nishizawa, S., T. Yamaura, and Y. Kajikawa, 2021, Influence of submesoscale topography on daytime precipitation associated with thermally driven local circulations over a mountainous region. J. Atmos. Sci., 78, 2511-2532, doi:10.1175/JAS-D-20-0332.1 .

  8. Ota, Y., M. Sekiguchi, and Y. Sato, 2021, Spatial-scale characteristics of a three-dimensional cloud-resolving solar radiation budget based on Monte Carlo radiative transfer simulation. SOLA, 17, 228-233, doi:10.2151/sola.2021-040 .

  9. Ruiz, J., G.-Y. Lien, K. Kondo, S. Otsuka, and T. Miyoshi, 2021, Reduced non-Gaussianity by 30 s rapid update in convective-scale numerical weather prediction. Nonlin. Processes Geophys., 28, 615-626, doi:10.5194/npg-28-615-2021 .

  10. Sato, Y., S. Hayashi, and A. Hashimoto, 2021, Difference in the lightning frequency between the July 2018 heavy rainfall event over central Japan and the 2017 northern Kyushu heavy rain fall event in Japan. Atmos. Sci. Lett., 23, e1067, doi:10.1002/asl.1067 .

  11. Sato, Y., Y. Miyamoto, and H. Tomita, 2021, Lightning frequency in an idealized hurricane-like vortex from initial to steady-state using a coupled meteorological and explicit bulk lightning model. Mon. Wea. Rev., 149, 753-771, doi:10.1175/MWR-D-20-0110.1 .

  12. Taylor, J., A. Amemiya, T. Honda, Y. Maejima, and T. Miyoshi, 2021, Predictability of the July 2020 heavy rainfall with the SCALE-LETKF. SOLA, 17, 48-56, doi:10.2151/sola.2021-008 .

  13. Taylor, J., A. Okazaki, T. Honda, S. Kotsuki, M. Yamaji, T. Kubota, R. Oki, T. Iguchi, and. T. Miyoshi, 2021, Oversampling reflectivity observations from a geostationary precipitation radar satellite: Impact on typhoon forecasts within a perfect model OSSE framework. J. Adv. Model. Earth Syst., 13, e2020MS002332, doi:10.1029/2020MS002332 .

  14. Yamazaki, K. and H. Miura, 2021, On the formation mechanism of cirrus banding: Radiosonde observations, numerical simulations, and stability analyses. J. Atmos. Sci., 78, 3477-3502, doi:10.1175/JAS-D-20-0356.1 .

2020

  1. Amemiya, A., T. Honda, and T. Miyoshi, 2020, Improving the Observation Operator for the phased array weather rader in the SCALE-LETKF system. SOLA, 16, 6-11, doi:10.2151/sola.2020-002 .

  2. Inatsu, M., S. Tanji, and Y. Sato, 2020, Toward predicting expressway closures due to blowing snow events. Cold Regions Sci. Tech., 177, 103123, doi:10.1016/j.coldregions.2020.103123 .

  3. Miyamoto, Y., Y. Sato, S. Nishizawa, H. Yashiro, T. Seiki, and A. T. Noda, 2020, An energy balance model for low-level clouds based on a simulation resolving mesoscale motions. J. Met. Soc. Japan, 98, 987-1004, doi:10.2151/jmsj.2020-051 .

  4. Necher, T., S. Geiss, M. Weissmann, J. Ruiz, T. Miyoshi, and G.-Y. Lien, 2020, A convective-scale 1,000-member ensemble simulation and potential applications. Q. J. R. Meteorol. Soc., 1-20, doi:10.1002/qj.3744 .

  5. Necker, T., M. Weissmann, Y. Ruckstuhl, J. Anderson, T. Miyoshi, 2020, Sampling error correction evaluated using a convective-scale 1000-member ensemble. Mon. Wea. Rev., 148, 1229-1249, doi:10.1175/MWR-D-19-0154.1 .

