The regional model‐based Mesoscale Ensemble Prediction System, <scp>MEPS</scp>, at the Japan Meteorological Agency

Ono, Kosuke; Kunii, Masaru; Honda, Yoshio

doi:10.1002/qj.3928

Cited by 26 publications

(39 citation statements)

References 38 publications

(46 reference statements)

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“…Recent studies have endeavored to improve heavy rainfall prediction by updating the prediction systems and adopting assimilation techniques (e.g., Kawabata et al, 2013; Kunii, 2014; Ono et al, 2021). However, heavy rainfall over Japan is sometimes difficult to predict, even with newer prediction systems, because these events consists of localized quasi‐stationary band‐shaped convective system (QBCS), especially in summertime.…”

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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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: 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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“…Correspondingly, various convective‐scale NWP model forecast products across a wide range of resolvable scales have emerged from operational centers and research agencies worldwide. For instance, the Japan Meteorological Agency (JMA) operates the Meso‐Scale Model (MSM; JMA, 2019) and MSM‐based Mesoscale Ensemble Prediction System (EPS; Ono et al ., 2021) at a horizontal grid spacing of 5 km together with a Local Forecast Model (LFM) at 2 km grid spacing to support disaster prevention, aviation safety, and short‐range precipitation forecasts. Based on the Unified Model (UM) model, the Met Office developed the 2.2‐km 12‐member Met Office Global and Regional Ensemble Prediction System (MOGREPS‐UK) to complement the 1.5‐km high‐resolution UK variable resolution (UKV) deterministic short‐term forecasting (Mittermaier and Csima, 2017).…”

Section: Introductionmentioning

confidence: 99%

Evaluation of an experimental Warn‐on‐Forecast 3DVAR analysis and forecast system on quasi‐real‐time short‐term forecasts of high‐impact weather events

Gao

Wang

et al. 2021

Quart J Royal Meteoro Soc

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The purpose of this study is to demonstrate the capability of an experimental, weather-adaptive, high-resolution, deterministic Warn-on-Forecast (WoF) analysis and forecast system (WoF3DVAR-AFS) for predicting high-impact severe weather events that occurred during the Hazardous Weather Testbed 2019 Spring Forecast Experiments. WoF3DVAR-AFS uses a three-dimensional variational (3DVAR) method as its core data assimilation system and the Advanced Research Version of the Weather Research and Forecasting (WRF-ARW) model as its forward model. Surface measurements provided in meteorological aviation reports and the Oklahoma Mesonet, Doppler radar data, and spaceborne total lightning observations provided by the Geostationary Lightning Mapper are assimilated at 15-min frequency over a target domain determined by the "Day 1" Convective Outlook product from the Storm Prediction Center. The chief goal of this system is to complement probabilistic forecasts generated by ensemble analysis and forecast systems, such as the experimental Warn-on-Forecast System (WoFS) with a higher-resolution deterministic member to aid forecasters' decision-making. We performed both qualitative and quantitative evaluations on 0-6 hr forecasts launched hourly from 1900 to 0300 UTC the next day for each of the 12 cases. Aggregated subjective forecast evaluation metrics from each individual case, as well as detailed comparison against available verification datasets, suggest that the forecasts are generally skillful in terms of composite reflectivity fields, quantitative precipitation forecasts, and the strength and location of rotation tracks and damaging winds. This study presents initial efforts to assess the performance of WoF3DVAR-AFS and provides possible directions for further improvements, including the development of a weather-adaptive, dual-resolution analysis and forecast system hybrid with an ensemble system, such as the experimental Warn-on-Forecast system.

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“…Then, after several comparative experiments, MEPS was introduced in 2015. Operational forecasting for MEPS began in 2019 with the launch of the 10th Generation Numerical Analysis and Prediction System (NAPS10) [48].…”

Section: Stochastic Approach Based On Ensemble Prediction Systemmentioning

confidence: 99%

Support Vector Quantile Regression for the Post-Processing of Meso-Scale Ensemble Prediction System Data in the Kanto Region: Solar Power Forecast Reducing Overestimation

2022

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Although the recent development of solar power forecasting through machine learning approaches, such as the machine learning models based on numerical weather prediction (NWP) data, has been remarkable, their extreme error requires an increase in the amount of reserve capacity procurement used for the power system safety. Hence, a reduction of the serious overestimation is necessary for efficient grid operation. However, despite the importance of the above issue, few studies have focused on the model design, suppressing serious errors, to the best of the authors’ knowledge. This study investigates a prediction model that can reduce the huge overestimation of the solar irradiance, which poses a risk to the power system. The specific approaches used are as follows: the employment of Support Vector Quantile Regression (SVQR), the utilization of Meso-scale Ensemble Prediction System (MEPS, Meso-scale EPS for the regions of Japan) data, which is based on the forecasts from Meso-scale Model (MSM) as explanatory variables, and the hyperparameter adjustment. The performance of the models is verified in the one day-ahead forecasting for surface solar irradiance at five sites in the Kanto region as the numerical simulation, where their forecasting errors are measured by the root mean square error (RMSE) and the 3σ error, which corresponds to the 99.87% quantile error of the order statistics. The test results indicate the following findings: the SVRs’ RMSE and 3σ error tend to be trade-offs in the case of varying the penalty of the regularization term; by using SVR as a post-processing tool for MSM or MEPS data, both of the score of their metrics can be improved from original data; the MEPS-based SVQR (MEPS-SVQR) could provide superior performance in both metrics in comparison with the MSM-based SVQR (MSM-SVQR) if the parameters are properly adjusted. Although the time period and the type of MEPS data used for the validation are limited, our report is expected to help the design of NWP-based machine learning models to enable short-term solar power forecasts with a low risk of overestimation.

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The regional model‐based Mesoscale Ensemble Prediction System, MEPS, at the Japan Meteorological Agency

Cited by 26 publications

References 38 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

Evaluation of an experimental Warn‐on‐Forecast 3DVAR analysis and forecast system on quasi‐real‐time short‐term forecasts of high‐impact weather events

Support Vector Quantile Regression for the Post-Processing of Meso-Scale Ensemble Prediction System Data in the Kanto Region: Solar Power Forecast Reducing Overestimation

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