Variational Data Assimilation System for Operational Regional Models at Japan Meteorological Agency

Ikuta, Yasutaka; Fujita, Tadashi; Ota, Yukinari; Honda, Yoshio

doi:10.2151/jmsj.2021-076

Cited by 13 publications

(4 citation statements)

References 52 publications

(42 reference statements)

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“…The elevation of the nearest grid point was 1,081 m. The MSM data was as follows: 5-km spatial resolution and 1-h temporal interval. MSM was initialized every 3 h with a mesoscale analysis assimilated with various observation data, such as weather radar data, satellite observations, and water vapor content, from ground-based GNSS (Ikuta et al, 2021). We reconstructed hourly time series data from the initial and two leading forecast data; however, the precipitation data https://doi.org/10.5194/nhess-2023-5 Preprint.…”

Section: Mesoscale Model Datamentioning

confidence: 99%

Spatial snowpack properties in a snow-avalanche release area: An extreme dry-slab avalanche case on Mt. Nodanishoji, Japan, in 2021

Katsuyama

Katsushima²,

Adachi³

et al. 2023

Preprint

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Abstract. An extreme dry-slab snow-avalanche occurred on 10 Jan. 2021 at Mt. Nodanishoji, Gifu, Japan, during a heavy snowfall. The avalanche ran down approximately 2,800 m and caused damage to trees and infrastructures. Although this avalanche was estimated to be the second largest in Japan, physical snowpack properties and their vertical structure and spatial distribution, that caused the avalanche, were not addressed in the release area just after the avalanche fall, mainly due to unsafe and lousy weather. Based on a snow depth distribution observed by an unmanned aerial vehicle and a numerical snowpack simulation in the avalanche release area, the spatial distributions of the mechanical snowpack stability and slab mass and their temporal evolutions were estimated in this study. The procedure was validated by comparing the calculation results with the observed snowpit and spatial snow depth data. The results indicated that two heavy snowfall events, ~3 and 10 days before the avalanche onset, generated two different weak layers made of precipitation particles and associated slabs above other weak layers. The older weak layer was only generated on the northward slope due to its low temperature, whereas the newer layer was predominant over the avalanche release area. The fraction of contributions of the slabs associated with the two weak layers to the total slab mass over the calculation domain was found to be 1 : 2.

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Section: Mesoscale Model Datamentioning

confidence: 99%

Spatial snowpack properties in a snow-avalanche release area: An extreme dry-slab avalanche case on Mt. Nodanishoji, Japan, in 2021

Katsuyama

Katsushima²,

Adachi³

et al. 2023

Preprint

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“…Initial conditions for the LFM were generated by the three-dimensional variational data assimilation, which assimilates radial velocity and reflectivity from doppler radars to help the spin-up in convective regions (Ikuta et al 2021). The lateral boundary conditions for the LFM were provided by the JMA's operational mesoscale model (JMA 2022) with a horizontal grid spacing of 5 km.…”

Section: Forecast Model and Its Initial And Lateral Boundary Conditionsmentioning

confidence: 99%

Multiscale Nonlinearity of Error Growth in High-Resolution Forecasts for Mesoscale Convective Systems

Ono¹,

Inatsu

2023

Preprint

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In this study, the nonlinearity in a weather forecast, defined as the extent to which the initial opposite-sign state vector does not keep the same magnitude and opposite direction in a forecast time, was examined in an environment containing a multiscale convective system. A pair of 18-h forecast experiments with initial perturbations of different signs was conducted for a heavy rainfall event in western Japan on 13 August 2021. Despite the initially different signs, the perturbations at a given forecast time had the same sign, which was emphasized by the low-pass filter. This result suggested that the nonlinearity was found not only on the convective scale but also on the meso-α scale. An additional forecast experiment with the initial perturbation only for water vapor applied in a limited area indicated that this meso-α scale nonlinearity originated from the asymmetry of water vapor perturbations to avoid supersaturation in the wet environment. The nonlinearity was propagated by large-scale gravity waves emitted from the perturbed convective areas. This explanation was supported by a pair of forecast experiments with dry and moisture perturbation areas on a scale larger than that of convection, in which the state vectors kept the opposite sign at a given forecast time.

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“…In Ref. [9], the primary field update method in the optimization process allowed the nonlinearity of the observation operator and numerical weather prediction model to be incorporated into the solution of the optimization problem in the incremental four-dimensional variational (4D-Var). The outer/inner models used in the incremental 4D-Var method are based on Unified Concept for Atmoshpere (ASUCA), with suitable configurations for each resolution and applied linearization.…”

Section: Introductionmentioning

confidence: 99%

Variational Data Assimilation Method Using Parallel Dual Populations Particle Swarm Optimization Algorithm

WU,

2024

Wuhan Univ. J. Nat. Sci.

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In recent years, numerical weather forecasting has been increasingly emphasized. Variational data assimilation furnishes precise initial values for numerical forecasting models, constituting an inherently nonlinear optimization challenge. The enormity of the dataset under consideration gives rise to substantial computational burdens, complex modeling, and high hardware requirements. This paper employs the Dual-Population Particle Swarm Optimization (DPSO) algorithm in variational data assimilation to enhance assimilation accuracy. By harnessing parallel computing principles, the paper introduces the Parallel Dual-Population Particle Swarm Optimization (PDPSO) Algorithm to reduce the algorithm processing time. Simulations were carried out using partial differential equations, and comparisons in terms of time and accuracy were made against DPSO, the Dynamic Weight Particle Swarm Algorithm (PSOCIWAC), and the Time-Varying Double Compression Factor Particle Swarm Algorithm (PSOTVCF). Experimental results indicate that the proposed PDPSO outperforms PSOCIWAC and PSOTVCF in convergence accuracy and is comparable to DPSO. Regarding processing time, PDPSO is 40% faster than PSOCIWAC and PSOTVCF and 70% faster than DPSO.

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Variational Data Assimilation System for Operational Regional Models at Japan Meteorological Agency

Cited by 13 publications

References 52 publications

Spatial snowpack properties in a snow-avalanche release area: An extreme dry-slab avalanche case on Mt. Nodanishoji, Japan, in 2021

Spatial snowpack properties in a snow-avalanche release area: An extreme dry-slab avalanche case on Mt. Nodanishoji, Japan, in 2021

Multiscale Nonlinearity of Error Growth in High-Resolution Forecasts for Mesoscale Convective Systems

Variational Data Assimilation Method Using Parallel Dual Populations Particle Swarm Optimization Algorithm

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