  6. Shima, S., Y. Sato, A. Hashimoto, and R. Misumi, 2020, Predicting the morphology of ice particles in deep convection using the super-droplet method: development and evaluation of SCALE-SDM 0.2.5-2.2.0, -2.2.1, and -2.2.2. Geosci. Model Dev., 13, 4107-4157, doi:10.5194/gmd-13-4107-2020 .

  7. Sato, Y., T. T. Sekiyama, S. Fang, M. Kajino, A. Quérel, D. Quélo, H. Kondo, H. Terada, M. Kadowaki, M. Takigawa, Y. Morino, J. Uchida, D. Goto, and H. Yamazawa, 2020, A model intercomparison of atmospheric 137Cs concentrations from the Fukushima Daiichi Nuclear Power Plant accident, phase III: Simulation with an identical source term and meteorological field at 1-km resolution. Atmos. Environ., 7, 100086, doi:10.1016/j.aeaoa.2020.100086 .

  8. Wing, A. A., Stauffer, C. L., Becker, T., Reed, K. A., Ahn, M.‐S., Arnold, N. P., et al., 2020, Clouds and convective self‐aggregation in a multimodel ensemble of radiative‐convective equilibrium simulations. J. Adv. Model. Earth Syst., 12, e2020MS002138, doi:10.1029/2020MS002138 .

  9. Yanase, T., S. Nishizawa, H. Miura, T. Takemi, and H. Tomita, 2020, New critcal length for the onset of self-aggregation of moist convection. Geophys. Res. Lett., 47, e2020GL088763, doi:10.1029/2020GL088763 .

2019

  1. Adachi, S. A., S. Nishizawa, K. Ando, T. Yamaura, R. Yoshida, H. Yashiro, Y. Kajikawa, and H. Tomita, 2019, An evaluation method for uncertainties in regional climate projections. Atmos. Sci. Lett., 20, e877, doi:10.1002/asl.877 .

  2. Honda, T., S. Takino, and T. Miyoshi, 2019, Improving a precipitation forecast by assimilating all-sky Himawari-8 satellite radiances: A case of Typhoon Malakas (2016). SOLA, 15, 7-11, doi:10.2151/sola.2019-002 .

  3. Masuda, R., H. Iwabuchi, K. S. Schmidt, A. Damiani, and R. Kudo, 2019, Retrieval of cloud optical thickness from sky-view camera images using a deep convolutional neural network based on three-dimensional radiative transfer. Remote Sens., 11(17), 1962, doi:10.3390/rs11171962 .

  4. Okazaki, A., T. Honda, S. Kotsuki, M. Yamaji, T. Kubota, R. Oki, T. Iguchi, and T. Miyoshi, 2019, Simulating precipitation radar observations from a geostationaly satellite. Atmos. Meas. Tech., 12, 3985-3996, doi:10.5194/amt-12-3985-2019 .

  5. Sato, Y., Y. Miyamoto, and H. Tomita, 2019, Large dependency of charge distribution in a tropical cyclone inner core upon aerosol number concentration. Prog. in Earth and Planet. Sci., 6(62), doi:10.1186/s40645-019-0309-7 .

  6. Sueki, K., T. Yamaura, H. Yashiro, S. Nishizawa, R. Yoshida, Y. Kajikawa, and H. Tomita, 2019, Convergence of convective updraft emsembles with respect to the grid spacing of atmospheric models. Geophys. Res. Lett., 46, 14817-14825, doi:10.1029/2019GL084491 .

  7. Tanji, S., and M. Inatsu, 2019, Case Study of Drifting Snow Potential Diagnosis with Dynamical Downscaling. SOLA, 15, 32-36, doi:10.2151/sola.2019-007 .

  8. Yoshida, R., S. Nishizawa, H. Yashiro, S. A. Adachi, T. Yamaura, H. Tomita, and Y. Kajikawa, 2019, Maintenance condition of back-building squall-line in a numerical simulation of a heavy rainfall event in July 2010 in Western Japan. Atmos. Sci. Lett., 20, e880, doi:10.1002/asl.880 .

