The JMA Nonhydrostatic Model and Its Applications to Operation and Research

Saitō, Kazuo

doi:10.5772/35368

Cited by 56 publications

(35 citation statements)

References 85 publications

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“…For example, the Japan Meteorological Agency (JMA) has used the meso-scale model (MSM) for operational forecast since 2001. The threat score of this model was about 0.2 in 2001 and increased to about 0.4 in 2011 when verifying on a 20-km grid with a threshold of 5 mm/3 hour (Saito, 2012). However, many difficulties remain in predicting intense rains of correct intensity, location and timing.…”

Section: Introductionmentioning

confidence: 99%

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Spatial-temporal fractions verification for high-resolution ensemble forecasts

Duc

Saitō

Seko

2013

Tellus A: Dynamic Meteorology and Oceanography

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A B S T R A C T Experiments with two ensemble systems of resolutions 10 km (MF10km) and 2 km (MF2km) were designed to examine the value of cloud-resolving ensemble forecast in predicting precipitation on small spatio-temporal scales. Since the verification was performed on short-term precipitation at high resolution, uncertainties from small-scale processes caused the traditional verification methods to be inconsistent with the subjective evaluation. An extended verification method based on the Fractions Skill Score (FSS) was introduced to account for these uncertainties. The main idea is to extend the concept of spatial neighbourhood in FSS to the time and ensemble dimension. The extension was carried out by recognising that even if ensemble forecast is used, small-scale variability still exists in forecasts and influences verification results. In addition to FSS, the neighbourhood concept was also incorporated into reliability diagrams and relative operating characteristics to verify the reliability and resolution of two systems.The extension of FSS in time dimension demonstrates the important role of temporal scales in short-term precipitation verification at small spatial scales. The extension of FSS in ensemble space is called the ensemble FSS, which is a good representative of FSS for ensemble forecast in comparison with the FSS of ensemble mean. The verification results show that MF2km outperforms MF10km in heavy rain forecasts. In contrast, MF10km was slightly better than MF2km in predicting light rains, suggesting that the horizontal resolution of 2 km is not necessarily enough to completely resolve convective cells.

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Section: Introductionmentioning

confidence: 99%

“…Both systems used the JMA non-hydrostatic model NHM (Saito et al, 2006;Saito, 2012) as the forecast model. Whereas MF10km applied the modified Kain-Fritsch convective scheme, MF2km did not use convective parameterisation.…”

Section: Design Of Experimentsmentioning

confidence: 99%

Spatial-temporal fractions verification for high-resolution ensemble forecasts

Duc

Saitō

Seko

2013

Tellus A: Dynamic Meteorology and Oceanography

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“…Two 11-member ensemble forecasts with different horizontal resolutions (10 and 2 km) were conducted for the 2011 Niigata-Fukushima heavy rainfall event using JMA-NHM (Saito et al, 2006;Saito, 2012) as the forecast model. The 10 km ensemble prediction system (EPS) uses the JMA's operational mesoscale 4D-Var analysis of 12:00 UTC (21:00 JST) on 28 July and the JMA's global spectral model (GSM) forecast from the same time as the initial and boundary conditions of the control run, respectively.…”

Section: Mesoscale Ensemble Prediction Systemmentioning

confidence: 99%

Ensemble flood simulation for a small dam catchment in Japan using 10 and 2 km resolution nonhydrostatic model rainfalls

Kobayashi

Otsuka

Apip³

et al. 2016

Nat. Hazards Earth Syst. Sci.

