Comparison of static mesoscale background-error covariances estimated by three different ensemble data assimilation techniques

2014 ◽  
Vol 141 (687) ◽  
pp. 413-425 ◽  
Author(s):  
Gergely Bölöni ◽  
Loïk Berre ◽  
Edit Adamcsek
Keyword(s):  
Data Assimilation ◽  
Background Error ◽  
Ensemble Data Assimilation ◽  
Ensemble Data

scholarly journals Improving Large-Domain Convection-Allowing Forecasts with High-Resolution Analyses and Ensemble Data Assimilation

2016 ◽  
Vol 144 (5) ◽  
pp. 1777-1803 ◽  
Author(s):  
Craig S. Schwartz
Keyword(s):  
Data Assimilation ◽  
Three Dimensional ◽  
Weather Research And Forecasting ◽  
Analysis Method ◽  
Grid Spacing ◽  
Background Error ◽  
Ensemble Data Assimilation ◽  
Ensemble Data ◽  
Horizontal Length ◽  
Horizontal Grid

Analyses with 20-km horizontal grid spacing were produced from continuously cycling three-dimensional variational (3DVAR), ensemble square root Kalman filter (EnSRF), and “hybrid” variational–ensemble data assimilation (DA) systems over a domain spanning the conterminous United States. These analyses initialized 36-h Weather Research and Forecasting Model forecasts containing a large convection-allowing 4-km nested domain, where downscaled 20-km 3DVAR, EnSRF, and hybrid analyses initialized the 4-km forecasts. Overall, hybrid analyses initialized the best 4-km precipitation forecasts. Furthermore, whether 4-km precipitation forecasts could be improved by initializing them with true 4-km analyses was assessed. As it was computationally infeasible to produce 4-km continuously cycling ensembles over the large 4-km domain, several “dual-resolution” hybrid DA configurations were adopted where 4-km backgrounds were combined with 20-km ensembles to produce 4-km hybrid analyses. Additionally, 4-km 3DVAR analyses were produced. In both hybrid and 3DVAR frameworks, initializing 4-km forecasts with true 4-km analyses, rather than downscaled 20-km analyses, yielded superior precipitation forecasts over the first 12 h. Differences between forecasts initialized from 4-km and downscaled 20-km hybrid analyses were smaller for 18–36-h forecasts, but there were occasionally meaningful differences. Continuously cycling the 4-km backgrounds and using static background error covariances with larger horizontal length scales in the hybrid led to better forecasts. All hybrid-initialized forecasts, including those initialized from downscaled 20-km analyses, were more skillful than forecasts initialized from 4-km 3DVAR analyses, suggesting the analysis method was more important than analysis resolution.

Comparing Limited-Area 3DVAR and Hybrid Variational-Ensemble Data Assimilation Methods for Typhoon Track Forecasts: Sensitivity to Outer Loops and Vortex Relocation

2013 ◽  
Vol 141 (12) ◽  
pp. 4350-4372 ◽  
Author(s):  
Craig S. Schwartz ◽  
Zhiquan Liu ◽  
Xiang-Yu Huang ◽  
Ying-Hwa Kuo ◽  
Chin-Tzu Fong
Keyword(s):  
Data Assimilation ◽  
Three Dimensional ◽  
Wrf Model ◽  
Limited Area ◽  
Background Error ◽  
Ensemble Data Assimilation ◽  
Ensemble Data ◽  
Typhoon Track ◽  
The Mean ◽  
The Impact

Abstract The Weather Research and Forecasting Model (WRF) “hybrid” variational-ensemble data assimilation (DA) algorithm was used to initialize WRF model forecasts of three tropical cyclones (TCs). The hybrid-initialized forecasts were compared to forecasts initialized by WRF's three-dimensional variational (3DVAR) DA system. An ensemble adjustment Kalman filter (EAKF) updated a 32-member WRF-based ensemble system that provided flow-dependent background error covariances for the hybrid. The 3DVAR, hybrid, and EAKF configurations cycled continuously for ~3.5 weeks and produced new analyses every 6 h that initialized 72-h WRF forecasts with 45-km horizontal grid spacing. Additionally, the impact of employing a TC relocation technique and using multiple outer loops (OLs) in the 3DVAR and hybrid minimizations were explored. Model output was compared to conventional, dropwindsonde, and TC “best track” observations. On average, the hybrid produced superior forecasts compared to 3DVAR when only one OL was used during minimization. However, when three OLs were employed, 3DVAR forecasts were dramatically improved but the mean hybrid performance changed little. Additionally, incorporation of TC relocation within the cycling systems further improved the mean 3DVAR-initialized forecasts but the average hybrid-initialized forecasts were nearly unchanged.

