Forecasts of the July 2020 Kyushu Heavy Rain Using a 1000-Member Ensemble Kalman Filter

Duc, Le; Kawabata, Takuya; Saitō, Kazuo; Oizumi, Tsutao

doi:10.2151/sola.2021-007

Cited by 15 publications

(10 citation statements)

References 20 publications

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“…The variability of intra‐daily precipitation is greatly dependent on the convection scheme, and the spectral scheme used is able to simulate this variability (Baba, 2020a). The present study focuses on regional‐scale precipitation over Japan (Kawase et al, 2019), so this resolution was used even though it cannot resolve the convective cells of QBCS (Duc et al, 2021; Kato, 2020). The total number of vertical layer is 34 which model top is set to 25 km.…”

Section: Model and Experimental Setupmentioning

confidence: 99%

“…For example, Kato (2020) suggested that possible favorable conditions for QBCS development include low and mid‐level moisture content, a large‐scale environment, and local wind properties. To reduce model's uncertainty and achieve more accurate heavy rainfall prediction, Duc et al (2021) recently performed an ensemble forecast with 1000 ensemble members that outperformed the operational forecast. However, this process is computationally too expensive for practical use, so other ways to improve the accuracy of heavy rainfall forecasting need to be considered.…”

Section: Introductionmentioning

confidence: 99%

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Importance of ocean prediction for heavy rainfall prediction over Japan in July 2020

Baba

2022

Atmospheric Science Letters

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Hindcast experiments were performed for heavy rainfall events over Japan in July 2020 using a regional atmospheric model and a regional coupled model to examine the importance of ocean prediction for predicting heavy rainfall events. Both models were able to predict the first peak of accumulated rainfall over western Japan occurring in the first half of July. However, only the coupled model predicted the second peak that occurred in the second half of July. Sea level pressure (SLP) and low-level moisture inflow originating from an existing atmospheric river (AR) were found to differ in each model. In the regional atmospheric model, the error associated with the inaccurate low-level moisture inflow grew with rising excessive latent heat flux, which enhanced convection and resulted in incorrect SLP patterns. This trend seems to be enhanced by having a prescribed sea surface temperature (SST), which affects the surface heat flux. When ocean conditions are predicted as in the coupled model, such error growth is suppressed by changes in SST that adjust surface heat flux, and it leads to generation of the correct SLP patterns. With correct SLP especially for Pacific high in this case, favorable conditions for inflow from the AR can also be predicted, thus making it possible to predict the heavy rainfall. In conclusion, considering the atmospheric feedback on SST, ocean prediction can improve the predictability of heavy rainfall over Japan, the conditions for which are influenced by the nearby AR. Ocean prediction may therefore extend the range of weather forecasting.

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Section: Model and Experimental Setupmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Importance of ocean prediction for heavy rainfall prediction over Japan in July 2020

Baba

2022

Atmospheric Science Letters

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“…One thousand ensemble members of a short-range regional-scale prediction were investigated for disaster prevention and evacuation. A reliable probabilistic prediction of a heavy rain event was achieved with mega-ensemble members of 1000 [21].…”

Section: Riken or Institute Of Physical And Chemical Research In Japanmentioning

confidence: 99%

High-Performance Computing in Meteorology under a Context of an Era of Graphical Processing Units

Nakaegawa

2022

Computers

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This short review shows how innovative processing units—including graphical processing units (GPUs)—are used in high-performance computing (HPC) in meteorology, introduces current scientific studies relevant to HPC, and discusses the latest topics in meteorology accelerated by HPC computers. The current status surrounding HPC is distinctly complicated in both hardware and software terms, and flows similar to fast cascades. It is difficult to understand and follow the status for beginners; they need to overcome the obstacle of catching up on the information on HPC and connecting it to their studies. HPC systems have accelerated weather forecasts with physical-based models since Richardson’s dream in 1922. Meteorological scientists and model developers have written the codes of the models by making the most of the latest HPC technologies available at the time. Several of the leading HPC systems used for weather forecast models are introduced. Each institute chose an HPC system from many possible alternatives to best match its purposes. Six of the selected latest topics in high-performance computing in meteorology are also reviewed: floating points; spectral transform in global weather models; heterogeneous computing; exascale computing; co-design; and data-driven weather forecasts.

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“…Under such circumstances, more accurate weather prediction is required to reduce the risks associated with extreme weather events such as heavy rainfall. Various prediction techniques and systems have been developed to accurately predict heavy rainfall as far in advance as possible (Duc et al 2021;Ono, Kunii and Honda 2021). However, accurate early prediction still remains difficult since many factors are involved in its occurrence (Kato 2020).…”

Section: Introductionmentioning

confidence: 99%

The Merits of Ocean Prediction for the Prediction of 2010, 2016, and 2021 Summer Heavy Rainfall Events in Japan

Baba

2023

Tellus A: Dynamic Meteorology and Oceanography

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The merits of ocean prediction for heavy rainfall prediction were examined using hindcast experiments for three summer heavy rainfall events in 2010, 2016, and 2021 in Japan. In these events, the rainfall stemmed from Baiu and stationary fronts. The hindcast experiments were conducted using regional atmospheric and coupled models (RUN-ATM and RUN-CPL). The results show that RUN-CPL predicted more accurate rainfall properties than RUN-ATM. RUN-ATM underestimated the accumulated rainfall compared with RUN-CPL, and the underestimation became more significant as the lead time increased. This was due to decreased horizontal vapor transport in the ocean southwest of Japan. Pressure patterns that dominated the vapor transport were different in each case. When an atmospheric model was used, the sea level pressure difference between the Pacific high and Japan was weakened, contributing to weaker vapor transport from the southwest because of the weakened anticyclonic and cyclonic circulations at the region of Pacific high and over Japan. The degraded pressure patterns generated by RUN-ATM stemmed from incorrect latent heat flux response to the sea surface temperature. When air-sea was decoupled in the atmospheric model, the decrease of sea surface temperature by latent heat flux did not occur, so the latent heat flux was overestimated. Also, this caused the decrease in the pressure difference between Pacific high and Japan areas, leading to a weaker moisture transport from the ocean southwest of Japan. The heat budget analysis in the ocean mixed layer suggests that ocean dynamics, especially vertical mixing, contributes to suppress the overestimation of latent heat flux around the Pacific high. It is concluded that heavy rainfall prediction that incorporates appropriate air-sea coupling and ocean prediction provides better results than atmosphere-only model prediction for front-derived heavy rainfall events.

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Forecasts of the July 2020 Kyushu Heavy Rain Using a 1000-Member Ensemble Kalman Filter

Cited by 15 publications

References 20 publications

Importance of ocean prediction for heavy rainfall prediction over Japan in July 2020

Importance of ocean prediction for heavy rainfall prediction over Japan in July 2020

High-Performance Computing in Meteorology under a Context of an Era of Graphical Processing Units

The Merits of Ocean Prediction for the Prediction of 2010, 2016, and 2021 Summer Heavy Rainfall Events in Japan

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