Representation of Model Error in Convective‐Scale Data Assimilation: Additive Noise, Relaxation Methods, and Combinations

Zeng, Yuefei; Janjić, Tijana; Lozar, Alberto de; Blahak, Ulrich; Reich, Hendrik; Keil, Christian; Seifert, Axel

doi:10.1029/2018ms001375

Cited by 28 publications

(41 citation statements)

References 92 publications

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“…; Zeng et al . give detailed descriptions of the synoptic situation). From 29 May to 3 June an upper‐level trough was responsible for synoptic lifting, whereas from 4 to 7 June the synoptic forcing was weak and precipitation was mostly triggered by local non‐equilibrium convection across Germany.…”

Section: Synoptic Regimes During a High‐impact Weather Period In 2016mentioning

confidence: 99%

Relative contribution of soil moisture, boundary‐layer and microphysical perturbations on convective predictability in different weather regimes

Keil

Baur

Bachmann³

et al. 2019

Quart J Royal Meteoro Soc

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The relative contributions of soil moisture heterogeneities, a stochastic boundary‐layer perturbation scheme and varied aerosol concentrations representing microphysical uncertainties on the diurnal cycle of convective precipitation and its spatial variability are examined conditional on the prevailing weather regime. To achieve this, separate perturbed‐parameter ensemble simulations are performed with the Consortium for Small‐scale Modeling (COSMO) model at convection‐permitting horizontal grid spacing for 10 days during a high‐impact weather episode in 2016 in Central Europe. We consider hourly precipitation amounts and their spatial distribution, focus on ensemble mean and spread aggregated over strong and weak forcing conditions, and employ spatial evaluation techniques. The convective adjustment time‐scale diagnostic is used to distinguish the different precipitation regimes. While the total amount of daily precipitation is hardly changed by the different perturbation approaches (less than 5%), the spatial variability of precipitation exhibits clear differences. Soil moisture heterogeneity primarily introduces variability during convection initiation causing a steeper increase in normalized rainfall spread prior to the onset of afternoon precipitation. The stochastic boundary‐layer perturbations lead to the largest spatial variability impacting precipitation from initial time onwards with an amplitude comparable to the operational ensemble spread. Similarly, perturbed aerosol concentrations impact spatial precipitation variability from the model start onwards, but to a smaller degree. Soil moisture heterogeneity shows the strongest weather regime dependence, with the greatest impact on convection during weak synoptic forcing. All types of perturbation increase dispersion of precipitation while maintaining the domain‐averaged precipitation rates.

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Section: Synoptic Regimes During a High‐impact Weather Period In 2016mentioning

confidence: 99%

Relative contribution of soil moisture, boundary‐layer and microphysical perturbations on convective predictability in different weather regimes

Keil

Baur

Bachmann³

et al. 2019

Quart J Royal Meteoro Soc

Self Cite

View full text Add to dashboard Cite

show abstract

“…In the first week (from 27 May to 2 June) under strong large‐scale forcing weather conditions, the convective activity was characterized by larger‐scale precipitation patterns caused by frontal ascent, whereas much more scattered convective cells triggered by local mechanisms prevailed in the second week (from 3 June to 9 June) under weak forcing conditions. It has been shown in Zeng et al () that the LAN, based on random samples from climatological atmospheric background error covariance used by the global EnVar data assimilation system, mimics large‐scale uncertainties arising from the global driving model and performs equally or even better than relaxation methods as well as combinations under strong forcing weather conditions. Its performance degrades a bit under weak forcing conditions, assumedly due to being less representative for small‐scale features.…”

Section: Brief Summary Of Experimental Setupmentioning

confidence: 99%

“…The experimental setup is given in Table , including combinations of the LAN and SAN, that is,

x^{a false(i false)} \leftarrow x^{a false(i false)} + α_{L} 𝛈_{L}^{false(i false)} + α_{S} 𝛈_{S}^{false(i false)},

where

𝛈_{L}^{false(i false)}

and

𝛈_{S}^{false(i false)}

are random large‐ and small‐scale samples, respectively. The tunable parameter α L for the LAN has been tuned in Zeng et al () and set to 0.1. With the LAN, u , v , q v , T , and p are perturbed.…”

