Recent Progress of Data Assimilation Methods in Meteorology

Tsuyuki, Tadashi; Miyoshi, Takemasa

doi:10.2151/jmsj.85b.331

Cited by 49 publications

(32 citation statements)

References 139 publications

(114 reference statements)

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“…Satellite precipitation estimates, such as TMPA, have not been widely used in DA because of several problems, including the non-Gaussian error distribution associated with precipitation, the error covariance between precipitation and other model variables, and the substantial model and observation errors (e.g., Errico et al, 2007;Tsuyuki and Miyoshi, 2007;Bauer et al, 2011;Lien et al, 2016a). Assimilation of all precipitation data without special treatments usually leads to no impacts or negative impacts.…”

Section: Background On the Precipitation Assimilation Studiesmentioning

confidence: 99%

Accelerating assimilation development for new observing systems using EFSO

Lien

Hotta

Kalnay

et al. 2018

Nonlin. Processes Geophys.

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Abstract. To successfully assimilate data from a new observing system, it is necessary to develop appropriate data selection strategies, assimilating only the generally useful data. This development work is usually done by trial and error using observing system experiments (OSEs), which are very time and resource consuming. This study proposes a new, efficient methodology to accelerate the development using ensemble forecast sensitivity to observations (EFSO). First, non-cycled assimilation of the new observation data is conducted to compute EFSO diagnostics for each observation within a large sample. Second, the average EFSO conditionally sampled in terms of various factors is computed. Third, potential data selection criteria are designed based on the non-cycled EFSO statistics, and tested in cycled OSEs to verify the actual assimilation impact. The usefulness of this method is demonstrated with the assimilation of satellite precipitation data. It is shown that the EFSO-based method can efficiently suggest data selection criteria that significantly improve the assimilation results.

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Section: Background On the Precipitation Assimilation Studiesmentioning

confidence: 99%

Accelerating assimilation development for new observing systems using EFSO

Lien

Hotta

Kalnay

et al. 2018

Nonlin. Processes Geophys.

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Section: Background Of the Precipitation Assimilation Studiesmentioning

confidence: 99%

Accelerating assimilation development for new observing systems using EFSO

Lien¹,

Hotta²,

Kalnay³

et al. 2017

Preprint

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Abstract. To successfully assimilate data from a new observing system, it is necessary to develop appropriate data selection strategies, assimilating only the generally useful data. This development work is usually done by trial-and-error using 10 observing system experiments, which are very time-and resource-consuming. This study proposes a new, efficient methodology to accelerate the development using the Ensemble Forecast Sensitivity to Observations (EFSO). First, noncycled assimilation of the new observation data is conducted to compute EFSO diagnostics for each observation. Second, the average EFSO conditionally sampled in terms of various factors is computed. Third, potential data selection rules are designed based on the EFSO results, and tested in cycled OSEs to verify the actual assimilation impact. The usefulness of 15 this method is demonstrated with the assimilation of satellite precipitation data. It is shown that the EFSO based method can efficiently suggest data selection rules that significantly improve the assimilation results.

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“…The conventional formulation of variational data assimilation has been described by several authors (e.g., Kalnay 2003;Tsuyuki and Miyoshi 2007). In this section, a new formulation of 4DVar that is appropriate for data assimilation in strong nonlinearity is presented on the basis of results in Section 2.…”

Section: Reformulation Of Variational Data Assimilationmentioning

confidence: 99%

“…In respect of the first conclusion, 4DVar is superior to EnKF, because EnKF assumes the prior PDF to be Gaussian in assimilating observational data, even in four-dimensional EnKF (Hunt et al 2004). It is well known that in 4DVar for a linear system, a Gaussian prior PDF that is evolved according to the evolution equation of the system is implicitly used in assimilating observational data (e.g., Thépaut et al 1993;Tsuyuki and Miyoshi 2007). The present result indicates that if a nonlinear system satisfies the divergence condition, this property of 4DVar is extended to include a non-Gaussian prior PDF.…”

Section: Reformulation Of Variational Data Assimilationmentioning

confidence: 99%

Deterministic Predictability of the Most Probable State and Reformulation of Variational Data Assimilation

Tsuyuki

2014

Journal of the Meteorological Society of Japan

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If the divergence in phase space of the evolution equation of a deterministic nonlinear system does not depend on the state variables (hereafter referred to as the divergence condition), the deterministic prediction starting from the mode of a probability density function (PDF) of the state variables remains the mode of the PDF at forecast time. For a system that does not satisfy the divergence condition, a condition for the forecast state to remain sufficiently close to the mode of the PDF is derived under assumption of a small forecast error. Calculation of the divergence in phase space for finite-dimensional analogs of several Eulerian equations of hydrodynamics shows that the divergence condition holds for the quasigeostrophic equations with lateral boundaries and the shallow water equations on a sphere.On the basis of the above results, a new formulation of four-dimensional variational data assimilation (4DVar) is presented. A Gaussian prior PDF at the beginning of an assimilation window is evolved up to the end of that window according to the Liouville equation. It is found that if the divergence condition holds, the cost function with the prior PDF thus evolved is equivalent to the conventional cost function of 4DVar. This result reveals that a non-Gaussian prior PDF which evolves according to the Liouville equation is implicitly used in 4DVar. Data assimilation experiments with toy models are conducted to demonstrate this advantage of 4DVar. The background error covariance at the beginning of the assimilation window is obtained from ensemble Kalman filter (EnKF). To alleviate the difficulty of multiple minima, when the convergence value of the cost function exceeds a certain threshold, the 4DVar analysis is replaced by the corresponding EnKF analysis. Results demonstrate that 4DVar cycles with the abovementioned modifications outperform EnKF cycles in terms of the accuracy of analysis in strong nonlinearity as well as in weak nonlinearity.

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Recent Progress of Data Assimilation Methods in Meteorology

Cited by 49 publications

References 139 publications

Accelerating assimilation development for new observing systems using EFSO

Accelerating assimilation development for new observing systems using EFSO

Accelerating assimilation development for new observing systems using EFSO

Deterministic Predictability of the Most Probable State and Reformulation of Variational Data Assimilation

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