A local ensemble Kalman filter for atmospheric data assimilation

Ott, Edward; Hunt, Brian R.; Szunyogh, Istvan; Zimin, Aleksey V.; Kostelich, Eric J.; Corazza, M.; Kalnay, Eugenia; Patil, D. J.; Yorke, James A.

doi:10.3402/tellusa.v56i5.14462

Cited by 436 publications

(273 citation statements)

References 24 publications

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“…These perturbations differ from the background perturbations as little as possible while still remaining a square root of P a (Ott et al, 2004). For a full discussion of the equivalence and relationship between (10)Á(12) and (1)Á(3), see Hunt et al (2007).…”

Section: Ensemble Kalman Filters (Enkfs)mentioning

confidence: 99%

“…(10)Á(12) and restricts the resulting analysis to that grid point alone. Observation localisation is motivated and explored in (Houtekamer and Mitchell, 1998;Ott et al, 2004;Hunt et al, 2007;Greybush et al, 2011). We provide a limited motivation here.…”

Section: Ensemble Kalman Filters (Enkfs)mentioning

confidence: 99%

“…ETKF. We tested ensemble data assimilation with FSA on the ETKF (Bishop et al, 2001;Wang et al, 2004) and the Local Ensemble Transform Kalman Filter (LETKF) (Ott et al, 2004;Hunt et al, 2007). We implemented both methods according to the formulation in Hunt et al (2007) (see Subsection 2.1.2 for the relationship between LETKF and ETKF).…”

Section: Ensemble Kalman Filters (Enkfs)mentioning

confidence: 99%

“…Ensemble data assimilation uses an ensemble of forecasts to provide a statistical sampling of the plausible atmospheric states, estimating the forecast error covariance through sample statistics. Ensemble Kalman filters (EnKFs) fit the ensemble mean to the observations and provide a collection of plausible estimates (the ensemble) to represent the remaining uncertainty (Evensen, 1994;Burgers et al, 1998;Houtekamer and Mitchell, 1998;Anderson and Anderson, 1999;Bishop et al, 2001;Ott et al, 2004;Wang et al, 2004). Propagating this analysis ensemble provides the sample mean and covariance for the next assimilation cycle.…”

Section: Introductionmentioning

confidence: 99%

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Ensemble data assimilation with an adjusted forecast spread

Rainwater¹,

Hunt

2013

Tellus A: Dynamic Meteorology and Oceanography

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Ensemble data assimilation typically evolves an ensemble of model states whose spread is intended to represent the algorithm's uncertainty about the state of the physical system that produces the data. The analysis phase treats the forecast ensemble as a random sample from a background distribution, and it transforms the ensemble according to the background and observation error statistics to provide an appropriate sample for the next forecast phase. We find that in the presence of model nonlinearity and model error, it can be fruitful to rescale the ensemble spread prior to the forecast and then reverse this rescaling after the forecast. We call this approach forecast spread adjustment, which we discuss and test in this article using an ensemble Kalman filter and a 2005 model due to Lorenz. We argue that forecast spread adjustment provides a tunable parameter, that is, complementary to covariance inflation, which cumulatively increases ensemble spread to compensate for underestimation of uncertainty. We also show that as the adjustment parameter approaches zero, the filter approaches the extended Kalman filter if the ensemble size is sufficiently large. We find that varying the adjustment parameter can significantly reduce analysis and forecast errors in some cases. We evaluate how the improvement provided by forecast spread adjustment depends on ensemble size, observation error and model error. Our results indicate that the technique is most effective for small ensembles, small observation error and large model error, though the effectiveness depends significantly on the nature of the model error

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Section: Ensemble Kalman Filters (Enkfs)mentioning

confidence: 99%

Section: Ensemble Kalman Filters (Enkfs)mentioning

confidence: 99%

Section: Ensemble Kalman Filters (Enkfs)mentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Ensemble data assimilation with an adjusted forecast spread

Rainwater¹,

Hunt

2013

Tellus A: Dynamic Meteorology and Oceanography

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“…The LETKF (Hunt et al, 2007) is an advanced data assimilation method based on the local ensemble Kalman filter (LEKF; Ott et al, 2004), where the ensemble update method of the ensemble transform Kalman filter (ETKF; Bishop et al, 2001) is applied to reduce computational cost. The LETKF has been coupled with a number of weather and climate models.…”

Section: Introductionmentioning

confidence: 99%

Performance evaluation of a throughput-aware framework for ensemble data assimilation: the case of NICAM-LETKF

et al. 2016

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Abstract. In this paper, we propose the design and implementation of an ensemble data assimilation (DA) framework for weather prediction at a high resolution and with a large ensemble size. We consider the deployment of this framework on the data throughput of file input/output (I/O) and multi-node communication. As an instance of the application of the proposed framework, a local ensemble transform Kalman filter (LETKF) was used with a Non-hydrostatic Icosahedral Atmospheric Model (NICAM) for the DA system. Benchmark tests were performed using the K computer, a massive parallel supercomputer with distributed file systems. The results showed an improvement in total time required for the workflow as well as satisfactory scalability of up to 10 K nodes (80 K cores). With regard to highperformance computing systems, where data throughput performance increases at a slower rate than computational performance, our new framework for ensemble DA systems promises drastic reduction of total execution time.

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Observation system simulation experiments using water vapor isotope information

Yoshimura

Miyoshi

Kanamitsu

2014

J. Geophys. Res. Atmos.

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Measurements of water vapor isotopes (δ 18 O and δD) have dramatically increased in recent years with the availability of new spectroscopic technology. To utilize these data more efficiently, this study first developed a new data assimilation system using a local transform ensemble Kalman filter (LETKF) and the Isotope-incorporated Global Spectral Model (IsoGSM). An observation system simulation experiment (OSSE) was then conducted. The OSSE used a synthetic data set of vapor isotope measurements, mimicking Tropospheric Emission Spectrometer (TES)-retrieved δD from the mid-troposphere, SCanning Imaging Absorption spectroMeter for Atmospheric CHartographY (SCIAMACHY)-retrieved δD from the water vapor column, and the virtual Global Network of Isotopes in Precipitation (GNIP)-like surface vapor isotope (both δD and δ 18 O) monitoring network. For TES and SCIAMACHY, we assumed a similar spatiotemporal coverage as that of the real data sets. For the virtual GNIP-like network, we assumed~200 sites worldwide and 6-hourly measurements. An OSSE with 20 ensemble members was then conducted for January 2006. The results showed a significant improvement in not only the vapor isotopic field but also meteorological fields, such as wind speed, temperature, surface pressure, and humidity, when compared with a test with no observations. For surface air temperature, the global root mean square error has dropped by 10%, with 40-60% of the decrease occurring in the east-southeast Asia where the concentration of observations is relatively higher. When there is a conventional radiosonde network, the improvement gained by adding isotopic measurements was small but positive for all variables.

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A local ensemble Kalman filter for atmospheric data assimilation

Cited by 436 publications

References 24 publications

Ensemble data assimilation with an adjusted forecast spread

Ensemble data assimilation with an adjusted forecast spread

Performance evaluation of a throughput-aware framework for ensemble data assimilation: the case of NICAM-LETKF

Observation system simulation experiments using water vapor isotope information

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