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Application of observability Gramian to targeted observation in WRF data assimilation Cover

Application of observability Gramian to targeted observation in WRF data assimilation

Open Access
|Jan 2020

Figures & Tables

Fig. 1.

Computational domain, geographical height, and streamline at the assimilation time (2017/2/7 12:00 GMT) for level 12. The strong northwest wind observed in the figure is caused by a typical pressure pattern of winter in Japan. A small turbulence is found in circle A.

Table 1.

Conditions for the observability calculation.

Computational conditionsTime period (Section 3.1)2017/2/7 06:00 – 2017/2/7 18:00Time period (Section 3.2)2017/2/7 06:00 – 2017/2/7 12:00Horizontal grid distance9 kmNumber of horizontal grids151 × 151Time step interval15 sNumber of vertical grids30Snapshot time interval for POD15 minTime interval for measurements15 minNumber of POD modes used for perturbations4Length of time integration12 hVariable to be measuredWind speed [m/s]Number of measurements1
Fig. 2.

Proper orthogonal decomposition (POD) bases for wind components u and v at level 12. Each mode is normalised in whole domain.

Table 2.

Computational conditions for the identical-twin experiment.

First guessTrue stateTime period2017/2/7 06:00 – 2017/2/7 18:002017/2/6 12:00 – 2017/2/7 18:00Horizontal grid distance9 kmNumber of horizontal grids151 × 151Length of time step15 sNumber of vertical grids30Time for assimilation2017/2/7 12:00Observed variable–Wind speed [m/s]/Wind direction [°]Number of Observations for each data assimilation1Initial/boundary conditionsNCEP GFSJMA MSM (NCEP GFS for soil variables)Time integration schemeRunge–Kutta 3rd-orderCumulus physicsKain–Fritsch schemeSurface-layer physicsRevised MM5 Monin–Obukhov schemeMicrophysicsWSM three-class simple ice schemePBL schemeYSU schemeRadiation schemeRRTM scheme (Long wave), Dudhia scheme (Short wave)
Fig. 3.

Schematic of identical-twin experiment and the calculation of observability.

Fig. 4.

Spatial distribution of minimum eigenvalues at each level, where (a), (b), (c), and (d) correspond to the distribution at level 2 (150 m), 7 (850 m), 12 (3200 m) and 17 (8000 m), respectively.

Fig. 5.

Correlations of the minimum eigenvalues and the actual distribution at levels 2, 7, 12, and 17. The vertical and horizontal axes correspond to the correlation coefficient and vertical height, respectively. The correlations with the time-averaged vorticity (red line), time-averaged perturbation of vorticity (blue line), and time-averaged temporal variation of vorticity (green line) are plotted. Each label on the left plot corresponds to the vorticity distributions on the right.

Fig. 6.

The distribution of eigenvalue, RMSE changes of wind magnitude, and time-averaged temporal variation of vorticity for levels 2, 7, 12, and 17, respectively. Area A shows the region where the correlation with the eigenvalue distribution is observed. Area B shows the region where RMSE reduction is observed regardless of almost zero eigenvalue. The first 6 h of the wind data (2017/2/7 6:00 GMT – 2017/2/7 12:00 GMT) are utilised to compose the Gramian.

Language: English
Page range: 1697602 - 1697602
Published on: Jan 1, 2020
Published by: Stockholm University Press
In partnership with: Paradigm Publishing Services

© 2020 Ryoichi Yoshimura, Aiko Yakeno, Takashi Misaka, Shigeru Obayashi, published by Stockholm University Press
This work is licensed under the Creative Commons Attribution 4.0 License.