Effect of doubling the ensemble size on the performance of ensemble prediction in the warm season using MOGREPS implemented at the KMA

Kay, Jun Kyung; Kim, Hyun Mee; Park, Young Youn; Son, Joohyung

doi:10.1007/s00376-012-2083-y

Cited by 15 publications

(13 citation statements)

References 30 publications

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“…The inflation factor ensures that the sum of the spreads of the ensemble 12-h forecast matches the sum of the variations in the error of the ensemblemean 12-h forecast in the observational space over the verification region (Bowler et al 2008(Bowler et al , 2009Kay et al 2013).…”

Section: B Etkf Formulationmentioning

confidence: 99%

“…The ensemble-based data assimilation system uses a covariance localization to decrease the long-range spurious correlations due to the limited ensemble size (Kay et al 2013). The basic idea of covariance localization is to reduce the elements of the forecast error covariance to remove the correlation between distant grid points (Houtekamer and Mitchell 2001;Hamill et al 2001).…”

Section: B Etkf Formulationmentioning

confidence: 99%

“…The inflation factors were calculated vertically for four layers: the lower troposphere (from the surface to 2 km), the mid-to upper troposphere (from approximately 2 to 15 km), the stratosphere (from approximately 16 to 56 km), and the mesosphere [from approximately 62 km to the top of the model (76 km)]. The detailed method of calculating the inflation factor is described in Kay et al (2013).…”

Section: Experimental Designmentioning

confidence: 99%

“…The inflation factors in Eq. (7) are calculated using sonde and ATOVS observations with 12-h ensemble forecasts because the error estimates of these observations are relatively easily obtained in the KMA data assimilation system (Kay et al 2013). The inflation factors were calculated vertically for four layers: the lower troposphere (from the surface to 2 km), the mid-to upper troposphere (from approximately 2 to 15 km), the stratosphere (from approximately 16 to 56 km), and the mesosphere [from approximately 62 km to the top of the model (76 km)].…”

Section: Experimental Designmentioning

confidence: 99%

“…Since 2011, the Korea Meteorological Administration (KMA) has operationally implemented the Unified Model (UM) and its related pre-/postprocessing systems, imported from the Met Office (Kay et al 2013). The new EPS of the KMA is based on MOGREPS and uses ETKF with localization as the initial ensemble perturbation generation method.…”

Section: Introductionmentioning

confidence: 99%

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Characteristics of Initial Perturbations in the Ensemble Prediction System of the Korea Meteorological Administration

Kay

Kim

2014

Weather and Forecasting

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In this study, the initial ensemble perturbation characteristics of the new Korea Meteorological Administration (KMA) ensemble prediction system (EPS), a version of the Met Office Global and Regional Ensemble Prediction System, were analyzed over two periods: from 1 June to 31 August 2011, and from 1 December 2011 to 29 February 2012. The KMA EPS generated the initial perturbations using the ensemble transform Kalman filter (ETKF). The observation effect was reflected in both the transform matrix and the inflation factor of the ETKF; it reduced (increased) uncertainties in the initial perturbations in regions with dense observations via the transform matrix (inflation factor). The reduction in uncertainties is generally governed by the transform matrix but locally modulated by the inflation factor. The sea ice significantly affects the initial perturbations near the lower boundary layer. The large perturbation energy in the lower stratosphere of the tropics was related to the dominant zonal wind, whereas the perturbation energy in the upper stratosphere of the winter hemispheres was related to the dominant polar night jet. In the early timeintegration stage, the initial perturbations decayed in the lower troposphere but grew rapidly in the mid-to upper troposphere. In the meridional direction, the initial perturbations grew greatest in the northern polar region and smallest in the tropics. The initial perturbations maintained a hydrostatic balance, especially during the summer in both hemispheres and during both the summer and winter in the tropics, associated with the smallest growth rates of the initial perturbations. The initial perturbations in the KMA EPS appropriately describe the uncertainties associated with atmospheric features.

