Takumi Matsunobu/Publication

Created Mon, 28 Feb 2022 13:38:14 +0100 Modified Thu, 06 Feb 2025 16:04:12 +0100
469 Words

In preparation/Submitted

  • Matsunobu, T., Keil, C. and Craig, C. G. (2025) A Variance Budget to estimate the Growth and Interaction of Uncertainties in Convective-scale Forecasts. Submitted.

Peer-reviewed articles

  • Tintó Prims, O., Redl, R., Rautenhaus, M., Selz, T., Matsunobu, T., Modali, K. R., and Craig, G. (2024) The effect of lossy compression of numerical weather prediction data on data analysis: a case study using enstools-compression 2023.11, Geosci. Model Dev., 17, 8909–8925, https://doi.org/10.5194/gmd-17-8909-2024.

  • Matsunobu, T., Puh, M. and Keil, C. (2024) Flow- and scale-dependent spatial predictability of convective precipitation combining different model uncertainty representations, Quart. J. Roy. Meteo. Soc., 150(761), 2364‒2381. https://doi.org/10.1002/qj.4713.

  • Matsunobu, T., Quinting, J. F., Grams, C. M., and Matsueda, M. (2023) Regional extreme precipitation events in wintertime Japan facilitated by East-Asian large-scale flow patterns, SOLA, 19, 253–260. https://doi.org/10.2151/sola.2023-033.

  • Matsunobu, T., Keil, C., and Barthlott, C. (2022) The impact of microphysical uncertainty conditional on initial and boundary condition uncertainty under varying synoptic control, Weather Clim. Dynam., 3(4), 1273–1289. https://doi.org/10.5194/wcd-3-1273-2022.

  • Barthlott, C., Zarboo, A., Matsunobu, T., and Keil, C. (2022) Impacts of combined microphysical and land-surface uncertainties on convective clouds and precipitation in different weather regimes. Atmos. Chem. Phys., 22(16), 10841–10860. https://doi.org/10.5194/acp-22-10841-2022.

  • Magnusson, L., Ackerley, D., Bouteloup, Y., Chen, J.-H., Doyle, J., Earnshaw, P., Kwon, Y. C., Köhler, M., Lang, S. T. K., Lim, Y.-J., Matsueda, M., Matsunobu, T., McTaggart-Cowan, R., Reinecke, A., Yamaguchi, M., and Zhou, L. (2022). Skill of Medium-Range Forecast Models Using the Same Initial Conditions. Bull. Amer. Meteor. Soc., 103(9), E2050-E2068. https://doi.org/10.1175/BAMS-D-21-0234.1.

  • Barthlott, C., Zarboo, A., Matsunobu, T., and Keil, C. (2022) Importance of aerosols and shape of the cloud droplet size distribution for convective clouds and precipitation. Atmos. Chem. Phys., 22(3), 2153–2172. https://doi.org/10.5194/acp-22-2153-2022.

  • Saito, K., Matsunobu, T., and Oizumi, T. (2022) Effect of Upper-Air Moistening by Northward Ageostrophic Winds Associated with a Tropical Cyclone on the PRE Enhancement. SOLA, 18, 81-87. https://doi.org/10.2151/sola.2022-014.

  • Saito, K., and Matsunobu, T. (2020) Northward Ageostrophic Winds Associated with a Tropical Cyclone. Part 2: Moisture Transport and Its Impact on PRE, SOLA, 16, 198-205. https://doi.org/10.2151/sola.2020-034.

  • Matsunobu, T., and Tanaka, H. L. (2019) Time series analysis of normal mode energetics for Rossby wave breaking and saturation using a simple barotropic model. Atmo. Sci. Lett., 20(11), e940. https://doi.org/10.1002/ASL.940.

  • Matsunobu, T., and Matsueda, M. (2019) Assessing the Predictability of Heavy Rainfall Events in Japan in Early July 2018 on Medium-Range Timescales. SOLA, 15A, 19–24. https://doi.org/10.2151/sola.15A-004.

Other publications

  • Matsunobu, T., and Matsueda, M. (2022) Medium-range forecast skill of various operational NWP models for the July 2018 severe rainfall event in Japan. In: Meteorological Research Note Vol. 246 (ed. H. Endo, H. Nakamura, Y. Tachibana), 107–116, Meteorological society of Japan (in Japanese).

  • Saito, K., Duc, L., Matsunobu, T., and Kurihana, T. (2022) Perturbations by the Ensemble Transform. In: Data Assimilation for Atmospheric, Oceanic and hydrologic Appplications (Vol. IV) (ed. S. K. Park, L. Xu), 115–141, Springer.