Optimal Localization for Ensemble Kalman Filter Systems

Periáñez, África; Reich, Hendrik; Potthast, Roland

doi:10.2151/jmsj.2014-605

Cited by 28 publications

(15 citation statements)

References 42 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Covariance localization was performed by using only those observations located within a specified horizontal radius of a given analysis point. An adaptive horizontal localization radius was used for the conventional observations (Perianez et al 2014); however, it was set to 35 km for the all-sky SEVIRI brightness temperatures given their uniform data coverage. The vertical localization scale was set to 0.7 in logarithm of pressure for the brightness temperatures, with the localization height determined using the peak of the satellite weighting function for the simulated brightness temperature from the deterministic run.…”

Section: B Kenda Data Assimilation Systemmentioning

confidence: 99%

Assimilation of All-Sky SEVIRI Infrared Brightness Temperatures in a Regional-Scale Ensemble Data Assimilation System

Otkin

Potthast

2019

Monthly Weather Review

Self Cite

View full text Add to dashboard Cite

Ensemble data assimilation experiments were performed to assess the ability of satellite all-sky infrared brightness temperatures and different bias correction (BC) predictors to improve the accuracy of short-range forecasts used as the model background during each assimilation cycle. Satellite observations sensitive to clouds and water vapor in the upper troposphere were assimilated at hourly intervals during a 3-day period. Linear and nonlinear conditional biases were removed from the infrared observations using a Taylor series polynomial expansion of the observation-minus-background departures and BC predictors sensitive to clouds and water vapor or to variations in the satellite zenith angle. Assimilating the all-sky infrared brightness temperatures without BC degraded the forecast accuracy based on comparisons to radiosonde observations. Removal of the linear and nonlinear conditional biases from the satellite observations substantially improved the results, with predictors sensitive to the location of the cloud top having the largest impact, especially when higher-order nonlinear BC terms were used. Overall, experiments employing the observed cloud-top height or observed brightness temperature as the bias predictor had the smallest water vapor, cloud, and wind speed errors, while also having less degradation to temperatures than occurred when using other predictors. The forecast errors were smaller during these experiments because the cloud-height-sensitive BC predictors were able to more effectively remove the large conditional biases for lower brightness temperatures associated with a deficiency in upper-level clouds in the model background.

show abstract

Section: B Kenda Data Assimilation Systemmentioning

confidence: 99%

Assimilation of All-Sky SEVIRI Infrared Brightness Temperatures in a Regional-Scale Ensemble Data Assimilation System

Otkin

Potthast

2019

Monthly Weather Review

Self Cite

View full text Add to dashboard Cite

show abstract

“…Anderson and Lei (2013) have presented a methodology to develop an empirical localization based on the observations. Perianez et al (2014) used the observation error, the density of measurements and the error of ensemble space in the localization construction to minimize the analysis error. Finally, in some studies the localization function is defined using optimal linear filtering and sample centred moments estimation in order to remove the noise from the background error covariance (Ménétrier et al, 2015a;2015b).…”

Section: Localizationmentioning

confidence: 99%

“…Since the results of these experiments could be dependent of the number of assimilated stations (Perianez et al, 2014), experiments 1 to 4 have also been conducted using the density of 1930 (not shown), i.e. closer to the average condition over the reanalysis period.…”

Section: Impact Of the Anamorphosis: Experiments 3 Andmentioning

confidence: 99%

See 1 more Smart Citation

A framework for high‐resolution meteorological surface reanalysis through offline data assimilation in an ensemble of downscaled reconstructions

