The ensemble Kalman filter in an operational regional NWP system: preliminary results with real observations

Bonavita, Massimo; Torrisi, L.; Marcucci, F.

doi:10.1002/qj.313

Cited by 25 publications

(32 citation statements)

References 30 publications

(40 reference statements)

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“…This larger value than the one used in the previous study (700 km: Bonavita et al, 2008) has been found to provide more accurate analysis and forecasts. Following Szunyogh et al (2008), the influence of each observation on the analysed grid point is decreased with its geophysical distance r through multiplication of the R −1 observation error matrix entries in Eqs.…”

Section: Ensemble Data Assimilation At Cnmcamentioning

confidence: 59%

“…Differently from Bonavita et al (2008), specific humidity has been added to the set of control variables in addition to temperature, zonal and meridional wind components and surface pressure. The analysis is now performed on the prognostic model's 40 vertical levels reaching up to 10 hPa, at the same horizontal resolution.…”

Section: Ensemble Data Assimilation At Cnmcamentioning

confidence: 99%

“…The model parametrization employed in Bonavita et al (2008) was a straightforward extension of the multiplicative covariance inflation technique introduced by Anderson and Anderson (1999). In the context of LETKF this inflation method is applied in the computation of the analysis error covariance Eq.…”

Section: Model Error Parametrization Experimentsmentioning

confidence: 99%

“…Thus, it is important to evaluate the magnitude of the systematic model errors and, if necessary, apply appropriate bias-removal techniques (Dee and Da Silva, 1998;Baek et al, 2006;Zupanski and Zupanski, 2006;Danforth et al, 2007). This paper is a follow-up to the earlier analysis reported in Bonavita et al (2008). In that study a prototypical EnKF data assimilation system had been set up and tested for application in the regional Numerical Weather Prediction (NWP) System of the Italian Meteorological Service (IMS).…”

Section: Introductionmentioning

confidence: 97%

“…The main results of that study had been that EnKF data assimilation is competitive with the currently operational variational technique (Bonavita and Torrisi, 2005) provided a temporally and spatially (in the vertical coordinate) varying multiplicative inflation correction is applied to the prior ensemble. In the present work many of the limitations and simplifying hypothesis invoked in Bonavita et al (2008) have been removed, thus providing results which are deemed more robust and representative of EnDA performance in a realistic NWP framework. An examination of the effectiveness of some of the above-mentioned methods to parametrize model error has been carried out, in terms of both the first and second moments of the ensemble forecast.…”

Section: Introductionmentioning

confidence: 99%

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Ensemble data assimilation with the CNMCA regional forecasting system

Bonavita¹,

Torrisi²,

Marcucci³

2010

Quart J Royal Meteoro Soc

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The Ensemble Kalman Filter (EnKF) is likely to become a viable alternative to variational methods for the next generation of meteorological and oceanographic data assimilation systems. In this work we present results from real-data assimilation experiments using the CNMCA regional numerical weather prediction (NWP) forecasting system and compare them to the currently operational variational-based analysis. The set of observations used is the same as the one ingested in the operational data stream, with the exception of satellite radiances and scatterometer winds. Results show that the EnKF-based assimilation cycle is capable of producing analyses and forecasts of consistently superior skill in the root mean square error metric than CNMCA operational 3D-Var.One of the most important issues in EnKF implementations lies in the filter tendency to become underdispersive for practical ensemble sizes. To combat this problem a number of different parametrizations of the model error unaccounted for in the assimilation cycle have been proposed. In the CNMCA system a combination of adaptive multiplicative and additive background covariance inflations has been found to give adequate results and to be capable of avoiding filter divergence in extended assimilation trials. The additive component of the covariance inflation has been implemented through the use of scaled forecast differences.Following suggestions that ensemble square-root filters can violate the gaussianity assumption when used with nonlinear prognostic models, the statistical distribution of the forecast and analysis ensembles has been studied. No sign of the ensemble collapsing onto one or a few model states has been found, and the forecast and analysis ensembles appear to stay remarkably close to the assumed probability distribution functions.

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Section: Ensemble Data Assimilation At Cnmcamentioning

confidence: 59%

Section: Ensemble Data Assimilation At Cnmcamentioning

confidence: 99%

Section: Model Error Parametrization Experimentsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 97%

Section: Introductionmentioning

confidence: 99%

See 3 more Smart Citations

Ensemble data assimilation with the CNMCA regional forecasting system

Bonavita¹,

Torrisi²,

Marcucci³

2010

Quart J Royal Meteoro Soc

Self Cite

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A review of operational methods of variational and ensemble‐variational data assimilation

Bannister

2017

Quart J Royal Meteoro Soc

309

246

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Variational and ensemble methods have been developed separately by various research and development groups and each brings its own benefits to data assimilation. In the last decade or so, various ways have been developed to combine these methods, especially with the aims of improving the background-error covariance matrices and of improving efficiency. The field has become confusing, even to many specialists, and so there is now a need to summarize the methods in order to show how they work, how they are related, what benefits they bring, why they have been developed, how they perform, and what improvements are pending. This article starts with a reminder of basic variational and ensemble techniques and shows how they can be combined to give the emerging ensemble-variational (EnVar) and hybrid methods. A key part of the article includes details of how localization is commonly represented.There has been a particular push to develop four-dimensional methods that are free of linearized forecast models. This article attempts to provide derivations of the formulations of most popular schemes. These are otherwise scattered throughout the literature or absent. It builds on the nomenclature used to distinguish between methods, and discusses further possible developments to the methods, including the representation of model error.Key Words: variational data assimilation; ensemble data assimilation; hybrid data assimilation; flow-dependent background-error covariances; localization; model error; nomenclature Data assimilation and uncertaintyDealing with uncertainty is at the heart of data assimilation (DA). Forecast models (here those used in numerical weather prediction, NWP) use initial conditions that are imperfect, and are based upon imperfect representations of physical processes. It is well known that a free-running model will accumulate errors until its forecast is no longer useful (Tribbia and Baumhefner, 2004). The only way to restore usefulness is to allow the model to be influenced by observations (Leith, 1993). DA (Daley, 1991;Kalnay, 2002;Rabier, 2005) is the procedure of maintaining the link between evolving models and reality by updating the model with fresh observations. Researchers are working towards formal and robust mathematical methods that work in ways that are consistent with the model, the data, and their degree of uncertainty. DA is related to approaches used in other fields and which go by different names, e.g. state estimation (Wunsch, 2012); optimization (Biegler, 1997); history matching (Emerick, 2012); retrieval production (Rodgers, 2000); inverse modelling (Tarantola, 2005)), and there are additionally many ways of solving a DA problem. Most known DA methods are based on probabilistic theories (most, if not all, exploit Bayes' Theorem (Lorenc, 1986)) and each is made practical by making approximations.Traditionally there are three Bayesian-based strategies that allow the DA problem to be solved in approximate (hence suboptimal) ways. These may be categorized as the following. (i) Variation...

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