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Abstract. This paper presents a study on short-term ensemble flood forecasting specifically for small dam catchments in Japan. Numerical ensemble simulations of rainfall from the Japan Meteorological Agency nonhydrostatic model (JMA-NHM) are used as the input data to a rainfall-runoff model for predicting river discharge into a dam. The ensemble weather simulations use a conventional 10 km and a highresolution 2 km spatial resolutions. A distributed rainfallrunoff model is constructed for the Kasahori dam catchment (approx. 70 km 2 ) and applied with the ensemble rainfalls. The results show that the hourly maximum and cumulative catchment-average rainfalls of the 2 km resolution JMA-NHM ensemble simulation are more appropriate than the 10 km resolution rainfalls. All the simulated inflows based on the 2 and 10 km rainfalls become larger than the flood discharge of 140 m 3 s −1 , a threshold value for flood control. The inflows with the 10 km resolution ensemble rainfall are all considerably smaller than the observations, while at least one simulated discharge out of 11 ensemble members with the 2 km resolution rainfalls reproduces the first peak of the inflow at the Kasahori dam with similar amplitude to observations, although there are spatiotemporal lags between simulation and observation. To take positional lags into account of the ensemble discharge simulation, the rainfall distribution in each ensemble member is shifted so that the catchment-averaged cumulative rainfall of the Kasahori dam maximizes. The runoff simulation with the positionshifted rainfalls shows much better results than the original ensemble discharge simulations.

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“…NHMÁ LETKF comprises a forecast model NHM, an assimilation module LETKF and a QC system. The driving model was the JMA's operational NHM (Saito et al, 2006;Saito, 2012). The version used was of April 2013.…”

Section: Data Assimilation Systemmentioning

confidence: 99%

Ensemble Kalman Filter data assimilation and storm surge experiments of tropical cyclone Nargis

Duc¹,

Kuroda²,

Saitō

et al. 2015

Tellus A: Dynamic Meteorology and Oceanography

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A B S T R A C T Data assimilation experiments on Myanmar tropical cyclone (TC), Nargis, using the Local Ensemble Transform Kalman Filter (LETKF) method and the Japan Meteorological Agency (JMA) non-hydrostatic model (NHM) were performed to examine the impact of LETKF on analysis performance in real cases. Although the LETKF control experiment using NHM as its driving model (NHMÁLETKF) produced a weak vortex, the subsequent 3-day forecast predicted Nargis' track and intensity better than downscaling from JMA's global analysis. Some strategies to further improve the final analysis were considered. They were sea surface temperature (SST) perturbations and assimilation of TC advisories. To address SST uncertainty, SST analyses issued by operational forecast centres were used in the assimilation window. The use of a fixed source of SST analysis for each ensemble member was more effective in practice. SST perturbations were found to have slightly positive impact on the track forecasts. Assimilation of TC advisories could have a positive impact with a reasonable choice of its free parameters. However, the TC track forecasts exhibited northward displacements, when the observation error of intensities was underestimated in assimilation of TC advisories. The use of assimilation of TC advisories was considered in the final NHMÁLETKF by choosing an appropriate set of free parameters. The extended forecast based on the final analysis provided meteorological forcings for a storm surge simulation using the Princeton Ocean Model. Probabilistic forecasts of the water levels at Irrawaddy and Yangon significantly improved the results in the previous studies.

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The JMA Nonhydrostatic Model and Its Applications to Operation and Research

Cited by 56 publications

References 85 publications

Spatial-temporal fractions verification for high-resolution ensemble forecasts

Spatial-temporal fractions verification for high-resolution ensemble forecasts

Ensemble flood simulation for a small dam catchment in Japan using 10 and 2 km resolution nonhydrostatic model rainfalls

Ensemble Kalman Filter data assimilation and storm surge experiments of tropical cyclone Nargis

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The JMA Nonhydrostatic Model and Its Applications to Operation and Research

Cited by 56 publications

References 85 publications

Spatial-temporal fractions verification for high-resolution ensemble forecasts

Spatial-temporal fractions verification for high-resolution ensemble forecasts

Ensemble flood simulation for a small dam catchment in Japan using 10 and 2 km resolution nonhydrostatic model rainfalls

Ensemble Kalman Filter data assimilation and storm surge experiments of tropical cyclone Nargis

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Ensemble flood simulation for a small dam catchment in Japan using 10 and 2 km resolution nonhydrostatic model rainfalls