Devers¹,

Vidal²,

Lauvernet³

et al. 2019

Quart J Royal Meteoro Soc

View full text Add to dashboard Cite

The knowledge of historical French weather has recently been improved through the development of the Spatially COherent Probabilistic Extended (SCOPE) climate reconstructions. This high‐resolution ensemble daily reconstruction dataset of precipitation and temperature covers the period 1871–2012 and is derived through a statistical downscaling of the Twentieth Century Reanalysis. Historical surface observations – even though rather scarce and sparse – do exist from at least the beginning of the period considered, and this information does not currently feed SCOPE Climate. We propose to construct a new high‐resolution surface reanalysis over France – called FYRE Climate (French hYdrometeorological REanalysis Climate) – by assimilating daily temperature and precipitation observations into SCOPE Climate through an offline Ensemble Kalman Filter. The goal of the study is to test methodological choices for developing the future FYRE Climate surface reanalysis. The data assimilation scheme is evaluated over the 2009–2012 period with an increasing assimilated observation density reproducing historical densities. A consistent set of independent stations is retained for validation in terms of continuous ranked probability score, daily bias and daily correlation. Results highlight the importance of the localization as well as the Gaussian anamorphosis for precipitation. They show that: (a) the reanalysis has a lower uncertainty than the initial SCOPE Climate reconstructions, (b) its reliability is similar, and (c) the data assimilation allows reaching the performance of the reference Safran surface reanalysis over France, even for a density of observations as low as the one of 1950. These results thus pave the way for a full 140‐year high‐resolution precipitation and temperature surface reanalysis over France.

show abstract

“…This accessible and relatively inexpensive model has recently been used as a test-bed for convective-scale DA (Perianez et al, 2014). This model provides a challenging environment for experimentation.…”

Section: Introductionmentioning

confidence: 99%

A weak-constraint 4DEnsembleVar. Part II: experiments with larger models

Goodliff¹,

Amezcua²,

Leeuwen

2017

Tellus A: Dynamic Meteorology and Oceanography

View full text Add to dashboard Cite

A B S T R A C TIn recent years, hybrid data-assimilation methods which avoid computation of tangent linear and adjoint models by using ensemble 4-dimensional cross-time covariances have become a popular topic in Numerical Weather Prediction. 4DEnsembleVar is one such method. In spite of its capabilities, its application can sometimes become problematic due to the not-trivial task of localising cross-time covariances. In this work we propose a formulation that helps to alleviate such issues by exploiting the presence of model error, i.e. a weak-constraint 4DEnsembleVar. We compare the weak-constraint 4DEnsembleVar to that of other data-assimilation methods. This is part II of a twopart paper. In part I, we describe the 4DEnsembleVar framework and problems with localised temporal crosscovariances associated with this method are discussed and illustrated on the Korteweg de Vries model. We also introduce our weak-constraint 4DEnsemble-Var formulation and show how it can alleviate-at least partially-the problem of having low-quality time cross-covariances. The second part of this paper deals with experiments on larger and more complicated models, namely the Lorenz 1996 model and a modified shallow-water model with simulated convection, both of them under the presence of model error. We investigate the performance of weak-constraint 4DEnsembleVar against strong-constraint 4DEnsembleVar (both with and without localisation) and other traditional methods (4DVar and the Local Ensemble Transform Kalman Smoother). Using the analysis root mean square error (RMSE) as a metric, these methods have been compared considering observation density (in time and space), observation period, ensemble sizes and assimilation window length. In this part we also explain how to perform outer loops in the EnVar methods. We show that their use can be counter-productive if the presence of model error is ignored by the assimilation method. We show that the addition of a weak-constraint generally improves the RMSE of 4DEnVar in cases where model error has time to develop, especially in cases with long assimilation windows and infrequent observations. We have assumed good knowledge of the statistics of this model error.

show abstract

Optimal Localization for Ensemble Kalman Filter Systems

Cited by 28 publications

References 42 publications

Assimilation of All-Sky SEVIRI Infrared Brightness Temperatures in a Regional-Scale Ensemble Data Assimilation System

Assimilation of All-Sky SEVIRI Infrared Brightness Temperatures in a Regional-Scale Ensemble Data Assimilation System

A framework for high‐resolution meteorological surface reanalysis through offline data assimilation in an ensemble of downscaled reconstructions

A weak-constraint 4DEnsembleVar. Part II: experiments with larger models

Contact Info

Product

Resources

About