2018

  1. Honda, T., T. Miyoshi, G.-Y. Lien, S. Nishizawa, R. Yoshida, S. A. Adachi, K. Terasaki, K. Okamoto, H. Tomita and K. Bessho, 2018, Assimilating All-Sky Himawari-8 Satellite Infrared Radiances: A Case of Typhoon Soudelor (2015). Mon. Weather Rev., 146, 213-229, doi:10.1175/MWR-D-16-0357.1 .

  2. Honda, T., S. Kotsuki, G.-Y. Lien, Y. Maejima, K. Okamoto and T. Miyoshi, 2018, Assimilation of Himawari-8 All-Sky Radiances Every 10 Minutes: Impact on Precipitation and Flood Risk Prediction. J. Geophys. Res.-Atmos., 123, 965-976, doi:10.1002/2017JD027096 .

  3. Nishizawa, S., S. A. Adachi, Y. Kajikawa, T. Yamaura, K. Ando, R. Yoshida, H. Yashiro, and H. Tomita, 2018, Decomposition of the large-scale atmospheric state driving downscaling: a perspective on dynamical downscaling for regional climate study. Prog. Earth Planet. Sci., 5, 2, doi:10.1186/s40645-017-0159-0 .

  4. Nishizawa, S. and Y. Kitamura, 2018, A surface flux scheme based on the Monin-Obukhov similarity for finite volume models. J. Adv. Model. Earth Syst., 10, 3159-3175, doi:10.1029/2018MS001534 .

  5. Sato, Y., S. Shima, and H. Tomita, 2018, Numerical Convergence of Shallow Convection Cloud Field Simulations: Comparison between Double-moment Eulerian and Particle-based Lagrangian Microphysics Coupled to the Same Dynamical Core. J. Adv. Model. Earth Syst., 10, 1495-1512, doi:10.1029/2018MS001285 .

  6. Sato, Y., M. Takigawa, T. T. Sekiyama, M. Kajino, H. Terada, H. Nagai, H. Kondo, J. Uchida, D. Goto, D. Quélo, A. Mathieu, A. Quérel, S. Fang, Y. Morino, P. von Schoenberg, H. Grahn, N. Brännström, S. Hirao, H. Tsuruta, H. Yamazawa, and T. Nakajim, 2018, Model Intercomparison of Atmospheric 137Cs from the Fukushima Daiichi Nuclear Power Plant Accident: Simulations Based on Identical Input Data. J. Geophys. Res. Atmos., 123, 11748-11765, doi:10.1029/2018JD029144 .

2017

  1. Adachi, S. A., S. Nishizawa, R. Yoshida, T. Yamaura, K. Ando, H. Yashiro, Y. Kajikawa, and H. Tomita, 2017, Contributions of changes in climatology and perturbation and the resulting nonlinearity to regional climate change. Nat. Commun., 8, 2224, doi:10.1038/s41467-017-02360-z .

  2. Iwabuchi, H., and R. Okamura, 2017, Multispectral Monte Carlo radiative transfer simulation by the maximum cross-section method. J. Quant. Spectrosc. Radiat. Transfer, 193, 40-46, doi:10.1016/j.jqsrt.2017.01.025 .

  3. Liao, J., B. Gerofi, G. Yuan Lien, T. Miyoshi, S. Nishizawa, H. Tomita, W. Keng Liao, A. Choudhary, and Y. Ishikawa, 2017, A flexible I/O arbitration framework for netCDF-based big data processing workflows on high-end supercomputers. Concurrency Computat. Pract. Exper., 29, e4161, doi:10.1002/cpe.4161 .

