The Data Assimilation Research Testbed: A Community Facility

Anderson, Jeffrey L.; Hoar, T. J.; Raeder, K.; Liu, Hui; Collins, Nancy; Torn, Ryan D.; Avellano, Avelino

doi:10.1175/2009bams2618.1

Cited by 569 publications

(458 citation statements)

References 46 publications

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“…The DART (Anderson et al, 2009) is used to perform the data assimilation calculations of the EnKF, and it is able to interface directly with WRF. The DART has a variety of algorithms for the ensemble filters, such as ensemble adjustment Kalman filter (EAKF; Anderson, 2001), ensemble Kalman filter with perturbed observations (Evensen, 1994), and particle filter (Snyder et al, 2008), to name a few.…”

Section: Wrf/dart Model Descriptionmentioning

confidence: 99%

“…The EnKF calculations are performed within the Data Assimilation Research Testbed (DART; Anderson et al, 2009). As in our previous papers, the HNEnKF is again compared to the EnKF and nudging applied separately, but here we perform a real-data study with truly independent/indirect verification of the data assimilation results against surface tracer observations.…”

Section: Introductionmentioning

confidence: 99%

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A hybrid nudging‐ensemble Kalman filter approach to data assimilation in WRF/DART

Lei

Stauffer

Deng

2012

Quart J Royal Meteoro Soc

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A hybrid nudging-ensemble Kalman filter (HNEnKF), previously tested in the Lorenz three-variable model and a two-dimensional shallow-water model, is now explored in WRF/DART with real observations using a Cross Appalachian Tracer Experiment case from September 1983 (CAPTEX-83). The HNEnKF, effectively combining observation nudging and the ensemble Kalman filter (EnKF), achieves a more gradual data assimilation and greatly reduces the insertion noise compared to the EnKF.Three-hourly surface observations and 12-hourly rawinsonde observations from the World Meteorological Organization (WMO) are assimilated. These assimilated meteorological observations are used to verify the priors of the experiments. It is found that the HNEnKF generally obtains better priors than the EnKF. To independently verify the HNEnKF approach, the hourly WRF experiment results are also used to drive the Second-Order Closure Integrated Puff (SCIPUFF) model to predict surface tracer concentrations that are verified against the observed surface concentration data. The HNEnKF analyses driving SCIPUFF produce better statistics of the independent tracer data than the EnKF. Thus there appear to be some advantages in the hourly dynamic analyses produced by the continuous HNEnKF compared to the intermittent EnKF. The analyses of surface pressure tendency demonstrate that the HNEnKF is able to provide better temporal smoothness and dynamic consistency in the hourly analyses than the EnKF.

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Section: Wrf/dart Model Descriptionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

A hybrid nudging‐ensemble Kalman filter approach to data assimilation in WRF/DART

Lei

Stauffer

Deng

2012

Quart J Royal Meteoro Soc

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“…A regional NWP system can be used to increase the model resolution of an area of interest at a relatively low cost. Therefore, regional EnKF systems have also been widely developed and tested (Zhang et al 2006;Meng and Zhang 2007;Torn and Hakim 2008;Anderson et al 2009;Miyoshi and Aranami 2006;Miyoshi and Kunii 2012;Kunii 2014), and they have shown promising results in analyzing and predicting a variety of mesoscale phenomena, such as tropical cyclones (e.g., Zhang et al 2009;Zhang and Weng 2015;Torn 2010) and convective rainstorms (e.g., Yussouf et al 2013;Kunii 2014;Jones et al 2015).…”

Section: Introductionmentioning

confidence: 99%

“…Meanwhile, we also aim to build a highly configurable regional LETKF data assimilation which can be conveniently used in a wide range of research. There have been a few existing ensemble data assimilation systems available for research use, such as the Data Assimilation Research Testbed (DART; Anderson et al 2009) and Community Gridpoint Statistical Interpolation (Community GSI; Shao et al 2015). The distinct foci of the SCALE-LETKF include 1) implementing the LETKF scheme which is not included in DART or Community GSI, 2) providing a data assimilation package for the SCALE-RM, and 3) having been tested on the Japan's flagship supercomputer, the K computer, with over 70,000 nodes during the development, aware of the parallelization efficiency of high performance computing.…”

