The impact of localization and observation averaging for convective-scale data assimilation in a simple stochastic model

2012 ◽  
Vol 139 (671) ◽  
pp. 515-523 ◽  
Author(s):  
George C. Craig ◽  
Michael Würsch
Keyword(s):  
Data Assimilation ◽  
Stochastic Model ◽  
Convective Scale ◽  
The Impact ◽  
Simple Stochastic Model ◽  
Scale Data

Sampling Error Correction Evaluated Using a Convective-Scale 1000-Member Ensemble

2019 ◽  
Vol 148 (3) ◽  
pp. 1229-1249 ◽  
Author(s):  
Tobias Necker ◽  
Martin Weissmann ◽  
Yvonne Ruckstuhl ◽  
Jeffrey Anderson ◽  
Takemasa Miyoshi
Keyword(s):  
Sensitivity Analysis ◽  
Data Assimilation ◽  
Error Correction ◽  
Degrees Of Freedom ◽  
Sampling Error ◽  
Lookup Table ◽  
Ensemble Prediction ◽  
Sampling Errors ◽  
Convective Scale ◽  
The Impact

Abstract State-of-the-art ensemble prediction systems usually provide ensembles with only 20–250 members for estimating the uncertainty of the forecast and its spatial and spatiotemporal covariance. Given that the degrees of freedom of atmospheric models are several magnitudes higher, the estimates are therefore substantially affected by sampling errors. For error covariances, spurious correlations lead to random sampling errors, but also a systematic overestimation of the correlation. A common approach to mitigate the impact of sampling errors for data assimilation is to localize correlations. However, this is a challenging task given that physical correlations in the atmosphere can extend over long distances. Besides data assimilation, sampling errors pose an issue for the investigation of spatiotemporal correlations using ensemble sensitivity analysis. Our study evaluates a statistical approach for correcting sampling errors. The applied sampling error correction is a lookup table–based approach and therefore computationally very efficient. We show that this approach substantially improves both the estimates of spatial correlations for data assimilation as well as spatiotemporal correlations for ensemble sensitivity analysis. The evaluation is performed using the first convective-scale 1000-member ensemble simulation for central Europe. Correlations of the 1000-member ensemble forecast serve as truth to assess the performance of the sampling error correction for smaller subsets of the full ensemble. The sampling error correction strongly reduced both random and systematic errors for all evaluated variables, ensemble sizes, and lead times.

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 The Impact of Mesoscale Environmental Uncertainty on the Prediction of a Tornadic Supercell Storm Using Ensemble Data Assimilation Approach

2013 ◽  
Vol 2013 ◽  
pp. 1-15 ◽  
Author(s):  
Nusrat Yussouf ◽  
Jidong Gao ◽  
David J. Stensrud ◽  
Guoqing Ge
Keyword(s):  
Data Assimilation ◽  
Environmental Variability ◽  
Ensemble Forecasts ◽  
Near Surface ◽  
Model Based ◽  
Supercell Storm ◽  
Convective Scale ◽  
Physics Ensemble ◽  
Background Fields ◽  
The Impact

Numerical experiments over the past years indicate that incorporating environmental variability is crucial for successful very short-range convective-scale forecasts. To explore the impact of model physics on the creation of environmental variability and its uncertainty, combined mesoscale-convective scale data assimilation experiments are conducted for a tornadic supercell storm. Two 36-member WRF-ARW model-based mesoscale EAKF experiments are conducted to provide background environments using either fixed or multiple physics schemes across the ensemble members. Two 36-member convective-scale ensembles are initialized using background fields from either fixed physics or multiple physics mesoscale ensemble analyses. Radar observations from four operational WSR-88Ds are assimilated into convective-scale ensembles using ARPS model-based 3DVAR system and ensemble forecasts are launched. Results show that the ensemble with background fields from multiple physics ensemble provides more realistic forecasts of significant tornado parameter, dryline structure, and near surface variables than ensemble from fixed physics background fields. The probabilities of strong low-level updraft helicity from multiple physics ensemble correlate better with observed tornado and rotation tracks than probabilities from fixed physics ensemble. This suggests that incorporating physics diversity across the ensemble can be important to successful probabilistic convective-scale forecast of supercell thunderstorms, which is the main goal of NOAA’s Warn-on-Forecast initiative.

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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