A local particle filter and its Gaussian mixture extension implemented with minor modifications to the LETKF
<p>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-dimensional dynamics. Among others, Penn...
| Main Authors: | , , , |
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| Format: | Article |
| Language: | English |
| Published: |
Copernicus Publications
2022-11-01
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| Series: | Geoscientific Model Development |
| Online Access: | https://gmd.copernicus.org/articles/15/8325/2022/gmd-15-8325-2022.pdf |
| Summary: | <p>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-dimensional dynamics. Among
others, Penny and Miyoshi (2016) developed an LPF in the form of the ensemble
transform matrix of the local ensemble transform Kalman filter (LETKF). The
LETKF has been widely accepted for various geophysical systems, including
numerical weather prediction (NWP) models. Therefore, implementing the LPF
consistently with an existing LETKF code is useful.</p>
<p>This study develops a software platform for the LPF and its Gaussian mixture
extension (LPFGM) by making slight modifications to the LETKF code with a
simplified global climate model known as Simplified Parameterizations,
Primitive Equation Dynamics (SPEEDY). A series of idealized twin experiments
were accomplished under the ideal-model assumption. With large inflation by
the relaxation to prior spread, the LPF showed stable filter performance
with dense observations but became unstable with sparse observations. The
LPFGM showed a more accurate and stable performance than the LPF with both
dense and sparse observations. In addition to the relaxation parameter,
regulating the resampling frequency and the amplitude of Gaussian kernels
was important for the LPFGM. With a spatially inhomogeneous observing
network, the LPFGM was superior to the LETKF in sparsely observed regions,
where the background ensemble spread and non-Gaussianity were larger. The
SPEEDY-based LETKF, LPF, and LPFGM systems are available as open-source
software on GitHub (<span class="uri">https://github.com/skotsuki/speedy-lpf</span>, last access: 16 November 2022) and
can be adapted to various models relatively easily, as in the case of the LETKF.</p> |
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| ISSN: | 1991-959X 1991-9603 |