Takemasa Miyoshi

Learn More
The purpose of the present study is to explore the feasibility of estimating and correcting systematic model errors using a simple and efficient procedure, inspired by papers by Leith as well as DelSole and Hou, that could be applied operationally, and to compare the impact of correcting the model integration with statistical corrections performed a(More)
In Ensemble Kalman Filter data assimilation, localization modifies the error covariance matrices to suppress the influence of distant observations, removing spurious long distance correlations. In addition to allowing efficient parallel implementation, this takes advantage of the atmosphere's lower dimensionality in local regions. There are two primary(More)
This study addresses the issue of model errors with the ensemble Kalman filter. Observations generated from the NCEP/NCAR reanalysis fields are assimilated into the SPEEDY model. Absent an effort to account for model errors, the performance of the Local Ensemble Transform Kalman Filter (LETKF) is seriously degraded when compared to the perfect model(More)
Shu-Chih Yang, Matteo Corazza, Alberto Carrassi, Eugenia Kalnay, and Takemasa Miyoshi 1 Earth System Science Interdisciplinary Center/Department of Atmospheric and Oceanic Science, University of Maryland, College Park, Maryland, 20742 2 Global Modeling Assimilation Office, NASA/ Goddard Space Flight Center, Greenbelt, Maryland, 20770 ARPAL – CFMI-PC, V.le(More)
Weather forecast and earth system models usually have a number of parameters, which are often optimized manually by trial and error. Several studies have proposed objective methods to estimate model parameters using data assimilation techniques. This paper provides a review of the previous studies and illustrates the application of ensemble-based data(More)
Powerful computers and advanced sensors enable precise simulations of the atmospheric state, requiring data assimilation to connect simulations to real-world sensor data using statistical mathematics and dynamical systems theory. Numerical weather prediction (NWP) thus enables simulations that more closely represent the real world. The authors explore the(More)
Past attempts to assimilate precipitation by nudging or variational methods have succeeded 1 in forcing the model precipitation to be close to the observed values. However, the model 2 forecasts tend to lose their additional skill after few forecast hours. In this study, a local 3 ensemble transform Kalman filter (LETKF) is used to effectively assimilate(More)
1College of Computer and Information Science, Southwest University of China, Chongqing, China 2State Key Laboratory for Novel Software Technology, Nanjing University, Jiangsu, China 3RIKENAdvanced Institute for Computational Science, Kobe, Japan 4Department of Electrical Engineering and Computer Science, Northwestern University, Evanston, IL, USA(More)