Section: Introductionmentioning

confidence: 99%

The Near-Real-Time SCALE-LETKF System: A Case of the September 2015 Kanto-Tohoku Heavy Rainfall

Lien

Miyoshi

Nishizawa

et al. 2017

SOLA

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With a goal of real-time, high-resolution, short-term prediction of heavy rainfall systems, the SCALE-LETKF was developed implementing the local ensemble transform Kalman filter with the Scalable Computing for Advanced Library and EnvironmentRegional Model (SCALE-RM). The system has been running in near real time experimentally since May 2015, configured for weather analyses and forecasts at 18-km resolution for a 5760 × 4320 km area around Japan. Among the data for more than one year, the near-real-time forecasts and the 3-km resolution downscaling simulations are demonstrated for a selected case of the September 2015 Kanto-Tohoku heavy rainfall associated with Typhoon Etau (2015). The typhoon track was successfully analyzed and predicted by the system, and the line-shaped rainband producing heavy rainfall can be reasonably forecasted by the downscaling simulation from the near-real-time data.

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“…Zhang et al (2007) then applied the EAKF to a coupled climate model and analyzed the meridional overturning circulation from the assimilated results. Anderson et al (2009) developed the Data Assimilation Research Testbed for data assimilation research, education, and development. This method was also used in other models, such as the El Niño/La Niña-Southern Oscillation models (Karspeck and Anderson 2007) and regional ocean models (Yin et al 2010a).…”

Section: Introductionmentioning

confidence: 99%

Using ensemble adjustment Kalman filter to assimilate Argo profiles in a global OGCM

Yin

Shu

2011

Ocean Dynamics

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An ensemble adjustment Kalman filter (EAKF) is used to assimilate Argo profiles of 2008 in a global version of the Modular Ocean Model version 4. Four assimilation experiments are carried out to compare with the simulation without data assimilation, which serves as the control experiment. All experiment results are compared with dataset of Global Temperature-Salinity Profile Program and satellite sea surface temperature (SST). The first experiment (Exp 1) is implemented by perturbing temperature of upper layers in the initial conditions (ICs) with an amplitude of 1.0°C and no ensemble inflation. The results from Exp 1 show that the simulated temperature (salinity) deviation in the upper 400 m (500 m) is reduced through Argo data assimilation; however, these deviations are increased in deeper layers. The error reduction in SST is much greater during January to June than during the rest of the year. Three more experiments are designed to understand the responses in different layers and months. Two of them test model sensitivities to ICs by perturbing them vertically: one over the vertical extent of the whole water column (Exp 2) and the other employs smaller perturbation amplitude of 0.1°C (Exp 3). Exp 2 shows that the simulated temperature and salinity deviations are systematically improved in the whole water column. Comparison between Exps 2 and 3 suggests that perturbation amplitude is important. Exp 4 tests the influence of the optimal inflation factor of 5%, which is determined by other set of numerical tests. Exp 4 improves assimilation performance much more than the other three experiments without inflation. Therefore, we conclude that the perturbation should be introduced to all model layers, proper perturbation amplitude is important for Ocean data assimilation using EAKF, and the ensemble inflation by an optimal inflation is critical to improve the skill of the EAKF analysis.

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The Data Assimilation Research Testbed: A Community Facility

Cited by 569 publications

References 46 publications

A hybrid nudging‐ensemble Kalman filter approach to data assimilation in WRF/DART

A hybrid nudging‐ensemble Kalman filter approach to data assimilation in WRF/DART

The Near-Real-Time SCALE-LETKF System: A Case of the September 2015 Kanto-Tohoku Heavy Rainfall

Using ensemble adjustment Kalman filter to assimilate Argo profiles in a global OGCM

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