  4. Okamura, R., H. Iwabuchi, and K. Sebastian Schmidt, 2017, Feasibility study of multi-pixel retrieval of optical thickness and droplet effective radius of inhomogeneous clouds using deep learning. Atmos. Meas. Tech., 10, 4747-4759, doi:10.5194/amt-10-4747-2017 .

  5. Sato, Y., S. Shima, and H. Tomita, 2017, A grid refinement study of trade wind cumuli simulated by a Lagrangian cloud microphysical model: the super-droplet method. Atmos. Sci. Lett, 18, 359-365, doi:10.1002/asl.764 .

  6. Tokutake, H., H. Hiraguri, and S. Nishizawa, 2017, Exploration of Wind Structure on Mars using an Airplane and Flight Feasibility Study. Trans. Jpn. Soc. Aeronaut. Space Sci., 60, 212-220, doi:10.2322/tjsass.60.212 .

  7. Yoshida, R., S. Nishizawa, H. Yashiro, S. A. Adachi, Y. Sato, T. Yamaura, and H. Tomita, 2017, CONeP: A Cost-Effective Online Nesting Procedure for Regional Atmospheric Models. Parallel Comput., 65, 21-31, doi:10.1016/j.parco.2017.04.004 .

2016

  1. Miyoshi, T., G. Yuan Lien, S. Satoh, T. Ushio, K. Bessho, H. Tomita, S. Nishizawa, R. Yoshida, S. A. Adachi, J. Liao, B. Gerofi, Y. Ishikawa, M. Kunii, J. Ruiz, Y. Maejima, S. Otsuka, M. Otsuka, K. Okamoto, and H. Seko, 2016, “Big Data Assimilation” Toward Post-Petascale Severe Weather Prediction: An Overview and Progress. Proceedings of the IEEE, 104, 2155-2179, doi:10.1109/JPROC.2016.2602560 .

  2. Nishizawa, S., M. Odaka, Y. O. Takahashi, K. Sugiyama, K. Nakajima, M. Ishiwatari, S. Takehiro, H. Yashiro, Y. Sato, H. Tomita, and Y.-Y. Hayashi, 2016, Martian dust devil statistics from high-resolution large-eddy simulations. Geophys. Res. Lett., 43, 41804188, doi:10.1002/2016GL068896 .

2015

  1. Nishizawa, S., H. Yashiro, Y. Sato, Y. Miyamoto, and H. Tomita, 2015, Influence of grid aspect ratio on planetary boundary layer turbulence in large-eddy simulations. Geosci. Model Dev., 8, 3393-3419, doi:10.5194/gmd-8-3393-2015 .

  2. Sato, Y., S. Nishizawa, H. Yashiro, Y. Miyamoto, Y. Kajikawa,and H. Tomita, 2015, Impacts of cloud microphysics on trade wind cumulus: which cloud microphysics processes contribute to the diversity in a large eddy simulation?. Prog. Earth Planet. Sci., 2, 23, doi:10.1186/s40645-015-0053-6 .

  3. Sato, Y., Y.Miyamoto, S. Nishizawa, H. Yashiro, Y. Kajikawa, R. Yoshida, T. Yamaura, and H. Tomita, 2015, Horizontal Distance of Each Cumulus and Cloud Broadening Distance Determine Cloud Cover. SOLA, 11, 75-79, doi:10.2151/sola.2015-019 .

2014

  1. Sato. Y, S. Nishizawa, H. Yashiro, Y. Miyamoto, and H. Tomita, 2014, Potential of Retrieving Shallow-Cloud Life Cycle from Future Generation Satellite Observations through Cloud Evolution Diagrams: A Suggestion from a Large Eddy Simulation. SOLA, 10, 10-14, doi:10.2151/sola.2014-003 .

2013

  1. Abe, T., T. Maeda, and M. Sato, 2013, Model checking stencil computations written in a partitioned global address space language. IEEE International Symposium on Parallel & Distributed Processing, Workshop an dPhd Forum, Cambridge, MA, 365-374, doi:10.1109/IPDPSW.2013.90 .