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

2019 ◽  
Vol 11 (3) ◽  
pp. 752-770 ◽  
Author(s):  
Yuefei Zeng ◽  
Tijana Janjić ◽  
Matthias Sommer ◽  
Alberto Lozar ◽  
Ulrich Blahak ◽  
...  
Keyword(s):  
Data Assimilation ◽  
Truncation Error ◽  
Additive Noise ◽  
Model Error ◽  
Convective Scale ◽  
Scale Data

Representation of model error in convective scale data assimilation

2020 ◽  
Author(s):  
Tijana Janjic ◽  
Yuefei Zeng ◽  
Alberto de Lozar ◽  
Yvonne Ruckstuhl ◽  
Ulrich Blahak ◽  
...  
Keyword(s):  
Data Assimilation ◽  
Additive Noise ◽  
Model Error ◽  
Ensemble Member ◽  
Radar Reflectivity ◽  
Model Errors ◽  
Estimation Of Parameters ◽  
Convective Scale ◽  
Reflectivity Data ◽  
Scale Data

<p>Model error is one of major contributors to forecast uncertainty. In addition, statistical representations of possible model errors substantially affect the data assimilation results. We investigate variety of methods of taking into account model error in ensemble based convective scale data assimilation. This is done using the operational convection-permitting COSMO model and data assimilation system KENDA of German weather service, for a two-week convective period in May 2016 over Germany. Conventional and radar reflectivity observations are assimilated hourly by the LETKF. For example, to take into account the model error due to unresolved scales and processes, we use the additive noise with samples coming from the difference between high-resolution model run and low-resolution experiment. We compare this technique for assimilation of radar reflectivity data to other methods such as RTPS, warm bubble initialization, stochastic boundary layer perturbation and estimation of parameters. To further improve on additive noise technique, which consists of perturbing each ensemble member with a sample from a given distribution, we propose a more flexible approach in which the model error samples are treated as additional synthetic ensemble members that are used in the update step of data assimilation but are not forecasted. In this way, the rank of the model error covariance matrix can be chosen independently of the ensemble. This altered additive noise method is analyzed as well.</p>

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

2018 ◽  
Vol 10 (11) ◽  
pp. 2889-2911 ◽  
Author(s):  
Yuefei Zeng ◽  
Tijana Janjić ◽  
Alberto Lozar ◽  
Ulrich Blahak ◽  
Hendrik Reich ◽  
...  
Keyword(s):  
Data Assimilation ◽  
Additive Noise ◽  
Model Error ◽  
Relaxation Methods ◽  
Convective Scale ◽  
Scale Data

scholarly journals Comparison of Methods Accounting for Subgrid-Scale Model Error in Convective-Scale Data Assimilation

2020 ◽  
Vol 148 (6) ◽  
pp. 2457-2477 ◽  
Author(s):  
Yuefei Zeng ◽  
Tijana Janjić ◽  
Alberto de Lozar ◽  
Stephan Rasp ◽  
Ulrich Blahak ◽  
...  
Keyword(s):  
Data Assimilation ◽  
Additive Noise ◽  
Stochastic Perturbation ◽  
Model Error ◽  
Scale Model ◽  
Ensemble Spread ◽  
Physically Based ◽  
Convective Scale ◽  
Convective Cells ◽  
Scale Data

Abstract Different approaches for representing model error due to unresolved scales and processes are compared in convective-scale data assimilation, including the physically based stochastic perturbation (PSP) scheme for turbulence, an advanced warm bubble approach that automatically detects and triggers absent convective cells, and additive noise based on model truncation error. The analysis of kinetic energy spectrum guides the understanding of differences in precipitation forecasts. It is found that the PSP scheme results in more ensemble spread in assimilation cycles, but its effects on the root-mean-square error (RMSE) are neutral. This leads to positive impacts on precipitation forecasts that last up to three hours. The warm bubble technique does not create more spread, but is effective in reducing the RMSE, and improving precipitation forecasts for up to 3 h. The additive noise approach contributes greatly to ensemble spread, but it results in a larger RMSE during assimilation cycles. Nevertheless, it considerably improves the skill of precipitation forecasts up to 6 h. Combining the additive noise with either the PSP scheme or the warm bubble technique reduces the RMSE within cycles and improves the skill of the precipitation forecasts, with the latter being more beneficial.

The development of a hybrid EnSRF-En3DVar system for convective-scale data assimilation

2019 ◽  
Vol 229 ◽  
pp. 208-223 ◽  
Author(s):  
Shibo Gao ◽  
Jinzhong Min ◽  
Limin Liu ◽  
Chuanyou Ren
Keyword(s):  
Data Assimilation ◽  
Convective Scale ◽  
Scale Data

scholarly journals Simulation of satellite infrared radiances for convective-scale data assimilation over the Mediterranean

2010 ◽  
Vol 115 (D15) ◽  
Author(s):  
Fanny Duffourg ◽  
Véronique Ducrocq ◽  
Nadia Fourrié ◽  
Geneviève Jaubert ◽  
Vincent Guidard
Keyword(s):  
Data Assimilation ◽  
Convective Scale ◽  
The Mediterranean ◽  
Infrared Radiances ◽  
Scale Data
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