Assimilation of SEVIRI Water Vapor Channels With an Ensemble Kalman Filter on the Convective Scale

Hutt, Axel; Schraff, Christoph; Anlauf, H.; Bach, Liselotte; Baldauf, Michael; Bauernschubert, Elisabeth; Cress, Alexander; Faulwetter, Robin; Fundel, Felix; Köpken-Watts, Christina; Reich, Hendrik; Schomburg, Annika; Schröttle, Josef; Stephan, Klaus; Stiller, Olaf; Weißmann, Martin; Potthast, Roland

doi:10.3389/feart.2020.00070

Cited by 7 publications

(10 citation statements)

References 74 publications

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“…Since these are not located at a single spatial site, it is nontrivial to include them in the LETKF that assumes a single observation location. To this end, several previous studies have suggested corresponding approaches [13,24,[30][31][32][33][34][35][36]. A reasonable approximation for the spatial location of a nonlocal observation is the location of the maximum sensitivity [10,37], i.e., max n H kn of nonlocal observation k. Although this approximation has been shown to yield good results, it introduces a considerable error for broad sensitivity functions, i.e., r H is large.…”

Section: Localizationmentioning

confidence: 99%

“…Moreover, several trials with the same parameters exhibit catastrophic filter divergence and the shown trial is a rare case. This divergence could have been avoided by implementing stabilizing features, such as ensemble enlargement [19], adaptive localization [29], adaptive inflation [18], or first guess check [13,45]. However, these methods would have introduced additional assimilation effects and the gained results would not have been comparable to findings and insights in the remaining work.…”

Section: Local and Nonlocal Observationsmentioning

confidence: 99%

“…In fact, present localization definitions [10,12] of nonlocal observations attempt to estimate the best single spatial location neglecting the spatial distribution of possible activity sources. A recent study [13] on satellite data assimilation proposes to choose the localization radius equal to the spatial distribution width of radiation sources. This spatial source distribution is the sensitivity function of the nonlocal observation and is part of the model system.…”

Section: Introductionmentioning

confidence: 99%

“…The underlying motivation of this work is the experience from meteorological data assimilation, that satellite data are detrimental to forecasts if assimilation procedure is not welltuned [12,13,22]. This effect is supposed to result from deficits in the underlying model.…”

Section: Introductionmentioning

confidence: 99%

“…What is the role of localization in this context? Is there a fundamental optimal relation between localization and sensitivity function as found in [13]? The present work addresses these questions in the following sections.…”

Section: Introductionmentioning

confidence: 99%

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Divergence of the Ensemble Transform Kalman Filter (LETKF) by Nonlocal Observations

Hutt

2020

Front. Appl. Math. Stat.

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Ensemble Kalman filters are powerful tools to merge model dynamics and observation data. For large system models, they are known to diverge due to subsampling errors at small ensemble size and thus possible spurious correlations in forecast error covariances. The Local Ensemble Transform Kalman filter (LETKF) remedies these disadvantages by localization in observation space. However, its application to nonlocal observations is still under debate since it is still not clear how to optimally localize nonlocal observations. The present work studies intermittent divergence of filter innovations and shows that it increases forecast errors. Nonlocal observations enhance such innovation divergence under certain conditions, whereas similar localization radius and sensitivity function width of nonlocal observations minimizes the divergence rate. The analysis of the LETKF reveals inconsistencies in the assimilation of observed and unobserved model grid points which may yield detrimental effects. These inconsistencies inter alia indicate that the localization radius should be larger than the sensitivity function width if spatially synchronized system activity is expected. Moreover, the shift of observation power from observed to unobserved grid points hypothesized in the context of catastrophic filter divergence is supported for intermittent innovation divergence. Further possible mechanisms yielding such innovation divergence are ensemble member alignment and a novel covariation between background perturbations in location and observation space.

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Section: Localizationmentioning

confidence: 99%

Section: Local and Nonlocal Observationsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Divergence of the Ensemble Transform Kalman Filter (LETKF) by Nonlocal Observations

Hutt

2020

Front. Appl. Math. Stat.

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Improving the representation of the atmospheric boundary layer by direct assimilation of ground‐based microwave radiometer observations

Vural,

Merker,

Löffler

et al. 2024

Quart J Royal Meteoro Soc

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In a joint effort, MeteoSwiss and Deutscher Wetterdienst (DWD) address the need for improving the initial state of the atmospheric boundary layer (ABL) by exploiting ground‐based profiling observations that aim to fill the existing observational gap in the ABL. We implemented the brightness temperature observations from ground‐based microwave radiometers (MWRs) into our data assimilation systems using a local ensemble transform Kalman filter (LETKF) with RTTOV‐gb (Radiative Transfer for TOVS, ground‐based) as a forward operator. We were able to obtain a positive impact on the brightness temperature first guess and analysis as well as a slight impact on the ABL humidity using two MWRs at MeteoSwiss. These results led to a subsequent operational implementation of the observing system at MeteoSwiss. Furthermore, we performed an extensive set of assimilation experiments at DWD to further investigate various aspects such as the vertical localisation of selected single channels. We obtained a positive impact on the 6 h‐forecast of ABL temperature and humidity by assimilating two channels employing a dynamical localisation based on the sensitivity functions of RTTOV‐gb but also with a static localisation in a single‐channel setup. Our experiments indicate the importance of the vertical localisation when using more than one channel, although reliable improvements are challenging to obtain without a larger number of observations for both assimilation and verification.This article is protected by copyright. All rights reserved.

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All-sky infrared radiance data assimilation of FY-4A AGRI with different physical parameterizations for the prediction of an extremely heavy rainfall event

Zhang

Liu

et al. 2023

Atmospheric Research

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Assimilation of SEVIRI Water Vapor Channels With an Ensemble Kalman Filter on the Convective Scale

Cited by 7 publications

References 74 publications

Divergence of the Ensemble Transform Kalman Filter (LETKF) by Nonlocal Observations

Divergence of the Ensemble Transform Kalman Filter (LETKF) by Nonlocal Observations

Improving the representation of the atmospheric boundary layer by direct assimilation of ground‐based microwave radiometer observations

All-sky infrared radiance data assimilation of FY-4A AGRI with different physical parameterizations for the prediction of an extremely heavy rainfall event

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