Improving the spin-up of regional EnKF for typhoon assimilation and
                        forecasting with Typhoon Sinlaku (2008)

Yang, Sheng-Hui; Lin, Kuan-Jen; Miyoshi, Takemasa; Kalnay, Eugenia

doi:10.3402/tellusa.v65i0.20804

Cited by 18 publications

(19 citation statements)

References 42 publications

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“…The WRF-LETKF analysis has shown reasonable skill for regional weather prediction (Yang et al 2013(Yang et al , 2014. Compared with the cold-start EPS, the ensemble perturbations are flow-dependent.…”

Section: Wrf-based Epssmentioning

confidence: 97%

“…In the following section, two types of WRF-based EPSs are used to investigate the potential of the MRC scheme. They are the cold-start EPS and LETKF-based EPS (Yang et al 2012(Yang et al , 2013. For the cold-start EPS, the initial perturbations are generated randomly at each initial time.…”

Section: Wrf-based Epssmentioning

confidence: 99%

“…The MRC scheme is applied to a Weather and Research Forecasting model (WRF)-based EPS. Two types of the WRF-EPS are used for evaluation: (1) the initial conditions are taken from the perturbed global analyses, and (2) the initial conditions are taken from the analysis ensemble from the WRF-local ensemble transform Kalman filter (WRF-LETKF) system (Yang et al 2012b(Yang et al , 2013(Yang et al , 2014. The former type is considered as a cold-start EPS, and the ensemble perturbations are independent from each initialization.…”

Section: Introductionmentioning

confidence: 99%

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Applications of the Mean Recentering Scheme to Improve Typhoon Track Prediction: A Case Study of Typhoon Nanmadol (2011)

Chang

Yang

Keppenne³

2014

Journal of the Meteorological Society of Japan

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and Christian KEPPENNE Global modeling and assimilation office, NASA/GSFC, U.S.A. (Manuscript received 6 January 2014, in final form 7 August 2014) AbstractUnder strong nonlinear dynamics, the assumption of a Gaussian distribution for an ensemble may be strongly violated, and thus the mean of the ensemble cannot be the best estimate for the atmosphere. A mean recentering (MRC) scheme is proposed to handle a track ensemble that has a strong non-Gaussian distribution when the track prediction is conducted under a highly uncertain condition. The validity of the MRC scheme is confirmed using a case study of Typhoon Nanmadol in 2011, which moves northward initially but turns westward sharply at 0000UTC 24 August. Factors contributing to Nanmadol's movement prediction include the saddle field between typhoons Nanmadol and Talas, the development of the subtropical high on the north side of both typhoons, and Nanmadol's own circulation.The MRC scheme successfully improves the typhoon track prediction with a regional ensemble prediction system based on the Weather and Research Forecasting (WRF) model. The corrections from the MRC scheme allow the ensemble to capture the realistic behavior when the original ensemble track prediction is poor. Such ensemble adjustment can provide positive feedback to the background error covariance used in the ensemble-based data assimilation system. Results from the WRF-local ensemble transform Kalman filter (WRF-LETKF) system incorporated with the MRC scheme show that the ensemble track prediction can be significantly improved during the WRF-LETKF's spin-up period when Nanmadol movement is highly uncertain. By dynamically adjusting the MRC scheme, the ensemble avoids suffering from the non-Gaussian and over-dispersive issues observed in the original ensemble prediction.

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Section: Wrf-based Epssmentioning

confidence: 97%

Section: Wrf-based Epssmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Applications of the Mean Recentering Scheme to Improve Typhoon Track Prediction: A Case Study of Typhoon Nanmadol (2011)

Chang

Yang

Keppenne³

2014

Journal of the Meteorological Society of Japan

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“…This procedure may be repeated multiple times per analysis cycle, but in this study it is only applied once per cycle. The RIP algorithm has been applied successfully in a simple Lorenz system, a quasi-geostrophic model (Kalnay and Yang, 2010;Yang et al, 2012a), observing system simulation experiments for typhoon prediction (Yang et al, 2012b) and the forecast of Typhoon Sinlaku (Yang et al, 2013). RIP is implemented here for the first time in the global domain with historical observation data.…”

Section: S G Penny Et Al: the Local Ensemble Transform Kalman Filtermentioning

confidence: 99%

The local ensemble transform Kalman filter and the running-in-place algorithm applied to a global ocean general circulation model

Penny

Kalnay

Carton

et al. 2013

Nonlin. Processes Geophys.