Assimilation of Precipitation-Affected Radiances in a Cloud-Resolving WRF Ensemble Data Assimilation System

2013 ◽  
Vol 141 (2) ◽  
pp. 754-772 ◽  
Author(s):  
Sara Q. Zhang ◽  
Milija Zupanski ◽  
Arthur Y. Hou ◽  
Xin Lin ◽  
Samson H. Cheung
Keyword(s):  
Data Assimilation ◽  
Prediction Models ◽  
Weather Prediction ◽  
Rain Event ◽  
Background Error ◽  
Ensemble Data Assimilation ◽  
Ensemble Data ◽  
Cross Covariance ◽  
Data Assimilation System ◽  
Assimilation System

Abstract Assimilation of remotely sensed precipitation observations into numerical weather prediction models can improve precipitation forecasts and extend prediction capabilities in hydrological applications. This paper presents a new regional ensemble data assimilation system that assimilates precipitation-affected microwave radiances into the Weather Research and Forecasting Model (WRF). To meet the challenges in satellite data assimilation involving cloud and precipitation processes, hydrometeors produced by the cloud-resolving model are included as control variables and ensemble forecasts are used to estimate flow-dependent background error covariance. Two assimilation experiments have been conducted using precipitation-affected radiances from passive microwave sensors: one for a tropical storm after landfall and the other for a heavy rain event in the southeastern United States. The experiments examined the propagation of information in observed radiances via flow-dependent background error auto- and cross covariance, as well as the error statistics of observational radiance. The results show that ensemble assimilation of precipitation-affected radiances improves the quality of precipitation analyses in terms of spatial distribution and intensity in accumulated surface rainfall, as verified by independent ground-based precipitation observations.

scholarly journals Model Error Representation Using the Stochastically Perturbed Hybrid Physical–Dynamical Tendencies in Ensemble Data Assimilation System

2020 ◽  
Vol 10 (24) ◽  
pp. 9010
Author(s):  
Sujeong Lim ◽  
Myung-Seo Koo ◽  
In-Hyuk Kwon ◽  
Seon Ki Park
Keyword(s):  
Data Assimilation ◽  
Weather Prediction ◽  
Stochastic Perturbation ◽  
Numerical Weather Prediction Model ◽  
Natural State ◽  
Model Errors ◽  
Background Error ◽  
Ensemble Data Assimilation ◽  
Ensemble Data ◽  
Stochastically Perturbed

Ensemble data assimilation systems generally suffer from underestimated background error covariance that leads to a filter divergence problem—the analysis diverges from the natural state by ignoring the observation influence due to the diminished estimation of model uncertainty. To alleviate this problem, we have developed and implemented the stochastically perturbed hybrid physical–dynamical tendencies to the local ensemble transform Kalman filter in a global numerical weather prediction model—the Korean Integrated Model (KIM). This approach accounts for the model errors associated with computational representations of underlying partial differential equations and the imperfect physical parameterizations. The new stochastic perturbation hybrid tendencies scheme generally improved the background error covariances in regions where the ensemble spread was not sufficiently expressed by the control experiment that used an additive inflation and the relaxation to prior spread method.

scholarly journals A hybrid variational ensemble data assimilation for the HIgh Resolution Limited Area Model (HIRLAM)

2014 ◽  
Vol 21 (1) ◽  
pp. 303-323 ◽  
Author(s):  
N. Gustafsson ◽  
J. Bojarova ◽  
O. Vignes
Keyword(s):  
Data Assimilation ◽  
Control Variable ◽  
Full Rank ◽  
Ensemble Member ◽  
Variational Data Assimilation ◽  
Limited Area ◽  
Background Error ◽  
Ensemble Data Assimilation ◽  
Ensemble Data ◽  
Improved Performance

Abstract. A hybrid variational ensemble data assimilation has been developed on top of the HIRLAM variational data assimilation. It provides the possibility of applying a flow-dependent background error covariance model during the data assimilation at the same time as full rank characteristics of the variational data assimilation are preserved. The hybrid formulation is based on an augmentation of the assimilation control variable with localised weights to be assigned to a set of ensemble member perturbations (deviations from the ensemble mean). The flow-dependency of the hybrid assimilation is demonstrated in single simulated observation impact studies and the improved performance of the hybrid assimilation in comparison with pure 3-dimensional variational as well as pure ensemble assimilation is also proven in real observation assimilation experiments. The performance of the hybrid assimilation is comparable to the performance of the 4-dimensional variational data assimilation. The sensitivity to various parameters of the hybrid assimilation scheme and the sensitivity to the applied ensemble generation techniques are also examined. In particular, the inclusion of ensemble perturbations with a lagged validity time has been examined with encouraging results.

