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Onodera, Naoyuki; Idomura, Yasuhiro; Hasegawa, Yuta; Asahi, Yuichi; Inagaki, Atsushi*; Shimose, Kenichi*; Hirano, Kohin*
Keisan Kogaku Koenkai Rombunshu (CD-ROM), 28, 4 Pages, 2023/05
We have developed a multi-scale wind simulation code named CityLBM that can resolve entire cities to detailed streets. CityLBM enables a real time ensemble simulation for several km square area by applying the locally mesh-refined lattice Boltzmann method on GPU supercomputers. On the other hand, real-world wind simulations contain complex boundary conditions that cannot be modeled, so data assimilation techniques are needed to reflect observed data in the simulation. This study proposes an optimization method for ground surface temperature bias based on an ensemble Kalman filter to reproduce wind conditions within urban city blocks. As a verification of CityLBM, an Observing System Simulation Experiment (OSSE) is conducted for the central Tokyo area to estimate boundary conditions from observed near-surface temperature values.
Hasegawa, Yuta; Onodera, Naoyuki; Asahi, Yuichi; Idomura, Yasuhiro
Keisan Kogaku Koenkai Rombunshu (CD-ROM), 27, 4 Pages, 2022/06
We developed GPU implementation of ensemble data assimilation (DA) using the local ensemble transform Kalman filter (LETKF) with the lattice Boltzmann method (LBM). The performance test was carried out upto 32 ensembles of two-dimensional isotropic turbulence simulations using the D2Q9 LBM. The computational cost of the LETKF was less than or nearly equal to that of the LBM upto eight ensembles, while the former exceeded the latter at larger ensembles. At 32 ensembles, their computational costs per cycle were respectively 28.3 msec and 5.39 msec. These results suggested that further speedup of the LETKF is needed for practical 3D LBM simulations.
Hasegawa, Yuta; Onodera, Naoyuki; Asahi, Yuichi; Idomura, Yasuhiro
no journal, ,
We implemented an ensemble data assimilation called local ensemble transform Kalman Filter (LETKF) into the turbulent flow simulation based on Lattice Boltzmann Method (LBM). The code was implemented on GPU, using CUDA for the LBM, and cuBLAS/cuSOLVER libraries for the matrix calculation and eigenvalue decomposition in the LETKF. The data assimilation experiment was carried out on the two-dimensional isotropic turbulence. The experiment showed that the LETKF realized more accurate results compared with the nudging, which is a simple data assimilation scheme.
Onodera, Naoyuki; Shimokawabe, Takashi*; Idomura, Yasuhiro; Kawamura, Takuma; Asahi, Yuichi; Hasegawa, Yuta; Ina, Takuya; Shimomura, Kazuya; Inagaki, Atsushi*; Hirano, Kohin*; et al.
no journal, ,
The project goal is to realize real-time wind prediction in urban areas by assimilating observed data into real-time wind simulations on GPU supercomputers. In FY2022, the first year of the project, we developed a dynamic optimization method for model variables by applying a particle filter (PF) based data assimilation method to reproduce wind conditions in the atmospheric boundary layer with high accuracy. The numerical simulations for the field experiment in Oklahoma City showed improvements of about 10 % for the standard deviation error of the all-day velocity compared to the results without the application of PF. In addition, a multi-scale analysis based on boundary conditions given by a geographic information system (GIS) and a cloud-resolving numerical model (CReSS) was realized for the Tokyo metropolitan area.