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Abstract. The most widely used methods of data assimilation in large-scale oceanography, such as the Simple Ocean Data Assimilation (SODA) algorithm, specify the background error covariances and thus are unable to refine the weights in the assimilation as the circulation changes. In contrast, the more computationally expensive Ensemble Kalman Filters (EnKF) such as the Local Ensemble Transform Kalman Filter (LETKF) use an ensemble of model forecasts to predict changes in the background error covariances and thus should produce more accurate analyses. The EnKFs are based on the approximation that ensemble members reflect a Gaussian probability distribution that is transformed linearly during the forecast and analysis cycle. In the presence of nonlinearity, EnKFs can gain from replacing each analysis increment by a sequence of smaller increments obtained by recursively applying the forecast model and data assimilation procedure over a single analysis cycle. This has led to the development of the "running in place" (RIP) algorithm by Kalnay and Yang (2010) and Yang et al. (2012a,b) in which the weights computed at the end of each analysis cycle are used recursively to refine the ensemble at the beginning of the analysis cycle. To date, no studies have been carried out with RIP in a global domain with real observations. This paper provides a comparison of the aforementioned assimilation methods in a set of experiments spanning seven years (1997)(1998)(1999)(2000)(2001)(2002)(2003) using identical forecast models, initial conditions, and observation data. While the emphasis is on understanding the similarities and differences between the assimilation methods, comparisons are also made to independent ocean station temperature, salinity, and velocity time series, as well as ocean transports, providing information about the absolute error of each. Comparisons to independent observations are similar for the assimilation methods but the observation-minus-background temperature differences are distinctly lower for LETKF and RIP. The results support the potential for LETKF to improve the quality of ocean analyses on the space and timescales of interest for seasonal prediction and for RIP to accelerate the spin up of the system.

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“…This extreme rainfall case differs from all the foregoing QPN studies in weather and terrain characteristics and therefore deserves exploration as well. Our radar data assimilation system is based on the system in Yang et al (2012Yang et al ( , 2013 that couples the local ensemble transform Kalman filter (LETKF; Hunt et al, 2007) with the WRF model, and its QPN skill is evaluated with observing system simulation experiments (OSSEs) first in this paper. In the next section, the LETKF algorithm and its features are introduced as well as the model setup and radar observation operator of our system.…”

Section: Introductionmentioning

confidence: 99%

Improving quantitative precipitation nowcasting with a local ensemble transform Kalman filter radar data assimilation system: observing system simulation experiments

Tsai¹,

Yang

Liou

2014

Tellus A: Dynamic Meteorology and Oceanography

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A B S T R A C TThis study develops a Doppler radar data assimilation system, which couples the local ensemble transform Kalman filter with the Weather Research and Forecasting model. The benefits of this system to quantitative precipitation nowcasting (QPN) are evaluated with observing system simulation experiments on Typhoon Morakot (2009), which brought record-breaking rainfall and extensive damage to central and southern Taiwan. The results indicate that the assimilation of radial velocity and reflectivity observations improves the three-dimensional winds and rain-mixing ratio most significantly because of the direct relations in the observation operator. The patterns of spiral rainbands become more consistent between different ensemble members after radar data assimilation. The rainfall intensity and distribution during the 6-hour deterministic nowcast are also improved, especially for the first 3 hours. The nowcasts with and without radar data assimilation have similar evolution trends driven by synoptic-scale conditions. Furthermore, we carry out a series of sensitivity experiments to develop proper assimilation strategies, in which a mixed localisation method is proposed for the first time and found to give further QPN improvement in this typhoon case.

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Improving the spin-up of regional EnKF for typhoon assimilation and forecasting with Typhoon Sinlaku (2008)

Cited by 18 publications

References 42 publications

Applications of the Mean Recentering Scheme to Improve Typhoon Track Prediction: A Case Study of Typhoon Nanmadol (2011)

Applications of the Mean Recentering Scheme to Improve Typhoon Track Prediction: A Case Study of Typhoon Nanmadol (2011)

The local ensemble transform Kalman filter and the running-in-place algorithm applied to a global ocean general circulation model

Improving quantitative precipitation nowcasting with a local ensemble transform Kalman filter radar data assimilation system: observing system simulation experiments

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