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Section: B Etkf Formulationmentioning

confidence: 99%

Section: B Etkf Formulationmentioning

confidence: 99%

Section: Experimental Designmentioning

confidence: 99%

Section: Experimental Designmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Characteristics of Initial Perturbations in the Ensemble Prediction System of the Korea Meteorological Administration

Kay

Kim

2014

Weather and Forecasting

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Evaluating the trade‐offs between ensemble size and ensemble resolution in an ensemble‐variational data assimilation system

Lei

Whitaker

2017

J Adv Model Earth Syst

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The current NCEP operational four‐dimensional ensemble‐variational data assimilation system uses a control forecast at T1534 resolution coupled with an 80 member ensemble at T574 resolution. Given an increase in computing resources, and assuming the control forecast resolution is fixed, would it be better to increase the ensemble size and keep the ensemble resolution the same, or increase the ensemble resolution and keep the ensemble size the same? To answer this question, experiments are conducted at reduced resolutions. Two sets of experiments are conducted which both use approximately four times more computational resources than the control experiment that uses a control forecast at T670 and an 80 member ensemble at T254. One increases the ensemble size to 320 but keeps the ensemble resolution at T254; and the other increases the ensemble resolution to T670 but retains an 80 ensemble size. When ensemble size increases to 320, turning off the static component of the background‐error covariance does not degrade performance. When the data assimilation parameters are tuned for optimal performance, increasing either ensemble size or ensemble resolution can improve the forecast performance. Increasing ensemble resolution is slightly, but significantly better than increasing ensemble size for these experiments, particularly when considering errors at smaller scales. Much of the benefit of increasing ensemble resolution comes about by eliminating the need for a deterministic control forecast and running all of the background forecasts at the same resolution. In this “single‐resolution” mode, the control forecast is replaced by an ensemble average, which reduces small‐scale errors significantly.

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Medium‐range global ensemble prediction system at 12 km horizontal resolution and its preliminary validation

Mamgain

Sarkar

Rajagopal

2019

Meteorological Applications

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Forecasts of high‐impact weather systems require sufficiently high resolution of state‐of‐the‐art numerical models in order to resolve the small‐scale features. At the National Centre for Medium Range Weather Forecasting (NCMRWF) in India, a global ensemble prediction system (EPS—called the NEPS) has been implemented operationally at 12 km horizontal grid size. The NEPS configuration is based on the UK Met Office Global and Regional Ensemble Prediction System (MOGREPS). Initial condition perturbations are generated by the ensemble transform Kalman filter (ETKF) method. Model uncertainties are taken care of by stochastic kinetic energy backscatter (SKEB) and random parameters (RP) schemes. Forecast perturbations obtained from 6 hr short forecasts of 22 ensemble members are updated by the ETKF four times a day (0000, 0600, 1200 and 1800 UTC). Perturbations of surface parameters such as sea‐surface temperature, soil moisture content and soil temperature are also included in the new NEPS. The NEPS aims to provide 10 day probabilistic long forecasts using 23 ensemble members (22 perturbed plus one control). The long forecast provided at 0000 UTC is the combination of 11 members from the 0000 UTC cycle and lagged 11 members from the 1200 UTC cycle. The new NEPS shows improvements in terms of forecast agreement among the members in comparison with the previously operational NEPS that was running at 33 km horizontal grid size with 44 perturbed members. The ratio between the root mean square error of the ensemble mean and ensemble spread as a function of lead time has improved in both the Northern and Southern Hemispheres in the new NEPS.

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Effect of doubling the ensemble size on the performance of ensemble prediction in the warm season using MOGREPS implemented at the KMA

Cited by 15 publications

References 30 publications

Characteristics of Initial Perturbations in the Ensemble Prediction System of the Korea Meteorological Administration

Characteristics of Initial Perturbations in the Ensemble Prediction System of the Korea Meteorological Administration

Evaluating the trade‐offs between ensemble size and ensemble resolution in an ensemble‐variational data assimilation system

Medium‐range global ensemble prediction system at 12 km horizontal resolution and its preliminary validation

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