Initiation of ensemble data assimilation

2006 ◽  
Author(s):  
M. Zupanski ◽  
S. J. Fletcher ◽  
I. M. Navon ◽  
B. Uzunoglu ◽  
R. P. Heikes ◽  
...  
Keyword(s):  
Data Assimilation ◽  
Ensemble Data Assimilation ◽  
Ensemble Data

Using a machine learning proxy for localization in ensemble data assimilation

2021 ◽  
Vol 25 (3) ◽  
pp. 931-944
Author(s):  
Johann M. Lacerda ◽  
Alexandre A. Emerick ◽  
Adolfo P. Pires
Keyword(s):  
Machine Learning ◽  
Data Assimilation ◽  
Ensemble Data Assimilation ◽  
Ensemble Data

Maximum Likelihood Ensemble Filter: Theoretical Aspects

2005 ◽  
Vol 133 (6) ◽  
pp. 1710-1726 ◽  
Author(s):  
Milija Zupanski
Keyword(s):  
Maximum Likelihood ◽  
Data Assimilation ◽  
Cost Function ◽  
Ensemble Data Assimilation ◽  
Error Covariance ◽  
Ensemble Data ◽  
Analysis Error ◽  
Nonlinear Observation ◽  
The Cost ◽  
Maximum Likelihood Ensemble Filter

Abstract A new ensemble-based data assimilation method, named the maximum likelihood ensemble filter (MLEF), is presented. The analysis solution maximizes the likelihood of the posterior probability distribution, obtained by minimization of a cost function that depends on a general nonlinear observation operator. The MLEF belongs to the class of deterministic ensemble filters, since no perturbed observations are employed. As in variational and ensemble data assimilation methods, the cost function is derived using a Gaussian probability density function framework. Like other ensemble data assimilation algorithms, the MLEF produces an estimate of the analysis uncertainty (e.g., analysis error covariance). In addition to the common use of ensembles in calculation of the forecast error covariance, the ensembles in MLEF are exploited to efficiently calculate the Hessian preconditioning and the gradient of the cost function. A sufficient number of iterative minimization steps is 2–3, because of superior Hessian preconditioning. The MLEF method is well suited for use with highly nonlinear observation operators, for a small additional computational cost of minimization. The consistent treatment of nonlinear observation operators through optimization is an advantage of the MLEF over other ensemble data assimilation algorithms. The cost of MLEF is comparable to the cost of existing ensemble Kalman filter algorithms. The method is directly applicable to most complex forecast models and observation operators. In this paper, the MLEF method is applied to data assimilation with the one-dimensional Korteweg–de Vries–Burgers equation. The tested observation operator is quadratic, in order to make the assimilation problem more challenging. The results illustrate the stability of the MLEF performance, as well as the benefit of the cost function minimization. The improvement is noted in terms of the rms error, as well as the analysis error covariance. The statistics of innovation vectors (observation minus forecast) also indicate a stable performance of the MLEF algorithm. Additional experiments suggest the amplified benefit of targeted observations in ensemble data assimilation.

scholarly journals Convection-Permitting Forecasts Initialized with Continuously Cycling Limited-Area 3DVAR, Ensemble Kalman Filter, and “Hybrid” Variational–Ensemble Data Assimilation Systems

2014 ◽  
Vol 142 (2) ◽  
pp. 716-738 ◽  
Author(s):  
Craig S. Schwartz ◽  
Zhiquan Liu
Keyword(s):  
Kalman Filter ◽  
Data Assimilation ◽  
Three Dimensional ◽  
Skill Score ◽  
Limited Area ◽  
Computational Domain ◽  
Ensemble Data Assimilation ◽  
Ensemble Data ◽  
Precipitation Characteristics ◽  
High Precipitation

Abstract Analyses with 20-km horizontal grid spacing were produced from parallel continuously cycling three-dimensional variational (3DVAR), ensemble square root Kalman filter (EnSRF), and “hybrid” variational–ensemble data assimilation (DA) systems between 0000 UTC 6 May and 0000 UTC 21 June 2011 over a domain spanning the contiguous United States. Beginning 9 May, the 0000 UTC analyses initialized 36-h Weather Research and Forecasting Model (WRF) forecasts containing a large convection-permitting 4-km nest. These 4-km 3DVAR-, EnSRF-, and hybrid-initialized forecasts were compared to benchmark WRF forecasts initialized by interpolating 0000 UTC Global Forecast System (GFS) analyses onto the computational domain. While important differences regarding mean state characteristics of the 20-km DA systems were noted, verification efforts focused on the 4-km precipitation forecasts. The 3DVAR-, hybrid-, and EnSRF-initialized 4-km precipitation forecasts performed similarly regarding general precipitation characteristics, such as timing of the diurnal cycle, and all three forecast sets had high precipitation biases at heavier rainfall rates. However, meaningful differences emerged regarding precipitation placement as quantified by the fractions skill score. For most forecast hours, the hybrid-initialized 4-km precipitation forecasts were better than the EnSRF-, 3DVAR-, and GFS-initialized forecasts, and the improvement was often statistically significant at the 95th percentile. These results demonstrate the potential of limited-area continuously cycling hybrid DA configurations and suggest additional hybrid development is warranted.

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