Zobrazeno 1 - 10
of 5 899
pro vyhledávání: '"T Miyoshi"'
Publikováno v:
Nonlinear Processes in Geophysics, Vol 30, Pp 457-479 (2023)
This study explores coupled land–atmosphere data assimilation (DA) for improving weather and hydrological forecasts by assimilating soil moisture (SM) data. This study integrates a land DA component into a global atmospheric DA system of the Nonhyd
Externí odkaz:
https://doaj.org/article/000a374ef3f44fd1ae146ff58c0c9607
Publikováno v:
Nonlinear Processes in Geophysics, Vol 30, Pp 117-128 (2023)
The control simulation experiment (CSE) is a recently developed approach to investigate the controllability of dynamical systems, extending the well-known observing system simulation experiment (OSSE) in meteorology. For effective control of chaotic
Externí odkaz:
https://doaj.org/article/285783ecf99248188c627ca1a12bcbd6
Publikováno v:
Nonlinear Processes in Geophysics, Vol 30, Pp 13-29 (2023)
The success of ensemble data assimilation systems substantially depends on localization, which is required to mitigate sampling errors caused by modeling background error covariances with undersized ensembles. However, finding an optimal localization
Externí odkaz:
https://doaj.org/article/ea3faef11e59401f8d605a28832a0b2e
Publikováno v:
Geoscientific Model Development, Vol 15, Pp 9057-9073 (2022)
A previous study proposed an adaptive observation error inflation (AOEI) method for an ensemble Kalman filter (EnKF)-based atmospheric data assimilation system to assimilate all-sky infrared brightness temperatures. Brightness temperature differences
Externí odkaz:
https://doaj.org/article/eb898843f74f4951a83fcd69821d1ab4
Publikováno v:
Geoscientific Model Development, Vol 15, Pp 8325-8348 (2022)
A particle filter (PF) is an ensemble data assimilation method that does not assume Gaussian error distributions. Recent studies proposed local PFs (LPFs), which use localization, as in the ensemble Kalman filter, to apply the PF efficiently for high
Externí odkaz:
https://doaj.org/article/2aa3cfe67930463a8f507a99809458fe
Publikováno v:
Geoscientific Model Development, Vol 15, Pp 8395-8410 (2022)
This study develops an ensemble Kalman filter (EnKF)-based regional ocean data assimilation system in which the local ensemble transform Kalman filter (LETKF) is implemented with version 1.0 of the Stony Brook Parallel Ocean Model (sbPOM) to assimila
Externí odkaz:
https://doaj.org/article/cb3ecaef3b8e4197812ad855995d8d8c
Autor:
T. Miyoshi, Q. Sun
Publikováno v:
Nonlinear Processes in Geophysics, Vol 29, Pp 133-139 (2022)
In numerical weather prediction (NWP), sensitivity to initial conditions brings chaotic behaviors and an intrinsic limit to predictability, but it also implies an effective control in which a small control signal grows rapidly to make a substantial d
Externí odkaz:
https://doaj.org/article/2285a943fa2b419bb500ac35f6745ae7
Publikováno v:
Journal of Advances in Modeling Earth Systems, Vol 15, Iss 2, Pp n/a-n/a (2023)
Abstract Systematic biases in numerical weather prediction models cause forecast deviation from reality. While model biases also affect data assimilation and degrade the analysis accuracy, observation information incorporated through data assimilatio
Externí odkaz:
https://doaj.org/article/09428aceb2ce4011872a84bb3d05e34e
Publikováno v:
Nonlinear Processes in Geophysics, Vol 28, Pp 615-626 (2021)
Non-Gaussian forecast error is a challenge for ensemble-based data assimilation (DA), particularly for more nonlinear convective dynamics. In this study, we investigate the degree of the non-Gaussianity of forecast error distributions at 1 km resolut
Externí odkaz:
https://doaj.org/article/607dcfde06584a41a5108259a6ae85da
Autor:
R. Fittipaldi, R. Hartmann, M. T. Mercaldo, S. Komori, A. Bjørlig, W. Kyung, Y. Yasui, T. Miyoshi, L. A. B. Olde Olthof, C. M. Palomares Garcia, V. Granata, I. Keren, W. Higemoto, A. Suter, T. Prokscha, A. Romano, C. Noce, C. Kim, Y. Maeno, E. Scheer, B. Kalisky, J. W. A. Robinson, M. Cuoco, Z. Salman, A. Vecchione, A. Di Bernardo
Publikováno v:
Nature Communications, Vol 12, Iss 1, Pp 1-9 (2021)
Strontium Ruthenate, Sr2RuO4, displays a remarkable number of intriguing physical phenomena, from superconductivity, to strain-induced ferromagnetism. Here, using low-energy muon spectroscopy, Fittipaldi et al. demonstrate the existence of unconventi
Externí odkaz:
https://doaj.org/article/4677350337054bb89953dc11716bc636