Section: Brief Summary Of Experimental Setupmentioning

confidence: 99%

“…where (i) L and (i) S are random large-and small-scale samples, respectively. The tunable parameter L for the LAN has been tuned in Zeng et al (2018) and set to 0.1. With the LAN, u, v, q v , T, and p are perturbed.…”

Section: Brief Summary Of Experimental Setupmentioning

confidence: 99%

“…By using this sort of adaptive technique, they were able to reduce thermodynamic biases. Zeng et al () showed that large‐scale additive noise (hereafter denoted by “LAN”) that is based on random samples from climatological atmospheric background error covariance used by the global EnVar data assimilation system may partially represent large‐scale error arising from the global driving model ICON (ICOsahedral Nonhydrostatic; Zängl et al, ). The LAN performs equally or even better than the relaxation methods RTPP (relaxation to prior perturbations; Zhang et al, ) and RTPS (relaxation to prior spread; Whitaker & Hamill, ) as well as combinations under strong forcing weather conditions, but the performance of the LAN degrades a bit under weak forcing conditions, assumedly due to being less representative for small‐scale features.…”

Section: Introductionmentioning

confidence: 99%

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Representation of Model Error in Convective‐Scale Data Assimilation: Additive Noise Based on Model Truncation Error

Zeng

Janjić

Sommer

et al. 2019

J Adv Model Earth Syst

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To account for model error on multiple scales in convective‐scale data assimilation, we incorporate the small‐scale additive noise based on random samples of model truncation error and combine it with the large‐scale additive noise based on random samples from global climatological atmospheric background error covariance. A series of experiments have been executed in the framework of the operational Kilometre‐scale ENsemble Data Assimilation system of the Deutscher Wetterdienst for a 2‐week period with different types of synoptic forcing of convection (i.e., strong or weak forcing). It is shown that the combination of large‐ and small‐scale additive noise is better than the application of large‐scale noise only. The specific increase in the background ensemble spread during data assimilation enhances the quality of short‐term 6‐hr precipitation forecasts. The improvement is especially significant during the weak forcing period, since the small‐scale additive noise increases the small‐scale variability which may favor occurrence of convection. It is also shown that additional perturbation of vertical velocity can further advance the performance of combination.

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Assimilating visible satellite images for convective‐scale numerical weather prediction: A case‐study

Scheck

Weißmann

Bach

2020

Quart J Royal Meteoro Soc

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Satellite images in the visible spectral range contain high‐resolution cloud information, but have not been assimilated directly before. This paper presents a case‐study on the assimilation of visible Meteosat SEVIRI images in a convective‐scale data assimilation system based on a local ensemble transform Kalman filter (LETKF) in a near‐operational set‐up. For this purpose, a fast look‐up table‐based forward operator is used to generated synthetic satellite images from the model state. Single‐observation experiments show that the assimilation of visible reflectances improves cloud cover under most conditions and often reduces temperature and humidity errors. In cycled experiments for two summer days with convective precipitation, the assimilation strongly reduces the errors of cloud cover and improves the precipitation forecast. While these results are promising, several issues are identified that limit the efficacy of the assimilation process. First, the linearity assumption of the LETKF can lead to errors as reflectance is a nonlinear function of the model state. Second, errors can arise from the fact that visible reflectances alone are ambiguous and only weakly sensitive to the water phase and cloud‐top height. And lastly, it is not obvious how to localise vertical covariances as visible reflectances are sensitive to clouds at all heights. For the latter reason, no vertical localisation was used in this study. To investigate the robustness of the results, the horizontal localisation scale, the assigned observation error and the spatial density of observations were varied in sensitivity experiments. The best results were obtained for an observation error close to the Desroziers estimate. High observation density combined with small localisation radii resulted in the smallest 1 hr forecast error. These settings were also beneficial for 3 hr forecasts, but forecasts at that lead time were less sensitive to the observation density and the localisation scale.

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Representation of Model Error in Convective‐Scale Data Assimilation: Additive Noise, Relaxation Methods, and Combinations

Cited by 28 publications

References 92 publications

Relative contribution of soil moisture, boundary‐layer and microphysical perturbations on convective predictability in different weather regimes

Relative contribution of soil moisture, boundary‐layer and microphysical perturbations on convective predictability in different weather regimes

Representation of Model Error in Convective‐Scale Data Assimilation: Additive Noise Based on Model Truncation Error

Assimilating visible satellite images for convective‐scale numerical weather prediction: A case‐study

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