The Local Ensemble Transform Kalman Filter (LETKF) with a Global NWP Model on the Cubed Sphere

Shin, So Jin; Kang, Ji-Sun; Jo, Young‐Heon

doi:10.1007/s00024-016-1269-0

Cited by 23 publications

(20 citation statements)

References 26 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…We test the use of ESVs as additive inflation using the LETKF data assimilation system implemented in the global NWP model at KIAPS (Shin et al ., ). The use of ESVs as an additive inflation was first suggested by Yang et al .…”

Section: Discussionmentioning

confidence: 97%

“…We choose a prior variance of the inflation parameter which allows a relatively strong temporal smoothing, since it results in a better performance of the LETKF (see more details in Shin et al . ()).…”

Section: Methodsmentioning

confidence: 94%

“…Since the forecast model is formulated with fully unstructured quadrilateral meshes based on the cubed‐sphere grid which is distributed irregularly when projected on the longitude and latitude grids, more careful examination is required to search for the closest background points surrounding the position of observation within an observation (forward) operator. The Jordan Curve Theorem is applied for a search algorithm to sample grid points enclosing the position of observation nearby in our LETKF system (Shin et al ., ). As a result, remapping between two different grid structures is not required within an observation operator.…”

Section: Methodsmentioning

confidence: 97%

“…We use the same spatial resolution as in Shin et al . (), and the average grid spacing at the Equator is 1° with the minimum grid spacing of 0.83° (Evans et al ., ). The simulations were run using the initial atmospheric states and ancillary data including sea‐surface temperature (SST), snow and ice obtained from the National Centers for Environmental Prediction (NCEP) Global Forecast System (GFS) analysis (Environmental Modeling Center, 2003).…”

Section: Methodsmentioning

confidence: 99%

See 3 more Smart Citations

Ensemble singular vectors as additive inflation in the Local Ensemble Transform Kalman Filter (LETKF) framework with a global NWP model

Shin

Kang

Yang

et al. 2018

Quart J Royal Meteoro Soc

Self Cite

View full text Add to dashboard Cite

We test an ensemble data assimilation system using the four-dimensional Local Ensemble Transform Kalman Filter (4D-LETKF) for a global numerical weather prediction (NWP) model with unstructured grids on the cubed-sphere. It is challenging to selectively represent structures of dynamically growing errors in background states under system uncertainties such as sampling and model errors. We compute Ensemble Singular Vectors (ESVs) in an attempt to capture fast-growing errors on the subspace spanned by ensemble perturbations, and use them as additive inflation to enlarge the covariance in the area where errors are flow-dependently growing. The performance of the 4D-LETKF system with ESVs is evaluated in real data assimilation, as well as Observing System Simulation Experiments (OSSEs). We find that leading ESVs help to capture fast-growing errors effectively, especially when model errors are present, and that the use of ESVs as additive inflation significantly improves the performance of the 4D-LETKF. KEYWORDS atmospheric global model, ensemble data assimilation, ensemble singular vectors, local ensemble transform Kalman filter, numerical weather prediction 1This is an open access article under the terms of the Creative Commons Attribution License, which permits use, distribution and reproduction in any medium, provided the original work is properly cited.

show abstract

Section: Discussionmentioning

confidence: 97%

Section: Methodsmentioning

confidence: 94%

Section: Methodsmentioning

confidence: 97%

Section: Methodsmentioning

confidence: 99%

See 2 more Smart Citations

Ensemble singular vectors as additive inflation in the Local Ensemble Transform Kalman Filter (LETKF) framework with a global NWP model

Shin

Kang

Yang

et al. 2018

Quart J Royal Meteoro Soc

Self Cite

View full text Add to dashboard Cite

show abstract

“…To obtain the ensemble forecasts every 6 h, we exploit the Local Ensemble Transform Kalman Filter at KIAPS (LETKF: Hunt et al ., ; Shin et al ., ; ). We use the four‐dimensional version of LETKF formulation and set 50 ensemble members for this study, which assimilates observations in a similar way to H4DEV in the aspect of using ensemble trajectory (Hunt et al ., ).…”

Section: Methods and Datamentioning

confidence: 99%

Effects of the wind–mass balance constraint on ensemble forecasts in the hybrid‐4DEnVar

Song

Kang

2019

Quart J Royal Meteoro Soc

View full text Add to dashboard Cite

Ensemble forecast covariance plays an important role in the hybridized background error covariance framework to improve the resultant analysis quality. However, a localization of ensemble samples is needed to fully take advantage of flow-dependent error modes. In this regard, it is an interesting and practical issue as to which variables, composing the ensemble perturbation, the localization should be applied to. This study examines this issue in a boreal-summer month by comparing two experimental sets: (a) a set of model variables, and (b) another set of model wind variables and unbalanced dry mass (temperature and surface pressure) used for the control variables. In the comparison, it is shown that imposing the wind-mass balance on the ensemble perturbation improves the Antarctic lower-stratospheric temperature and tropical mid-/lower-tropospheric humidity. The nonlinear wind-mass balance relationship smooths an evolution of humidity forecast forced by the resultant analysis increments.

show abstract

The Advantages of Hybrid 4DEnVar in the Context of the Forecast Sensitivity to Initial Conditions

Song

Shin

et al. 2017

JGR Atmospheres

Self Cite

View full text Add to dashboard Cite

Hybrid four‐dimensional ensemble variational data assimilation (hybrid 4DEnVar) is a prospective successor to three‐dimensional variational data assimilation (3DVar) in operational weather prediction centers currently developing a new weather prediction model and those that do not operate adjoint models. In experiments using real observations, hybrid 4DEnVar improved Northern Hemisphere (NH; 20°N–90°N) 500 hPa geopotential height forecasts up to 5 days in a NH summer month compared to 3DVar, with statistical significance. This result is verified against ERA‐Interim through a Monte Carlo test. By a regression analysis, the sensitivity of 5 day forecast is associated with the quality of the initial condition. The increased analysis skill for midtropospheric midlatitude temperature and subtropical moisture has the most apparent effect on forecast skill in the NH including a typhoon prediction case. Through attributing the analysis improvements by hybrid 4DEnVar separately to the ensemble background error covariance (BEC), its four‐dimensional (4‐D) extension, and climatological BEC, it is revealed that the ensemble BEC contributes to the subtropical moisture analysis, whereas the 4‐D extension does to the midtropospheric midlatitude temperature. This result implies that hourly wind‐mass correlation in 6 h analysis window is required to extract the potential of hybrid 4DEnVar for the midlatitude temperature analysis to the maximum. However, the temporal ensemble correlation, in hourly time scale, between moisture and another variable is invalid so that it could not work for improving the hybrid 4DEnVar analysis.

show abstract

The Local Ensemble Transform Kalman Filter (LETKF) with a Global NWP Model on the Cubed Sphere

Cited by 23 publications

References 26 publications

Ensemble singular vectors as additive inflation in the Local Ensemble Transform Kalman Filter (LETKF) framework with a global NWP model

Ensemble singular vectors as additive inflation in the Local Ensemble Transform Kalman Filter (LETKF) framework with a global NWP model

Effects of the wind–mass balance constraint on ensemble forecasts in the hybrid‐4DEnVar

The Advantages of Hybrid 4DEnVar in the Context of the Forecast Sensitivity to Initial Conditions

Contact Info

Product

Resources

About