Development of the Real‐Time 30‐s‐Update Big Data Assimilation System for Convective Rainfall Prediction With a Phased Array Weather Radar: Description and Preliminary Evaluation

Honda, Tomohiro; Amemiya, Arata; Otsuka, Shigenori; Lien, Guo-Yuan; Taylor, James D.; Maejima, Yuji; Nishizawa, Seiya; Yamaura, Tsuyoshi; Sueki, Kenta; Tomita, Hirofumi; Satoh, Shinsuke; Ishikawa, Yutaka; Miyoshi, Takemasa

doi:10.1029/2021ms002823

Cited by 11 publications

(17 citation statements)

References 67 publications

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“…Honda et al. (2022) briefly evaluated the performance of the real‐time SCALE‐LETKF during this event and showed that the real‐time SCALE‐LETKF successfully assimilated the MP‐PAWR observations every 30 s. This study further investigates the performance of the real‐time SCALE‐LETKF system by comparing with the JMA nowcast. Following Honda et al.…”

Section: Methodsmentioning

confidence: 88%

“…The system is called SCALE-LETKF first developed by Lien et al (2017). Recently, the SCALE-LETKF system has been extended by Honda et al (2022) to update real-time extended forecasts every 30 s with the assimilation of MP-PAWR observations. This study describes the application of the real-time version of SCALE-LETKF for a past precipitation event.…”

Section: Data Assimilation Systemmentioning

confidence: 99%

“…The thinning method by Honda et al ( 2022) with a thinning grid interval of four has been applied in this study. Refer to Honda et al (2022) for further details of the real-time SCALE-LETKF system.…”

Section: Data Assimilation Systemmentioning

confidence: 99%

“…In general, nowcasting and NWP are expected to outperform each other at shorter and longer lead times, respectively (e.g., Kotsuki et al., 2019; Sun et al., 2014). However, as mentioned above, the latest experimental NWP (Honda et al., 2022) and the JMA nowcast systems rely on different radar observations and their updating time intervals are different. Although the new NWP system with MP‐PAWR can update extended forecasts every 30 s, the advantage of such frequently updating NWP for the prediction of convective precipitation systems has not been investigated so far.…”

Section: Introductionmentioning

confidence: 99%

“…More recently, Honda et al. (2022) developed a new experimental real‐time NWP system to assimilate PAWR observations every 30 s from the multiparameter PAWR (MP‐PAWR) at Saitama University, Japan (Takahashi et al., 2019). Their system used a convection‐resolving horizontal grid spacing (500 m) and provided real‐time 30‐min forecasts updated every 30 s in the summer of 2020 as will be reported by a separate paper.…”

Section: Introductionmentioning

confidence: 99%

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Advantage of 30‐s‐Updating Numerical Weather Prediction With a Phased‐Array Weather Radar Over Operational Nowcast for a Convective Precipitation System

Honda

Amemiya

Otsuka

et al. 2022

Geophysical Research Letters

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Convective precipitation systems in the summer often cause sudden heavy precipitation and largely affect various human activities, but the rapid evolution limits our predicting capability. Phased‐array weather radars (PAWRs) with a high spatiotemporal resolution are useful for observing such precipitation system. A recently developed numerical weather prediction (NWP) system assimilates PAWR observations with a 500‐m mesh NWP model. It initiates 30‐min extended forecasts every 30 s, much more frequently than the operational NWP and nowcasting systems. This study investigates the benefits of the 30‐s‐updating NWP system in a single but representative convective precipitation event in which a convective cloud developed within 10 min, and its evolution was not well predicted by operational precipitation nowcasting. The rapidly updating NWP system successfully predicts the evolution of the convective cloud. Assimilating the PAWR observations every 30 s continuously modifies the moisture and dynamical fields and improves the forecast accuracy consistently.

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Section: Methodsmentioning

confidence: 88%

Section: Data Assimilation Systemmentioning

confidence: 99%

Section: Data Assimilation Systemmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 3 more Smart Citations

Advantage of 30‐s‐Updating Numerical Weather Prediction With a Phased‐Array Weather Radar Over Operational Nowcast for a Convective Precipitation System

Honda

Amemiya

Otsuka

et al. 2022

Geophysical Research Letters

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Ensemble based methods for leapfrog integration in the simplified parameterizations, primitive‐equation dynamics model

Niño-Ruiz

Ortega

Lucini

2023

Quart J Royal Meteoro Soc

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This paper presents efficient and practical implementations of sequential data assimilation methods for the Simplified Parameterizations, primitive-Equation DYnamics (SPEEDY) Model, a well-known numerical model, into the data assimilation community for climate prediction. In the SPEEDY model, the time evolution of dynamics is performed via the second-order Leapfrog integration scheme; this time integrator relies on two steps: the position and the velocity. The computational implementation of SPEEDY blends the time integrator and the spatial discretization of dynamics to accelerate algebraic computations. Thus, there is no access to the right-hand side function of the ordinary differential equations governing the time evolution of model dynamics. Consequently, the SPEEDY model is often treated as a black box wherein positions and velocities work as inputs and outputs. Since observations in operational data assimilation only match position states, we can exploit augmented vector states to propagate analysis innovations from positions to velocities. For this purpose, we formulate three variants of ensemble-based filters and perform numerical experiments to assess their accuracies. We consider two scenarios for the experiments: an ideal case wherein positions and velocities can be observed and a more realistic one wherein measurements are only accessible for position states. Besides, we discuss the effects of the ensemble size on the accuracies of our formulations and, even more, the typical case in which velocities are not updated across assimilation steps. The results reveal that all filter formulations' accuracies remain the same in terms of Root-Mean-Square-Error by neglecting observations from velocities (a realistic scenario) even for cases wherein the number of measurements decreases to 6% of model components. Furthermore, for all discussed filter implementations, the propagation of analysis increments from position to velocities improves up to 100% the performance of filter implementations wherein velocities are not updated, a typical operational scenario.

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Forecast and Numerical Simulation Studies on Meso/Micro-scale High-Impact Weathers Using High-Performance Computing in Japan

Saito,

Kawabata,

Seko

et al. 2023

Springer Atmospheric Sciences

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Development of the Real‐Time 30‐s‐Update Big Data Assimilation System for Convective Rainfall Prediction With a Phased Array Weather Radar: Description and Preliminary Evaluation

Abstract: Radar observations are essential for capturing and predicting the evolution of convective precipitation. Numerous studies have assimilated radar observations to improve regional-scale analyses and forecasts (e.g.

Cited by 11 publications

References 67 publications

Advantage of 30‐s‐Updating Numerical Weather Prediction With a Phased‐Array Weather Radar Over Operational Nowcast for a Convective Precipitation System

Advantage of 30‐s‐Updating Numerical Weather Prediction With a Phased‐Array Weather Radar Over Operational Nowcast for a Convective Precipitation System

Ensemble based methods for leapfrog integration in the simplified parameterizations, primitive‐equation dynamics model

Forecast and Numerical Simulation Studies on Meso/Micro-scale High-Impact Weathers Using High-Performance Computing in Japan

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